DETAILED ACTION
The present office action represents a nonfinal action on the merits.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/4/2025 has been entered.
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 .
Priority
This application claims the priority date of a 371 of PCT Foreign Application PCT/JP2019/040353 of October 14, 2019 and Foreign Application JP2018-194703 of October 15, 2018.
Status of Claims
Claims 1 and 14 are amended and claims 1-11 and 14 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-11 and 14 are drawn to a health management system, which is within the four statutory categories (i.e., machine). Claim 14 is drawn to computer method is a health management system, which is within the four statutory categories (i.e., manufacture.)
Claims 1-11 recite a health management system comprising:
a communication interface configured to receive, from user equipment, vital data of a user;
a first storage configured to store an estimation model for predicting vital data based on an intake amount of food components, the estimation model being created by multivariate analysis of the intake amount of the food component ingested by the user and the vital data of the user, wherein the multivariate analysis includes performing multiple regression analysis to determine a coefficient for each of the food components in relation to the vital data;
a reference value storage configured to store a reference value of the intake amount of the food component ingested by the user;
a processor; and
a memory coupled to the processor, the memory including computer-readable instructions which, when executed by the processor, cause the processor to:
receive a target value of the vital data for the user;
set the reference value as a target intake amount of each of the food components;
determining a food component having a positive coefficient as an improving food component and a food component having a negative coefficient as a deteriorating food component, when a first vital data predicted by applying a first set target intake amount to the estimation model is greater than current vital data and is closer to a predefined target value, then determining a food component term, based on a difference between the first target intake and a next target intake, wherein a food component term having a positive coefficient is determined as an improving food component term, and a food component term having a negative coefficient is determined as a deteriorating food component term, when a vital data predicted by applying the next target intake amount to the estimation model is greater than the vital data predicted by the first target intake;
determining a food component having a positive coefficient as a deteriorating food component and a food component having a negative coefficient as an improving food component, when a first vital data predicted by applying a first set target intake amount to the estimation model is smaller than current vital data and is closer to a predefined target value, then determining a food component term based on a difference between the first target intake and a next target intake, wherein a food component term having a positive coefficient is determined as a deteriorating component term, and a food component term having a negative coefficient is determined as an improving component term, when a vital data predicted by applying the next target intake amount to the estimation model is greater than the vital data predicted by the first target intake;
calculate optimal amounts of food components by performing at least one of increasing the target intake amount of the improving food component and decreasing the target intake amount of the deteriorating food component so that a second predicted vital data predicted by applying the optimal amounts of food components to the estimation model becomes the target value;
generating a prepared menu, the prepared menu including the optimal amounts of food components associated with the target value; and
transmitting, to the user equipment, the prepared menu including the optimal amounts of the food components,
wherein the food component is a nutritional or non-nutritional component of a food item.
Claim 14 recites a computer implemented method comprising:
receiving, from a user equipment, vital data of a user;
storing, in a storage of the computer, an estimation model for predicting vital data based on an intake amount of food components, the estimation model being created by multivariate analysis of the intake amount of the food component ingested by a user and the vital data of the user, wherein the multivariate analysis includes performing multiple regression analysis to determine a coefficient for each of the food components in relation to the vital data;
storing a reference value of the intake amount of the food component ingested by the user;
receiving a target value of the vital data for the user;
setting a reference value as a target intake amount of each of the food components;
processing, by a processor of the computer, the set target intake amounts using the estimation model to determine predicted vital data;
determining differences between the predicted vital data and the target values;
identifying, based on the difference, an improving food component of the food components that is a food component which improves the vital data in the estimation model and a deteriorating food component that is a food component of the food components which deteriorates the vital data in the estimation model;
performing at least one of increasing the target intake amount of the identified improving food component and decreasing the target intake amount of the identified deteriorating food component so that the target intake amount causes the predicted vital data to become the target value; and
transmitting, to the user equipment, a signal indicating at least one of the increased target intake amount and the decreased target intake amount.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity (e.g., gathering user information; managing user information, in this case providing datasets of vital data and dietary ingredients.) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP 2106.04(a)(2) and mathematical concepts (e.g., the estimation model). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-11 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
The dependent claims include additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 14.
The additional elements from claim 1 include:
a communication interface configured to (apply it, MPEP 2106.05(f)).
a first storage configured to (apply it, MPEP 2106.05(f)).
store (extra-solution activity, MPEP 2106.05(g)).
a reference value storage configured to (apply it, MPEP 2106.05(f)).
store (extra-solution activity, MPEP 2106.05(g)).
a processor (apply it, MPEP 2106.05(f)).
a memory coupled to the processor, the memory including computer-readable instructions (apply it, MPEP 2106.05(f)).
user equipment (apply it, MPEP 2106.05(f)).
