DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action is in response to applicant’s communication filed 2 April 2026, in response to the Office Action mailed 5 January 2026. The applicant’s remarks and any amendments to the claims or specification have been considered, with the results that follow.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statements, dated 11 March 2026 and 8 May 2026, are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 forms, initialed and dated by the examiner, are attached to the instant office action.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claim(s) 1-3, 9-13, and 15-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kallonen (WO 2022/074300) in view of Gesley (US 10,936,921 – cited in an IDS).
As per claim 1, Kallonen teaches a system comprising: memory hardware configured to store instructions; and processor hardware configured to execute the instructions [the system includes one or more data processing devices, which comprise one or more processors (processor hardware) and at least one connected memory storing instructions to be executed by the processor(s) (pg. 18, line 18 to pg. 19, line 15; fig. 1; etc.)], wherein the instructions include: loading a machine learning model, loading a training data set, loading baseline hyperparameters, configuring the machine learning model with the baseline hyperparameters [the system includes a data processing device which comprises one or more processors (processor hardware) and at least one connected memory storing instructions to be executed by the processor(s), which includes computerized models (CMs) and their parameters, as well as training data for the models (pg. 18, line 18 to pg. 19, line 15; fig. 1; etc.), and can also include stored CM performance metrics and setting (baseline) hyperparameters of the CMs (pg. 29, lines 23-32; pg. 39, lines 3-24; figs. 1 and 18; etc.); where the CM may include an ensemble of transformer networks or CNNs, etc. (pg. 3, lines 8-20; pg. 42, lines 23-29; etc.); so that the initial CM/hyperparameters are the machine learning model with baseline hyperparameters], providing the training data set as inputs to the machine learning model configured with the baseline hyperparameters to determine baseline performance metrics [the CMs can be trained using training sample sequences input to the model(s) (pg. 18, line 18 to pg. 19, line 15; pg. 21, line 16 to pg. 22, line 16; fig. 3; etc.), including setting (baseline) hyperparameters of the CMs (pg. 29, lines 23-32; pg. 39, lines 3-24; fig. 18; etc.)], determining whether the baseline performance metrics are above a threshold [training of a CM may include determining whether an expected performance (metric) of the CM has been achieved by comparing it to a threshold (pg. 40, lines 1-3; pg. 41, line 19 to pg. 42, line 6; fig. 18; etc.)], in response to determining that the baseline performance metrics are above the threshold, saving the baseline hyperparameters as optimal hyperparameters, configuring the machine learning model with the optimal hyperparameters [training of a CM may include determining whether an expected performance (metric) of the CM has been achieved by comparing it to a threshold, and finishing training when the performance exceeds the threshold, at which point the CM model and optimized hyperparameters are saved as ready for use (pg. 40, lines 1-3; pg. 41, line 19 to pg. 42, line 6; fig. 18; etc.); where the finished model with optimized hyperparameters are the machine learning model with optimal hyperparameters], loading input variables, providing the input variables as inputs to the machine learning model configured with the optimal hyperparameters to generate output variables [when the performance exceeds the threshold, the CM model and optimized hyperparameters are saved as ready for use (pg. 40, lines 1-3; pg. 41, line 19 to pg. 42, line 6; fig. 18; etc.) where, after the model is finished training, measured biosignals are sent to the trained CM as inputs (variables), and a prediction of patient conditions are provided as output (variables) (pg. 8, lines 3-11; see also: pg. 9, lines 10-16; pg. 21, lines 16-28; pg. 28, lines 1-21; figs. 6 and 18; etc.)], saving the output variables to a database [the outputs may be stored in a database with patient identifiers and time-domain sample sequences from patients (pg. 11, lines 17-28; see also: pg. 12, lines 5-14; pgs. 15-17; pg. 26, line 19 to pg. 27, line 7; figs. 1 and 5; etc.)], and generating a graphical user interface, wherein the graphical user interface is configured to access the output variables from the database and display the output variables to a user [the system may include a user interface device providing a user interface display (GUI) (pg. 17, lines 26 to pg. 18, line 3) which can be used to display the conditions output by the CM (pg. 19, lines 16-27; etc.)].
While Kallonen/Gesley teaches preprocessing inputs (see, e.g., Kallonen: pg. 27, line 29 to pg. 28, line 6; etc.), it has not been relied upon for teaching loading an input variable binning data structure, wherein the input variable binning data structure indicates whether each of the input variables belongs to a first bin or a second bin, for each respective input variable of the input variables, parsing the input variable binning data structure to classify the respective input variable into the first bin or the second bin, assigning input variables classified into the first bin as fixed effects and input variables classified into the second bin as mixed effects, [and] providing the input variables with fixed effects assignments and mixed effects assignments as inputs to the machine learning model.
