Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The following Office action in response to communications received November 25, 2025. As stated in Remarks, claims 1 and 11 have been amended. Therefore, claims 1-20 are pending and addressed below.
Applicant’s amendments to the claims are sufficient to overcome the 35 U.S.C. 112(b) rejections in reference to insufficient antecedent basis set forth in the previous office action dated April 23, 2025.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. In particular, the claim amendments “determine, using a machine-learning process, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function” are not taught in original claims or anywhere in specification. For example, and/or specifically for “determine, using a machine-learning process, a cost associated with the edible of interest” portion of the amendments is not taught in specification. Cost is only mentioned in paragraph 54 and has no teaching or even mentioning of a machine-learning process to determine cost. Therefore, the follow-up limitations wherein determining the cost comprises…., are uncertain. Appropriate clarification and correction are required.
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 1-20 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1 and 11 are directed to a system for calculating an edible score, including retrieve, a performance profile relating to a user; determine, an edible of interest; receive, nourishment information relating to the edible of interest; generate, an output an edible score; and display, the edible score.
Independent Claim 1 recites “determine an edible of interest relating to a user; receive nourishment information relating to the edible of interest to the user, wherein the nourishment information comprises a plurality of ingredients; calculate one or more nutrient biodiversity scores as a function of the nourishment information comprising: evaluating each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes: extracting at least a nutrient from each ingredient of the plurality of ingredients; and calculating a nutrient biodiversity score for the at least a nutrient; determine a nutritional requirement as a function of at least the nourishment information; determine, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function; and display the cost nutritional requirement, and the one or more nutrient biodiversity scores of the edible of interest.”
Independent Claim 11 recites “determining, an edible of interest relating to a user; receiving, nourishment information relating to the edible of interest to the user, wherein the nourishment information comprises a plurality of ingredients; calculating, one or more nutrient biodiversity scores as a function of the nourishment information comprising: evaluating each ingredient of the plurality of ingredients, wherein evaluating each ingredient includes: extracting at least a nutrient from each ingredient of the plurality of ingredients; and calculating a nutrient biodiversity score for the at least a nutrient; determining, a nutritional requirement as a function of at least the nourishment information; determining, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function; and displaying, the cost, nutritional requirement, and the one or more nutrient biodiversity scores of the edible of interest.”
The limitations of Claims 1 and 11, as drafted, under its broadest reasonable interpretation, covers the performance of a Mental Process concepts performed in the human mind (including an observation, evaluation, judgment, opinion) OR Mathematical Concepts which are concepts performed that encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations, but for the recitation of generic computer components. That is, other than reciting, “computing device, machine-learning, display interface” nothing in the claim element precludes the step from practically being performed in the mind and using concepts performed that encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, but for the “computer device” language, “obtaining” in the context of this claim encompasses the user manually determine an edible of interest. Similarly, the receiving, nourishment information relating to the edible of interest to the user, under its broadest reasonable interpretation, covers performance of the limitation in the mind and using concepts performed that encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and using concepts performed that encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts and Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “computing device, machine-learning, display interface” to perform all of the “determining; receiving; retrieving; generating and displaying” steps. The “computing device, machine-learning, display interface” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 has the following additional elements (i.e., computing device, machine-learning, display interface). Claim 11 has the following additional elements (i.e., computing device, machine-learning, display interface). Looking to the specification, these components are described at a high level of generality (¶ 9 and ¶ 14; A user client device 116 may include without limitation, an additional computing device such as a mobile device, laptop, desktop computer, and the like. A user client device 116 may include, without limitation, a display in communication with computing device 104.). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-10 and 12-20). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Mathematical Concepts and Mental Processes,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 1-20 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
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, 6-7, 9-11, 16-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No.: US 20190286656 A1 to Yerva et al. in view of Pub. No.: US 20170330481 A1 to Sabourian-Tarwe.
