CTFR 17/811,623 CTFR 92914 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. This action is responsive to the Amendment filed on February 5, 2026. Claims 6 and 8 are cancelled. Claims 1-5, 7, and 9-11 are pending in the case. Claims 1, 8, 10, and 11 are the independent claims. This action is final. Applicant’s Response In the Amendment filed on February 5, 2026, Applicant amended the claims and provided arguments in response to the interpretation of the claims under 25 USC 112(f) and rejections of the claims under 35 USC 101, 102, and 103 in the previous office action. Response to Argument/Amendment Applicant’s amendments to the claims in response to the interpretation of the claims under 35 USC 112(f) are acknowledged, and Applicant’s associated arguments have been fully considered. Applicant’s amendments to the claims, which include cancelling claim 8 and amending claim 9 to no longer invoke 35 USC 112(f), render the interpretation moot. Therefore it is withdrawn. Applicant’s amendments to the claims in response to the rejection of the claims under 35 USC 101 are acknowledged, and Applicant’s associated arguments have been fully considered. Applicant argues that the amended independent claims render the rejection moot because they integrate any purported abstract idea into a practical application which recites significantly more than any abstract idea. Applicant argues that the recited involvement of both a central server and plurality of information processing devices and transmission of information including data distributions and models between them leads to significant improvements in the training of machine learning models, including reducing the use of computational resources of the central server and reducing the data traffic between the central server and each of the information processing devices. Applicant points to paragraph 74 of the published application, which states that “it is possible to improve the accuracy of evaluating a global model by including a first acquirer configured to acquire a global model based on data sets regarding a plurality of cohorts, a second acquirer configured to acquire a local model based on a first data set regarding at least one of the plurality of first cohorts, a weight calculator configured to calculate the weight based on a first data distribution with respect to the first data set and a second data distribution with respect to a second data set regarding the plurality of cohorts, and an index value calculator configured to calculate an index value regarding the global model on the basis of the first data set and the weight.” Applicant concludes, comparing the instant application to the decision in Desjardins, that the claims are directed to a specific technical solution to a specific technical problem, noting that the claims are directed to training and obtaining an improved global model. These arguments are not persuasive. Applicant’s essential arguments appear to be that the various recited components of the claimed invention combine to provide various improvements and/or technical solutions to technical problems, including reduction of use of computational resources by the central server, reduction of data traffic between the central server and other devices, improved global model accuracy, and/or other improvements to the global model. However, unlike Desjardins, none of the purported improvements and solution argued by Applicant are actually reflected in the claims as currently recited. For example, while the claims do generally recite calculation of distributions at corresponding devices and transmitting these to the central server, calculating a second distribution at the central server and transmitting the second distribution and global model to the devices, and further generally recite acquiring the second distribution, acquiring the global mode, acquiring a local model, calculating a weight based on the first data distribution, and calculation of index values regarding the global model, none of these recited limitations appear to reflect or otherwise be tied to any of the actual improvements, such as reduction in computational resource use, reduction of data traffic, or any particular improvement to the global model. Therefore, the rejection under 35 USC 101 is maintained below . Applicant’s amendments to the claims in response to the rejections of the claims under 35 USC 102 and 103 are acknowledged, and Applicant’s associated arguments have been fully considered. Applicant argues that Barhak (the ‘523 application) fails to disclose “a plurality of information processing devices and a central server, wherein each of the plurality of information processing devices calculates a first data distribution of a data set regarding a corresponding cohort and transmits the first data distribution to the central server, the central server calculates a second data distribution of data sets regarding a plurality of cohorts based on a plurality of first data distributions transmitted from the plurality of information processing devices, and transmits. to each of the plurality of information processing devices, the second data distribution and a global model based on the data sets regarding the plurality of cohorts, and wherein each of the plurality of information processing devices comprises processing circuitry configured to acquire the second data distribution and the global model, acquire a local model based on the data set regarding each cohort, and calculate a weight based on the first data distribution and the second data distribution.” For example, Applicant appears to regard Barhak as especially failing to disclose the sending of the first data distribution from a plurality of information processing devices to the central server and the sending of the second data distribution and the global model from the central server to the plurality of information processing devices, as recited in the amended claims. This argument is persuasive with respect to Barhak’s failure to explicitly disclose the sending of the first data distribution from a plurality of information processing devices to the central server and the sending of the second data distribution and the global model from the central server to the plurality of information processing devices. Therefore, the rejection under 35 USC 102 is withdrawn . Applicant argues that Ghose (the ‘872 application) is directed to a continuous federated learning framework comprising a global model at a global site and respective local models derived from the global model at respective local sites, including retraining or retuning the global model and respective local models without sharing actual datasets between the global site and respective local sites but instead sharing synthetic datasets generated from the actual datasets. However, Applicant further argues that Ghose fails to disclose the same limitations as are discussed above with respect to Barhak. This argument is not persuasive. Ghose clearly teaches: an information processing system comprising a plurality of formation processing devices ( e.g. paragraph 0038, Fig. 1, multiple local sites or nodes 14; paragraph 0043, Fig. 3, local sites or nodes 14 include servers or computing devices; paragraph 0045, Fig. 5, describing combination of software and hardware, including processor-based system, for implementing global and local sites ) and a central server ( e.g. paragraph 0038, Fig. 1, global site 12, which is a central or main site; global site 12 including global model 16; paragraph 0043, Fig. 3, global site 12 located at central/main server or computing device at central or main site; paragraph 0045, Fig. 5, describing combination of software and hardware, including processor-based system, for implementing global and local sites ) used to perform a method; a non-transitory computer-readable storage medium storing programs used for the information processing system comprising the plurality of information processing devices and the central server, the programs comprising a first program for each of the