The dependent claims include additional elements in addition to those in the independent claims, including:
wherein the memory includes additional instructions which, when executed by the processor, cause the processor to (apply it, MPEP 2106.05(f)).
receive an input of a target value of the vital data (extra-solution activity, MPEP 2106.05(g)).
a menu information storage configured to store (apply it, MPEP 2106.05(f)).
storage device (apply it, MPEP 2106.05(f)).
configured to store menu information (extra-solution activity, MPEP 2106.05(g)).
the memory includes additional instructions (apply it, MPEP 2106.05(f)).
an input device configured to receive an input of lifestyle information indicating a lifestyle of the user (apply it, MPEP 2106.05(f)).
receiving, from a user equipment (apply it, MPEP 2106.05(f)).
storing, in a storage of the computer extra-solution activity, MPEP 2106.05(g)).
Claims 1-11 and 14 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of “memory”, “the storage device”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see Specification Paragraphs [0016], [0023]-[0024], [0028], and [0051]. (See MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea – for example, the recitation of receiving or storing data, which amounts to mere data gathering, and/or the recitation of analyzing the relationship between vital data and intake of a food component, (claims 1, 3, 4, and 11), which amounts to insignificant extra-solution activity. (See MPEP 2106.05(g));
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Paragraphs [0016] and [0054], of the Specification discloses that the additional elements (i.e., computer, processor, memory, storage device) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g., MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g., see Intellectual Ventures v. Symantec – similarly, the current invention receives data relating to vital data of a user and intake of a food component;
Storing and retrieving information in memory, e.g., see Versata Dev. Group, Inc., v. SAP Am., Inc. – the current invention recites storing vital data of a user and intake of a food component.
Dependent claims 2-11 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves acquiring and analyzing vital data and food component data or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-11 and 14 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claims 1-3, 5-11 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hadad (U.S. Pub. No. 2019/0295440 A1) in view of Chapela (U.S. Pub. No. 2018/0189636 A1) and Solari (U.S. Pub. No. 2018/0233223 A1).
Regarding claim 1, Hadad discloses a health management system comprising:
a communication interface configured to receive, from user equipment, vital data of a user (Paragraphs [0097], [0136]-[0137], [0248], and FIGS 26 discuss a food analysis system with a device that can device/data hub can automatically aggregate biomarker and health data of the user (e.g., sleep, exercise, blood tests, genetic tests, etc.) from multiple application programming interfaces and an interface for blood glucose logging.);
a first storage configured to store an estimation model for predicting vital data based on an intake amount of food components, the estimation model being created by multivariate analysis of the intake amount of the food component ingested by a user and the vital data of the user, wherein performing analysis to determine information for each of the food components in relation to the vital data (Paragraph [0060] and [0265] discuss a tangible computer readable medium storing instructions that, when executed by one or more processors, causes one or more processors to perform a computer-implemented method for determining effects of food consumption on a user's body by applying a predictive model to (1) data indicative of foods consumed by the user, (2) data indicative of physiological inputs associated with the user, and (3) information about the foods consumed by the user from a food ontology, to thereby generate a plurality of personalized food and health metrics for the user, for example, a recommendation to eat less carbohydrate, the user may eat pizza often for lunch, and the insights and recommendation engine can detect that consumption of pizza is correlated with a spiked increase in the user's blood glucose level and identify other foods that, when eaten with pizza, can reduce the blood glucose level and also identify one or more alternative food items to replace pizza.);
a reference value storage configured to store a reference value of the intake amount of the food component ingested by the user (Paragraphs [0061] and [0135]-[0138] discuss generating a food baseline profile of a user can comprise monitoring effect of different foods on the user's body and the platform can be in communication with the Internet and database(s) (e.g., other food, nutrition, or healthcare providers) to store any data or information that is collected and generated by the platform.);
a processor (Paragraphs [0034] and [0044] discuss one or more processors.); and
a memory coupled to the processor, the memory including computer-readable instructions which, when executed by the processor, cause the processor to (Paragraphs [0034] and [0044] discuss computer readable medium can store instructions that when executed by one or more processors causes one or more processors to perform a computer-implemented method.):
receive a target value of the vital data for the user (Paragraph [0302] and FIGS. 34A-34C discuss an interface may display to a user details about blood glucose, including the user's average weekly glucose level and its standard deviation value, the percentage of glucose measurements that have been in a predetermined target range, below the target range, and above the target range.);
set the reference value as a target intake amount of each of the food components (Paragraph [0302] discusses interface window may also display popular eating hours, represented by the average of number of meals that have been consumed per every hour throughout the day, and a distribution plot of carbohydrates consumed (e.g., in grams) per every hour throughout the day and indicate whether the average glucose values at 2 hours post-meal are above or below the predetermined target range of blood glucose level and an assessment of the balance of the average meal nutrition breakdown.);
