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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 29, 2026 has been entered.
Status of Claims
This office action for the 18/476014 application is in response to the communications filed April 29, 2026.
Claims 1 and 13 were amended April 29, 2026.
Claims 1, 2, 4-13 and 15-22 are currently pending and considered below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 2, 4-13 and 15-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claims 1 and 13,
These claims recite the limitation of:
“determining a topic for the cohort predicted to cause a certain percentage of the cohort to interact with the topic based at least in part on the range of values of the risk prediction score of the cohort”
The as-filed specification does not provide any written description for this limitation. The closest written description that the examiner could find to support this limitation was:
“The number of guided engagement tasks and prompts per week is adjusted to maintain a specific percentage (e.g., 40% or about 40%) of users interacting and a specific percentage (e.g., 50% or about 50%) of users adhering to medical plans. These metrics indicate the ease of use of each feature, identifying where qualitative tests could indicate improvement tactics.” (Paragraph [0056] of the as-filed specification)
However, this description does not provide adequate basis for predicting a level of engagement that a topic would provide. The as-filed specification merely describes that engagement should be maintained at a particular percentage without any sort of detail about how to predict that a given topic should achieve such a level of engagement. Accordingly, these claims lack written description.
As pe claims 2, 4-12 and 15-22,
These claims are dependent from a base claim that was determined to lack written description. Accordingly, these claims also lack written description.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4-13 and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over McGovern et al. (US 2020/0342958; herein referred to as McGovern) in view of Jain et al. (US 11,417,418; herein referred to as Jain).
As per claim 1,
McGovern teaches a computer-implemented method for training a machine learning (ML) model to assess the risk of a human subject for developing at least one disorder, the method comprising:
(Abstract and Paragraphs [0003]-[0011] of McGovern. The teaching describes assessing inflammatory disease or condition in subjects using Deep Learning (DL) prediction models - assaying a biological sample of the subject to generate a dataset comprising genetic data - processing the dataset at a plurality of genomic loci to determine quantitative measures of each genomic locus of the plurality of genomic loci. Applying a deep learning prediction model to the inflammatory disease profile to identify a presence of the inflammatory disease or condition in the subject, or a likelihood that the subject will develop the inflammatory disease or condition. Applying the deep learning prediction model (e.g., a deep learning classifier) to a set of clinical health data of the subject)
McGovern further teaches receiving an input dataset including at least medical claim data corresponding to a plurality of human subjects over a target prediction period:
(Paragraphs [0003]-[0011], [0049]-[0053] and [0084]-[0091] of McGovern. The teaching describes a set of clinical health data comprises one or more of familial history of an inflammatory disease or disorder, age, hypertension or pre-hypertension, diabetes or pre-diabetes, overweight or obesity, environmental exposure, lifestyle risk factors (e.g., smoking, alcohol consumption, or drug use), or presence of other risk factors). Biological samples may be obtained or derived from a human subject - inflammatory disease or condition may comprise a likelihood, risk, or susceptibility of having an inflammatory disease in the future)
McGovern further teaches splitting the input dataset into a first dataset corresponding to a first portion of the plurality of human subjects and a second dataset corresponding to a second portion of the plurality of human subjects:
(Paragraphs [0116]-[0122] of McGovern. The teaching describes that a DeepLearning algorithm may be used to apply a machine learning classifier to a plurality of inflammatory disease-associated genomic loci that are associated with two or more classes of individuals inputted into a machine learning model, in order to classify a subject into one of the two or more classes of individuals. For example, the DeepLearning algorithm may be used to apply a machine learning classifier to a plurality of inflammatory disease-associated genomic loci that are associated with individuals with known conditions (e.g., an inflammatory disease or disorder, such as an IBD) and individuals not having the condition (e.g., healthy individuals, or individuals who do not have an inflammatory disease or disorder, such as an IBD), in order to classify a subject as having the condition (e.g., positive test outcome) or not having the condition (e.g., negative test outcome). The plurality of input variables or features may comprise one or more datasets indicative of the presence (e.g., positive test result) or absence (e.g., negative test result) of one or more inflammatory disease or conditions.)
