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 .
Status of Claims
This office action for the 18/517566 application is in response to the communications filed January 02, 2025.
Claims 1, 11 and 20 were amended January 02, 2025.
Claims 1-20 are currently pending and considered below.
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.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Frank et al. (US 2018/0025368; herein referred to as Frank) in view of Short et al. (US 2019/0231240; herein referred to as Short) in further view of Mirabile (US 2014/0236759).
As per claim 1,
Frank teaches a system for nourishment refinement using psychiatric markers, the system comprising: a computing device designed and configured to: retrieve a plurality of psychiatric markers relating to a user: wherein the plurality of psychiatric markers comprises: a physical measurement obtained from the user's physiological extraction of a deoxyribonucleic acid related sample, wherein the physical measurement indicates a psychiatric condition:
(Paragraph [0120] and [0318] of Frank. The teaching describes a system that includes sensors that measure the affective response of a person or group of people after having eaten certain foods. The teaching further describes that “a measurement of affective response of a user may include a physiological signal derived from a biochemical measurement of the user. For example, the biochemical measurement may be indicative of the concentration of one or more chemicals in the body of the user (e.g., electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity)”. These chemicals in the body are considered as psychiatric markers because, for example, levels of neurotransmitters in the body reflect at least part of the psychiatric condition of a patient. Metabolites such as protein are a deoxyribonucleic acid (DNA) related sample. Proteins are formed by the sequences of DNA used to construct it.)
Frank further teaches wherein the psychiatric marker comprises: a physical measurement obtained from the user's physiological extraction of a deoxyribonucleic acid related sample, wherein the physical measurement indicates a psychiatric condition and a subjective response describing the user's current emotional state related to a psychological energy level:
(Paragraphs [0109], [0120] and [0318] of Frank. The teaching describes “affective response” refer to physiological and/or behavioral manifestation of an entity's emotional state. The teaching further describes a system that includes sensors that measure the affective response of a person or group of people after having eaten certain foods. The teaching further describes that “a measurement of affective response of a user may include a physiological signal derived from a biochemical measurement of the user. For example, the biochemical measurement may be indicative of the concentration of one or more chemicals in the body of the user (e.g., electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity)”. These chemicals in the body are considered as a psychiatric marker because for example, the level of neurotransmitters in the body reflect the psychiatric state of a patient.)
Frank further teaches identify a nutrient variation as a function of the plurality of psychiatric markers:
(Paragraphs [0145]-[0152] and [0318] of Frank. The teaching describes that the affective response from a person measured among a population of people. This measurement of affective response differs from person to person through the personalization of a preference score for the patient. This preference score defines the experience, i.e. affective response, which corresponds to consuming a certain type of food. The affective response is a function of the type of food the person ate and the preference score outputs data classifying the variability of scores between people as a function of the affective response. The affective response can be considered a variability in the level of biochemical measurement of the user such as electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity. Accordingly, the patient eats food of a specific type, experiences an affective response, this affective response defines any experience that the patient has including blood levels such as a neurotransmitter, i.e. the psychiatric marker, and the preference score defines difference in food consumption, i.e. identifying a nutrient variation, from patient to patient according to this affective response.)
Frank does not explicitly teach wherein identifying the nutrient variation comprises: determining a degree of psychiatric impairment as a function of the physical measurement; and identifying the nutrient variation as a function of the degree of psychiatric impairment; establish a nourishment possibility as a function of the nutrient variation and the degree of psychiatric impairment.
However, Short teaches wherein identifying further comprises determining a degree of psychiatric impairment and establish nourishment possibilities as a function of the degree of psychiatric impairment:
(Paragraphs [0191]-[0193] and [0215] of Short. The teaching describes a predictive system that correlates a biomarker such as C-reactive protein with a mental condition such as depression. The system provides many responses to such a prediction including information, advice or guidance to the user pertaining to nutrition and eating habits based on the predicted mental condition. Here the mental condition is considered a psychiatric condition.)