plurality of information processing devices and a second program for the central server wherein the first and second programs respectively cause the plurality of information processing devices and central server to perform the method ( e.g. paragraph 0036, instructions, software routines, etc., stored in computer-readable media such as memory, mass storage device, etc., including information and software routines/programs for providing described implementations ); and the information processing method, using the information processing system comprising the plurality of information processing devices and the central server, the method comprising: using each of the plurality of information processing devices, calculating a first data distribution of a data set regarding a corresponding cohort and transmitting the first data distribution to the central server ( e.g. paragraph 0041, Fig. 2, at local sites 14, local dataset utilized in local retuning/retraining of global model to generate a new local model 18; utilizing local dataset to synthesize or generate a synthetic or generated dataset 30 that reflects the distribution of the actual or true data in the local dataset; new local models and local generated datasets 30 from each local site 14 encrypted and sent to central server; i.e. the local sites determine a distribution of a true local dataset and generate a synthetic dataset which reflects this distribution, and sends this to the global/central site ), using the central server, calculating a second data distribution of data sets regarding a plurality of cohorts based on a plurality of first data distributions transmitted from the plurality of information processing devices ( e.g. paragraph 0040, Fig. 2, global site/central server 12 using primary dataset which is a global dataset to synthesize or generate a dataset 26 which reflects the distribution of the actual or true data in the primary dataset 17; paragraph 0041, updating/retuning/retraining of global model 16 at global site/central server, using the transmitted local model and generated local dataset; repeating the process iteratively; i.e. as part of the repeated, iterative process, the central server receives the generated local datasets reflecting corresponding local distributions from a plurality of local sites and utilizes this dataset/distribution (along with the existing primary/global dataset) as a collective second data distribution of datasets regarding the plurality of local sites to retrain the model and generate a new generated global dataset ), and transmitting, to each of the plurality of information processing devices, the second data distribution and a global model based on the data sets regarding the plurality of cohorts ( e.g. paragraph 0040, the model 24 and generated dataset 26 distributed to each of the local sites 14; paragraph 0041, repeating the process iteratively; i.e. as part of the repeated, iterative process, in a subsequent repetition/iteration, the new global dataset/distribution (based on previously received local datasets/distributions) is again transmitted to each of the local sites along with a new/retrained global model ), and wherein each of the plurality of information processing devices comprises processing circuitry ( e.g. paragraph 0043, Fig. 3, local sites or nodes 14 include servers or computing devices; paragraph 0045, Fig. 5, describing combination of software and hardware, including processor-based system, for implementing global and local sites ) configured to perform further method steps including using each of the plurality of information processing devices, acquiring the second data distribution and the global model ( e.g. paragraph 0040, the model 24 and generated dataset 26 distributed to each of the local sites 14; paragraph 0041, repeating the process iteratively; i.e. as part of the repeated, iterative process, in a subsequent repetition/iteration, the new global dataset/distribution (based on previously received local datasets/distributions) is again transmitted to each of the local sites along with a new/retrained global model ); acquiring a local model based on the data set regarding each cohort ( e.g. paragraph 0041, Fig. 2, at local sites 14, local dataset utilized in local retuning/retraining of global model to generate a new local model 18; utilizing local dataset to synthesize or generate a synthetic or generated dataset 30 that reflects the distribution of the actual or true data in the local dataset; new local models and local generated datasets 30 from each local site 14 encrypted and sent to central server; repeating the process iteratively; i.e. the local sites determine a distribution of a true local dataset and generate a synthetic dataset which reflects this distribution, and sends this to the global/central site ). Applicant’s arguments do not appear to consider the above-cited teachings of Ghose, or to make any distinction between these teachings of Ghose and the instant application, other than the bare allegation that Ghose does not teach the limitations. However, Ghose does appear to teach most of the limitations which Applicant alleges that it does not teach. Although Applicant points out that Ghose teaches sharing synthetic datasets generated from the actual datasets instead of sharing the actual datasets between the global and local sites, Examiner notes that the claim limitations do not appear to explicitly require sharing of actual datasets. Instead, the claims teach transmitting calculated first and second data distributions between the central server and plurality of devices. As noted by Applicant Ghose teaches sharing of synthetic datasets; as cited above, Ghose further teaches that these synthetic datasets reflect and/or have the same distribution as the actual datasets. Therefore, since Ghose does teach transmission of these synthetic datasets, which reflect the calculated distributions of the actual datasets, one of ordinary skill in the art would understand Ghose as teaching the transmission of the respective local and global distributions (i.e. as reflected/encapsulated in the corresponding datasets) between the central/global server and the local devices. Although Ghose does not appear to explicitly disclose “calculate a weight based on the first data distribution and the second data distribution; and calculate an index value regarding the global model based on the data set regarding each cohort and the weight,” as cited in the previous office action, Barhak appears to teach: calculating a weight based on the first data distribution and the second data distribution ( e.g. paragraph 0034, evaluating fitness of individual models based on contributions of individual models to overall prediction; coefficients for each model indicating contribution of corresponding model; determining the coefficients for the models; paragraph 0035, coefficients indicate influence (i.e. weight) of the individual models; coefficients of individual models can sub to one and can have values ranging from 0 to 1; coefficients with values closer to 1 have more influence; paragraph 0037, coefficients based upon initial conditions; paragraph 0058, initial guesses regarding coefficients for the different models; simulations using virtual populations compared to observed outcomes (i.e. based on real population for given clinical study) to determine fitness; i.e. coefficients are determined for each of the plurality of models, the coefficients indicating relative weighting/importance of the individual models with respect to one another; where each model is representative of/based on particular clinical study data/dataset, the determination of this relative importance/weighting for a given model would be with respect to at least a first dataset (such as the dataset which the model is based upon) and a second dataset (such as another dataset/model which is also having a relative weighting/importance determined, among all of the models and/or a virtual population based on aggregate data) ); and calculating an index value regarding the global model based on the data set regarding each cohort and the weight ( e.g. paragraphs 0059-0061, applying optimization techniques in order to determine fitness of the models; determining values of coefficients at local minimum; evaluating aggregate model by determining fitness of the aggregate model with the values of the coefficients, including by comparing results of simulations with observed outcomes of similar population; differences between results and observed outcomes used to determine a fitness score; see also paragraphs 0099 and 0117, describing evaluating fitness of the aggregate model ). Therefore, as cited above, the combination of Barhak and Ghose appears to teach the amended independent claims. New grounds of rejection are provided below. 