Gesley teaches loading an input variable binning data structure, wherein the input variable binning data structure indicates whether each of the input variables belongs to a first bin or a second bin, for each respective input variable of the input variables [for a number of input variables associated with a number of objects (col. 14, lines 44-67; etc.) have their measurement data values binned using a tri-bin approach for mixed or unanimous/non-mixed (fixed) decision bins (col. 34, line 42 to col. 35, line 38; tables I-III; etc.)], parsing the input variable binning data structure to classify the respective input variable into the first bin or the second bin, assigning input variables classified into the first bin as fixed effects and input variables classified into the second bin as mixed effects [for a number of input variables associated with a number of objects (col. 14, lines 44-67; etc.) have their measurement data values binned using a tri-bin approach for mixed or unanimous/non-mixed (fixed) decision bins (col. 34, line 42 to col. 35, line 38; tables I-III; etc.)], and providing the input variables with fixed effects assignments and mixed effects assignments as inputs to the machine learning model [for a number of input variables associated with a number of objects (col. 14, lines 44-67; etc.) have their measurement data values binned using a tri-bin approach for mixed or unanimous/non-mixed (fixed) decision bins and provided to a ML classifier model (col. 34, line 42 to col. 35, line 38; tables I-III; etc.); for the input variables provided to the machine learning model of Kallonen, above].
Kallonen and Gesley are analogous art, as they are within the same field of endeavor, namely training machine learning models for classification of images.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the tri-bin (or more) binning of inputs for the ML classifier, as taught by Gesley, in the preprocessing of inputs for the ML classifier model of the system taught by Kallonen.
Gesley provides motivation as [the binning reduces dimensionality of the inputs (col. 2, line 56 to col. 3, line 4; etc.) and a distinct set may not necessarily be disjoint, as mentioned previously. Thus, in some cases, it is possible that training class sample object measurement vector score distributions may not result in disjoint sets, but may nonetheless be sufficiently distinct to be resolvable from one another, as is desirable… Thus, as illustrated by example, a binary decision may be employed to filter training class sample object member measurement vectors (col. 33, lines 1-47; etc.)].
As per claim 2, Kallonen/Gesley teaches wherein: the input variables include an identifier of an entity in a population [along with the time sequence data, patient identifiers and timestamps may be used as the time sequence samples received by the data processing system to use as inputs to the CMs (Kallonen: pg. 12, lines 1-13; see also: pg. 16, lines 5-15; pg. 27, lines 5-8; pg. 39, lines 25-32; etc.); where the patients are entities of a population]; the output variables include a score for the entity indicated by the identifier [the output of the CMs may include one or more scores or a composite score of multiple (ensemble) CNNs associated with the patient identifier (Kallonen: pg. 21, lines 19-25; pg. 43, lines 4-20; fig. 21; etc.)]; and the score indicates a likelihood of a feature of merit exceeding a threshold [the output of the CMs may include one or more scores or a composite score of multiple (ensemble) CNNs, which scores indicate classes which indicate predicted conditions as well as a risk score (Kallonen: pg. 21, lines 19-25; pg. 43, lines 4-28; figs. 21-22; etc.) where the scores generated by the CMs provide a probability of a life-threatening condition or need for treatment, or a distribution of such probability values (Kallonen: pg. 20, line 19 to pg. 21, line 28; pg. 34, lines 5-12; etc.) which may be compared to a determined decision threshold for each condition (Kallonen: pg. 36, line 13 to pg. 37, line 2; pg. 42, lines 30-33; etc.); where the life-threatening condition or need for treatment is the feature of merit exceeding the decision threshold].
As per claim 3, Kallonen/Gesley teaches wherein the instructions include: generating a plurality of scores for a plurality of entities in the population [patient identifiers and associated time sequence samples and model outputs may be stored for a number of patients (Kallonen: pg. 18, lines 4-12; see also: pg. 11, lines 17-28; pg. 12, lines 5-14; pgs. 15-17; pg. 26, line 19 to pg. 27, line 7; figs. 1 and 5; etc.)]; and clustering the plurality of scores into a plurality of clusters [additional models can include unsupervised learning, including clustering, where clustering is performed on the outputs of the prior (classification) models (Kallonen: pg. 44, line 13 to pg. 45, line 11; etc.)].
