CTNF 18/544,782 CTNF 99207 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. Claim Rejections - 35 USC § 112 Regarding 112(b): 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In regard to Claim 1: Claim 1 recites the limitation "for an AI/ML model" in “receive one or more criteria for an artificial intelligence or machine learning (AI/ML) model monitoring operation; perform, for an AI/ML model, the AI/ML model monitoring operation…”. There is insufficient antecedent basis for this limitation in the claim. One of ordinary skill cannot tell if the AI/ML model in “the AI/ML model monitoring operation” is the same or different than the model noted to have received the criteria for, thus one of ordinary skill unable to tell which model is being referenced by “the AI/ML model”. In regards to claim analogous to claim 1: Claims analogous to claim 1 are seen as reciting the same 112(b) rejections as claim 1, thus are also rejected under 112(b). In regards to claim 5: Claim 5 is rejected as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 5 recites “and wherein the data is collected at least the second amount of time from the monitoring time”. One of ordinary skill in the art cannot determine what the recited limitation means. A first interpretation of the limitation could be the data should be from after a first amount of time, then also after the second amount of time from the monitoring time (indicated by the “at least the second amount of time from the monitoring time”). A second interpretation of the limitation could be that the data is between the first amount of time after the monitoring time and the second amount of time after the monitoring time (support for this interpretation comes from figure 9 indicating data after the second time is not included in the data, thus the data should be between the first and second). A third interpretation of the limitation could be should only be data at the second amount of time (as the “collected at least the second amount of time from the monitoring time” does not provide an indication of whether the wanted time should be before, after, or at the second amount of time). For 103 interpretation, the limitation is seen as indicating there is a second amount of time after the monitoring time to indicate a form of time window with the first amount of time. In regards to dependent claims: Any claim dependent upon a claim rejected under 112(b) is rejected under 112(b) for being dependent upon a rejected claim. 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 towards an abstract idea without significantly more. In regards to Claim 1: Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter? Yes, the claim is directed towards a machine. Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 1 recites the following abstract ideas: perform, for an AI/ML model, the AI/ML model monitoring operation based on a first distribution of inference data associated with the AI/ML model satisfying the one or more criteria This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation, observation, and judgement. Generically performing a monitoring operation based on a criteria is a task or processes performable by a human. No steps are recited that indicate a form of data that is unconventional for a human to manage is used, nor is there an indication of a step being performed that a human could not do. The steps in this limitation indicates monitoring ( perform, for an AI/ML model, the AI/ML model monitoring operation ) should the decision to monitor ( satisfying the one or more criteria ) be decided on. perform, for the AI/ML model, an action based on the AI/ML model monitoring operation This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation. Generically performing an action is an action a person/human can do. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 1 recites the following additional elements: A network entity for wireless communication, comprising: a processing system configured to This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). receive one or more criteria for an artificial intelligence or machine learning (AI/ML) model monitoring operation This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 1 recites the following additional elements: A network entity for wireless communication, comprising: a processing system configured to This limitation is directed towards linking or indicating a field of use or technological environment (see MPEP 2106.05(h)). Limitations that amount to merely linking/indicating to a field of use or technological environment, such as a computer environment (see MPEP 2106.05(h)(x) or see MPEP 2106.05(h)(iv)), do not amount to significantly more than the exception itself. The note for the limitations being for a device for wireless communication does not provide more than indicating that the claims are being applied to the field of communication or networks. receive one or more criteria for an artificial intelligence or machine learning (AI/ML) model monitoring operation This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). In regards to Claim 2: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 2 recites the following abstract ideas: compare, for the AI/ML model, the first distribution to a second distribution of training data associated with the AI/ML model based on the first distribution satisfying the one or more criteria This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation and judgement. In regards to Claim 3: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 3 recites the following abstract ideas: wherein the one or more criteria include timing information associated with the first distribution This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. In regards to Claim 4: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 4 recites the following abstract ideas: wherein the timing information includes an amount of time This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 3. wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: perform the AI/ML model monitoring operation using