McGovern further teaches selecting at least one risk factor associated with developing the at least one disorder from the first dataset:
(Paragraphs [0003]-[0011] and [0092]-[0096] of McGovern. The teaching describes that the biological sample may be taken from a subject at risk of developing an inflammatory disease or condition due to factors such as familial history, age, hypertension or pre-hypertension, diabetes or pre-diabetes, overweight or obesity, environmental exposure, lifestyle risk factors (e.g., smoking, alcohol consumption, or drug use), or presence of other risk factors)
McGovern further teaches training a machine learning (ML) model using the first dataset and the at least one risk factor, the ML model including at least one logistic regression model:
(Paragraphs [0003]-[0011] and [0120]-[0126] of McGovern. The teaching describes that a deep learning prediction model is trained using a first set of independent training samples associated with a presence of the inflammatory disease or condition and a second set of independent training samples associated with an absence of the inflammatory disease or condition - applying the deep learning prediction model (e.g., a deep learning classifier) to a set of clinical health data of the subject. DeepLearning algorithm may comprise a classifier, such that each of the one or more output values comprises one of a fixed number of possible values (e.g., a linear classifier, a logistic regression classifier, etc.)
McGovern further teaches providing the second dataset to the ML model to generate a risk prediction for developing the at least one disorder by the end of the target prediction period for each human subject included in the second portion of the plurality of human subjects:
(Paragraphs [0003]-[0011], [0049]-[0053], [0084]-[0091] and [0222]-[0227] of McGovern. The teaching describes that inflammatory disease or condition may comprise a likelihood, risk, or susceptibility of having an inflammatory disease in the future (e.g., within about 1 hour- about 10 years, or more than about 10 years. Prediction model of CD risk was constructed using DeepLearning algorithms using genetic data from the IIBDGC cohort - best performance of risk prediction of complex human diseases using genetic data - DL predicted risk score is also strongly associated with disease clinical phenotypes of CD including disease location, severity and need for surgery.)
McGovern further teaches tuning at least one parameter of the ML model based on the generated risk predictions for the second portion of the plurality of human subjects:
(Paragraphs [0221]-[0227] of McGovern. The teaching describes that intensive tuning of the hyperparameters was performed in the Deep Learning Model, rather than using arbitrarily selected numbers of neurons and/or layers. Tuning of the parameters in Deep Learning Models may have important impact on performance of the models - Deep Learning-based algorithms were effectively utilized to predict CD risk using genetic data. Results demonstrated that this algorithm significantly increased the prediction accuracy, and that the predicted disease risk is associated with disease clinical characteristics)
McGovern further teaches further comprising: creating a third dataset corresponding to a certain human subject over a time period; and providing the third dataset to the ML model with the at least one parameter to generate a risk prediction for developing the at least one disorder over the time period for the certain human subject, wherein the risk prediction score is within a range of values:
(Paragraphs [0003]-[0011], [0084]-[0091 and [0222]-[0227] of McGovern. The teaching describes prediction model of CD risk was constructed using Deep Learning algorithms using genetic data from the IIBDGC cohort - best performance of risk prediction of complex human diseases using genetic data - DL predicted risk score is also strongly associated with disease clinical phenotypes of CD including disease location, severity and need for surgery.)