It would have been obvious to one of ordinary skill in the art before the time of filing to modify the analysis made in the recommendation module of Frank with the predictive psychiatric diet recommendation teachings of Short. Paragraph [0012] of Short teaches the “references discussed above fail to appreciate that biomarkers in a sample from an individual may be used directly to assist the individual, optionally in real-time to maintain or improve the individual's wellness. The present invention provides a method and system for maintaining or improving an individual's wellness based on measurements of a presence and/or concentration of one or more biomarkers in a sample from the individual. The invention provides individualized information, advice, or guidance for maintaining or improving wellness”. This suggests that the enclosed embodiments of Short would have improved patient outcome in Frank thereby leading to an improved system. One of ordinary skill in the art would have modified the teaching of Frank with the teaching of Short based on this incentive without yielding unexpected results.
The combined teaching of Frank and Short would then teach training a first machine learning model using training data configured to input the plurality of psychiatric markers and output a degree of psychiatric impairment, wherein identifying the nutrient variation comprises: determining a degree of psychiatric impairment as a function of the physical measurement and the trained first machine learning model; and identifying the nutrient variation as a function of the degree of psychiatric impairment; establish a nourishment possibility as a function of the nutrient variation and the degree of psychiatric impairment:
(Paragraphs [0145]-[0152], [0270] and [0318] of Frank. The teaching describes that the affective response from a person measured among a population of people. This measurement of affective response differs from person to person through the personalization of a preference score for the patient. This preference score defines the experience, i.e. affective response, which corresponds to consuming a certain type of food. The affective response is a function of the type of food the person ate and the preference score outputs data classifying the variability of scores between people as a function of the affective response. The affective response can be considered a variability in the level of biochemical measurement of the user such as electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity. Accordingly, the patient eats food of a specific type, experiences an affective response, this affective response defines any experience that the patient has including blood levels such as a neurotransmitter, i.e. the psychiatric marker, and the preference score defines difference in food consumption, i.e. identifying a nutrient variation, from patient to patient according to this affective response. Various approaches may be utilized, in embodiments described herein, to learn parameters of the function mentioned above from the measurements of affective response. In some embodiments, the parameters of the function may be learned utilizing an algorithm for training a predictor. For example, the algorithm may be one of various known machine learning-based training algorithms that may be used to create a model for a machine learning-based predictor that may be used to predict target values of the function for different domain values of the function.)
(Paragraphs [0191]-[0193], [0215] and [0221] of Short. The teaching describes a predictive system that correlates a biomarker such as C-reactive protein with a mental condition such as depression. The system provides many responses to such a prediction including information, advice or guidance to the user pertaining to nutrition and eating habits based on the predicted mental condition. Here the mental condition is considered a psychiatric condition. the present invention provides a system for providing assistance to an individual for maintaining or improving the individual's wellness, comprising a measuring device 11 for measuring a presence and/or concentration of one or more biomarkers in a sample from the individual, a predictor 12 for predicting a wellness need of the individual to maintain or improve the individual's wellness based on the measured presence and/or concentration of the one or more biomarkers where the one or more biomarkers are indicators of the wellness need)
The combined teaching of Frank and Short would then teach generate a nourishment program, wherein generating the nourishment program comprises: training a second machine learning model as a function of a training set containing the plurality of psychiatric markers and nutrient variations determined as a function of the first machine learning model as input correlated to a plurality of nourishment programs as output and generating the nourishment program as a function of the plurality of psychiatric markers and the nourishment possibility, using the trained second machine learning model.