data, from the inference data, that is collected after the amount of time from a monitoring time This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. In regards to Claim 5: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 5 recites the following abstract ideas: wherein the timing information includes a first amount of time and a second amount of time This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 3. wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: perform the AI/ML model monitoring operation using data, from the inference data, that is collected after the first amount of time from a monitoring time, and wherein the data is collected at least the second amount of time from the monitoring time This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. In regards to Claim 6: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 6 recites the following abstract ideas: wherein the one or more criteria include a quantity of measurement samples to be included in the inference data This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. In regards to Claim 7: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 7 recites the following abstract ideas: wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. In regards to Claim 8: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 8 recites the following abstract ideas: wherein the AI/ML model is configured to perform a function, and wherein the one or more criteria include at least one criterion that is associated with the function This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. In regards to Claim 9: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 9 recites the following abstract ideas: wherein the one or more criteria are associated with one or more condition parameters This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: detect the one or more condition parameters This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as observation and judgement. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 9 recites the following additional elements: apply the one or more criteria to the performance of the AI/ML model monitoring operation based on the detection of the one or more condition parameters At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 9 recites the following additional elements: apply the one or more criteria to the performance of the AI/ML model monitoring operation based on the detection of the one or more condition parameters At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “apply” or equivalent does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 10: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 10 recites the following abstract ideas: compare, for the AI/ML model, the first distribution to a second distribution of training data associated with the AI/ML model, the comparison being associated with a similarity of the first distribution and the second distribution This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as observation and evaluation. In regards to Claim 11: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 11 recites the following abstract ideas: perform the action based on whether the similarity metric satisfies a threshold, wherein the threshold is based on a quantity of measurement samples included in the inference data This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as judgement and evaluation (as the “perform the action” continues the abstract idea of evaluation in claim 1). In regards to Claim 12: This claim is analogous to claim 1 In regards to Claim 13: This claim is analogous to claim 3 In regards to Claim 14: This claim is analogous to claim 6 In regards to Claim 15: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 15 recites the following abstract ideas: wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data, and wherein performing the AI/ML model monitoring operation comprises: performing the AI/ML model monitoring operation based on the inference data including measurement samples having respective time gaps that are less than or equal to the allowable time gap This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. In regards to Claim 16: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 16 recites the following abstract ideas: wherein the one or more criteria are based on the recommendation information This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 16 recites the following additional elements: transmitting recommendation information for the AI/ML model monitoring operation This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 16 recites the following additional elements: transmitting recommendation information for the AI/ML model monitoring operation This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). In regards to Claim 17: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 17 recites the following abstract ideas: wherein the one or more criteria are based on the one or more capabilities This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. This limitation is seen as a continuation of performing model monitoring in claim 1. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 17 recites the following additional elements: transmitting a capability report indicating one or more capabilities for the AI/ML model monitoring operation This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 17 recites the following additional elements: transmitting a capability report indicating one or more capabilities for the AI/ML model monitoring operation This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). In regards to Claim 18: This claim is analogous to claim 1 In regards to Claim 19: This claim is analogous to claim 3 In regards to Claim 20: This claim is analogous to claim 6 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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1, 3-5, 8, 10, 12, 13, 18, 19 is/are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Zhang et al (WO2024176170A1), referred to as Zhang in this document . Regarding Claim 1: Zhang teaches: A network entity for