As per Claim 1, Yerva et al. teaches a system for calculating a score for an edible in a display interface, the system comprising a computing device configured to:
-- determine an edible of interest relating to a user (see Yerva et al. paragraphs 5, 39, 53 and 93; The user data 222 includes at least user profiles 232 and corresponding consumable logs 234. The user profiles 232 include a profile data for each user of the health tracking system 100. Each user profile includes demographic information for the users such as name, age, gender, height, weight, performance level (e.g., beginner, intermediate, professional, etc.) and/or other information for the user. In at least one embodiment, the consumable logs (e.g., mapping/charting) include a consumable diary/log for each user (which may also be referred to herein as a “food diary"). The consumable diary/log allows the user to track consumables that are consumed by the user over a period of days and any nutritional data (e.g. nourishment information) associated with the food consumed. For example, the consumable diary/log may allow the user to enter particular consumable that is consumed (e.g. edible of interest) by the user and keep track of the associated calories, macronutrients, micronutrients, sugar, fiber, and/or any of various other nutritional data associated with the consumables entered by the user in the consumable diary/log. In some embodiments, the user data 222 further includes various activity and fitness data collected by sensors (not shown) associated with the health tracking devices 110. In some embodiments, the memory 310 includes program instructions for a graphical user interface configured to provide a client-side health tracking application 316. The memory 310 may further be configured to store certain user data 318, such as e.g., user gender, height, weight, user identifier, password, etc. Additionally, health related data (e.g., data collected from one or more sensors and/or manually entered) may be stored. The processor 308 is configured to read the program instructions from the memory 310 and execute the program instructions to provide the health tracking application 316 to the user so for the purpose of performing health and fitness related tasks for the user, including displaying, modifying, and analyzing the user data 318.).
-- receive nourishment information relating to the edible of interest to the user (see Yerva et al. paragraphs 5, 39, 53 and 93; The user data 222 includes at least user profiles 232 and corresponding consumable logs 234. The user profiles 232 include a profile data for each user of the health tracking system 100. Each user profile includes demographic information for the users such as name, age, gender, height, weight, performance level (e.g., beginner, intermediate, professional, etc.) and/or other information for the user. In at least one embodiment, the consumable logs 234 include a consumable diary/log for each user (which may also be referred to herein as a “food diary"). The consumable diary/log allows the user to track consumables that are consumed by the user over a period of days and any nutritional data (e.g., nourishment information) associated with the food consumed. For example, the consumable diary/log may allow the user to enter particular consumable that is consumed (e.g., edible of interest) by the user and keep track of the associated calories, macronutrients, micronutrients, sugar, fiber, and/or any of various other nutritional data associated with the consumables entered by the user in the consumable diary/log. In some embodiments, the user data 222 further includes various activity and fitness data collected by sensors (not shown) associated with the health tracking devices 110. In some embodiments, the memory 310 includes program instructions for a graphical user interface configured to provide a client-side health tracking application 316. The memory 310 may further be configured to store certain user data 318, such as e.g., user gender, height, weight, user identifier, password, etc. Additionally, health related data (e.g., data collected from one or more sensors and/or manually entered) may be stored. The processor 308 is configured to read the program instructions from the memory 310 and execute the program instructions to provide the health tracking application 316 to the user so for the purpose of performing health and fitness related tasks for the user, including displaying, modifying, and analyzing the user data 318.).
-- training a score machine-learning process using edible training data, wherein edible training data contains a plurality of data entries, each data entry containing elements of the performance profile and the nourishment information correlated to an edible score (see Yerva et al. paragraphs 5-7, 39, 53 and 93; In accordance with yet another exemplary embodiment, a method of operating a health tracking system to train a machine learning model is disclosed. The method comprises the steps of: receiving, with a processor of the health tracking system, a plurality of training inputs, each training input including (i) a query string, (ii) a first descriptive string and first nutritional data labeled as corresponding to a correct output, and (iii) a second descriptive string and second nutritional data labeled as corresponding to an incorrect output; and for each training input: generating, with the processor, (i) a first feature vector based on the first descriptive string, (ii) a second feature vector based on the second descriptive string, and (iii) a third feature vector based on the query string, using at least one first embedding function of the machine learning model; determining, with the processor, (i) a first nutrition information vector from the first nutritional data and (ii) a second nutrition information vector from the second nutritional data; generating, with the processor, a third nutrition information vector based on the query string, using a second embedding function of the machine learning model; determining, with the processor, a hinge loss based on the first feature vector, the second feature vector, the third feature vector, first nutrition information vector, the second nutrition information vector, and the third nutrition information vector; and adjusting, with the processor, parameter values of the machine learning model based on the hinge loss.) and
-- display the edible score of the edible of interest through a display interface (see Yerva et al. paragraph 31; display of the consumable records).
Yerva et al. fails to explicitly teach:
-- retrieve a performance profile comprising a plurality of logged user performance metrics;
-- generate an edible score of the edible of interest, wherein generating the edible score comprises:
-- generating the edible score as a function of the score machine-learning process;
-- determine, using a machine-learning process, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function.