McGovern further teaches grouping the certain human subject into a cohort comprising a plurality of similar users wherein each similar user of the plurality of similar users has a risk prediction score that is within the range of values:
(Paragraph [0130] of McGovern. The teaching describes that the classifier may be configured to classify samples by assigning an output value of “indeterminate” or 2 if the sample is not classified as “positive”, “negative”, 1, or 0. In this case, a set of two cutoff values is used to classify samples into one of the three possible output values or classes of individuals (e.g., corresponding to outcome groups of individuals having “low risk,” “intermediate risk,” and “high risk” of having one or more inflammatory disease or conditions, such as an inflammatory disease or disorder). Examples of sets of cutoff values may include {1%, 99%}, {2%, 98%}, {5%, 95%}, {10%, 90%}, {15%, 85%}, {20%, 80%}, {25%, 75%}, {30%, 70%}, {35%, 65%}, {40%, 60%}, and {45%, 55%}. Similarly, sets of n cutoff values may be used to classify samples into one of n+1 possible output values or classes of individuals, where n is any positive integer.)
McGovern further teaches providing an activity to the certain human subject and the plurality of similar users:
(Paragraphs [0074], [0148] and [0150] of McGovern. The teaching describes upon identifying a subject as having elevated risk of developing an inflammatory disease with the DeepLeaning model described herein, a primary intervention may be administered to the subject to prevent or delay the onset of the inflammatory disease or condition. The therapeutic intervention may comprise recommending the subject for a secondary clinical test to confirm a diagnosis of the inflammatory disease or condition. This secondary clinical test may comprise a blood test, fetal occult blood test (FOBT), colonoscopy, sigmoidoscopy, endoscopy, enteroscopy, X-ray scan, computerized tomography (CT) scan, positron emission tomography (PET) scan, PET-CT scan, magnetic resonance imaging (MRI), ultrasound scan, or a combination thereof. Progression of an inflammatory disease may be tracked before treatment, after treatment, or during the course of treatment, to determine the treatment's effectiveness. For example, a method as described herein may be performed on a subject prior to, and after, treatment of a subject with an inflammatory disease therapy to measure the subject's disease progression or regression in response to the inflammatory disease therapy.)
McGovern does not explicitly teach determining a topic for the cohort predicted to cause a certain percentage of the cohort to interact with the topic based at least in part on the range of values of the risk prediction score of the cohort.
However, Jain teaches determining a topic for a cohort predicted to cause a certain percentage of the cohort to interact with the topic based at least in part on the range of values of the risk prediction score of the cohort:
(Columns 14 Lines 43-52, and 15 Lines 57-63, and Figures 2A and 2B of Jain. The teaching describes a technique for prioritizing program opportunities based on two program scoring criteria relating to user burden and compliance is shown. In this example, research requirement data 252A specifies a research focus criterion for a sleep study and a cohort preference criterion for high retention and/or compliance for participants enrolled in the sleep study. The server 110 prioritizes programs 304A and 304B using similar techniques discussed in reference to FIGS. 2A and 2B. In this example, the server 110 customizes prioritization for each user and in relation to the research parameters 302. Thus, the server 110 balances the research parameters 302 and the preferences of each user in a different fashion, which results in different prioritizations being provided to each user. FIGS. 2A and 2B illustrate examples of techniques for prioritizing program opportunities based on using weights to evaluate program scoring criteria. Referring initially to FIG. 2A, a technique for prioritizing program opportunities based on two program scoring criteria relating to user preference and research value is shown. In this example, user data 202B indicates that the user is interesting in improving his/her personal fitness and genomic data indicating that he/she is part of an at-risk population for an infection to be addressed by a vaccine being investigated by researchers. Here the predicted topic for improved engagement prediction is the future study the cohort of patients can participate in.)
Jain further teaches generating a notification to the cohort comprising an indication of the topic that is predicted to cause the certain percentage of the cohort to interact with the topic:
(Column 19 Lines 5-13 and Figure 5A-5C of Jain. The teaching describes that FIGS. 5A-5C illustrate examples of user interfaces 500A-500C that provide information related to program opportunities and selection result data for prioritized programs. Each of interfaces 500A-500C can be provided through a computing device, such as device 130 shown in FIG. 1A. In some implementations, interfaces 500A-500C are accessed through a mobile application running on the device 130, while in others, the interfaces 500A-500C are accessed through a webpage through a web browser application. Program information can be presented through interfaces 500A-500C at different time points of the program participation process. In some instances, program information is presented before users register to participate in a program. In such instances, program information presented through interfaces 500A-500C can be used to sponsor registration or recruitment of a program by incentivizing the user's participation. In other instances, program information is presented while the user is participating in a program to, for example, maintain user engagement within the program, indicate other program opportunities that have recently become available and may be better aligned with a user's objectives. It can be seen that the topic for improved engagement is presented to the cohort of patients for which improved engagement is predicted.)