wireless communication, comprising: [Zhang 0005]: “Some general embodiments that address the problems of model drifting in wireless networks [A network entity for wireless communication, comprising:] are summarized below” a processing system configured to: [Zhang 0100]: “The processing circuitry 502 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs [computer readable storage medium] in the memory 510 such as instructions for performing the multi-stage drift monitoring described herein. The processing circuitry 502 may be implemented as… general-purpose processors… [a processing system configured to:] ” Support that the processing circuitry and other aspects of components are related to the UE [Zhang 0099]: “The UE 500 includes processing circuitry 502 that is operatively coupled via a bus 504 to an input/output interface 506, a power source 508, a memory 510, a communication interface 512, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 5. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.” Support for the UE is a wireless connection based device [Zhang 0097]: “Figure 5 shows a UE 500 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs.” receive one or more criteria for an artificial intelligence or machine learning (AI/ML) model monitoring operation; perform, for an AI/ML model, the AI/ML model monitoring operation based on a first distribution of inference data associated with the AI/ML model satisfying the one or more criteria; [Zhang 0074]: “In stage 1 215, the MDM entity performs a distance metric calculation of the model inference dataset 260 against the training dataset for the model to obtain a distance metric. If the training dataset is not available at the MDM, the MDM requests the training dataset from an entity that stores the training dataset. The distance metric calculation may be performed like as described earlier herein. The distance metric calculation is periodically carried out and the distance metric is compared to a threshold. If the distance metric of the datasets (the model inference dataset 260 and the training dataset) exceeds a predefined threshold [receive one or more criteria for an artificial intelligence or machine learning (AI/ML) model monitoring operation as a predefined threshold is seen as an input for a condition thus a received criteria ] , then it triggers a state change to enter the second stage 225 of detection [perform, for an AI/ML model, the AI/ML model monitoring operation based on a first distribution of inference data associated with the AI/ML model satisfying the one or more criteria where performing the monitoring based on a criteria is further supported by paragraph 75 of Zhang indicating that the second stage is a performance based monitoring, thus a criteria for an AI/ML model, such as a distance metric, indicated a form of monitoring ] . Otherwise, there is no state change, and the monitoring stays at stage 1 215. As shown in Figure 2, the state transitions to stage 2 225 when the distance metric (Ml) is greater than the threshold_l 220.” Further support for being able to receive data is also given in [Zhang 0118], as the hardware for a system is noted to be able to receive data from a communication interface. and perform, for the AI/ML model, an action based on the AI/ML model monitoring operation [Zhang 0080]: “In summary, with above stage transition policy and procedure, the model monitoring function continuously monitors either or both the distribution distance metric and/or performance metric and shifts its monitoring stages. The stage changes according to whether the metric values surpassing certain thresholds at certain time windows. If the model drifting happens, it will detect it in a high likelihood and send out an alarm message [and perform, for the AI/ML model, an action based on the AI/ML model monitoring operation] to the AI/ML model monitored for it to start a fine tuning or retraining.” Regarding Claim 3: The network entity of claim 1 is taught by Zhang. Zhang teaches: wherein the one or more criteria include timing information associated with the first distribution [Zhang 0077]: “If the continuous monitoring does not result in an alarm triggering criterion being satisfied within an extended time [wherein the one or more criteria include timing information associated with the first distribution where this data is related to the first distribution as the data being checked is inferred data from the model and not the second distribution which is the training data ] period (time window W), the monitoring function will re-enter stage 1 215 and follow the monitoring procedure at stage 1.” Regarding Claim 4: The network entity of claim 3 is taught by Zhang. Zhang teaches: wherein the timing information includes an amount of time, and wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: perform the AI/ML model monitoring operation using data, from the inference data, that is collected after the amount of time from a monitoring time [Zhang 0077]: “At sub stage 2 235, the MDM entity keeps monitoring the performance metric in a sliding window to determine whether the alarm triggering criterion is satisfied. If the continuous monitoring does not result in an alarm triggering criterion being satisfied within an extended time period (time window W), the monitoring function will re-enter stage 1 215 and follow the monitoring procedure at stage 1. The sliding window can take the form of [x(t), x(t+1), ... x(t+s)] [wherein the timing information includes an amount of time, and wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: perform the AI/ML model monitoring operation using data, from the inference data, that is collected after the amount of time from a monitoring time where t is an amount of time from a monitoring time and any data after t (shown by x(t + 1)) would be data collected after the amount of time ] where s is the window size.” Regarding Claim 5: The network entity of claim 3 is taught by Zhang. Zhang teaches: wherein the timing information includes a first amount of time and a second amount of time, and wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: perform the AI/ML model monitoring operation using data, from the inference data, that is collected after the first amount of time from a monitoring time, and wherein the data is collected at least the second amount of time from the monitoring time [Zhang 0077]: “At sub stage 2 235, the MDM entity keeps monitoring the performance metric in a sliding window to determine whether the alarm triggering criterion is satisfied. If the continuous monitoring does not result in an alarm triggering criterion being satisfied within an extended time period (time window W), the monitoring function will re-enter stage 1 215 and follow the monitoring procedure at stage 1. The sliding window can take the form of [x(t), x(t+1), ... x(t+s)] [wherein the timing information includes a first amount of time and a second amount of time, and wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: perform the AI/ML model monitoring operation using data, from the inference data, that is collected after the first amount of time from a monitoring time, and wherein the data is collected at least the second amount of time from the monitoring time where t is an amount of time from a monitoring time and t+s is the second amount of time from the monitoring time ] where s is the window size.” Regarding Claim 8: The network entity of claim 1 is taught by Zhang. Zhang teaches: wherein the AI/ML model is configured to perform a function, [Zhang 0003]: “ML has been found to be an effective tool in radio positioning [wherein the AI/ML model is configured to perform a function as this indicates a function for the ML model ] , for instance, 3GPP has now been investigating an AI/ML based positioning method that can include channel state information or time of arrival measurements based on a so-called fingerprint method for positioning, especially for indoor.” and wherein the one or more criteria include at least one criterion that is associated with the function [Zhang 0008]: “The method further includes performing, at the second stage of the multi-stage drift monitoring, a performance-based metric calculation that measures a quality of model output to obtain a performance metric [and wherein the one or more criteria include at least one criterion that is associated with the function as the performance metric is a measure of the model output (thus a measure associated with the model function) and the performance metric is used in a criteria with a threshold ] , comparing the performance threshold against a second threshold, and determining, based on the comparison, that the performance metric exceeds the second threshold, and responsive to this determination, transmitting a message that indicates that a drift of the radio positioning model is detected.” Regarding Claim 10: The network entity of claim 1 is taught by Zhang. Zhang teaches: wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: compare, for the AI/ML model, the first distribution to a second distribution of training data associated with the AI/ML model, the comparison being associated with a similarity of the first distribution and the second distribution [Zhang 0082]:“Performing the distance metric calculation may include Kullback- Leibler (KL) divergence [wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: compare, for the AI/ML model, the first distribution to a second distribution of training data associated with the AI/ML model, the comparison being associated with a similarity of the first distribution and the second distribution] of probability density functions (PDF) or derivative of cumulative density function (CDF) of the training dataset and the model inference dataset. The first stage may be performed without a ground truth label (e.g., the actual position of the radio) or a performance metric.” Further details on the indication of distance between a first and second distribution are given in the mapping for claim 2. Here in claim 10 the comparing distributions from claim 2 is being indicated to utilize a distance/similarity. This indicated by the distance being able to be Kullback- Leibler (KL) divergence which is noted in the specification to be a measure of similarity ([Current Application 0137]: “For example, the configuration information may indicate one or more thresholds for a similarity metric used to compare an inference data distribution to training data distributions for one or more AI/ML models. Additionally, or alternatively, the one or more thresholds may be defined, or otherwise fixed, by a wireless communication standard, such as the 3GPP. As an example, the AI/ML model monitoring operation may include determining whether a similarity metric (e.g., indicating a similarity level between an inference data distribution and a training data distribution) satisfies the one or more thresholds. The similarity metric may be a KL divergence metric, a KS distance (or KS statistic), an EMD, and/or another similarity metric indicative of a similarity between two data distributions.”). Regarding Claim 12: This claim is analogous to claim 1. Regarding Claim 13: This claim is analogous to claim 3. Regarding Claim 18: This claim is analogous to claim 1. Regarding Claim 19: This claim is analogous to claim 3 . 