As per claim 9, Kallonen/Gesley teaches wherein the instructions include, in response to determining that the baseline metrics are not above the threshold, adjusting the baseline hyperparameters [if the performance metric is not satisfactory (not above the threshold – see above) the method proceeds to permute the hyperparameters according to a defined permutation function and proceeds to the next iteration of training (Kallonen: pg. 40, lines 27-33; fig. 18; etc.)].
As per claim 10, Kallonen/Gesley teaches wherein the instructions include configuring the machine learning model with the adjusted hyperparameters [if the performance metric is not satisfactory (not above the threshold – see above) the method proceeds to permute the hyperparameters according to a defined permutation function and proceeds to the next iteration of training (Kallonen: pg. 40, lines 27-33; fig. 18; etc.); where the next training iteration proceeds with the adjusted model hyperparameters (see, e.g., Kallonen: fig. 18)].
As per claim 11, Kallonen/Gesley teaches wherein the instructions include providing the training data set as inputs to the machine learning model configured with the adjusted hyperparameters to determine updated performance metrics [if the performance metric is not satisfactory (not above the threshold – see above) the method proceeds to permute the hyperparameters according to a defined permutation function and proceeds to the next iteration of training (Kallonen: pg. 40, lines 27-33; fig. 18; etc.), which includes using training sample sequences input to the model(s) (Kallonen: pg. 18, line 18 to pg. 19, line 15; pg. 21, line 16 to pg. 22, line 16; fig. 3; etc.)].
As per claim 12, Kallonen/Gesley teaches wherein the instructions include determining whether the updated performance metrics are more optimal than the baseline performance metrics [each iteration includes determining whether the updated performance metric is satisfactory and, if the performance metric is not satisfactory (not above the threshold – see above) the method proceeds to permute the hyperparameters according to a defined permutation function and proceeds to the next iteration of training (Kallonen: pg. 40, lines 27-33; fig. 18; etc.); which is determining whether the updated performance metrics are an improvement (see, e.g., Kallonen: fig. 18)].
As per claim 13, Kallonen/Gesley teaches wherein the instructions include, in response to determining that the updated performance metrics are more optimal than the baseline performance metrics, saving the adjusted hyperparameters as the baseline hyperparameters [each iteration includes determining whether the updated performance metric is satisfactory and, if the performance metric is not satisfactory (not above the threshold – see above) the method proceeds to permute the hyperparameters according to a defined permutation function and proceeds to the next iteration of training (Kallonen: pg. 40, lines 27-33; fig. 18; etc.); where the permuted hyperparameters are saved with the model(s) (Kallonen: pg. 29, lines 23-32; pg. 39, lines 3-24; figs. 1 and 18; etc.)].
As per claim 15, Kallonen/Gesley teaches wherein the output variables include at least one of (i) a per-patient risk score, (ii) a patient identifier, (iii) a physician identifier, (iv) a physician state, and (v) a patient state [the output of the CMs may include one or more scores or a composite score of multiple (ensemble) CNNs, which scores indicate classes which indicate predicted conditions as well as a risk score (Kallonen: pg. 21, lines 19-25; pg. 43, lines 4-28; figs. 21-22; etc.) where the scores generated by the CMs provide a probability of a life-threatening condition or distribution of probability values (Kallonen: pg. 20, line 19 to pg. 21, line 28; pg. 34, lines 5-12; etc.); which includes at least per-patient risk scores indicating high-risk episodes/high-cost treatment, patient identifiers, and patient state].
As per claim 16, Kallonen/Gesley teaches wherein the input variables are stored on one or more storage devices [the system includes a data processing device which comprises one or more processors (processor hardware) and at least one connected memory storing instructions to be executed by the processor(s), which includes computerized models (CMs) and their parameters, as well as training data for the models (Kallonen: pg. 18, line 18 to pg. 19, line 15; fig. 1; etc.); where the memory is the storage device storing the input variables (training data including time sequence data, patient identifiers, etc. – see above)].
As per claim 17, Kallonen/Gesley teaches wherein the processor hardware is configured to access the one or more storage devices via one or more networks [the processor(s) may access the memory via a wired or wireless network(s) (Kallonen: pg. 10, lines 5-7; etc.)].
As per claim 18, see the rejection of claim 1, above.
As per claim 19, see the rejection of claim 9, above.
As per claim 20, see the rejection of claim 10, above.
As per claim 21, see the rejection of claim 11, above.
As per claim 22, see the rejection of claim 12, above.
As per claim 23, see the rejection of claim 13, above.
Claim(s) 4-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kallonen (WO 2022/074300), in view of Gesley (US 10,936,921 – cited in an IDS), and further in view of Rivas (US 2021/0065846).