determining a food component having a positive impact as an improving food component and a food component having a negative impact (Examiner notes that the prior art references food that scores a higher value versus a food that scores less and does not reference negative or positive) as a deteriorating food component, when a first vital data predicted by applying a first set target intake amount to the estimation model is greater than current vital data and is closer to a predefined target value, then determining a food component term, based on a difference between the first target intake and a next target intake, wherein a food component term having a positive impact is determined as an improving food component term, and a food component term having a negative impact is determined as a deteriorating food component term, when a vital data predicted by applying the next target intake amount to the estimation model is greater than the vital data predicted by the first target intake (Examiner notes that the prior art reference does not contain “first”, “predefined”, or “next”, however, it includes the broadest reasonable interpretation of the claims.) (Paragraph [0060], [0257], [0311]and FIG. 21 discuss determining effects of food consumption on a user's body by applying a predictive model to generate a plurality of personalized food and health metrics for the user, for example, a recommendation to eat less carbohydrate, the user may eat pizza often for lunch, and the insights and recommendation engine can detect that consumption of pizza is correlated with a spiked increase in the user's blood glucose level and identify other foods that, when eaten with pizza, can reduce the blood glucose level and also identify one or more alternative food items to replace pizza; the insights and recommendation engine can (1) access the food ontology in the food analysis system , (2) access a plethora of personal biomarkers data from the device/data hub, (3) analyze how foods affect a user's biomarkers, and (4) continually generate personal nutrition recommendations to the user, and upon analyzing and validating how foods may affect the user's biomarkers, the insights and recommendation engine can generate one or more personalized digital signatures unique for the user to estimate the response of a specific biomarker of the user to consumption of a specific food item. For example, a recommendation can compare two food items and suggest if one is healthier and a the report may focus on factors that can influence blood glucose level: food, activity, and sleep, inform a pre-defined target glucose level range (e.g. 70-170 mg/dL) for the user along with the user's average glucose level. The report can include assessment of one or more meals based on how the user's glucose level responded to one or more meals and utilize a rating system (e.g., “A” for a balanced glucose response, “F” for a poor glucose response, etc.), and show recommendations .);
determining a food component having a positive impact as a food component and a food component having a negative impact as a food component, when a first vital data predicted by applying a first set target intake amount to the estimation model is smaller than current vital data and is closer to a predefined target value, then determining a food component term based on a difference between the first target intake and a next target intake, wherein a food component term having a positive impact is determined as a deteriorating component term, and a food component term having a negative impact is determined as an improving component term, when a vital data predicted by applying the next target intake amount to the estimation model is greater than the vital data predicted by the first target intake (Paragraph [0060], [0257], [0311]and FIG. 21 discuss determining effects of food consumption on a user's body by applying a predictive model to generate a plurality of personalized food and health metrics for the user, for example, a recommendation to eat less carbohydrate, the user may eat pizza often for lunch, and the insights and recommendation engine can detect that consumption of pizza is correlated with a spiked increase in the user's blood glucose level and identify other foods that, when eaten with pizza, can reduce the blood glucose level and also identify one or more alternative food items to replace pizza; the insights and recommendation engine can (1) access the food ontology in the food analysis system , (2) access a plethora of personal biomarkers data from the device/data hub, (3) analyze how foods affect a user's biomarkers, and (4) continually generate personal nutrition recommendations to the user, and upon analyzing and validating how foods may affect the user's biomarkers, the insights and recommendation engine can generate one or more personalized digital signatures unique for the user to estimate the response of a specific biomarker of the user to consumption of a specific food item. For example, a recommendation can compare two food items and suggest if one is healthier and a the report may focus on factors that can influence blood glucose level: food, activity, and sleep, inform a pre-defined target glucose level range (e.g. 70-170 mg/dL) for the user along with the user's average glucose level. The report can include assessment of one or more meals based on how the user's glucose level responded to one or more meals and utilize a rating system (e.g., “A” for a balanced glucose response, “F” for a poor glucose response, etc.), and show recommendations.);
calculate optimal amounts of food components by performing at least one of increasing the target intake amount of the improving food component and decreasing the target intake amount of the deteriorating food component so that a second predicted vital data predicted by applying the optimal amounts of food components to the estimation model becomes the target value (Paragraphs [0048]-[0050], [0068]-[0069], [0131], [0254], and [0311] discuss a plurality of personalized food and health metrics can comprise a predicted impact of one or more of the foods on the user's health or well-being that is continuously updated in real-time based on effects of foods on user’s body, recommended actions can include a recommendation to reduce or increase consumption of one or more selected foods, or compare two food items consumed by the user and suggest if one of the two food items is a healthier option than the other of the two food items based on the user's physiological responses, for example, a recommendation can compare two types of breads (whole grain bread vs. white bread) and recommend swapping white bread for whole grain alternatives.);
generating a prepared menu, the prepared menu including the food components associated with the target value (Paragraph [0267] discusses the insights and recommendation engine can (1) monitor a user's food intake, blood glucose levels (either continuously from a CGM device or in a discrete manner using a conventional glucose meter), as well as insulin levels for users using the insulin injection therapy, and (2) analyze relationships among specific food types, insulin injections, and blood glucose level responses and use the GUI-based software interface in the user device to display the recommendations to the user, for example, a recommendation may suggest that, “When you added avocado to your sandwiches your glucose response was over 30% lower [occurred 5 out of 6 times].”); and
transmitting, to the user equipment, the prepared menu including the food components (Paragraphs [0031] and [0243] discuss a graphical user interface (GUI) for receiving the data from and/or sending the data to a user, of the food ontology.),
wherein the food component is a nutritional or non-nutritional component of a food item (Paragraphs [0011]-[0015] discuss food ontology includes graphical representation displaying ingredients, micro/macro-nutrients, food characteristics, etc.).