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 1-5, 7, and 9-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental steps) without significantly more. This judicial exception is not integrated into a practical application because any additional elements amount to implementing the abstract idea on a generic computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding independent claims 1, 10, and 11 , and relying on the evaluation flowchart in MPEP 2106: Step 1 (Is the claim to a process, machine, manufacture, or composition of matter?) : Yes. Claim 1 is a system (machine). Claim 10 is a method (process). Claim 11 is a storage medium (article of manufacture). Step 2a Prong One (Does the claim recite an abstract idea?) : Yes. Claims 1, 10, and 11 recite: calculating a first data distribution of a dataset regarding a corresponding cohort (a mental process involving performing a mathematical calculation, with or without the aid of pen and paper, such as a human mentally performing calculations to determine a first data distribution for a cohort); calculating a second data distribution of data sets regarding a plurality of cohorts based on a plurality of first data distributions (a mental process involving performing a mathematical calculation, with or without the aid of pen and paper, such as a human mentally performing calculations to determine a second data distribution for a multiple cohorts based on a plurality of distributions); calculating a weight based on the first data distribution and the second data distribution (a mental process involving performing a mathematical calculation, with or without the aid of pen and paper, such as a human mentally performing calculations for a weight based on first and second data distributions); calculating an index value regarding the global model based on the data set regarding each cohort and the weight (a mental process involving performing a mathematical calculation, with or without the aid of pen and paper, such as a human mentally performing calculations for an index value using the data sets and the weight). Under the broadest reasonable interpretation, these steps may be performed mentally, using mental observation and mental determination, including by a human using a physical aid such as pen and paper, including a human mentally performing observations and mentally performing mathematical calculations, and therefore correspond to the Mental Processes grouping. Step 2a Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) : No. Claims 1, 10, and 11 additionally recite: transmitting the first data distribution to the central server…plurality of first data distributions transmitted from the plurality of information processing devices (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g)); transmitting the second data distribution and a global model based on the data sets regarding the plurality of cohorts (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g)); acquiring the second data distribution and the global model (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g) with respect to the acquiring limitation); acquiring a local model based on the first data set regarding each cohort (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g) with respect to the acquiring limitation). Claim 1 additionally recites an information processing system comprising a plurality of information processing devices; and a central server, wherein each of the plurality of information processing devices perform the first calculating step and first transmitting step, the central server performs the second calculating step and second transmitting step, and wherein each of the plurality of information processing devices comprises processing circuitry configured to perform the limitations discussed above including the acquiring steps and the steps for calculating the weight and calculating the index value (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Claim 10 additionally recites using an information processing system comprising a plurality of information processing devices and a central server to perform the method, including using each of the plurality of information processing devices to perform the first calculating step and first transmitting step, using the central server to perform the second calculating step and second transmitting step, and using each of the plurality of information processing devices to perform the limitations discussed above including the acquiring steps and the steps for calculating the weight and calculating the index value (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Claim 11 additionally recites a non-transitory computer-readable storage medium storing programs used for an information processing system comprising a plurality of information processing devices and a central server, the programs comprising a first program for each of the plurality of information processing devices and a second program for the central server, wherein the first program causes each of the plurality of information processing devices to perform the first calculating step and first transmitting step, the second program causes the central server to perform the second calculating step and second transmitting step, and the first program further causes each of the plurality of information processing devices to perform the limitations discussed above including the acquiring steps and the steps for calculating the weight and calculating the index value (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Therefore, in view of the considerations set forth in MPEP 2106.04(d), 2106.05(a)-(c) and (e)-(h), the additional elements as disclosed above alone or in combination do not integrate the judicial exception into a practical application as they are mere insignificant extra solution activity, combined with implementing the abstract idea using generic computer components. Step 2b (Does the claim recite additional elements that amount to siqnificantly more than the judicial exception) : No. Relying on the same analysis as Step 2a Prong Two (see MPEP 2106.05.I.A: Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:…Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP 2106.05(f));…Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception...; Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g);…) ), claims 1, 10, and 11 do not recite any additional elements that amount to significantly more than the abstract idea. As discussed above, Claims 1, 10, and 11 recite: transmitting the first data distribution to the central server…plurality of first data distributions transmitted from the plurality of information processing devices (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g)); transmitting the second data distribution and a global model based on the data sets regarding the plurality of cohorts (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g)); acquiring the second data distribution and the global model (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g) with respect to the acquiring limitation); acquiring a local model based on the first data set regarding each cohort (insignificant extra-solution activity of mere data gathering and/or transmitting data over a network as discussed in MPEP 2106.05(g) with respect to the acquiring