Jain further teaches providing an activity to the cohort wherein the activity comprises a discussion for the cohort on the topic at least in part be transmitting one or more electronic communications between the certain human subject and the plurality of similar users, wherein the one or more electronic communications correspond to the topic:
(Column 22 Lines 35-67 of Jain. The teaching describes that the program modules that the server 110 provides can cause the devices that receive and install the modules to perform many different types of interactions, for delivery of therapy, for assessment and information gathering, and more. These include interactions that are designed to change the user's behavior, such as to prompt changes to sleep, diet, exercise, medication usage, and other actions that are separate from a user's interaction with devices. The programs can cause targeted interactions that are relevant to the user's current needs or estimated future needs. The interactions can take many forms, and can be based on the content delivered in the original configuration data or program module and/or through later or ongoing communication with the server 110 over a network. A client device used for a program can, either with the module alone or with additional interaction with the server 110, providing media to a user for the user to read or view, generate an interactive form such as a survey, send a notification message or alert, provide a test or assessment of the user, provide recommendations, provide instructional activities or games, provide content from a social media platform, prompt a user to take an action, record a measurement from a device, initiate communication with a health service provider, or communicate with family, friends, or others regarding a user's goals or status. The client device, as directed by the downloaded program module and/or further messages from the server 110, can initiate a challenge to the user, such as challenging, reminding, or informing the user about a goal for the user. The program can involve prompting a user to set, adjust, or view a goal. The interactions of a client device with a user can include interactions involving visual output, audio output, voice input, haptic output, gesture input, and other input/output modalities.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the teachings of McGovern, the medically-based social media platform of Jain. Column 31 Lines 4-24 describes that the disclosed methods provide a basis for patients experiencing similar medical conditions to improve their physical and mental well-being by providing a platform in which they can learn more about their condition at a higher level of engagement that they would have otherwise. One of ordinary skill in the art in possession of McGovern would have looked to Jain for this improved cohort treatment and use this platform to improve the outcomes in their own patient cohorts. One of ordinary skill in the art would have added to the teaching of McGovern, the teaching of Jain based on this incentive without yielding unexpected results.
As per claim 2,
The combined teaching of McGovern and Jain teaches the limitations of claim 1.
McGovern further teaches wherein the first dataset is a training dataset and the second dataset is a validation dataset:
(Paragraphs [0110]-[0122], [0182]-[0184] and [0222]-[0232] of McGovern. The teaching describes that after building up different deep learning models in the training dataset, those models may be fitted in the test dataset to obtain the predictions. DeepLearning algorithm may be used to apply a machine learning classifier to a plurality of inflammatory disease-associated genomic loci that are associated with individuals with known conditions (e.g., an inflammatory disease or disorder, such as an IBD} and individuals not having the condition (e.g., healthy individuals, or individuals who do not have an inflammatory disease or disorder, such as an IBD), in order to classify a subject as having the condition (e.g., positive test outcome) or not having the condition (e.g., negative test outcome). Such classifications include training and validation cohorts, and external validation datasets)
As per claim 4,
The combined teaching of McGovern and Jain teaches the limitations of claim 1.