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 2, 6, 9, 11, 14, 16, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (WO2024176170A1), referred to as Zhang in this document, and further in combination with Ku et al (US20240196242), referred to as Ku in this document . Regarding Claim 2: The network entity of claim 1 is taught by Zhang. wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: compare, for the AI/ML model, the first distribution to a second distribution of training data associated with the AI/ML model [Zhang 0074]: “In stage 1 215, the MDM entity performs a distance metric calculation of the model inference dataset 260 against the training dataset for the model to obtain a distance metric. If the training dataset is not available at the MDM, the MDM requests the training dataset from an entity that stores the training dataset. The distance metric calculation may be performed like as described earlier herein. The distance metric calculation is periodically carried out and the distance metric is compared to a threshold. If the distance metric of the datasets (the model inference dataset 260 and the training dataset) [wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: compare, for the AI/ML model, the first distribution to a second distribution of training data associated with the AI/ML model] exceeds a predefined threshold, then it triggers a state change to enter the second stage 225 of detection. Otherwise, there is no state change, and the monitoring stays at stage 1 215. As shown in Figure 2, the state transitions to stage 2 225 when the distance metric (Ml) is greater than the threshold_l 220.” Zhang does not explicitly teach: based on the first distribution satisfying the one or more criteria Zhang teaches fitting a criteria, but to better fit the interpretation of a criteria that must be met before doing a distribution comparison, Ku is used (as Ku teaches a criteria should be met for statistical observation). Ku teaches: based on the first distribution satisfying the one or more criteria [Ku 0106]: “Number of samples: This parameter indicates the minimum number of monitoring samples that must be collected overtime before applying the statistical threshold [based on the first distribution satisfying the one or more criteria] . For example, the network could configure that at least 100 instances are required to determine if 60% of them exceed the per-instance 90% accuracy level.” One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Zhang and Ku. Zhang and Ku are in the same fields of endeavor of machine learning and monitoring of AI/ML models. One of ordinary skill in the art would have been motivated to combine Zhang and Ku in order to ensure that amount of data collected is enough to perform a statistical observation about the data ([Ku 0107]: “These additional parameters allow the network to specify thresholds both for individual monitoring instances as well as aggregate performance over time when determining if the AI/ML model meets the desired performance criteria. The number of samples indicates the minimum set size for the statistical observation.”). Regarding Claim 6: The network entity of claim 1 is taught by Zhang. Zhang does not explicitly teach: wherein the one or more criteria include a quantity of measurement samples to be included in the inference data Ku teaches: wherein the one or more criteria include a quantity of measurement samples to be included in the inference data [Ku 0106]: “Number of samples: This parameter indicates the minimum number of monitoring samples that must be collected overtime before applying the statistical threshold [wherein the one or more criteria include a quantity of measurement samples to be included in the inference data] . For example, the network could configure that at least 100 instances are required to determine if 60% of them exceed the per-instance 90% accuracy level.” The motivation to combine with Ku is the same motivation for combining with Ku in claim 2. Regarding Claim 9: The network entity of claim 1 is taught by Zhang. Zhang teaches: wherein the one or more criteria are associated with one or more condition parameters, and wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: detect the one or more condition parameters; [Zhang 0074]: “In stage 1 215, the MDM entity performs a distance metric calculation of the model inference dataset 260 against the training dataset for the model to obtain a distance metric. If the training dataset is not available at the MDM, the MDM requests the training dataset from an entity that stores the training dataset. The distance metric calculation may be performed like as described earlier herein. The distance metric calculation is periodically carried out and the distance metric is compared to a threshold. If the distance metric of the datasets (the model inference dataset 260 and the training dataset) exceeds a predefined threshold, then it triggers a state change [wherein the one or more criteria are associated with one or more condition parameters, and wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: detect the one or more condition parameters;] to enter the second stage 225 of detection. Otherwise, there is no state change, and the monitoring stays at stage 1 215. As shown in Figure 2, the state transitions to stage 2 225 when the distance metric (Ml) is greater than the threshold_l 220.” and apply the one or more criteria to the performance of the AI/ML model monitoring operation based on the detection of the one or more condition parameters [Zhang 0008]: “The method further includes performing, at the second stage of the multi-stage drift monitoring, a performance-based metric calculation that measures a quality of model output to obtain a performance metric, comparing the performance threshold against a second threshold [and apply the one or more criteria to the performance of the AI/ML model monitoring operation based on the detection of the one or more condition parameters where the criteria here can be the performance metric threshold that was used as a criteria as a result of a condition being met (distance metric meeting a threshold) ] , and determining, based on the comparison, that the performance metric exceeds the second threshold, and responsive to this determination, transmitting a message that indicates that a drift of the radio positioning model is detected.” Alternative mapping from Ku: [Ku 0108]: “More specially, in this example, based on the capability report from the UE 1004, the base station 1002 determines that the UE 1004 supports monitoring AI/ML-based beam management models [wherein the one or more criteria are associated with one or more condition parameters, and wherein the processing system, to perform the AI/ML model monitoring operation, is configured to: detect the one or more condition parameters] and configures a CSI-ReportConfig configuration for the UE 1004. A “enableAIreporting” parameter is set to “ON”. This instructs the UE to monitor the AI/ML model and report its findings according to the specified parameters [and apply the one or more criteria to the performance of the AI/ML model monitoring operation based on the detection of the one or more condition parameters] .” Ku is seen as teaching in the above quote that a condition can involve a form ability to do something (part of the capability of the system) and then as a result have criteria applied to the monitoring (such as monitoring and reporting on specified parameters). The motivation to combine with Ku related to the capability or condition of the system is the same as for the capability report in claim 17. Regarding Claim 11: The network entity of claim 10 is taught by Zhang. Zhang teaches: wherein the processing system, to perform the action, is configured to: perform the action based on whether the similarity metric satisfies a threshold, [Zhang 0074]: “In stage 1 215, the MDM entity performs a distance metric calculation of the model inference dataset 260 against the training dataset for the model to obtain a distance metric. If the training dataset is not available at the MDM, the MDM requests the training dataset from an entity that stores the training dataset. The distance metric calculation may be performed like as described earlier herein. The distance metric calculation is periodically carried out and the distance metric is compared to a threshold. If the distance metric of the datasets (the model inference dataset 260 and the training dataset) exceeds a predefined threshold, then it triggers a state change [wherein the processing system, to perform the action, is configured to: perform the action based on whether the similarity metric satisfies a threshold where a state change would be a possible action. An alternative for the action is also performing fine tuning or retraining as indicated by [Zhang 0080], as such an action is being performed after the criteria is met where the only reason the fine tuning/retraining has a second criteria of the performance metric is that Zhang is a multistage monitoring setup ] to enter the second stage 225 of detection. Otherwise, there is no state change, and the monitoring stays at stage 1 215. As shown in Figure 2, the state transitions to stage 2 225 when the distance metric (Ml) is greater than the threshold_l 220.” Zhang does not explicitly teach: wherein the threshold is based on a quantity of measurement samples included in the inference data Ku teaches: wherein the threshold is based on a quantity of measurement samples included in the inference data [Ku 0105]: “Statistic threshold of the metrics: In addition to a per-instance threshold, the network can configure a statistical threshold applied over multiple monitoring samples [wherein the threshold is based on a quantity of measurement samples included in the inference data] . For example, this could be the percentage of monitoring instances over time where the metric must exceed the threshold. If the statistical threshold is 60% and the per-instance threshold is 90%, then at least 60% of the monitoring samples would need to have a per-instance accuracy greater than 90% to consider the overall performance good.” One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Zhang and Ku. Zhang and Ku are in the same fields of endeavor of machine learning and monitoring of AI/ML models. One of ordinary skill in the art would have been motivated to combine Zhang and Ku in order to ensure the overall performance of the model is good by scaling the check to the size of the data ([Ku 0105]: “Statistic threshold of the metrics: In addition to a per-instance threshold, the network can configure a statistical threshold applied over multiple monitoring samples. For example, this could be the percentage of monitoring instances over time where the metric must exceed the threshold. If the statistical threshold is 60% and the per-instance threshold is 90%, then at least 60% of the monitoring samples would need to have a per-instance accuracy greater than 90% to consider the overall performance good.”). Regarding Claim 14: This claim is analogous to claim 6. Regarding Claim 16: The method of claim 12 is taught by Zhang Zhang does not explicitly teach: transmitting recommendation information for the AI/ML model monitoring operation, wherein the one or more criteria are based on the recommendation information Ku teaches: transmitting recommendation information for the AI/ML model monitoring operation, wherein the one or more criteria are based on the recommendation information [Ku 0097]: “For UE side model, even though the monitoring method is up to UE's implementation, there can be a mechanism which allows the network (NW) to configure cell-specific monitoring conditions for the UE to monitor its AI/ML model/functionality. This means that while the UE can choose its own method for monitoring its AI/ML model, the network can still configure some standardized parameters [transmitting recommendation information for the AI/ML model monitoring operation, wherein the one or more criteria are based on the recommendation information] to align the monitoring conditions with the UE.” Alternatively: [Ku 0103]: “Further, the network can configure additional parameters [transmitting recommendation information for the AI/ML model monitoring operation, wherein the one or more criteria are based on the recommendation information] for the UE to determine if the AI/ML model is performing well or poorly” [Ku 0107]: “These additional parameters allow the network to specify thresholds both for individual monitoring instances as well as aggregate performance over time when determining if the AI/ML model meets the desired performance criteria. The number of samples indicates the minimum set size for the statistical observation” One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Zhang and Ku. Zhang and Ku are in the same fields of endeavor of machine learning and monitoring. One of ordinary skill in the art would have been motivated