DETAILED ACTION
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
The examiner acknowledges that the instant application is a continuation of US Patent No. 10878458, filed 07/21/2016 and US Patent No. 12327275, filed 12/10/2020.
Information Disclosure Statement
Information Disclosure Statement received 06/09/2025 has been reviewed and considered.
Status of Claims
• The following is an office action in response to the communication filed 07/21/2025.
• Claims 1-20 have been canceled.
• Claims 21-40 have been added.
• Claims 21-40 are currently pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 and 17-19 of U.S. Patent No. 12327275 B2 (referred to as “‘275”), Hadad et. al. (US 20140255882 A1, herein referred to as Hadad).
‘275 teaches:
Claim 21
‘275 [Claim 1]
A method for operating a health tracking system, the method comprising:
A method for operating a health tracking system, the method comprising:
receiving a data record comprising at least a descriptive string and nutritional data regarding a consumable item to which the data record corresponds
receiving a crowd-sourced data record comprising at least a descriptive string and nutritional data regarding a consumable item to which the data record corresponds
determining one of a plurality of possible tastes associated to the consumable item by:
determining one of a plurality of possible tastes associated to the consumable item by:
applying a first statistical model to the descriptive string to determine a first set of probabilities that the consumable item has each of the plurality of possible tastes, wherein the plurality of possible tastes includes a plurality of fundamental flavors
applying a first statistical model to the descriptive string to determine a first set of probabilities that the consumable item has each of the plurality of possible tastes, wherein the plurality of possible tastes includes a plurality of fundamental flavors
applying a second statistical model to the nutritional data to determine a second set of probabilities that the consumable item has each of the plurality of possible tastes; and
applying a second statistical model to the nutritional data to determine a second set of probabilities that the consumable item has each of the plurality of possible tastes, and
determining the one of the plurality of possible tastes for the consumable item based on the first set of probabilities and the second set of probabilities
determining the one of the plurality of possible tastes for the consumable item based on the first set of probabilities and the second set of probabilities
updating a database in order to associate the determined taste with the data record in the database
updating a crowd-sourced database in order to associate the determined taste with the data record in the crowd-sourced database
performing the receiving, the determining, and the associating with respect to each of a plurality of data records stored in the database such that each of the plurality of data records is associated with a taste
performing the acts of receiving, determining, and associating updating with respect to each of a plurality data records stored in the crowd-sourced database such that each of the plurality of data records is associated to a taste
‘275 does not disclose:
enabling a health tracking device associated with a user to access the database and search the plurality of data records having the tastes associated therewith.
However, Hadad teaches:
enabling a health tracking device associated with a user to access the database and search the plurality of data records having the tastes associated therewith {Hadad: fig 5, #520 diet profiler; [0007] installing the application on computing devices belonging to a plurality of users and creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm; [0045] the application queries the user about culinary preferences. The application tries to infer the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen; [0082] eating habits 514 are input to the diet profiler 520; [0049] nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--the user's activity profile (training activities schedule), medical profile (the user's medical condition), different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences. Using this data, the first module creates a nutritional schedule; [0066] Create a module that will be able to query and analyze the user's inputs efficiently, generating value to both the algorithm and the user. Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the application as taught by Hadad in the taste profile method of ‘275 in order to provide improve the quality of results (Hadad: [0020]).
