DETAILED ACTION
The present office action represents a final action on the merits.
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, claims 15—20 are new, and claims 1-11 and 14-20 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 14-20 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 15-17 are drawn to a health management system, which is within the four statutory categories (i.e., machine). Claims 14 and 18-20 are drawn to computer method of a health management system, which is within the four statutory categories (i.e., manufacture.)
Claims 1-11 and 15-17 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, starting from the target intake amount set to the reference value, repeatedly applying the target intake amount of each of the food components to the estimation model to determine predicted vital data, determining a difference between the predicted vital data and the target value, updating the target intake amount 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 in a direction determined based on the difference and the coefficient of each of the food components, by a predetermined amount or ratio, and constraining each updated target intake amount of each of the food components within a predetermined range based on the reference value of the food component, and determining, as the optimal amounts of food components, the target intake amount that causes the predicted vital data to be closest to the target value among the predicted vital data obtained by the repeated applying; and
transmitting, to the user equipment, the prepared menu including the optimal amounts of the food components.
Claims 14 and 18-20 recite 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;
iteratively updating the target intake amount, starting from the reference value, by 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 in a direction determined based on the difference and the coefficient of each food component, by a predetermined amount or ratio, applying the updated target intake amount to the estimation model to determine updated predicted vital data, determining an updated difference between the updated predicted vital data and the target value, and constraining each updated target intake amount within a predetermined range based on the reference value, and determining, as the target intake amount, an updated target intake amount that causes the predicted vital data to be closest to the target value among the predicted vital data obtained by the repeated applying; 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 and 15-20 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 14-20 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 and 15-20 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 14-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free from Prior Art
Examiner acknowledges the limitations in claims 1 and 14:
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;
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; and
calculate optimal amounts of food components by, starting from the target intake amount set to the reference value, repeatedly applying the target intake amount of each of the food components to the estimation model to determine predicted vital data, determining a difference between the predicted vital data and the target value, updating the target intake amount 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 in a direction determined based on the difference and the coefficient of each of the food components, by a predetermined amount or ratio, and constraining each updated target intake amount of each of the food components within a predetermined range based on the reference value of the food component, and determining, as the optimal amounts of food components, the target intake amount that causes the predicted vital data to be closest to the target value among the predicted vital data obtained by the repeated applying.
are free from prior art when considered in combination with the other limitations, and are not subject to any prior art rejections under 103. The closest prior art is:
Hadad (U.S. Pub. No. 2019/0295440 A1) 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.
Hadad (U.S. Pub. No. 2019/0295440 A1) 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.
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) 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.
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) 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.
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) 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.
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) 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.
It would not be obvious to combine all of the references, accordingly, the 103 rejection is withdrawn.
Response to Arguments
Applicant’s arguments filed June 26, 2026 have been fully considered.
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 is integrated into a practical application. As amended, the claim is not directed merely to gathering, analyzing, and presenting data. Rather, the claim recites a specific feedback-control process: the predicted vital data (the output) is driven toward the target value (the set point) by iteratively updating the food component intake amounts (the manipulated variables) through the estimation model based on a difference (error) signal, while constraining each updated intake amount within a physiologically meaningful boundary based on the reference value (e.g., a tolerable upper limit, an estimated average requirement, or a permissible deviation range). This is a particular, technical optimization mechanism. The improvement does not reside in a generic computer (the interface, storage, or processor), but in the optimization process itself that is implemented by the estimation model.” (Remarks, page 9). Examiner respectfully disagrees. The Application analyzes a relationship between an intake of the dietary ingredient and improvement or deterioration of the vital data, and a dietary ingredient output unit configured to output the dietary ingredient of improving the vital data and the dietary ingredient of deteriorating the vital data, and is not a technical problem rooted in the technology. Here, there is no improvement to the estimation model. The use of an estimation model and performing multiple regression analysis to determine a coefficient for each food component to dynamically classifying a food component as improving or deteriorating – the optimization process - is directed to the 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, “The iterative, constraint-based convergence is a technological improvement to a health management system, and therefore the claim integrates the recited judicial exception into a practical application.” (Remarks, page 10). Examiner respectfully disagrees. Here, the claim does not integrate the recited judicial exception into a practical application. The additional elements, the “interface”, “storage device” or “processor”, etc., 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 fails to overcome the 35 U.S.C. 101 rejection.
Rejections under 35 U.S.C. 103:
Applicant’s arguments with regard to 103 are moot because the 103 rejection has been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
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/DAWN T. HAYNES/
Art Unit 3686/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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