As per claim 4, Kallonen/Gesley teaches the system of claim 3, as described above.
While Kallonen/Gesley teaches clustering the plurality of output scores (see above), it has not been relied upon for teaching wherein the plurality of clusters is three clusters.
Rivas teaches wherein the plurality of clusters is three clusters [a risk score model, including multiple hyperparameters, is used to produce risk scores for patients (paras. 0033, 0056, etc.), which are clustered using k-means clustering to cluster high risk profiles, which clusters can be associated with 3 types of genetic subtypes (paras. 0033, 0044; figs. 3 and 15; etc.); so, this includes 3 clusters (type 1, type 2, and type 3 – see, e.g., fig. 3)].
Kallonen and Rivas are analogous art, as they are within the same field of endeavor, namely predicting individual/patient risk scores using machine learning models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include clustering the risk scores into different risk profiles, including 3 types, as taught by Rivas, for the clustering being performed on the predicted scores in the system taught by Kallonen.
Rivas provides motivation as [the determined clusters can be used to separate each subtype into relevant groupings (para. 0033, etc.) and used to asses which components drive risk (para. 0127, etc.)].
As per claim 5, Kallonen/Rivas teaches wherein: the plurality of clusters includes a particular cluster associated with a greatest risk [the clusters can include identifying outliers, which includes individuals with the greatest risk (Rivas: paras. 0033, 0043-44, etc.)]; and the instructions include adapting the graphical user interface in response to the score being assigned to the particular cluster [the clusters can include identifying outliers, which includes individuals with the greatest risk (Rivas: paras. 0033, 0043-44, etc.); and the system may include a user interface device providing a user interface display (GUI) (Kallonen: pg. 17, lines 26 to pg. 18, line 3) which can be used to display the conditions output by the CM (Kallonen: pg. 19, lines 16-27; etc.) as well as displaying increased risk (Kallonen: pg. 34, lines 5-12; etc.); which display would thus be adapted to provide the outlier risk cluster assignment (increased risk)].
As per claim 6, Kallonen/Rivas teaches wherein the score is a value between zero and one hundred inclusive [the scores generated by the CMs provide a probability of a life-threatening condition or distribution of probability values (Kallonen: pg. 20, line 19 to pg. 21, line 28; pg. 34, lines 5-12; etc.) where risk can be measured as percentage (Rivas: para. 0039, etc.); and where probability is a percentage, which is a score between zero and one hundred, inclusive].
As per claim 7, Kallonen/Rivas teaches wherein: the population includes entities that consume services; and the feature of merit is a measure of service consumption of the entity [the output of the CMs may include one or more scores or a composite score of multiple (ensemble) CNNs, which scores indicate classes which indicate predicted conditions as well as a risk score (Kallonen: pg. 21, lines 19-25; pg. 43, lines 4-28; figs. 21-22; etc.) where the scores generated by the CMs provide a probability of a life-threatening condition or need for treatment, or a distribution of such probability values (Kallonen: pg. 20, line 19 to pg. 21, line 28; pg. 34, lines 5-12; etc.) which may be compared to a determined decision threshold for each condition (Kallonen: pg. 36, line 13 to pg. 37, line 2; pg. 42, lines 30-33; etc.); where the need for treatment is the feature of merit that is a measure of service consumption (treatment)].
Claim(s) 8 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kallonen (WO 2022/074300), in view of Gesley (US 10,936,921 – cited in an IDS), further in view of Rivas (US 2021/0065846), and further in view of DeLong et al. (Comparing Risk-Adjustment Methods for Provider Profiling, 1997, pgs. 2645-2664).
As per claim 8, Kallonen/Gesley/Rivas teaches the system of claim 7, as described above.
While Kallonen/Gesley/Rivas teaches using population data that includes healthcare service data (see above), it has not been relied upon for teaching wherein: the population includes entities that coordinate services; and the feature of merit is an amount of services advised by the entity.
DeLong teaches wherein: the population includes entities that coordinate services; and the feature of merit is an amount of services advised by the entity [a risk assessment prediction can be made for providers to compile provider profiles (pg. 2645, Summary) using a risk-adjustment prediction model (pg. 2647, section 2.2 and 2.4; etc.) which can include analyzing observed patient data (pg. 2648, section 2.4.1; etc.) which is the feature of merit including an amount of services advised (i.e., patients seen, etc.); which can be a risk score in the system of Kallonen/Rivas above].