Dependent claims 22-33 are anticipated by ‘275 as follows:
Instant claims
‘275
Claim 22
Claim 3
Claim 23
Claim 4
Claim 24
Claim 11
Claim 25
Claim 1
Claim 26
Claim 1
Claim 27
Claim 1
Claim 28
Claim 2
Claim 29
Claim 1
Claim 30
Claim 5
Claim 31
Claim 6
Claim 32
Claim 8
Claim 33
Claim 7
Claim 34
‘275 [Claim 9]
A health tracking system comprising:
A health tracking system comprising:
a crowd-sourced 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 to which the data record corresponds,
a database configured to store a plurality of crowd-sourced data records, each of the plurality of data records comprising at least a descriptive string and nutritional data regarding the consumable item to which the data record corresponds
a data processor in communication with the crowd-sourced database, the data processor being configured to, for each respective data record of the plurality of data records,
a data processor in communication with the database, the data processor being configured to
(i) determine one of a plurality of possible tastes associated to the respective consumable item and (ii) store the determined taste in the crowd-sourced database in association with the respective data record, the one of the plurality of possible tastes for the respective consumable item being determined by:
(ii) determine a taste aspect for the consumable item to which each of the received ones of the one or more of the plurality of data records corresponds
[Claim 10] – wherein the data processor is further configured to store the determined taste aspects in the database in association with the respective ones of the one or more of the plurality of data records
evaluating the nutritional data of the respective data record to determine an accuracy of the nutritional data with respect to the respective consumable item, in response to determining that the nutritional data is accurate, (i) applying a first statistical model to the descriptive string to determine a first set of probabilities that the respective consumable item has each of the plurality of possible tastes, wherein the plurality of possible tastes includes a plurality of fundamental flavors, (ii) applying a second statistical model to the nutritional data to determine a second set of probabilities that the respective consumable item has each of the plurality of possible tastes, and (iii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities and the second set of probabilities;
wherein the evaluation of at least one of the descriptive string and the nutritional data comprises: for each of the one or more of the plurality of data records, determine whether the nutritional data thereof is accurate with respect to the consumable item; when it is determined that the nutritional data is accurate, apply a first mathematical model to the nutritional data to calculate a probability that respective ones of the consumable item have a first taste aspect and based on the result thereof, apply a second mathematical model to the descriptive string, wherein applying the second mathematical model comprises (i) vectorizing the descriptive string, (ii) comparing the vectorized descriptive string to a set of training vectors, and (iii) using a weighted k-nearest neighbor algorithm to calculate a first set of probabilities that the consumable item has each of the plurality of possible tastes based on a similarity between the vectorized descriptive string and vectors in the set of training vectors
in response to determining that the nutritional data is not accurate, (i) applying the first statistical model to the descriptive string to determine the first set of probabilities that the respective consumable item has each of the plurality of possible tastes and (ii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities
[Claim 19] – when the nutritional data is determined to be inaccurate, determining the taste by applying the first statistical model to the descriptive string and omitting to utilize the nutritional data; update the database by storing the determined tastes in the database associated to the respective ones of the plurality of data records
‘275 does not disclose:
the plurality of data records of the crowd-sourced database being accessible and searchable by a health tracking device associated with a user.
However, Hadad teaches:
the plurality of data records of the crowd-sourced database being accessible and searchable by a health tracking device associated with a user {Hadad: fig 5, #520 diet profiler; [0007] installing the application on computing devices belonging to a plurality of users and creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm; [0045] the application queries the user about culinary preferences. The application tries to infer the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen; [0082] eating habits 514 are input to the diet profiler 520; [0049] nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--the user's activity profile (training activities schedule), medical profile (the user's medical condition), different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences. Using this data, the first module creates a nutritional schedule; [0066] Create a module that will be able to query and analyze the user's inputs efficiently, generating value to both the algorithm and the user. Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the application as taught by Hadad in the taste profile method of ‘275 in order to provide improve the quality of results (Hadad: [0020]).
Dependent claims 35-38 are anticipated by ‘275 as follows:
Instant claims
‘275
Claim 35
Claim 9
Claim 36
Claim 17
Claim 37
Claim 18
Claim 38
Claim 11
In regards to claim 39, claim 39 is directed to a medium. Claim 39 recites limitations that are substantially parallel in nature to those addressed above for claim 34 which is directed towards a system. The combined method of ‘275/Hadad teaches the limitations of claim 34 as noted above. ‘275 further teaches a non-transitory computer-readable medium for operating a health tracking system, the computer-readable medium having a plurality of instructions stored thereon that, when executed by a processor, cause the processor to (‘275: [Claim 19]). Claim 39 is therefore rejected for the reasons set forth above in claim 34 and in this paragraph.
Dependent claim 40 is anticipated by ‘275 as follows:
Instant claim
‘275
Claim 40
Claim 19
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 21-33 are directed to a process, claims 34-38 are directed to a machine, and claims 39-40 are directed to a manufacture. Therefore, claims 21-40 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 21 as representative, claim 21 recites at least the following limitations that are believed to recite an abstract idea:
receiving a data record comprising at least a descriptive string and nutritional data regarding a consumable item to which the data record corresponds;
determining one of a plurality of possible tastes associated to the consumable item by:
applying a first statistical model to the descriptive string to determine a first set of probabilities that the consumable item has each of the plurality of possible tastes, wherein the plurality of possible tastes includes a plurality of fundamental flavors;
applying a second statistical model to the nutritional data to determine a second set of probabilities that the consumable item has each of the plurality of possible tastes;
and determining the one of the plurality of possible tastes for the consumable item based on the first set of probabilities and the second set of probabilities;
updating in order to associate the determined taste with the data record;
performing the receiving, the determining, and the associating with respect to each of a plurality of data records stored such that each of the plurality of data records is associated with a taste; and
enabling a health tracking associated with a user to access and search the plurality of data records having the tastes associated therewith.