(Paragraphs [0145]-[0152], [0168], [0270], [0389] [0552], [0557] and [0692] of Frank. The teaching describes that the system creates recommendations for food to the user based on the preference score which is determined from the affective response. The teaching further describes that to compute preference score for the patient, a machine learning model may be used. The machine learning model is trained by input measurements of affective response and returns a result of a score that is considered to reflect the experience of the patient from consuming the food, i.e. the preference score. The affective response is a function of the type of food the person ate and the preference score outputs data classifying the variability of scores between people as a function of the affective response. In this embodiment, the patient consumes the food, experiences an affective response, a preference score is generated through a trained machine learning process that is a function of affective response and difference in food consumed, and food recommendations are made based upon this preference score. Various approaches may be utilized, in embodiments described herein, to learn parameters of the function mentioned above from the measurements of affective response. In some embodiments, the parameters of the function may be learned utilizing an algorithm for training a predictor. For example, the algorithm may be one of various known machine learning-based training algorithms that may be used to create a model for a machine learning-based predictor that may be used to predict target values of the function for different domain values of the function. In some embodiments, an affective value scorer may be implemented by a predictor, which may utilize an Emotional State Estimator (ESE) and/or itself be an ESE. Training a personalized ESE for a user may require acquiring appropriate training samples. These samples typically comprise measurements of affective response of the user (from which feature values may be extracted) and labels corresponding to the samples, representing an emotional response the user had when the measurements were taken. a software agent (or another module) that is tasked with training a personalized ESE for a certain user may start off by utilizing a general ESE to determine emotional states of the user. These labeled samples may be added to a pool of training samples used to train the personalized ESE. As the body of labeled samples increases in size, the estimator trained on them will begin to represent the particular characteristics of how the user expresses emotions.)
(Paragraphs [0191]-[0193], [0215] and [0221] of Short. The teaching describes a predictive system that correlates a biomarker such as C-reactive protein with a mental condition such as depression. The system provides many responses to such a prediction including information, advice or guidance to the user pertaining to nutrition and eating habits based on the predicted mental condition. Here the mental condition is considered a psychiatric condition. the present invention provides a system for providing assistance to an individual for maintaining or improving the individual's wellness, comprising a measuring device 11 for measuring a presence and/or concentration of one or more biomarkers in a sample from the individual, a predictor 12 for predicting a wellness need of the individual to maintain or improve the individual's wellness based on the measured presence and/or concentration of the one or more biomarkers where the one or more biomarkers are indicators of the wellness need)
The combined teaching of Frank and Short does not explicitly teach establish a nourishment possibility as a function of the nutrient variation and the degree of psychiatric impairment, wherein the nourishment possibility includes: a first list of one or more meals available to purchase from a food delivery company; and a second list of one or more meals available to purchase from a restaurant located within a certain geographical location of the user.
However, Mirabile teaches establish a nourishment possibility as a function of the nutrient variation and the degree of psychiatric impairment, wherein the nourishment possibility includes: a first list of one or more meals available to purchase from a food delivery company; and a second list of one or more meals available to purchase from a restaurant located within a certain geographical location of the user:
(Paragraphs [0029] and [0041] of Mirabile. The teaching describes method stores all of a person's submitted data; cross-references this data with a stored database of restaurants and associated meals; identifies restaurants and meals that fulfill person's schedule, including the possibility for delivery to geographic location and during date and time of day, and determines which meals best satisfy the person's specified meal requirements and preferences. In one embodiment a list of Viable Food Options is compiled. In another embodiment, a particular meal is automatically delivered to the person. In another embodiment, a particular meal is delivered to the person according to a predefined schedule. In another embodiment, a person is presented with an opt-in confirmation option for a meal. In another embodiment, a person is presented with an opt-out alert for preventing fulfillment of an otherwise automatically delivered or fulfilled meal. “Personal Information” is information about a human being. For any given human being, categories of Personal Information include physical