Hadad does not explicitly disclose:
wherein the multivariate analysis includes performing multiple regression analysis to determine a coefficient for each of the food components in relation to the vital data;
determining a food component having a positive coefficient as an improving food component and a food component having a negative coefficient as a deteriorating food component, wherein a food component term having a positive coefficient is determined as an improving component term, and a food component term having a negative coefficient is determined as a deteriorating component term;
determining a food component having a positive coefficient as a deteriorating food component and a food component having a negative coefficient as an improving food component, wherein a food component term having a positive coefficient is determined as a deteriorating component term, and a food component term having a negative coefficient is determined as an improving component term;
the prepared menu including the optimal amounts of food components.
Chapela teaches:
wherein the multivariate analysis includes performing multiple regression analysis to determine information in relation to the vital data (Paragraphs [0023], [0025]-[0026] discuss multivariate causation system receive empirical feedback about the user and their behavior from the computing device and the external sensors, such as wearables (e.g., smart glasses, watches, etc.), sleep sensors, blood pressure monitors, glucose monitors and insulin pumps, blood pressure sensors, respiration monitors, pulse oximeters, heart rate meters, etc., and the multivariate causation system can deeply analyze user feedback and determine specific variations on behaviors and determine how they affect the desired outcomes.);
Therefore, it would have been obvious to one of ordinary skill in the art to modify Hadad to include wherein the multivariate analysis includes performing multiple regression analysis to determine information in relation to the vital data, as taught by Chapela, in order to better learn the relationships between different food elements and groupings; and a contextual filtering and adherence scoring system that identifies and selects recipes, food products, supplements, medications and restaurant menu items according to a personalized plan. (Chapela Paragraph [0003]).
Solari teaches:
determine a coefficient for each of the food components (Paragraphs [0021], [0026], and FIG. 4 discuss the system composes or aggregates the component scores into aggregate scores based on a personalized set of weighting parameters ascribed to each nutrient that reflect the overall nutritional health impact of the consumable for the individual.);
determining a food component having a positive coefficient as an improving food component and a food component having a negative coefficient as a deteriorating food component, wherein a food component term having a positive coefficient is determined as an improving component term, and a food component term having a negative coefficient (Paragraphs [0022], [0026], and [0087]-[0089], [0092], [0096] FIGS. 2-4 discuss provide the score of a meal as built, and can provide an optimal score that might be achieved if additional food items are consumed or if certain consumed foods are removed or reduced from a diet, the system stores an indication of the curve by storing a lower healthy range value, an upper healthy range value, a weighting value, and a sensitivity value for each individual or population of individuals to whom the nutritional health score is tailored, individuals may have their own arrangement of weighting values and/or sensitivity values tailored to their own personal health conditions and the increasing returns of consuming a nutrient under a lower healthy range value, the decreasing returns of consuming a nutrient above an upper healthy range value, or the eventual negative returns of consuming a nutrient, for example, it is less unhealthy for an individual to consume extra calcium than it is for an individual to consume extra saturated fat.);
determining a food component having a positive coefficient as a deteriorating food component and a food component having a negative coefficient as an improving food component, wherein a food component term having a positive coefficient is determined as a deteriorating component term, and a food component term having a negative coefficient is determined as an improving component term (Paragraphs [0022], [0026], and [0087]-[0089], [0092], [0096] FIGS. 2-4 discuss provide the score of a meal as built, and can provide an optimal score that might be achieved if additional food items are consumed or if certain consumed foods are removed or reduced from a diet, the system stores an indication of the curve by storing a lower healthy range value, an upper healthy range value, a weighting value, and a sensitivity value for each individual or population of individuals to whom the nutritional health score is tailored, individuals may have their own arrangement of weighting values and/or sensitivity values tailored to their own personal health conditions and the increasing returns of consuming a nutrient under a lower healthy range value, the decreasing returns of consuming a nutrient above an upper healthy range value, or the eventual negative returns of consuming a nutrient, for example, it is less unhealthy for an individual to consume extra calcium than it is for an individual to consume extra saturated fat, for example, the sensitivity for calcium is “2,” while the sensitivity for saturated fat is “1.” This indicates that more extra calcium (above the u.sub.nt value for calcium) is needed for the calcium to begin to have a negative impact on the individual's nutritional health (reducing the overall nutritional health score) than extra saturated fat. Put another way, it is less unhealthy for an individual to consume extra calcium than it is for an individual to consume extra saturated fat.);