Sabourian-Tarwe teaches a system that recommends one or more recipes to a user, wherein the system collects various data, including a rating/score that the user (and/or other users) assigns to one or more recipes; and wherein the system implements one or more machine learning algorithms (e.g., Al) to perform the data analysis; and wherein the system further generates an aggregate health benefit score for one or more of recipes; and thereby the system determines/recommends a pertinent recipe to the user based on the aggregate health benefit score of the recipe (see Sabourian-Tarwe paragraphs 29, and 82-84: e.g., the aggregate health benefit score calculated above corresponds to the score for the edible).
In some embodiments, the culinary application 145 can assign a score, e.g., points, to the user 115, for performing certain activities. For example, the culinary application 145 can provide points for any of the following: creating a user profile with the culinary application 145, answering the questions posted by the culinary application 145 as part of the trivia 130, cooking a meal using the recipe 125, posting an image of the meal cooked using the recipe to a social networking application, posting a recipe to the culinary application 145, buying products or services from the culinary application 145. Different number of loyalty points can be awarded for different types of activities (see Sabourian-Tarwe paragraph 46; e.g., points, to the user 115, for performing certain activities).
The difficulty level can be defined based on (e.g. determined by) various attributes, such as time taken to prepare the dish, number of steps or activities to be performed to prepare the dish, number of ingredients required to prepared the dish, cost of the ingredients (e.g. determined cost), ease of availability of the ingredients (see Sabourian-Tarwe paragraph 90).
In another example, the rule engine 1450 can determine one or more recipes for the user 115 based on recipe selection pattern of other users, such as users who are similar to the user 115. Users can register with the culinary application 145 to create a user profile, which can store profile information of the user 115, such as name, age, ethnicity, grade level, dietary preferences, preferences regarding a cuisine, etc. The rule engine 1450 can use the user profile information of the users to determine which users are considered similar. The administrator 140 can specify the criteria based on which two users are considered similar. For example, two users of the same age range are considered similar. In another example, two users of the same age range and same ethnicity are considered similar. In yet another example, two users of the same age range are considered less similar than two users of the same age range and same ethnicity. The recipe selection pattern can consider the level of engagement of the users for various recipes and/or the user scores assigned to the recipes in determining the recipes to be recommended to the user 115. For example, if the level of engagement for a set of recipes by a set of users is below a specified threshold, the culinary application 145 may not recommend the set of recipes to the user 115. The culinary application 145 may recommend those of the recipes whose level of engagement is above the specified threshold. In another example, if the user score assigned to a set of recipes is below a specified threshold, the culinary application 145 may not recommend the set of recipes to the user 115 (see Sabourian-Tarwe paragraph 91; user profile).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Yerva et al. in view of Sabourian-Tarwe; for example, by upgrading the system’s algorithm, so that the system collects one or more additional parameters that the user and/or other users provided with respect to each of one or more meals/ingredients in the database; such as, a rating/score that each user assigned to one or more meals/ingredients, each user’s frequency of selection of one or more of the meals/ingredients, etc., wherein the system’s algorithm further incorporates one or more machine learning algorithms—e.g., an artificial intelligence—so that it calculates, per each of the meals/ingredients in its database, an aggregate health benefit score that signifies the healthfulness of a meal/ingredient, etc., so that the system further recommends to the user at least one meal based on the aggregate health benefit score of the meal; and thereby the system helps the user to customize a healthy meal.
As per Claim 6, Yerva et al. and Sabourian-Tarwe teach the system of claim 1, wherein the nourishment information comprises a caloric input (see Yerva et al. paragraph 27; … configured to receive a plurality of consumable records which include item descriptions, as well as caloric and nutritional contents of a respective plurality of consumable items which are entered at the health tracking devices…).
The obviousness of combining the teachings of Yerva et al. and Sabourian-Tarwe are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 7, Yerva et al. and Sabourian-Tarwe teach the system of claim 1, wherein the nourishment information comprises a nutrient input (see Yerva et al. paragraph 27; … configured to receive a plurality of consumable records which include item descriptions, as well as caloric and nutritional contents of a respective plurality of consumable items which are entered at the health tracking devices…).