attributes, for example, sex, age, height, current weight, body mass index (“BMI”), body measurements (e.g., waist, bust, arm, chest, etc.). Personal information also includes date of birth, weight, body fat percentage, sex, race or ethnicity, living & work conditions, ZIP code, size of home and/or workplace, time spent outdoors, time spent under natural sunlight, occupation, time spent sitting vs. standing, time spent watching television or in front of a computer, medical history and current medical conditions and ailments (e.g., diabetes, cancer [including type and status], ADHD, depression, obesity, heart disease, physical injury, low libido/sex drive, etc.), cholesterol levels (e.g., total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, ApoA1, ApoB, LP(a), Lp-PLA2, Omega-3 Fatty Acids, Free Fatty Acids, etc.), liver & kidney health (e.g., BUN/Creatinine, AST & ALT, Total Bilirubin, Albumin, Total Protein, etc.), performance hormones (e.g., DHEA, Free Testosterone, Testosterone, Estradiol, SHBG, etc.), metabolic hormones, (e.g., cortisol, IGF-1, insulin), thyroid & blood sugar (e.g., glucose, HbA1c, etc.), advanced thyroid (e.g., Total T4, T3 uptake, free T4 index, TSH, total T3, free T3, reverse T3, free T4, etc.), advanced Inflammation (e.g., hs-CRP, fibrinogen, homocysteine, etc.), blood count and advanced nutrients (e.g., complete blood count with differential, calcium, electrolytes, bicarbonate, ferritin [serum], total Iron binding capacity [TIBC], folate, vitamin B12, RBC Magnesium, 25-Hydroxy vitamin D, etc.), womens' reproductive panel (e.g., progesterone, FSH, Luteinizing Hormone, etc.), blood pressure (diastolic, systolic), food allergens, food sensitivities, hormone levels (via blood, urine and/or saliva tests), assessment of physical/athletic health (i.e., recent physical activity, upload of heart rate/activity information, etc.), resting heart rate, active heart rate and Heart Rate Variability during specific activities (i.e., while working, while walking “x” mph for “y” minutes, etc.), VO2 max, genetic testing—DNA sequencing, neural waves (e.g., EEG, HEG and other readings), sleep effectiveness (total time, time per sleep stage, number of sleep stages, etc.), physical activity (type of activity[ies], duration, and any quantified self outputs (heart rate, skin temperature, horizontal and vertical distance, weight of self and equipment, repetitions, etc.). This means that the system is generating a list of food options for purchase based upon psychiatric conditions such as depression and generating this list in tandem with a list of food available to be delivered to the user with the intention of the food being able to address the medical conditions that the user is experiencing for which the food may remedy.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the food recommendation platform of the combined teaching of Frank and Short, the food recommendation and delivery methods of Mirabile. Paragraph [0152] of Mirabile teaches that the disclosed system allows for the improvement in user outcomes by improving future recommendations of food items over time to best assist the user in their medical deficiency. One of ordinary skill in the art in possession of the combined teaching of Frank and Short, particularly with regard to their teaching on a food’s affective response in a user, would have looked to Mirabile to achieve these improvements. One of ordinary skill in the art would have added to the combined teaching of Frank and Short, the teaching of Mirabile based on this incentive without yielding unexpected results.
As per claim 2,
The combined teaching of Frank, Short and Mirabile teaches the limitations of claim 1.
Frank further teaches wherein the subjective response comprises a psychological energy level related to an impulsive behavior:
(Paragraphs [0004], [0145]-[0152] and [0318] of Frank. The teaching describes that the affective response from a person measured among a population of people. This measurement of affective response differs from person to person through the personalization of a preference score for the patient. This preference score defines the experience, i.e. affective response, which corresponds to consuming a certain type of food. The affective response is a function of the type of food the person ate and the preference score outputs data classifying the variability of scores between people as a function of the affective response. The affective response can be considered a variability in the level of biochemical measurement of the user such as electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity. Accordingly, the patient eats food of a specific type, experiences an affective response, this affective response defines any experience that the patient has including blood levels such as a neurotransmitter, i.e. the psychiatric marker, and the preference score defines difference in food consumption, i.e. identifying a nutrient variation, from patient to patient according to this affective response. The measurements of affective response may be used to determine how users feel while or after consuming certain types of food. In one example, the measurements may be indicative of the extent the users feel one or more of the following emotions: pain, anxiety, annoyance, stress, aggression, aggravation, fear, sadness, drowsiness, apathy, anger, happiness, contentment, calmness, attentiveness, affection, and excitement.)
As per claim 3,
The combined teaching of Frank, Short and Mirabile teaches the limitations of claim 1.