the prepared menu including the optimal amounts of food components (Paragraphs [0003], [0186]-[0188] and FIG. 10 discuss “Make It Optimal” calculates nutritional health score of each food, stores serving size and ensures it does not exceed a single serving size and recommend consumables that help meet goals by ensuring the consumed nutrients are within a healthy range.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Hadad to include determine a coefficient for each of the food components, determining a food component having a positive coefficient as an improving food component and a food component having a negative coefficient as a deteriorating food component, wherein a food component term having a positive coefficient is determined as an improving component term, and a food component term having a negative coefficient, and determining a food component having a positive coefficient as a deteriorating food component and a food component having a negative coefficient as an improving food component, wherein a food component term having a positive coefficient is determined as a deteriorating component term, a food component term having a negative coefficient is determined as an improving component term, and the prepared menu including the optimal amounts of food components, as taught by Solari, in order to help an individual meet his or her goals by ensuring that consumed nutrients are within a healthy range for a given time period, such as for a given day or week. (Solari Paragraph [0003]).
Regarding claim 2, Hadad discloses wherein the memory includes additional instructions which, when executed by the processor, cause the processor to perform at least one of:
increasing the intake amount of the improving food component above the associated reference value, and decreasing the intake amount of the deteriorating food component below the associated reference value, according to at least one of the target value, a coefficient of the improving food component, and a coefficient of the deteriorating food component (Paragraphs [0230], [0278], [0302], and [0311] discuss recommendation can compare two food items consumed by the user and suggest if one of the two food items is a healthier option than the other of the two food items based on the user's physiological responses, for example, a recommendation can compare two types of breads (whole grain bread vs. white bread) and recommend swapping white bread for whole grain alternatives; the food analysis system can estimate nutrients, provide a meal nutrition breakdown (protein, carbohydrates, fats, etc.) and make food recommendations.).
Regarding claim 3, Hadad discloses further comprising:
wherein the memory includes additional instructions which, when executed by the processor, cause the processor to (Paragraphs [0034] and [0044] discuss computer readable medium can store instructions that when executed by one or more processors causes one or more processors to perform a computer-implemented method.):
determine an intake amount that yields vital data closest to the target value when the intake amount is input into the estimation model (Paragraphs [0311] discuss after completion of the baseline data collection, the user can have access to a report of analyses and insights generated by the insights and recommendation engine and the report can inform a pre-defined target glucose level range (e.g. 70-170 mg/dL) for the user along with the user's average glucose level, assessment of one or more meals based on how the user's glucose level responded to one or more meals, utilizing a rating system (e.g., “A” for a balanced glucose response, “F” for a poor glucose response, etc.) and show recommendations.).
Regarding claim 5, Hadad discloses further comprising:
a menu information storage configured to store menu information (Paragraphs [0019] and [0031] discuss food ontology can include elementary foods, packaged foods, food recipes, food dishes, menus, etc. and a database for storing data.); and
wherein the memory includes additional instructions which, when executed by the processor, cause the processor to (Paragraphs [0034] and [0044] discuss computer readable medium can store instructions that when executed by one or more processors causes one or more processors to perform a computer-implemented method.):
determine a menu including the improving food component of the determined target intake amount by referring to the stored menu information (Paragraphs [0031] and [0311] discuss a database for storing data and a recommendation can compare two food items consumed by the user and suggest if one of the two food items is a healthier option than the other of the two food items based on the user's physiological responses, for example, a recommendation can compare two types of breads (whole grain bread vs. white bread) and recommend swapping white bread for whole grain alternatives.).
Regarding claim 6, Hadad discloses further comprising:
a storage device configured to store menu information (Paragraphs [0220], [0240], [0251]-[0252], [0255], and FIG. 15 discuss data accessible through the user’s device and a data hub can include data in cloud storage services and a menu item analysis model and a table of information abstracted from menus.); and
wherein the memory includes additional instructions which, when executed by the processor, cause the processor to (Paragraphs [0034] and [0044] discuss computer readable medium can store instructions that when executed by one or more processors causes one or more processors to perform a computer-implemented method.):
identify an amount of the improving food component included in the menu information and modify the menu information so that the amount of the improving food component becomes the target intake amount (Paragraph [0311] discusses recommendation can compare two food items consumed by the user and suggest if one of the two food items is a healthier option than the other of the two food items based on the user's physiological responses, for example, a recommendation can compare two types of breads (whole grain bread vs. white bread) and recommend swapping white bread for whole grain alternatives.).