The obviousness of combining the teachings of Yerva et al. and Sabourian-Tarwe are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 9, Yerva et al. and Sabourian-Tarwe teach the system of claim 1, wherein the computing device is further configured to:
-- generate a plurality of compatible edibles based on the edible score; and display, through the display interface, the plurality of compatible edibles based on the edible score (see Sabourian-Tarwe paragraphs 29, and 82-84: e.g., the aggregate health benefit score calculated above corresponds to the score for the edible).
The obviousness of combining the teachings of Yerva et al. and Sabourian-Tarwe are discussed in the rejection of claim 1, and incorporated herein.
As per Claim 10, Yerva et al. and Sabourian-Tarwe teach the system of claim 1, wherein generating the edible score further comprises narrowing an edible score range, wherein the edible score range relates to a nutritional impact the edible of interest has on the user based on feedback received (see Sabourian-Tarwe paragraphs 29, and 82-84: e.g., the aggregate health benefit score calculated above corresponds to the score for the edible).
The obviousness of combining the teachings of Yerva et al. and Sabourian-Tarwe are discussed in the rejection of claim 1, and incorporated herein.
As per Claims 11, 16-17 and 19-20, Claims 11, 16-17 and 19-20 are directed to a method for calculating a score for an edible in a display interface. Claims 11, 16-17 and 19-20 recite the same or substantially similar limitations as those addressed above for Claims 1, 6-7 and 9-10 as taught by Yerva et al. and Sabourian-Tarwe. Claims 11, 16-17 and 19-20 are therefore rejected for the same reasons as set forth above for Claims 1, 6-7 and 9-10 respectively.
Claims 2-5, 8, 12-15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yerva et al. and Sabourian-Tarwe as applied to claims 1, 6-7, 9-11, 16-17 and 19-20 above, and further in view of WO 2013086363 A2 to BAARMAN.
As per Claim 2, Yerva et al. and Sabourian-Tarwe fail to explicitly teach the system of claim 1, wherein the performance profile comprises a biological extraction.
BAARMAN teaches the biological sensors on the user may also record the state of the user before and while the user is taking the food and given timestamp information. Once the data is collected— or while it is being collected— personal device and the refrigerator can sync the data to one another, or can sync information to a common hub or bridge, or set of bridges, or can store the information along with the timing and location data on their own internal memory storages to be later downloaded to a central bridge or hub. Information can then be processed to determine the state of the user before food was taken (stressed, relaxed, dehydrated, tired, etc.), what foods were consumed by the user, and what effect it had on the user (became relaxed, woke up, felt nauseas, fell asleep) by tracking the biological data for a period of time after the food was consumed. By tracking these before and after states and correlating them to the event trigger, the system can detect foods or activities that have either positive or negative effects on a user. For example, a food allergy can be detected by correlating a nausea feeling with eating certain foods over a long period of time. The system may also be able to detect patterns of eating, drinking, and activity with a user's physical and emotional state (see BAARMAN paragraph 248).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference BAARMAN with the systems/methods as taught by reference Yerva et al. and Sabourian-Tarwe with the motivation of providing a behavior tracking and modification devices using a substantial amount of user control to track and modify both desired and non-desired behaviors, in doing so, these systems seek to modify a user's behavior by presenting pertinent information to the user in hopes that their actions may change (see BAARMAN paragraphs 8-9).
As per Claim 3, Yerva et al. and Sabourian-Tarwe fail to explicitly teach the system of claim 1, wherein the logged user performance metric comprises a timestamp associated with a consumption of an edible.
BAARMAN teaches the biological sensors on the user may also record the state of the user before and while the user is taking the food and given timestamp information. Once the data is collected— or while it is being collected— personal device and the refrigerator can sync the data to one another, or can sync information to a common hub or bridge, or set of bridges, or can store the information along with the timing and location data on their own internal memory storages to be later downloaded to a central bridge or hub. Information can then be processed to determine the state of the user before food was taken (stressed, relaxed, dehydrated, tired, etc.), what foods were consumed by the user, and what effect it had on the user (became relaxed, woke up, felt nauseas, fell asleep) by tracking the biological data for a period of time after the food was consumed. By tracking these before and after states and correlating them to the event trigger, the system can detect foods or activities that have either positive or negative effects on a user. For example, a food allergy can be detected by correlating a nausea feeling with eating certain foods over a long period of time. The system may also be able to detect patterns of eating, drinking, and activity with a user's physical and emotional state (see BAARMAN paragraph 248).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference BAARMAN with the systems/methods as taught by reference Yerva et al. and Sabourian-Tarwe with the motivation of providing a behavior tracking and modification devices using a substantial amount of user control to track and modify both desired and non-desired behaviors, in doing so, these systems seek to modify a user's behavior by presenting pertinent information to the user in hopes that their actions may change (see BAARMAN paragraphs 8-9).