to combine Zhang and Ku in order to allow a more controlled or better determination of the AI/ML model performance ([Ku 0103]: “Further, the network can configure additional parameters for the UE to determine if the AI/ML model is performing well or poorly”). Regarding Claim 17: The method of claim 12 is taught by Zhang Zhang does not explicitly teach: transmitting a capability report indicating one or more capabilities for the AI/ML model monitoring operation, wherein the one or more criteria are based on the one or more capabilities Zhang teaches aspects related to transmitting reports related to some capabilities, such as the capabilities of the AI/ML model (as taught in 76 of Zhang), but to better address the teaching of a capability report for capabilities of the monitoring operation Ku is used. Ku teaches: transmitting a capability report indicating one or more capabilities for the AI/ML model monitoring operation, wherein the one or more criteria are based on the one or more capabilities [Ku 0084]: “The capability report [transmitting a capability report indicating one or more capabilities for the AI/ML model monitoring operation, wherein the one or more criteria are based on the one or more capabilities] includes several components that portray the capabilities of the UE 1004: 1. Support for Monitoring Reporting: This boolean parameter indicates whether the UE 1004 can perform reporting functions for AI/ML-based beam management model or functionality monitoring. If the UE 1004 is capable, the value is True; if not, the value is False. 2. Supported Performance Metrics: This component specifies the types of performance metrics that the UE 1004 can support. The metrics include: Beam Prediction Accuracy: It refers to the UE's ability to derive and report how accurately it can predict the best beam or beams for communication. This metric involves the probability of a UE to accurately predict the best beam or beams for communication…” Further supported by the indication the UE sends the report and that the report is for the capability of monitoring or reporting [Ku 0083]: “wherein the UE 1004 is capable of transmitting a capability report that outlines its abilities with regard to monitoring and reporting AI/ML-based beam management functionality” One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Zhang and Ku. Zhang and Ku are in the same fields of endeavor of machine learning and monitoring. One of ordinary skill in the art would have been motivated to combine Zhang and Ku in order to be able to better manage the reporting and control to better suit the use case ([Ku 0096]: “Each of these capabilities, as reported by the UE 1004, enables better network management by the base station 1002, allowing it to configure beam management operations that best suit the UE's abilities, thus optimizing communication efficiency and maintaining a high-quality service.”). Regarding Claim 20: This claim is analogous to claim 6 . 07-21-aia AIA Claim s 7, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (WO2024176170A1), referred to as Zhang in this document, and further in combination with Ben Jones (“Avoiding Data Pitfalls, Part 1: Gaps Between Data and Reality”), referred to as Ben Jones in this document . Regarding Claim 7: The network entity of claim 1 is taught by Zhang. Zhang does not explicitly teach: wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data Ben Jones teaches: wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data [Ben Jones Example #1: Actual vs. Recorded Earthquakes]: “Are we really to believe that earthquakes have increased in frequency by this much? Obviously not. The world that measured and collected earthquakes in the early 20th century was very different than the one that did so in the last decade. Comparisons across decades, and even within some decades (the 1960s), aren’t “apples-to-apples” due to the changes in technology… When it comes to earthquakes, the gap between data and reality is getting smaller. The problem is that the “data-reality gap” [wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data] is changing over the time period we’re considering. And it’s hard to know for sure exactly how many magnitude 6.0 earthquakes we missed in any particular year.” Further supporting the information in the above quote [Ben Jones How to Avoid Confusing Data with Reality]: “Notice that in these three examples – 1. earthquakes (a dubious trend), 2. bicycle counting (a spike or outlier), and 3. Ebola deaths (a downward slope in a cumulative line plot) – something in the view of the data itself alerted us to a potential “data-reality gap”. Visualizing the data can be one of the best ways to find problems with it.” The reason that time gap data would be utilized as a criteria for measurements in data is that keeping track of gaps is important in order to properly formulate opinions or make decisions based off of data ([Ben Jones In Conclusion]: “We can’t ever perfectly know the “data-reality gap” because that would require perfect data. What we can do, though, is seek to identify any gaps that may exist, and take that into account when we use data to form our opinions.”). Zhang teaches criteria for a model in claim 1. Adding time gap as a criteria works well with Ku, as Ku indicates a minimum number of samples for statistical observations [Ku 0016]. Here the time gap criteria acts as a method to ensure that the enough data to be useful for a statistical observation from Ku doesn’t have the issue of the data being inaccurate for statistical observations (as Ben Jones teaches in the above quotes that gaps give false statistical indications such as an increase in earthquake occurrences). One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Zhang and Ben Jones. Zhang and Ben Jones are in the same field of endeavor of monitoring data. One of ordinary skill in the art, prior to the effective filing date would have been motivated to combine Zhang and Ben Jones in order to have a criteria for a gap in information collected or keeping track of gaps, as Ben Jones teaches that keeping tracks