Regarding claim 7, Hadad discloses wherein the memory includes additional instructions which, when executed by the processor, cause the processor to (Paragraphs [0034] and [0044] discuss computer readable medium can store instructions that when executed by one or more processors causes one or more processors to perform a computer-implemented method.):
modify the menu information within a pre-specified modification range (Paragraph [0049]-[0051] and [0053] discuss recommendations to reduce or increase consumption of one or more selected foods where the predictive model can be configured to determine the effects of different foods on the individual's body, by analyzing the changes to one or more biomarkers.).
Regarding claim 8, Hadad discloses further comprising
an input device configured to receive an input of lifestyle information indicating a lifestyle of the user (Paragraphs [0038] discuss the plurality of physiological inputs can relate to sleep patterns, exercise, etc. of the user.):
wherein the memory includes additional instructions which, when executed by the processor, cause the processor to (Paragraphs [0034] and [0044] discuss computer readable medium can store instructions that when executed by one or more processors causes one or more processors to perform a computer-implemented method.):
perform the multivariate analysis by adding the lifestyle information as an explanatory variable (Paragraph [0052] discusses the data indicative of foods consumed by the user can comprise time series data, and the predictive model can be configured to plot the time series data that can comprise measurements of changes to one or more biomarkers in the user's body over a time period and the biomarkers can be affected by sleep, exercise, blood test(s), genetics, stress, medication(s), menstrual cycle, and/or mood of the user where the biomarkers can comprise a glucose level, blood pressure, antioxidant level, cortisol level, cholesterol values, and/or body temperature of the user and the predictive model can determine the effects of different foods on the individual's body, by analyzing the changes to one or more biomarkers.).
Regarding claim 9, Hadad discloses wherein the menu information includes at least foodstuff information and quantity information (Paragraphs [0210], [0217], and [0241] discuss the food analysis system can use the labeling machine to analyze and map multiple types of data related to foods into the food ontology and can automatically obtain data related to the foods (e.g. nutrition facts labels, manufacturer's product information, restaurant menu, recipes, serving, etc.) from one or more sources (e.g., the Internet, grocery store websites, restaurant websites, recipe blogs, user input, etc.).
Regarding claim 10, Hadad discloses wherein the vital data is a blood pressure value (Paragraphs [0035] and [0037] discuss collecting health data from blood pressure monitors.).
Regarding claim 11, Hadad discloses wherein the food components comprise at least one of proteins, fats, carbohydrates, sodium, saturated fatty acids, n-3 fatty acids, n-6 fatty acids, cholesterols, carbohydrates, sugars, dietary fiber, zinc, potassium, calcium, chromium, selenium, iron, copper, magnesium, manganese, molybdenum, iodine, phosphorus, niacin, pantothenic acid, biotin, vitamins A, B1, B2, BE, B12, C, D, E, and K, and folic acid (Paragraphs [0230], [0278], and [0302] discuss the food analysis system can estimate nutrients from food including total fat, carbohydrate, protein, etc.).
Regarding claim 14, Hadad discloses a computer implemented method comprising:
receiving, from a user equipment, vital data of a user (Paragraphs [0097], [0136]-[0137], [0248], and FIGS 26 discuss a food analysis system with a device that can device/data hub can automatically aggregate biomarker and health data of the user (e.g., sleep, exercise, blood tests, genetic tests, etc.) from multiple application programming interfaces and an interface for blood glucose logging.);
storing, in a storage of the computer, an estimation model for predicting vital data based on an intake amount of food components, the estimation model being created by multivariate analysis of the intake amount of the food component ingested by a user and the vital data of the user, wherein the multivariate analysis includes performing multiple regression analysis to determine a coefficient for each of the food components in relation to the vital data (Paragraph [0060] and [0265] discuss a tangible computer readable medium storing instructions that, when executed by one or more processors, causes one or more processors to perform a computer-implemented method for determining effects of food consumption on a user's body by applying a predictive model to (1) data indicative of foods consumed by the user, (2) data indicative of physiological inputs associated with the user, and (3) information about the foods consumed by the user from a food ontology, to thereby generate a plurality of personalized food and health metrics for the user, for example, a recommendation to eat less carbohydrate, the user may eat pizza often for lunch, and the insights and recommendation engine can detect that consumption of pizza is correlated with a spiked increase in the user's blood glucose level and identify other foods that, when eaten with pizza, can reduce the blood glucose level and also identify one or more alternative food items to replace pizza.);
storing a reference value of the intake amount of the food component ingested by the user (Paragraphs [0061] and [0135]-[0138] discuss generating a food baseline profile of a user can comprise monitoring effect of different foods on the user's body and the platform can be in communication with the Internet and database(s) (e.g., other food, nutrition, or healthcare providers) to store any data or information that is collected and generated by the platform.);