Frank further teaches wherein the psychiatric marker further comprises data related to neuroimaging:
(Paragraphs [0303]-[0308] of Frank. The teaching describes that a measurement of affective response of a user comprises, and/or is based on, a physiological signal of the user, which reflects a physiological state of the user including brain activity determined based on functional magnetic resonance imaging (fMRI))
As per claim 4,
The combined teaching of Frank, Short and Mirabile teaches the limitations of claim 1.
Frank further teaches wherein the psychiatric condition comprises at least one nutrition related disorder:
(Paragraph [0564] of Frank. The teaching describes that a software agent may function as a virtual assistant and/or “virtual wingman” that assists a user by making decisions on behalf of a user, making suggestions to the user, and/or issuing warnings to the user. Optionally, the software agent may make the decisions, suggestions, and/or warnings based on a model of the users' biases. Optionally, the software agent may make decisions, suggestions, and/or warnings based on crowd-based scores for experiences. In one example, the software agent may suggest to a user certain experiences to have (e.g., to go biking in the park), places to visit (e.g., when on a vacation in an unfamiliar city), and/or content to select. In another example, the software agent may warn a user about situations that may be detrimental to the user or to the achievement of certain goals of the user. For example, the agent may warn about experiences that are bad according to crowd-based scores, suggest the user take a certain route to avoid traffic, and/or warn a user about excessive behavior (e.g., warn when excessive consumption of alcohol is detected when the user needs to get up early the next day).)
As per claim 5,
The combined teaching of Frank, Short and Mirabile teaches the limitations of claim 1.
Frank further teaches wherein the computing device is further configured to modify the nourishment program based on an intervention assistance marker related to therapy:
(Paragraph [1305] of Frank. The teaching describes the experience for which the aftereffect function is computed involves partaking in an exercise activity, such as Yoga, Zumba, jogging, swimming, golf, biking, etc. The aftereffect function in this embodiment may describe how well user feels (e.g., on a scale from 1 to 10) at a certain time after completing the exercise; the certain time in this embodiment may be 0 to 12 hours from when the user finished the exercise. Optionally, a prior measurement of the user may be taken before the user starts exercising (or while the user is exercising), and a subsequent measurement is taken at a time Δt after the user finishes exercising. Optionally, in addition to the input value indicative of Δt, the aftereffect function may receive additional input values. For example, in one embodiment, the aftereffect function receives an additional input value d that is indicative of the duration of the exercise and/or of the difficulty level of the exercise. Thus, in this example, the aftereffect function may be considered to behave like a function of the form ƒ(Δt,d)=v, and it may describe the affective response v, a user is expected to feel at a time Δt after partaking an exercise for a duration d (and/or the exercise has a difficulty level that equals d). This means that the computing device can modify the nutrition program based on exercise, a type of therapy related to nutrition.)
As per claim 6,
The combined teaching of Frank, Short and Mirabile teaches the limitations of claim 1.
Frank further teaches wherein the computing device is further configured to modify the nourishment program based on an intervention assistance marker related to exercise:
(Paragraph [1305] of Frank. The teaching describes the experience for which the aftereffect function is computed involves partaking in an exercise activity, such as Yoga, Zumba, jogging, swimming, golf, biking, etc. The aftereffect function in this embodiment may describe how well user feels (e.g., on a scale from 1 to 10) at a certain time after completing the exercise; the certain time in this embodiment may be 0 to 12 hours from when the user finished the exercise. Optionally, a prior measurement of the user may be taken before the user starts exercising (or while the user is exercising), and a subsequent measurement is taken at a time Δt after the user finishes exercising. Optionally, in addition to the input value indicative of Δt, the aftereffect function may receive additional input values. For example, in one embodiment, the aftereffect function receives an additional input value d that is indicative of the duration of the exercise and/or of the difficulty level of the exercise. Thus, in this example, the aftereffect function may be considered to behave like a function of the form ƒ(Δt,d)=v, and it may describe the affective response v, a user is expected to feel at a time Δt after partaking an exercise for a duration d (and/or the exercise has a difficulty level that equals d). This means that the computing device can modify the nutrition program based on exercise, a type of therapy related to nutrition.)
As per claim 7,
The combined teaching of Frank, Short and Mirabile the limitations of claim 1.