McGovern further teaches further comprising: determining whether each human subject of the plurality of human subjects has developed the at least one disorder by the end of the target prediction period; labeling a portion of the plurality of human subjects who have developed the at least one disorder by the end of the target prediction period as positive for the disorder; and labeling a remaining portion of the plurality of human subjects as healthy:
(Paragraphs [0084]-[0091], [0116]-[0122] and [0131] of McGovern. The teaching describes that an inflammatory disease or condition may comprise a likelihood, risk, or susceptibility of having an inflammatory disease in the future (e.g., within about 1 hour – about 10 years, or more than about 10 years. DeepLearning algorithm may be used to apply a machine learning classifier to a plurality of inflammatory disease-associated genomic loci that are associated with individuals with known conditions (e.g., an inflammatory disease or disorder, such as an IBD) and individuals not having the condition (e.g., healthy individuals, or individuals who do not have an inflammatory disease or disorder, such as an IBD), in order to classify a subject as having the condition (e.g., positive test outcome) or not having the condition (e.g., negative test outcome).)
As per claim 5,
The combined teaching of McGovern and Jain teaches the limitations of claim 4.
McGovern further teaches wherein determining that a human subject has developed the at least one disorder includes detecting at least one identifying factor in a final year of the target prediction period:
(Paragraph [0131] of McGovern. The teaching describes independent training samples may comprise samples and associated datasets and outputs obtained at a plurality of different time points from the same subject (e.g., on a regular basis such as weekly, biweekly, or monthly), as part of a longitudinal monitoring of a subject before, during, and after a course of treatment for one or more inflammatory disease or conditions of the subject)
As per claim 6,
The combined teaching of McGovern and Jain teaches the limitations of claim 4.
McGovern further teaches wherein the first portion of the plurality of human subjects has a first ratio of positive to healthy human subjects and the second portion of the plurality of human subjects has a second ratio of positive to healthy human subjects:
(Paragraphs [0003]-[0011], [0116]-[0122], [0200] and [0222]-[0232] of McGovern. The teaching describes improvement in prediction accuracy from the DL approach led to greatly enriched CD cases in the extreme of the DL score, with an OR of 19.25 in the top 10 percent, and an OR of 26.32 for DL compared to 6.29 for LDPred approach in top 5 percent - By producing such a high Odds Ratio (OR), DL-based approaches may enable cost-effective genetic screening (e.g., to a general population or a high-risk population such as individuals with family history and/or symptoms of CD) in the extremes of DL prediction)
As per claim 7,
The combined teaching of McGovern and Jain teaches the limitations of claim 6.
McGovern further teaches wherein the first ratio and the second ratio are different:
(Paragraphs [0003]-[0011], [0116]-[0122], [0200] and [0222]-[0232] of McGovern. The teaching describes improvement in prediction accuracy from the DL approach led to greatly enriched CD cases in the extreme of the DL score, with an OR of 19.25 in the top 10 percent, and an OR of 26.32 for DL compared to 6.29 for LDPred approach in top 5 percent - By producing such a high Odds Ratio (OR), DL-based approaches may enable cost-effective genetic screening (e.g., to a general population or a high-risk population such as individuals with family history and/or symptoms of CD) in the extremes of DL prediction)
As per claim 8,
The combined teaching of McGovern and Jain teaches the limitations of claim 1.
McGovern further teaches wherein selecting the at least one risk factor associated with developing the at least one disorder includes identifying at least one risk factor in a first year of the target prediction period associated with a diagnosis of the at least one disorder by the end of the target prediction period:
(Paragraphs [0003]-[0011], [0049]-[0053], [0084]-[0091] and [0222]-[0227] of McGovern. The teaching describes that inflammatory disease or condition may comprise a likelihood, risk, or susceptibility of having an inflammatory disease in the future (e.g., within about 1 hour- about 10 years, or more than about 10 years. Prediction model of CD risk was constructed using DeepLearning algorithms using genetic data from the IIBDGC cohort - best performance of risk prediction of complex human diseases using genetic data - DL predicted risk score is also strongly associated with disease clinical phenotypes of CD including disease location, severity and need for surgery.)
As per claim 9,
The combined teaching of McGovern and Jain teaches the limitations of claim 1.