receiving a target value of the vital data for the user (Paragraph [0302] and FIGS. 34A-34C discuss an interface may display to a user details about blood glucose, including the user's average weekly glucose level and its standard deviation value, the percentage of glucose measurements that have been in a predetermined target range, below the target range, and above the target range.);
setting a reference value as a target intake amount of each of the food components (Paragraph [0302] discusses interface window may also display popular eating hours, represented by the average of number of meals that have been consumed per every hour throughout the day, and a distribution plot of carbohydrates consumed (e.g., in grams) per every hour throughout the day and indicate whether the average glucose values at 2 hours post-meal are above or below the predetermined target range of blood glucose level and an assessment of the balance of the average meal nutrition breakdown.);
processing, by a processor of the computer, the set target intake amounts using the estimation model to determine predicted vital data (Paragraphs [0004], [0030], [0034], [0302], [0305] and FIGS. 34A-34C discuss processor performs mapping foods and for example, display details about blood glucose, including the user's average weekly glucose level and its standard deviation value, the percentage of glucose measurements that have been in a predetermined target range (Time in target), below the target range, and above the target range, display popular eating hours, represented by the average of number of meals that have been consumed per every hour throughout the day, and a distribution plot of carbohydrates consumed (e.g., in grams) per every hour throughout the day and display a minute-to-minute (or other intervals) change in blood glucose level during the 2 hour post-meal period, an average meal nutrition breakdown (e.g., protein, carbohydrates, fats, etc.), and an assessment of the balance of the average meal nutrition breakdown and the platform can use data and predictions to set baseline for the user.);
determining differences between the predicted vital data and the target values (Paragraphs [0004], [0302], [0311] discuss predict correlations between an individual’s consumed foods and biomarkers and provided personalized nutrition recommendations based on health and metabolic state and details about blood glucose, including the user's average weekly glucose level and its standard deviation value, the percentage of glucose measurements that have been in a predetermined target range (Time in target), below the target range, and above the target range and a report can inform a pre-defined target glucose level range (e.g. 70-170 mg/dL) for the user along with the user's average glucose level.);
identifying, based on the difference, an improving food component of the food components that is a food component which improves the vital data in the estimation model and a deteriorating food component that is a food component of the food components which deteriorates the vital data in the estimation model (Paragraphs [0311] discuss after completion of the baseline data collection, the user can have access to a report of analyses and insights generated by the insights and recommendation engine and the report can inform a pre-defined target glucose level range (e.g. 70-170 mg/dL) for the user along with the user's average glucose level, assessment of one or more meals based on how the user's glucose level responded to one or more meals, utilizing a rating system (e.g., “A” for a balanced glucose response, “F” for a poor glucose response, etc.) and show recommendations.);
performing at least one of increasing the target intake amount of the identified improving food component and decreasing the target intake amount of the identified deteriorating food component so that the target intake amount causes the predicted vital data to become the target value (Paragraphs [0048]-[0050], [0131] and [0311] discuss a plurality of personalized food and health metrics can comprise a predicted impact of one or more of the foods on the user's health or well-being that is continuously updated, recommended actions can include a recommendation to reduce or increase consumption of one or more selected foods, or compare two food items consumed by the user and suggest if one of the two food items is a healthier option than the other of the two food items based on the user's physiological responses, for example, a recommendation can compare two types of breads (whole grain bread vs. white bread) and recommend swapping white bread for whole grain alternatives.); and
transmitting, to the user equipment, a signal indicating at least one of the increased target intake amount and the decreased target intake amount (Paragraphs [0031], [0243], [0267], and [0311] discuss a graphical user interface (GUI) for receiving the data from and/or sending the data to a user, of the food ontology and the insights and recommendation engine can (1) monitor a user's food intake, blood glucose levels (either continuously from a CGM device or in a discrete manner using a conventional glucose meter), as well as insulin levels for users using the insulin injection therapy, and (2) analyze relationships among specific food types, insulin injections, and blood glucose level responses and use the GUI-based software interface in the user device to display the recommendations to the user, for example, a recommendation may suggest that, “When you added avocado to your sandwiches your glucose response was over 30% lower [occurred 5 out of 6 times].”, also , for example, a recommendation can compare two types of breads (whole grain bread vs. white bread) and recommend swapping white bread for whole grain alternatives.).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Hadad in view of Chapela and Solari and in further view of Engelsen (U.S. Pub. No. 2011/0004453 A1).