Frank further teaches wherein the degree of psychiatric impairment comprises a severity of the psychiatric condition:
(Paragraphs [0145]-[0152], [0168] and [0692] of Frank. The teaching describes that the system creates recommendations for food to the user based on the preference score which is determined from the affective response. The teaching further describes that to compute preference score for the patient, a machine learning model may be used. The machine learning model is trained by input measurements of affective response and returns a result of a score that is considered to reflect the experience of the patient from consuming the food, i.e. the preference score. The affective response is a function of the type of food the person ate and the preference score outputs data classifying the variability of scores between people as a function of the affective response. In this embodiment, the patient consumes the food, experiences an affective response, a preference score is generated through a trained machine learning process that is a function of affective response and difference in food consumed, and food recommendations are made based upon this preference score.)
As per claim 8,
The combined teaching of Frank, Short and Mirabile teaches the limitations of claim 1.
Short further teaches wherein establishing the nourishment possibilities comprises: generating a query relating to the psychiatric marker and the nutrient variation; and establishing the nourishment possibilities by searching a nutrient database using the generated query:
(Paragraphs [0191]-[0193], [0215] and [0216] of Short. The teaching describes a predictive system that correlates a biomarker such as C-reactive protein with a mental condition such as depression. The system provides many responses to such a prediction including information, advice or guidance to the user pertaining to nutrition and eating habits based on the predicted mental condition. Here the mental condition is considered a psychiatric condition. The nutritional information, advice, or guidance may also be information, advice, or guidance on daily limits on total number of calories, amount of fiber, proteins, sugar, salt, and bad fat (saturated fat and trans fat), or any other nutrients or ingredients, in grams or equivalent teaspoons or tablespoons and a total daily count indicator for each nutrient. The nutritional information, advice, or guidance may also be a rank-ordered list of suggestions for meal preparation and choices of food. In providing nutritional information, advice, or guidance, the individual's food allergy information, or any food contraindicated to the individual's medical conditions, a list of favorites, excluded, preferred, and non-preferred foods may be considered as personal preferences in the database 14.)
As per claim 9,
The combined teaching of Frank, Short and Mirabile teaches the limitations of claim 1.
Frank further teaches wherein the computing device is further configured to: evaluate the user regarding a behavior marker; and update the nourishment program as a function of the behavior marker:
(Paragraph [0544] of Frank. The teaching describes that context may be given by identifying a situation the user was in when the measurement was taken. Examples of situations may include a mood of the user, a health state of the user, the type of activity the user is partaking in (e.g., relaxing, exercising, working, and/or shopping), the location the user is at (e.g., at home, in public, or at work), and/or the alertness level of the user. The additional situation information may be used by the ESE to improve the estimation of the emotional state of the user from the measurement.)
As per claim 11,
Claim 11 is substantially similar to claim 1. Accordingly, claim 11 is rejected for the same reasons as claim 1.
As per claim 12,
Claim 12 is substantially similar to claim 2. Accordingly, claim 12 is rejected for the same reasons as claim 2.
As per claim 13,
Claim 13 is substantially similar to claim 3. Accordingly, claim 13 is rejected for the same reasons as claim 3.
As per claim 14,
Claim 14 is substantially similar to claim 4. Accordingly, claim 14 is rejected for the same reasons as claim 4.
As per claim 15,
Claim 15 is substantially similar to claim 5. Accordingly, claim 15 is rejected for the same reasons as claim 5.
As per claim 16,
Claim 16 is substantially similar to claim 6. Accordingly, claim 16 is rejected for the same reasons as claim 6.
As per claim 17,
Claim 17 is substantially similar to claim 7. Accordingly, claim 17 is rejected for the same reasons as claim 7.
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 19. Accordingly, claim 19 is rejected for the same reasons as claim 9.
Response to Arguments
Applicant's arguments filed September 30, 2025 have been fully considered.
Applicant’s arguments pertaining to 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).
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, PETER H CHOI can be reached on (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHAD A NEWTON/Primary Examiner, Art Unit 3686