limitation). Claim 1 additionally recites an information processing system comprising a plurality of information processing devices; and a central server, wherein each of the plurality of information processing devices perform the first calculating step and first transmitting step, the central server performs the second calculating step and second transmitting step, and wherein each of the plurality of information processing devices comprises processing circuitry configured to perform the limitations discussed above including the acquiring steps and the steps for calculating the weight and calculating the index value (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Claim 10 additionally recites using an information processing system comprising a plurality of information processing devices and a central server to perform the method, including using each of the plurality of information processing devices to perform the first calculating step and first transmitting step, using the central server to perform the second calculating step and second transmitting step, and using each of the plurality of information processing devices to perform the limitations discussed above including the acquiring steps and the steps for calculating the weight and calculating the index value (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). Claim 11 additionally recites a non-transitory computer-readable storage medium storing programs used for an information processing system comprising a plurality of information processing devices and a central server, the programs comprising a first program for each of the plurality of information processing devices and a second program for the central server, wherein the first program causes each of the plurality of information processing devices to perform the first calculating step and first transmitting step, the second program causes the central server to perform the second calculating step and second transmitting step, and the first program further causes each of the plurality of information processing devices to perform the limitations discussed above including the acquiring steps and the steps for calculating the weight and calculating the index value (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f)). The additional elements as discussed above, in combination with the abstract idea, are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination with generic computer functions and components used to implement the abstract idea. Regarding dependent claim 2: Step 2a Prong One: incorporates the rejection of claim 1. Claim 2 additionally recites selects the provided model based on the index value calculated (mental process involving performing a determination or selection, with or without the aid of pen and paper, such as a human mentally evaluating a model to select based on an index value). Step 2a Prong Two: the claims additionally recite wherein the index value is an index value for selecting a provided model used to generate the global model, and the processing circuitry performs the selection (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the “processing circuitry” and recitation of using a provided model to generate the global model, and field of use and technological environment discussed in MPEP 2106.05(h) with respect to the limitations indicating the index value is used for selection of a provided model). Step 2b: the claims additionally recite wherein the index value is an index value for selecting a provided model used to generate the global model, and the processing circuitry performs the selection (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the “processing circuitry” and recitation of using a provided model to generate the global model, and field of use and technological environment discussed in MPEP 2106.05(h) with respect to the limitations indicating the index value is used for selection of a provided model). Regarding dependent claim 3: Step 2a Prong One: incorporates the rejection of claim 1. The claims additionally recite wherein the weight is a global density ratio calculated based on the first data distribution and the second data distribution (a mental process involving performing a mathematical calculation, with or without the aid of pen and paper, such as a human mentally performing calculations for a weight as a global density ratio based on first and second data distributions) Step 2a Prong Two: the claims do not recite any other limitations in addition to the abstract idea discussed above. Step 2b: the claims do not recite any other limitations in addition to the abstract idea discussed above. Regarding dependent claim 4: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite wherein the processing circuitry is further configured to train at least one of the global model or the local model using the data set regarding each cohort (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f); i.e. performing generic training of a generic model using a dataset). Step 2b: the claims additionally recite wherein the processing circuitry is further configured to train at least one of the global model or the local model using the data set regarding each cohort (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f); i.e. performing generic training of a generic model using a dataset). Regarding dependent claim 5: Step 2a Prong One: incorporates the rejection of claim 1; the claim further recites select the provided model (a mental process of evaluation, such as a human mentally determining/selecting a provided model). Step 2a Prong Two: the claims additionally recite wherein the processing circuitry is further configured to select the provided model from a model group including global models and trained global models (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the use of processing circuitry and generic models, and field of use and technological environment discussed in MPEP 2106.05(h) with respect to a model group of global models and trained global models). Step 2b: the claims additionally recite wherein the processing circuitry is further configured to select the provided model from a model group including global models and trained global models (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to the use of processing circuitry and generic models, and field of use and technological environment discussed in MPEP 2106.05(h) with respect to a model group of global models and trained global models). Regarding dependent claim 7: Step 2a Prong One: incorporates the rejection of claim 1. Claim 7 additionally recites select an operation local model…from a plurality of models (a mental process of evaluation, such as a human mentally determining a model to select). Step 2a Prong Two: the claims additionally recite wherein the processing circuitry is further configured to select…operation local model operated in medical practice…a plurality of models including the global model and the local model (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to use of processing circuitry to perform the recited function, the plurality of models including the global model and the local model, and a field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the operation local model being operated in medical practice). Step 2b: the claims additionally recite wherein the processing circuitry is further configured to select…operation local model operated in medical practice…a plurality of models including the global model and the local model (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f) with respect to use of processing circuitry to perform the recited function, the plurality of models including the global model and the local model, and a field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the operation local model being operated in medical practice). Regarding dependent claim 9: Step 2a Prong One: incorporates the rejection of claim 1. Step 2a Prong Two: the claims additionally recite wherein the central server further comprises second circuitry configured to integrate the global models provided by the plurality of information processing devices into a new global model (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f), with respect to use of the central server, further comprising an integrator, to perform the recited function, the plurality of provided models, and the new global model, field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the plurality of sites providing models, and outputting results of integrating models as a new global model). Step 2b: the claims additionally recite wherein the central server further comprises second circuitry configured to integrate the global models provided by the plurality of information processing devices into a new global model (mere instructions to apply the exception using generic computer components as discussed in MPEP 2106.05(f), with respect to use of the central server, further comprising an integrator, to perform the recited function, the plurality of provided models, and the new global model, field of use and technological environment as discussed in MPEP 2106.05(h) with respect to the plurality of sites providing models, and outputting results of integrating models as a new global model). 