McGovern further teaches wherein the at least one risk factor corresponds to at least one Clinical Classifications Software Refined (CCSR) category:
(Paragraphs [0072]-[0075] of McGovern. The teaching describes that a sample may be taken from a subject at risk of developing an inflammatory disease or disorder due to factors such as familial history, age, hypertension or pre-hypertension, diabetes or pre-diabetes, overweight or obesity, environmental exposure, lifestyle risk factors (e.g., smoking, alcohol consumption, or drug use), or presence of other risk factors)
As per claim 10,
The combined teaching of McGovern and Jain teaches the limitations of claim 1.
McGovern further teaches wherein the at least one disorder is breast cancer:
(Paragraph [0089] of McGovern. The teaching describes that the system can detect disorders including Breast Cancer, Breast Cancer in Men - HER2-Positive Breast Cancer)
As per claim 11,
The combined teaching of McGovern and Jain teaches the limitations of claim 1.
McGovern further teaches wherein the trained ML model is configured to receive input data corresponding to a user and provide a risk prediction indicating the user's risk of being diagnosed with the at least one disorder by the end of the target prediction period:
(Paragraphs [0003]-[0011] and [0116]-[0122] of McGovern. The teaching describes DeepLeaming algorithm may be used to apply a machine learning classifier to a plurality of inflammatory disease-associated genomic loci that are associated with individuals with known conditions (e.g., an inflammatory disease or disorder, such as an IBD) and individuals not having the condition (e.g., healthy individuals, or individuals who do not have an inflammatory disease or disorder, such as an IBD), in order to classify a subject as having the condition (e.g., positive test outcome) or not having the condition (e.g., negative test outcome))
As per claim 12,
The combined teaching of McGovern and Jain teaches the limitations of claim 11.
McGovern further teaches wherein the risk prediction includes a risk score:
(Paragraphs [0003]-[0011], [0049]-[0053], [0084]-[0091] and [0222]-[0227] of McGovern. The teaching describes that inflammatory disease or condition may comprise a likelihood, risk, or susceptibility of having an inflammatory disease in the future (e.g., within about 1 hour- about 10 years, or more than about 10 years. Prediction model of CD risk was constructed using DeepLearning algorithms using genetic data from the IIBDGC cohort - best performance of risk prediction of complex human diseases using genetic data - DL predicted risk score is also strongly associated with disease clinical phenotypes of CD including disease location, severity and need for surgery.)
As per claim 13,
Claim 13 is substantially similar to claim 1. Accordingly, claim 13 is rejected for the same reasons as claim 1.
As per claim 15,
Claim 15 is substantially similar to claim 4. Accordingly, claim 15 is rejected for the same reasons as claim 4.
As per claim 16,
Claim 16 is substantially similar to claim 5. Accordingly, claim 16 is rejected for the same reasons as claim 5.
As per claim 17,
Claim 17 is substantially similar to claim 6. Accordingly, claim 17 is rejected for the same reasons as claim 6.
As per claim 18,
Claim 18 is substantially similar to claim 8. Accordingly, claim 18 is rejected for the same reasons as claim 8.
As per claim 19,
Claim 19 is substantially similar to claim 9. Accordingly, claim 19 is rejected for the same reasons as claim 9.
As per claim 20,
Claim 20 is substantially similar to claim 10. Accordingly, claim 20 is rejected for the same reasons as claim 10.
As per claim 21,
Claim 21 is substantially similar to claim 11. Accordingly, claim 21 is rejected for the same reasons as claim 11.
As per claim 22,
Claim 22 is substantially similar to claim 12. Accordingly, claim 22 is rejected for the same reasons as claim 12.
Response to Arguments
Applicant's arguments filed April 29, 2026 have been fully considered.
Applicant’s arguments pertaining to the rejections made under 35 U.S.C. 103 are rendered moot in light of the new combination of references used in the current rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H. CHOI can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