Regarding claim 4, Hadad discloses wherein the memory includes additional instructions which, when executed by the processor, cause the processor to (Paragraph [0034] discusses computer readable medium can store instructions that when executed by one or more processors causes them to perform a computer implemented method for mapping foods.):
estimate a the food components by the analysis (Paragraph [0231]-[0233] discuss the food processing algorithm can include specific processing parameters, (e.g., an absorption coefficient, evaporation coefficient, diffusion coefficient, etc.) for one or more nutrients and a an additional pipeline to estimate unknown nutrients from at least one food recipe can include: (1) receiving a free text of a food recipe including ingredients, cooking methods, and cooking time, (2) parsing and classifying the ingredients and their respective amounts by using at least the machine labeler and the food processing algorithm, and (3) superposing estimated nutritional values of the ingredients.
Hadad does not explicitly disclose:
estimate a regression coefficient of the food components by the multivariate analysis.
Engelsen teaches estimate a regression coefficient of the food components by the multivariate analysis (Paragraphs [0044] and [0050] discuss preparing regression coefficients for predicting a quantity using a multivariate data analysis.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Hadad to include estimate a regression coefficient of the food components by the multivariate analysis, as taught by Engelsen, in order to measure and monitor fat uptake as a function of diet, lifestyle, and health status for assessment and understanding of disease processes and risks.). (Engelsen Paragraph [0002].).
Response to Arguments
Applicant’s arguments filed December 4, 2025 have been fully considered.
Claim objections:
Examiner withdraws the claim objections in light of Applicant’s amendments.
Rejections under 35 U.S.C. 101:
With respect to claim 1 and the Prong 1 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity or mathematical concepts. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Applicant states, “the claim integrates them into a practical application: (1) The system uses regression coefficients and scenario-based classification (improving vs. deteriorating) to automatically recast the user’s menu. (2) This results in physically changing the amounts of nutritional or non-nutritional components in a prepared menu. (3) No prior or conventional method teaches “flipping” the classification of positive/negative coefficients based on two different conditions (predicted vital data being greater or smaller than current data, each also being compared to a target).” (Remarks, page 7). Performing multiple regression analysis to determine a coefficient for each food component, and to dynamically classifying a food component as improving or deteriorating, is not a technical problem rooted in the technology. The use of an estimation model and performing multiple regression analysis to determine a coefficient for each food component, and to dynamically classifying a food component as improving or deteriorating is directed to the abstract idea.
When evaluating a claim to determine whether it recites an abstract idea, examiners should keep in mind that while "all inventions at some level embody, use, reflect, rest upon, or apply laws of nature, natural phenomenon, or abstract ideas", not all claims recite an abstract idea. See Alice Corp. Pty. Ltd. v. CLS Bank, Int’l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1980-81 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 US 66, 71, 101 USPQ2d 1961, 1965 (2012)). The Step 2A Prong One analysis articulated in MPEP 2106.04 accounts for this cautionary principle by requiring a claim to recite (i.e., set forth or describe) an abstract idea in Prong One before proceeding to the Prong Two inquiry about whether the claim is directed to that idea, thereby separating claims reciting abstract ideas from those that are merely based on or involve an abstract idea.
While practical application is a way to overcome the Prong 2 35 U.S.C. 101 rejection, here, claim 1 fails to integrate the recited judicial exception into a practical application. Applicant states, “In Ex Parte Desjardins (Sept. 26, 2025, precedential), the director explained that the improvements to technology can be improvements to elements of the claim which may be implemented in software. In Desjardins, those improvements were to the claimed machine learning model (a software model), and not the computer that was recited in the claims. See Desjardins at 8.” (Remarks, page 8). Examiner respectfully disagrees. Here, the Application is distinguishable from Dejardins. The multiple regression analysis is used to determine a coefficient for each food component, not the regression analysis. The additional elements, the “interface”, “storage device” or “processor”, do not result in a practical application as it is recited at an apply it level, as stated above. Here, the improvement is to the abstract idea - acquiring vital data of a user and a food component, and analyzing a relationship between an intake of the food component and improvement or deterioration of the vital data; therefore, Applicant’s amendment fails to overcome the rejection. All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. For the reasons stated above, claim 14 similarly fail to overcome the 35 U.S.C. 101 rejection. Applicant’s claims are directed to gathering user vital data, predicting vital data based on an intake amount of food components, and delivering the information.
Rejections under 35 U.S.C. 103:
Applicant argues the amendments overcome the previous rejection. Regarding the prior art rejection, Applicant argues “none of these disclosures disclose or suggest calculating optimal amounts of food components so that a second predicted vital data predicted by applying optimal amounts of food components to an estimation model becomes a target value.” (Remarks, pages 910). Examiner interprets Hadad to include predict correlations between an individual’s consumed foods and biomarkers, in real-time, therefore predicting subsequent values. Applicant’s arguments are well taken and with respect to claim 1 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim 1 and 14 amendments.
Conclusion
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/DAWN T. HAYNES/
Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686