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 set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. 07-20-02-fti This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102€, (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a). 07-21-aia AIA Claim s 1-3, 7, and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Barhak (US 20210183523 A1) in view of Ghose et al. (US 20230004872 A1) . With respect to claims 1, 10, and 11, Barhak teaches an information processing system comprising a plurality of formation processing devices and a central server used to perform a method ( e.g. paragraph 0065, Fig. 6, computing device 602 can be implemented with one or more processing units and memory, which can be distributed across one or more physical or logical locations; operations described as being performed by computing device 602 can be performed by multiple computing devices; paragraph 0074, functional components of computing device 602 executed in the one or more processing units include clinical data import module 616, virtual population generation module 618, and model evaluation module; modules 616, 618, and 620 used to implement frameworks 100, 220, 300, 400 of Figs. 1-4 ); a non-transitory computer-readable storage medium storing programs used for the information processing system comprising the plurality of information processing devices and the central server, the programs comprising a first program for each of the plurality of information processing devices and a second program for the central server, wherein the first and second programs cause the plurality of information processing devices and the central server to perform respective method steps ( e.g. paragraphs 0065-0068, Fig. 6, computing device 600 to evaluate models implemented with processing units 604 and memory 606, the memory providing storage of computer readable instructions, etc. ); and an information processing method, using the information processing system comprising the plurality of information processing devices and the central server, the method comprising: using each of the plurality of information processing devices, calculating a first data distribution of a data set regarding a corresponding cohort ( e.g. paragraph 0027, using rules that generate distributions for a population that participated in a clinical study; paragraph 0039, information extracted from clinical studies data includes information related to populations that participated in individual clinical studies including baseline population distributions; paragraph 0311, executing models separately on protected populations within each institution’s secure environment; paragraph 0313, multiple institutions holding datasets; each institution executing model components locally on their own protected data ); using the central server, calculating a second data distribution of data sets regarding a plurality of cohorts based on a plurality of first data distributions ( e.g. paragraph 0026, generating synthetic population/aggregate population from number of different clinical studies to determine virtual population having corresponding distributions; virtual population may have distributions that are derived from number of clinical study populations, but do not actually match the populations that participated in the clinical studies, although describing similar statistics; paragraph 0048, Fig. 2, virtual populations having characteristics that correspond with aggregate characteristics of actual populations studied in clinical studies; paragraph 0054, statistical distributions for virtual population; paragraph 0075, clinical data import module 616 causing computing device to extract data about the clinical studies; ), and providing the second data distribution and a global model based on the data sets regarding the plurality of cohorts ( e.g. paragraph 0035, Fig. 1, producing aggregate model; paragraph 0041, using cooperative framework, obtaining models being evaluated and populations utilized to evaluate the models, including virtual populations based on aggregated information; paragraph 0048, Fig. 2, virtual populations having characteristics that correspond with aggregate characteristics of actual populations studied in clinical studies; paragraph 0049, models obtained from clinical studies data in light of virtual populations; evaluating models in light of virtual populations; producing aggregate model comprised of the individual models; paragraph 0075, clinical data import module 616 causing computing device to extract data about the clinical studies; paragraph 0080, model evaluation module 620; aggregated model produced from plurality of models; i.e. an aggregate/global model is acquired/produced based on a plurality of individual models of individual populations/cohorts, each representative of a corresponding data set/clinical study data ), and using each of the plurality of information processing devices, acquiring the second data distribution and the global model ( e.g. paragraph 0035, Fig. 1, producing aggregate model; paragraph 0041, using cooperative framework, obtaining models being evaluated and populations utilized to evaluate the models, including virtual populations based on aggregated information (i.e. where these virtual populations have their own statistical distributions as previously cited); paragraph 0048, Fig. 2, virtual populations having characteristics that correspond with aggregate characteristics of actual populations studied in clinical studies; paragraph 0049, models obtained from clinical studies data in light of virtual populations; evaluating models in light of virtual populations; producing aggregate model comprised of the individual models; i.e. an aggregate/global model is acquired/produced based on a plurality of individual models of individual populations/cohorts, each representative of a corresponding data set/clinical study data ); acquiring a local model based on based on the first data set regarding each cohort ( e.g. paragraph 0032, Fig. 1, deriving models from clinical studies data; models derived from results of individual clinical studies; paragraph 0048, Fig. 2, virtual populations having characteristics that correspond with aggregate characteristics of actual populations studied in clinical studies; paragraph 0049, models obtained from clinical studies data in light of virtual populations; i.e. a first model is obtained based on a first virtual population corresponding to first clinical studies data, among a plurality of virtual populations corresponding to a plurality of clinical studies ); calculating a weight based on the first data distribution and the second data distribution ( e.g. paragraph 0034, evaluating fitness of individual models based on contributions of individual models to overall prediction; coefficients for each model indicating contribution of corresponding