As per Claim 4, Yerva et al. and Sabourian-Tarwe fail to explicitly teach the system of claim 1, wherein the computing device is further configured to identify positive and negative trends in a consumption of edibles correlated to the timestamp.
BAARMAN teaches the biological sensors on the user may also record the state of the user before and while the user is taking the food and given timestamp information. Once the data is collected— or while it is being collected— personal device and the refrigerator can sync the data to one another, or can sync information to a common hub or bridge, or set of bridges, or can store the information along with the timing and location data on their own internal memory storages to be later downloaded to a central bridge or hub. Information can then be processed to determine the state of the user before food was taken (stressed, relaxed, dehydrated, tired, etc.), what foods were consumed by the user, and what effect it had on the user (became relaxed, woke up, felt nauseas, fell asleep) by tracking the biological data for a period of time after the food was consumed. By tracking these before and after states and correlating them to the event trigger, the system can detect foods or activities that have either positive or negative effects on a user. For example, a food allergy can be detected by correlating a nausea feeling with eating certain foods over a long period of time. The system may also be able to detect patterns of eating, drinking, and activity with a user's physical and emotional state (see BAARMAN paragraph 248).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference BAARMAN with the systems/methods as taught by reference Yerva et al. and Sabourian-Tarwe with the motivation of providing a behavior tracking and modification devices using a substantial amount of user control to track and modify both desired and non-desired behaviors, in doing so, these systems seek to modify a user's behavior by presenting pertinent information to the user in hopes that their actions may change (see BAARMAN paragraphs 8-9).
As per Claim 5, Yerva et al. and Sabourian-Tarwe fail to explicitly teach the system of claim 1, wherein determining the edible of interest comprises determining the edible of interest as a function of a user dietary habit.
BAARMAN teaches the data and information may suggest that the user might have an interest in a specific product. As a few examples, the locations frequented by a user, the types of activities of the user and the consumption habits of a user may alone or in combination allow the system to determine products or services of potential interest to a user (see BAARMAN paragraphs 18 and 228).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference BAARMAN with the systems/methods as taught by reference Yerva et al. and Sabourian-Tarwe with the motivation of providing a behavior tracking and modification devices using a substantial amount of user control to track and modify both desired and non-desired behaviors, in doing so, these systems seek to modify a user's behavior by presenting pertinent information to the user in hopes that their actions may change (see BAARMAN paragraphs 8-9).
As per Claim 8, Yerva et al. and Sabourian-Tarwe fail to explicitly teach the system of claim 1, wherein the edible score of the edible of interest is based on a timestamp of consumption.
BAARMAN teaches the biological sensors on the user may also record the state of the user before and while the user is taking the food and given timestamp information. Once the data is collected— or while it is being collected— personal device and the refrigerator can sync the data to one another, or can sync information to a common hub or bridge, or set of bridges, or can store the information along with the timing and location data on their own internal memory storages to be later downloaded to a central bridge or hub. Information can then be processed to determine the state of the user before food was taken (stressed, relaxed, dehydrated, tired, etc.), what foods were consumed by the user, and what effect it had on the user (became relaxed, woke up, felt nauseas, fell asleep) by tracking the biological data for a period of time after the food was consumed. By tracking these before and after states and correlating them to the event trigger, the system can detect foods or activities that have either positive or negative effects on a user. For example, a food allergy can be detected by correlating a nausea feeling with eating certain foods over a long period of time. The system may also be able to detect patterns of eating, drinking, and activity with a user's physical and emotional state (see BAARMAN paragraph 248).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include systems/methods as taught by reference BAARMAN with the systems/methods as taught by reference Yerva et al. and Sabourian-Tarwe with the motivation of providing a behavior tracking and modification devices using a substantial amount of user control to track and modify both desired and non-desired behaviors, in doing so, these systems seek to modify a user's behavior by presenting pertinent information to the user in hopes that their actions may change (see BAARMAN paragraphs 8-9).
As per Claims 12-15 and 18, Claims 12-15 and 18 are directed to a method for calculating a score for an edible in a display interface. Claims 12-15 and 18 recite the same or substantially similar limitations as those addressed above for Claims 2-5 and 8 as taught by Yerva et al., Sabourian-Tarwe and BAARMAN. Claims 12-15 and 18 are therefore rejected for the same reasons as set forth above for Claims 2-5 and 8 respectively.