of gaps is important for gaps can indicate inaccurate data that doesn’t properly represent the environment ([Ben Jones In Conclusion]: “We can’t ever perfectly know the “data-reality gap” because that would require perfect data. What we can do, though, is seek to identify any gaps that may exist, and take that into account when we use data to form our opinions.”), such as the earthquake data showing a dubious trend as the gap in earthquakes recorded gives a false indication of an increase of earthquake occurrences (indicated in the quotes from Ben Jones above). Regarding Claim 15: The method of claim 12 is taught by Zhang Zhang does not explicitly teach: wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data, and wherein performing the AI/ML model monitoring operation comprises: performing the AI/ML model monitoring operation based on the inference data including measurement samples having respective time gaps that are less than or equal to the allowable time gap Zhang notes performing the AI/ML monitoring operation with criteria, as taught in claim 1. Ben Jones teaches: wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data, and wherein performing the AI/ML model monitoring operation comprises: performing the AI/ML model monitoring operation based on the inference data including measurement samples having respective time gaps that are less than or equal to the allowable time gap [Ben Jones Example #1: Actual vs. Recorded Earthquakes]: “Are we really to believe that earthquakes have increased in frequency by this much? Obviously not. The world that measured and collected earthquakes in the early 20th century was very different than the one that did so in the last decade. Comparisons across decades, and even within some decades (the 1960s), aren’t “apples-to-apples” due to the changes in technology… When it comes to earthquakes, the gap between data and reality is getting smaller. The problem is that the “data-reality gap” [wherein the one or more criteria include an allowable time gap between measurement samples to be included in the inference data, and wherein performing the AI/ML model monitoring operation comprises: performing the AI/ML model monitoring operation based on the inference data including measurement samples having respective time gaps that are less than or equal to the allowable time gap] is changing over the time period we’re considering. And it’s hard to know for sure exactly how many magnitude 6.0 earthquakes we missed in any particular year.” Claim 15 teaches the same subject matter as claim 7, thus further detail on the mapping is provided in the mapping for claim 7. Claim 15 noting the addition of performing the AI/ML model monitoring operation based on the inference data having a limit on time gaps is not seen as adding limitations beyond what is taught in claim 7, as claim 7 notes the criteria includes an allowable time gap, thus since the AI/ML monitoring is already based on satisfying criteria the limitation related to “performing the AI/ML model monitoring operation based on…” in claim 15 is seen as already indicated by the same teachings used in claim 7. The motivation to combine with Ben Jones is the same motivation as provided in claim 7 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Fehrenbach et al (US 20260128958) is relevant art that discusses AI/ML models in wireless communication that involves using samples to update or retrain a AI/ML model or even deciding to switch to another AI/ML model based on feedback from the device the AI/ML model is running in. Gama et al (“Learning with Drift Detection”) is relevant art that discusses finding distribution drift and how distribution drift leads to increase in error of AI/ML models. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER D DEVORE whose telephone number is (703)756-1234. The examiner can normally be reached Monday-Friday 7:30 am - 5 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, Michael J Huntley can be reached at (303) 297-4307. 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. /C.D.D./Examiner, Art Unit 2129 /SCHYLER S SANKS/Primary Examiner, Art Unit 2129 Application/Control Number: 18/544,782 Page 2 Art Unit: 2129 Application/Control Number: 18/544,782 Page 3 Art Unit: 2129 Application/Control Number: 18/544,782 Page 4 Art Unit: 2129 Application/Control Number: 18/544,782 Page 5 Art Unit: 2129 Application/Control Number: 18/544,782 Page 6 Art Unit: 2129 Application/Control Number: 18/544,782 Page 7 Art Unit: 2129 Application/Control Number: 18/544,782 Page 8 Art Unit: 2129 Application/Control Number: 18/544,782 Page 9 Art Unit: 2129 Application/Control Number: 18/544,782 Page 10 Art Unit: 2129 Application/Control Number: 18/544,782 Page 11 Art Unit: 2129 Application/Control Number: 18/544,782 Page 12 Art Unit: 2129 Application/Control Number: 18/544,782 Page 13 Art Unit: 2129 Application/Control Number: 18/544,782 Page 14 Art Unit: 2129 Application/Control Number: 18/544,782 Page 15 Art Unit: 2129 Application/Control Number: 18/544,782 Page 16 Art Unit: 2129 Application/Control Number: 18/544,782 Page 17 Art Unit: 2129 Application/Control Number: 18/544,782 Page 18 Art Unit: 2129 Application/Control Number: 18/544,782 Page 19 Art Unit: 2129 Application/Control Number: 18/544,782 Page 20 Art Unit: 2129 Application/Control Number: 18/544,782 Page 21 Art Unit: 2129 Application/Control Number: 18/544,782 Page 22 Art Unit: 2129 Application/Control Number: 18/544,782 Page 23 Art Unit: 2129 Application/Control Number: 18/544,782 Page 24 Art Unit: 2129 Application/Control Number: 18/544,782 Page 25 Art Unit: 2129 Application/Control Number: 18/544,782 Page 26 Art Unit: 2129 Application/Control Number: 18/544,782 Page 27 Art Unit: 2129 Application/Control Number: 18/544,782 Page 28 Art Unit: 2129 Application/Control Number: 18/544,782 Page 29 Art Unit: 2129 Application/Control Number: 18/544,782 Page 30 Art Unit: 2129 Application/Control Number: 18/544,782 Page 31 Art Unit: 2129 Application/Control Number: 18/544,782 Page 32 Art Unit: 2129
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