model; determining the coefficients for the models; paragraph 0035, coefficients indicate influence (i.e. weight) of the individual models; coefficients of individual models can sub to one and can have values ranging from 0 to 1; coefficients with values closer to 1 have more influence; paragraph 0037, coefficients based upon initial conditions; paragraph 0058, initial guesses regarding coefficients for the different models; simulations using virtual populations compared to observed outcomes (i.e. based on real population for given clinical study) to determine fitness; i.e. coefficients are determined for each of the plurality of models, the coefficients indicating relative weighting/importance of the individual models with respect to one another; where each model is representative of/based on particular clinical study data/dataset, the determination of this relative importance/weighting for a given model would be with respect to at least a first dataset (such as the dataset which the model is based upon) and a second dataset (such as another dataset/model which is also having a relative weighting/importance determined, among all of the models and/or a virtual population based on aggregate data) ); and calculating an index value regarding the global model based on the data set regarding each cohort and the weight ( e.g. paragraphs 0059-0061, applying optimization techniques in order to determine fitness of the models; determining values of coefficients at local minimum; evaluating aggregate model by determining fitness of the aggregate model with the values of the coefficients, including by comparing results of simulations with observed outcomes of similar population; differences between results and observed outcomes used to determine a fitness score; see also paragraphs 0099 and 0117, describing evaluating fitness of the aggregate model ). Barhak does not explicitly disclose: transmitting the first data distribution to the central server, such that the first data distributions are transmitted from the plurality of information processing devices; transmitting, to each of the plurality of information processing devices, the second data distribution and a global model based on the data sets regarding the plurality of cohorts. However, Ghose teaches an information processing system comprising a plurality of formation processing devices ( e.g. paragraph 0038, Fig. 1, multiple local sites or nodes 14; paragraph 0043, Fig. 3, local sites or nodes 14 include servers or computing devices; paragraph 0045, Fig. 5, describing combination of software and hardware, including processor-based system, for implementing global and local sites ) and a central server ( e.g. paragraph 0038, Fig. 1, global site 12, which is a central or main site; global site 12 including global model 16; paragraph 0043, Fig. 3, global site 12 located at central/main server or computing device at central or main site; paragraph 0045, Fig. 5, describing combination of software and hardware, including processor-based system, for implementing global and local sites ) used to perform a method; a non-transitory computer-readable storage medium storing programs used for the information processing system comprising the plurality of information processing devices and the central server, the programs comprising a first program for each of the plurality of information processing devices and a second program for the central server wherein the first and second programs respectively cause the plurality of information processing devices and central server to perform the method ( e.g. paragraph 0036, instructions, software routines, etc., stored in computer-readable media such as memory, mass storage device, etc., including information and software routines/programs for providing described implementations ); and the information processing method, using the information processing system comprising the plurality of information processing devices and the central server, the method comprising: using each of the plurality of information processing devices, calculating a first data distribution of a data set regarding a corresponding cohort and transmitting the first data distribution to the central server ( e.g. paragraph 0041, Fig. 2, at local sites 14, local dataset utilized in local retuning/retraining of global model to generate a new local model 18; utilizing local dataset to synthesize or generate a synthetic or generated dataset 30 that reflects the distribution of the actual or true data in the local dataset; new local models and local generated datasets 30 from each local site 14 encrypted and sent to central server; i.e. the local sites determine a distribution of a true local dataset and generate a synthetic dataset which reflects this distribution, and sends this to the global/central site ), using the central server, calculating a second data distribution of data sets regarding a plurality of cohorts based on a plurality of first data distributions transmitted from the plurality of information processing devices ( e.g. paragraph 0040, Fig. 2, global site/central server 12 using primary dataset which is a global dataset to synthesize or generate a dataset 26 which reflects the distribution of the actual or true data in the primary dataset 17; paragraph 0041, updating/retuning/retraining of global model 16 at global site/central server, using the transmitted local model and generated local dataset; repeating the process iteratively; i.e. as part of the repeated, iterative process, the central server receives the generated local datasets reflecting corresponding local distributions from a plurality of local sites and utilizes this dataset/distribution (along with the existing primary/global dataset) as a collective second data distribution of datasets regarding the plurality of local sites to retrain the model and generate a new generated global dataset ), and transmitting, to each of the plurality of information processing devices, the second data distribution and a global model based on the data sets regarding the plurality of cohorts ( e.g. paragraph 0040, the model 24 and generated dataset 26 distributed to each of the local sites 14; paragraph 0041, repeating the process iteratively; i.e. as part of the repeated, iterative process, in a subsequent repetition/iteration, the new global dataset/distribution (based on previously received local datasets/distributions) is again transmitted to each of the local sites along with a new/retrained global model ), and wherein each of the plurality of information processing devices comprises processing circuitry ( e.g. paragraph 0043, Fig. 3, local sites or nodes 14 include servers or computing devices; paragraph 0045, Fig. 5, describing combination of software and hardware, including processor-based system, for implementing global and local sites ) configured to perform further method steps including using each of the plurality of information processing devices, acquiring the second data distribution and the global model ( e.g. paragraph 0040, the model 24 and generated dataset 26 distributed to each of the local sites 14; paragraph 0041, repeating the process iteratively; i.e. as part of the repeated, iterative process, in a subsequent repetition/iteration, the new global dataset/distribution (based on previously received local datasets/distributions) is again transmitted to each of the local sites along with a new/retrained global model ); acquiring a local model based on the data set regarding each cohort ( e.g. paragraph 0041, Fig. 2, at local sites 14, local dataset utilized in local retuning/retraining of global model to generate a new local model 18; utilizing local dataset to synthesize or generate a synthetic or generated dataset 30 that reflects the distribution of the actual or true data in the local dataset; new local models and local generated datasets 30 from each local site 14 encrypted and sent to central server; repeating the process iteratively; i.e. the local sites determine a distribution of a true local dataset and generate a synthetic dataset which reflects this distribution, and sends this to the global/central site ). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Barhak and Ghose in front of him to have modified the teachings of Barhak (directed to analysis and verification of models derived from clinical studies data extracted from a database), to incorporate the teachings of Ghose (directed to deep learning techniques utilizing continuous federated learning with a distributed data generative model) to include the capability to arrange the distributed/federated computing capabilities (i.e. of Barhak) in a centralized arrangement, such that the global/aggregated model and corresponding distribution is obtained at a central server, and local/distributed sites acquire the global/aggregated model provided by the central server and send, to the central server, their own local models and distributions. One of ordinary skill would have been motivated to perform such a modification in order to enable local learning at the site level to account for site-specific preferences while maintaining global performance, mitigating the issue of catastrophic forgetting, ensuring better performance, using a model that by design is generalizable across multiple industries as described in Ghose (paragraph 0025-0026). With respect to claim 9, Barhak in view of Ghose teaches all of the limitations of claim 1 as previously discussed, and Barhak further teaches wherein the central server further comprises second circuitry configured to integrate the global models provided by the plurality of information processing devices into a new global model ( e.g. paragraph 0035, Fig. 1, producing aggregate model with coefficients indicating contribution of each individual model; aggregate model represented as aA+bB+cC+dD, where A, B, C, D are functions that represent the individual models and a, b, c, d are the coefficients indicating the influence of the individual models A, B, C, and D on the prediction; paragraph 0110, iterative process determining final aggregate model ). With respect to claim 2, Barhak in view of Ghose teaches all of the limitations of claim 1 as previously discussed, and Barhak further teaches wherein the index value is an index value for selecting a provided model used to generate the global model, and the processing circuitry selects the provided model based on the index value calculated ( e.g. paragraph 0020, evaluating coefficients for each model that provide best fitness; coefficients having greatest contribution identified as models that have best fitness; paragraph 0037, fitness of aggregate model evaluated and set of values for individual coefficients of aggregate equation having best fitness determined; paragraph 0055, determining fitness of combination of individual models by evaluating aggregate model; paragraph 0061, determining fitness of aggregate model by comparing results of simulations with observed outcomes; differences between results of simulations and observed outcomes used to determine a fitness score for initial iteration; performing simulations for subsequent combinations and determining corresponding fitness scores; if fitness scores improve, iterative process continues until criteria are satisfied; paragraph 0110, iterative process determining final aggregate model; i.e. different combinations of coefficients of models comprising the aggregated/global model (therefore comprising different aggregated models) are iteratively evaluated with respect to their respective fitness scores, until an aggregated model having a fitness score meeting a given criteria is determined, where the fitness score meeting the criteria appears to indicate that this particular model (i.e. having the best fitness) is to be utilized/selected (such that the fitness score/index provides a basis for selection) ). With respect to claim 3, Barhak in view of Ghose teaches all of the limitations of claim 1 as previously discussed, and Barhak further teaches wherein the weight is a global density ratio calculated based on the first data distribution and the second data distribution ( e.g. paragraph 0031, clinical studies data includes population data for populations participating in individual studies, including baseline population distributions; paragraph 0034, evaluating fitness of individual models based on contributions of individual models to overall prediction; coefficients for each model indicating contribution of corresponding model; paragraph 0035, coefficients indicate influence (i.e. weight) of the individual models; coefficients of individual models can sum to one and can have values ranging from 0 to 1; coefficients with values closer to 1 have more influence; paragraph 0045, statistical distributions for virtual populations; paragraph 0048, Fig. 2, virtual populations having characteristics that correspond with aggregate characteristics of actual populations studied in clinical studies; paragraph 0049, models obtained from clinical studies data in light of virtual populations; evaluating models in light of virtual populations; producing aggregate model comprised of the individual models; paragraph 0085, information regarding populations including statistical distribution; i.e. the coefficient/weight measures a relative/proportional contribution of a given model corresponding to a given dataset/distribution with respect to the aggregate/global contribution of all models, where the relative/proportional contribution for each model is a number between 0 and 1, and these are summed to equal 1, representative of the aggregate/global contribution of all models corresponding to the aggregate/global data set/distribution, such that the coefficient/weight for a given model represents a proportion/ratio/fraction of that model/dataset with respect to aggregate/global models/datasets; compare with page 12, final paragraph, of the specification of the instant application, indicating that global density ration is calculated by calculating the ratio of the global data distribution to the local data distribution (i.e. individual models, representative of populations associated with individual studies, having a particular distribution are determined as having a particular importance/weighting/contribution with respect to the aggregate model, representative of all relevant populations associated with all relevant studies, having corresponding distributions, such that the coefficient associated with a particular mode/population/distribution is representative of a ratio of the local/individual data/model with respect to the global/aggregate data/model) ). With respect to claim 7, Barhak in view of Ghose teaches all of the limitations of claim 1 as previously discussed, and Barhak further teaches wherein the processing circuitry is further configured to select an operation local model operated in medical practice from a plurality of models including the global model and the local model ( e.g. paragraph 0035, aggregate model with coefficients indicating influence of individual models on prediction of progression of biological condition such as diabetes; paragraph 0036, fitness of each model for prediction progression of disease determined; paragraph 0042, clinical studies related to heart disease; paragraph 0088, models that predict progression of biological condition, derived from respective clinical studies; paragraph 0110, iterative process determining final aggregate model; paragraph 0112, identifying plurality of models that predict progression of biological condition; i.e. various individual/local models corresponding to specific studies are selected/identified, where these models are related to prediction of disease progression and therefore operated in medical practice ) . 