Response to Arguments
Applicant’s arguments filed November 25, 2025 have been fully considered but they are not persuasive. In the remarks applicant argues:
(1) Rejection of claims under 35 U.S.C. § 101
(2) Rejection of claims under 35 U.S.C. § 103: Yerva/Sabourian-Tarwe
Claims 1, 6-7, 9-11, 16-17 and 19-20 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over Pub. No.: US 20190286656 Al to Yerva et al. in view of Pub. No.: US 20170330481 Al to Sabourian-Tarwe. (Office Action, p. 5). Applicant respectfully traverses the rejection.
Claim 1
The Office asserts that Yerva discloses "a system for calculating a score for an edible in a display interface" citing paragraph 5, stating:
In accordance with one exemplary embodiment of the disclosures, a method of operating a health tracking system is disclosed. The health tracking system has a processor and a database configured to store a plurality of data records, each of the plurality of data records comprising at least a descriptive string and nutritional data regarding a respective consumable item. The method comprises the steps of: receiving, with the processor, a query string; retrieving, with the processor, a first data record of the plurality of data records and a second data record of the plurality of data records from the database; generating, with the processor, (i) a first feature vector based on the descriptive string of the first data record, (ii) a second feature vector based on the descriptive string of the second data record, and (iii) a third feature vector based on the query string, using at least one first embedding function of a machine learning model, the at least one first embedding function being learned in a training process of the machine learning model; generating, with the processor, (i) a first nutrition information vector from the nutritional data of the first data record and (ii) a second nutrition information vector from the nutritional data of the second data record; generating, with the processor, a third nutrition information vector based on the query string, using a second embedding function of the machine learning model, the second embedding function being learned in the training process of the machine learning model; and determining, with the processor, which of the first data record and the second data record is more relevant to the query string based on the first feature vector, the second feature vector, the third feature vector, first nutrition information vector, the second nutrition information vector, and the third nutrition information vector.
However, Applicant asserts Yerva does not disclose "determine, using a machine-learning process, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function," as in claim 1.
Sabourian-Tarwe fails to cure the deficiencies of Yerva. The Office has not asserted that Sabourian-Tarwe teaches, suggests, or motivates "determine, using a machine-learning process, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function," as in claim 1. Applicant respectfully submits that Sabourian- Tarwe does not teach, suggest, or motivate "determine, using a machine-learning process, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function," as in claim 1.
Accordingly, Applicant respectfully submits that claim 1 as amended is patentably distinguishable over Yerva and Sabourian-Tarwe, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of these rejections.
Claim 11
Claim 11 recites similar limitations to claim 1. As noted above, claim 1 is patentably distinguishable over Yerva and Sabourian-Tarwe, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claim 11 as amended is patentably distinguishable over Yerva and Sabourian-Tarwe, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of these rejections.
Claims 6-7, 9-10, 16-17 and 19-20
Each of claims 6-7, 9-10, 16-17 and 19-20 depends, directly or indirectly, from claims 1 or 11. As noted above, claims 1 and 11 are patentably distinguishable over Yerva and Sabourian-Tarwe, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 6-7, 9-10, 16-17 and 19-20 are patentably distinguishable over Yerva and Sabourian-Tarwe, alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of these rejections.
Yerva/Sabourian-Tarwe/BAARMAN
Claims 2-5, 8, 12-15 and 18 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over Pub. No.: US 20190286656 Al to Yerva et al. in view of Pub. No.: US 20170330481 Al to Sabourian-Tarwe further in view of WO 2013086363 A2 to BAARMAN. (Office Action, p. 11). Applicant respectfully traverses the rejection.
Each of claims 2-5, 8, 12-15 and 18 depends, directly or indirectly, from claims 1 or 11. As noted above, claims 1 and 11 are patentably distinguishable over Yerva and Sabourian-Tarwe, alone or in combination, for at least the reasons discussed above. Accordingly, Applicant respectfully submits that claims 2-5, 8, 12-15 and 18 are patentably distinguishable over Yerva and Sabourian-Tarwe, alone or in combination, for at least the reasons discussed above.