Kallonen/Gesley/Rivas and DeLong are analogous art, as they are within the same field of endeavor, namely risk assessment/prediction in healthcare services using machine learning models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include healthcare provider risk assessment prediction, as taught by DeLong, in the patient risk assessment predictions in the system taught by Kallonen/Gesley/Rivas.
DeLong provides motivation as [provider risk profiling is a useful tool for measuring quality and value of health care (pg. 2645, Summary, etc.)].
As per claim 25, Kallonen/Gesley/Rivas/DeLong teaches wherein: the output variables include (i) per-provider risk scores and (ii) clusters for the per-provider risk scores; and each cluster indicates a risk category [additional models can include unsupervised learning, including clustering, where clustering is performed on the outputs of the prior (classification) models (Kallonen: pg. 44, line 13 to pg. 45, line 11; etc.); where the classifiers provide risk scores (Kallonen: pg. 21, lines 19-25; pg. 43, lines 4-28; figs. 21-22; etc.) which are clustered using k-means clustering to cluster high risk profiles, which clusters can be associated with different types (Rivas; paras. 0033, 0044; figs. 3 and 15; etc.), and which can include per provider risk assessments (DeLong: pg. 2647, section 2.2 and 2.4; etc.)].
Examiner’s Note: the reasoning and motivation for the combination is provided in the rejection of claim 8, above.
Claim(s) 14 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kallonen (WO 2022/074300), in view of Gesley (US 10,936,921 – cited in an IDS), and further in view of Zheng et al. (Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model, Aug 2021, pgs. 1-12).
As per claim 14, Kallonen/Gesley teaches the system of claim 1, as described above.
While Kallonen/Gesley teaches using a machine learning model for making the patient risk predictions (see above), it has not been relied upon for teaching wherein the machine learning model is a light gradient-boosting machine (LightGBM) regressor model.
Zheng teaches wherein the machine learning model is a light gradient-boosting machine (LightGBM) regressor model [a light gradient boosting machine (LightGBM) model using logistic regression is used to make patient risk assessment predictions (pg. 1, Abstract; etc.)].
Kallonen and Zheng are analogous art, as they are within the same field of endeavor, namely patient risk assessment prediction using machine learning models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to utilize a LightGBM model to make the patient risk assessment, as taught by Zheng, as the machine learning model(s) for predicting patient risk scores in the system taught by Kallonen.
Zheng provides motivation as [Risk stratification based on the LightGBM model showed better discriminative ability than traditional model in predicting 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. Individual patients’ prognosis could also be obtained, and the subgroup of patients with a higher risk of mortality could be identified. It can help clinicians identify and manage high- and low-risk patients and carry out more targeted intervention measures to realize precision medicine and the optimal allocation of health care resources (pg. 1, Abstract)].
As per claim 24, Kallonen/Gesley/Zheng teaches wherein the machine learning model is a light gradient-boosting machine (LightGBM) regressor model or a LightGBM classifier model [a light gradient boosting machine (LightGBM) model using logistic regression is used to make patient risk assessment predictions (Zheng: pg. 1, Abstract; etc.)].
Examiner’s Note: the reasoning and motivation for the combination is provided in the rejection of claim 14, above.
Response to Arguments
The rejection of claim 15 under 35 U.S.C. 112(b) has been withdrawn due to the amendments filed.
Applicant’s arguments, see the remarks, filed 2 April 2026, with respect to the rejection(s) of claim(s) 1-25 under 35 U.S.C. 102 and 103 have been fully considered and are persuasive in view of the amendments made to the independent claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Gesley, which has been relied upon for teaching the claimed input binning structure(s) (see above).
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-25 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Drake (US 2021/0174958) – discloses a system including hyperparameter optimization for a ML classifier for analyzing blood-based diagnostic tests.
Hancock et al. (Leveraging LightGBM for Categorical Big Data, Aug 2021, pgs. 149-154) – discloses using LightGBM models for assessments on multiple kinds of healthcare data, including provider fraud detection/assessment.
Huang et al. (Deep significance clustering: a novel approach for identifying risk-stratified and predictive patient subgroups, Sept 2021, pgs. 2641-2653) – discloses deep significance clustering (DICE) for self-supervised training and prediction of risk-based patient groupings.
Xiong et al. (A binning method for analyzing mixed longitudinal data measured at distinct time points, May 2010, pgs. 1919-1931) – discloses preprocessing including binning of input patient data for mixed effects.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
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 GEORGE GIROUX whose telephone number is (571)272-9769. The examiner can normally be reached M-F 10am-6pm.
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, Omar Fernandez Rivas can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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