07-21-aia AIA Claim s 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Barhak in view of Ghose, further in view of Isaksson et al. (US 20240296342 A1) . With respect to claim 4, Barhak in view of Ghose teaches all of the limitations of claim 1 as previously discussed. Although Barhak further teaches training of at least one of the global model or the local model in general ( e.g. paragraph 0026, discussing training and validation of models, where training apparently includes using simulations to generate an aggregate model, etc.; paragraph 0061, iterative process of determining fitness of aggregate model with respect to simulations, performing further simulations for subsequent guess combinations, etc. until given criteria are satisfied; paragraph 0062, iterative process optimizing the aggregate model; i.e. Barhak appears to teach an iterative optimization process intended to minimize a difference/loss between the model under evaluation and real world/ground truth results, in order to find a model having a best fitness for making predictions, and therefore appears to teach at least that the global model is trained, in addition to teaching training of models in general ), Barhak does not explicitly disclose wherein the processing circuitry is further configured to train at least one of the global model or the local model using the data set regarding each cohort. However, Isakkson teaches wherein the processing circuitry is further configured to train at least one of the global model or the local model using the data set regarding each cohort ( e.g. paragraph 0036, distributed and decentralized ML setting that includes K clients, each having access to a local data partition; paragraph 0037, each client partition of data divided into set of training data and test data; paragraphs 0049, 0052, performing training rounds on the local training data using the selected global ML model; paragraph 0053, new model is the global model selected based on evaluation of the metric, and the new ML model is trained on the set of data; paragraph 0055, performing training rounds on local training data using selected global ML model; paragraph 0112, training local ML model 303 as shown in Fig. 3 ). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Barhak, Ghose, and Isaksson in front of him to have modified the teachings of Barhak (directed to analysis and verification of models derived from clinical studies data extracted from a database) and Ghose (directed to deep learning techniques utilizing continuous federated learning with a distributed data generative model), to incorporate the teachings of Isaksson (directed to selection of global machine learning models for collaborative machine learning in a communication network) to include the capability to train at least one of the local model (i.e. an individual model of Barhak, based on an individual clinical trial and corresponding population and dataset) and the global model (i.e. the aggregated model of Barhak, based on combined models of combined clinical trials, populations and datasets) using the first data set (i.e. a local training data set of Isaksson specific to a particular/individual client device, analogous to an individual dataset associated with an individual population and corresponding clinical trial of Barhak). One of ordinary skill would have been motivated to perform such a modification in order to handle non-IID data distribution among distributed clients to improve or optimize performance of ML models, improve convergence, and prime ML models as described in Isaksson (paragraph 0011-0012). With respect to claim 5, Barhak in view of Ghose teaches all of the limitations of claim 2 as previously discussed. Although Barhak generally teaches selection of a model ( e.g. paragraph 0032, Fig. 1, deriving models from clinical studies data; models derived from results of individual clinical studies; paragraph 0049, models obtained from clinical studies data in light of virtual populations; paragraph 0110, iterative process determining final aggregate model ), Barhak does not explicitly disclose wherein the processing circuitry is further configured to select the provided model from a model group including global models and trained global models. However, Isaksson teaches wherein the processing circuitry is further configured to select the provided model from a model group including global models and trained global models ( e.g. paragraph 0041, selecting global ML model from a plurality of global ML models; paragraphs 0049, 0052, performing training rounds on the local training data using the selected global ML model ) . Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention having the teachings of Barhak, Ghose, and Isaksson in front of him to have modified the teachings of Barhak (directed to analysis and verification of models derived from clinical studies data extracted from a database) and Ghose (directed to deep learning techniques utilizing continuous federated learning with a distributed data generative model), to incorporate the teachings of Isaksson (directed to selection of global machine learning models for collaborative machine learning in a communication network) to include the capability to select a provided model from a group of global ML models, where at least some of the global models are trained models. One of ordinary skill would have been motivated to perform such a modification in order to handle non-IID data distribution among distributed clients to improve or optimize performance of ML models, improve convergence, and prime ML models as described in Isaksson (paragraph 0011-0012). It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L STANLEY whose telephone number is (469)295-9105. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM CST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar, can be reached at telephone number (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center and the Private Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from Patent Center or Private PAIR. Status information for unpublished applications is available through Patent Center and Private PAIR for authorized users only. Should you have questions about access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form. /JEREMY L STANLEY/ Primary Examiner, Art Unit 2127 Application/Control Number: 17/811,623 Page 2 Art Unit: 2127 Application/Control Number: 17/811,623 Page 3 Art Unit: 2127 Application/Control Number: 17/811,623 Page 4 Art Unit: 2127 Application/Control Number: 17/811,623 Page 5 Art Unit: 2127 Application/Control Number: 17/811,623 Page 6 Art Unit: 2127 Application/Control Number: 17/811,623 Page 7 Art Unit: 2127 Application/Control Number: 17/811,623 Page 8 Art Unit: 2127 Application/Control Number: 17/811,623 Page 9 Art Unit: 2127 Application/Control Number: 17/811,623 Page 10 Art Unit: 2127 Application/Control Number: 17/811,623 Page 11 Art Unit: 2127 Application/Control Number: 17/811,623 Page 12 Art Unit: 2127 Application/Control Number: 17/811,623 Page 13 Art Unit: 2127 Application/Control Number: 17/811,623 Page 14 Art Unit: 2127 Application/Control Number: 17/811,623 Page 15 Art Unit: 2127 Application/Control Number: 17/811,623 Page 16 Art Unit: 2127 Application/Control Number: 17/811,623 Page 17 Art Unit: 2127 Application/Control Number: 17/811,623 Page 18 Art Unit: 2127 Application/Control Number: 17/811,623 Page 19 Art Unit: 2127 Application/Control Number: 17/811,623 Page 20 Art Unit: 2127 Application/Control Number: 17/811,623 Page 21 Art Unit: 2127 Application/Control Number: 17/811,623 Page 22 Art Unit: 2127 Application/Control Number: 17/811,623 Page 23 Art Unit: 2127 Application/Control Number: 17/811,623 Page 24 Art Unit: 2127 Application/Control Number: 17/811,623 Page 25 Art Unit: 2127 Application/Control Number: 17/811,623 Page 26 Art Unit: 2127 Application/Control Number: 17/811,623 Page 27 Art Unit: 2127 Application/Control Number: 17/811,623 Page 28 Art Unit: 2127 Application/Control Number: 17/811,623 Page 29 Art Unit: 2127 Application/Control Number: 17/811,623 Page 30 Art Unit: 2127 Application/Control Number: 17/811,623 Page 31 Art Unit: 2127 Application/Control Number: 17/811,623 Page 32 Art Unit: 2127 Application/Control Number: 17/811,623 Page 33 Art Unit: 2127 Application/Control Number: 17/811,623 Page 34 Art Unit: 2127
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