BAARMAN fails to cure the deficiencies of Yerva and Sabourian-Tarwe. The Office has not asserted that BAARMAN teaches, suggests, or motivates "determine, using a machine- learning process, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function," as in claim 1. Applicant respectfully submits that BAARMAN does not teach, suggest, or motivate "determine, using a machine-learning process, a cost associated with the edible of interest, wherein determining the cost comprises: representing the edible of interest as an edible of interest expression; comparing the edible of interest expression to a loss function representing a plurality of goal parameters, wherein comparing comprises minimizing the loss function as a function of the edible of interest; and determining the cost associated with the edible of interest as a function of comparing the edible of interest expression to the loss function," as in claim 1.
Accordingly, Applicant respectfully submits that claims 2-5, 8, 12-15 and 18 are patentably distinguishable over Yerva, Sabourian-Tarwe, and BAARMAN alone or in combination, for at least the reasons discussed above. Therefore, Applicant respectfully requests the withdrawal of these rejections.
In response to argument (1), Examiner respectfully disagrees. The Examiner has considered the claim and determines that claim 1 does not overcome the rejection under 35 U.S.C. 101 because it is directed to a judicial exception and does not integrate that exception into a practical application. Under Step 2A, Prong One, the claims recite abstract subject matter falling within at least two recognized categories of abstract ideas. First, the amended claims recite mathematical concepts, as it requires calculating nutrient biodiversity scores, representing an edible as an expression, comparing that expression to a loss function, and minimizing the loss function to determine a cost. These steps constitute mathematical relationships, calculations, and optimization techniques. Second, the claims recite a mental process, because the core operations—evaluating ingredients, determining nutritional requirements, weighing goals, and presenting a score—are forms of observation, evaluation, and judgment that can be performed conceptually or with pen and paper, even if performed more quickly by a computer. The recitation of a machine-learning process does not remove the claim from these categories because it is used as a tool to perform data analysis and evaluation rather than to improve computer technology itself.
Under Step 2A, Prong Two, the claims do not integrate the abstract idea into a practical application. The additional elements—a computing device, display interface, and generic data receipt—perform their ordinary functions of receiving information, processing it, and presenting results. The claim does not recite a specific technological improvement to computer functionality, data processing architecture, machine-learning operation, or display technology. Instead, the computer components merely implement the abstract analysis in a computerized environment and link the abstract idea to a field of use involving nutrition evaluation. The output of the claims are informational in nature (scores, cost, and requirements), which does not affect a technological transformation or control of a technical process.
Under Step 2B, the claims also lack an inventive concept because the additional elements, individually and in combination, amount to well-understood, routine, and conventional computer functions for receiving data, performing calculations, and displaying results. Accordingly, the claims remain directed to abstract ideas in the form of mathematical concepts and mental processes without significantly more, and the rejection under 35 U.S.C. § 101 is maintained.
In response to argument (2), Examiner respectfully disagrees. The difficulty level can be defined based on (e.g. determined by) various attributes, such as time taken to prepare the dish, number of steps or activities to be performed to prepare the dish, number of ingredients required to prepared the dish, cost of the ingredients (e.g. determined cost), ease of availability of the ingredients (see Sabourian-Tarwe paragraph 90).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10720235 B2; A method for improving food-related personalized for a user including determining food-related preferences associated with a plurality of users to generate a user food preferences database; collecting dietary inputs from a subject matter expert (SME) at an SME interface associated with the user food preferences database; determining personalized food parameters for the user based on the user food-related preferences and the dietary inputs; receiving feedback associated with the personalized food parameters from the user; and updating the user food preferences database based on the feedback.
US 8647121 B1; Systems and methods of grading food items include a user customizable profile that allows a user to specify nutritional needs, dietary goals, or a medical state. The user profile is used to provide a user specific grade to one or more food items. This grade is configured for a user to compare food items and learn which provides better nutrition according to their profile. The user profile is optionally configured to take into account a medical state such as celiac disease, diabetes, or a nutritional deficiency. A grade can be provided for a single food item or to a list of food items.
US 20140074510 A1; A computer may receive health related data from a plurality of data sources. Upon receiving the health related data, the computer may standardize the health related data based on a reference database. Further, based on a person-centric data framework, the computer may categorize the health related data into historical health related data and current health related data. Then, the computer may calculate a risk score based on the historical health related data and the current health related data. Once the risk score is calculated, the computer may determine a cost associated with a risk represented by the risk score. Further, the computer may generate an overall health score based on the risk score and the determined cost, which may then be transmitted for presentation to authorized end users.
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 EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683