Additionally taking claim 34 as representative, claim 34 recites at least the following limitations that are believed to recite an abstract idea:
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 to which the data record corresponds, the plurality of data records being accessible and searchable by a health tracking associated with a user; and
for each respective data record of the plurality of data records,
(i) determine one of a plurality of possible tastes associated to the respective consumable item and
(ii) store the determined taste in association with the respective data record,
the one of the plurality of possible tastes for the respective consumable item being determined by:
evaluating the nutritional data of the respective data record to determine an accuracy of the nutritional data with respect to the respective consumable item,
in response to determining that the nutritional data is accurate, (i) applying a first statistical model to the descriptive string to determine a first set of probabilities that the respective consumable item has each of the plurality of possible tastes, wherein the plurality of possible tastes includes a plurality of fundamental flavors,
(ii) applying a second statistical model to the nutritional data to determine a second set of probabilities that the respective consumable item has each of the plurality of possible tastes, and
(iii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities and the second set of probabilities; and
in response to determining that the nutritional data is not accurate,
(i) applying the first statistical model to the descriptive string to determine the first set of probabilities that the respective consumable item has each of the plurality of possible tastes and
(ii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities.
The above limitations recite the concept of recommending consumable items. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, recommending items is a sales and marketing activity and pertains to managing personal behavior. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the determinations are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Independent claims 39 recites similar limitations as claim 34 and, as such, falls within the same identified grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 21, 34, and 39 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1 and 7-8 recite additional elements, such as a health tracking system; a database; a health tracking device; a crowd-sourced database; a data processor; and a non-transitory computer-readable medium for operating a health tracking system, the computer-readable medium having a plurality of instructions stored thereon that, when executed by a processor, cause the processor to. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 21, 34, and 39 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 21, 34, and 39 merely recite a commonplace business method (i.e., recommending consumable items) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 21, 34, and 39 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 21, 34, and 39 specifying that the abstract idea of recommending consumable items is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 21, 34, and 39 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 21, 34, and 39 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 21, 34, and 39 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 21, 34, and 39, these claims recite additional elements, such as a health tracking system; a database; a health tracking device; a crowd-sourced database; a data processor; and a non-transitory computer-readable medium for operating a health tracking system, the computer-readable medium having a plurality of instructions stored thereon that, when executed by a processor, cause the processor to. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 21, 34, and 39 are manual processes, e.g., receiving information, analyzing information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 21, 34, and 39 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims specifying that the abstract idea of recommending consumables is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 21, 34, and 39 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 21, 34, and 39 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 22-33, 35-38, and 40, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 22-33, 35-38, and 40 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. These claims, under their broadest reasonable interpretation, further fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 22-23, 26-28, 30-33, and 35-37 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 24-25, 29, 38, and 40 further identify the additional elements of a machine learning model; a weighted k-nearest neighbor algorithm; and transmitting data. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 22-33, 35-38, and 40 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 26, 37, and 44, dependent claims 22-33, 35-38, and 40 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 21-40 are ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 21, 25-26, 29-30, 32-33 are rejected under 35 U.S.C. 103 as being unpatentable over Hadad et. al. (US 20140255882 A1, herein referred to as Hadad), in view of Pickelsimer (US 20130339179 A1, herein referred to as Pickelsimer).
Claim 21:
Hadad discloses:
A method for operating a health tracking system {Hadad: [0007] A method is disclosed to provide an application for personal recommendations for nutrition based on preferences, medical and activity profiles of a plurality of users}, the method comprising:
receiving a data record comprising at least a descriptive string and nutritional data regarding a consumable item to which the data record corresponds {Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships, and previously inputted user culinary preferences; [0051] The diet calculator relies on a multitude of inputs: User culinary preferences, Nutrition Database, Nutritional values of each food item (i.e., nutritional data); [0068] nutrition crowdsourcing};
determining one of a plurality of possible tastes associated to the consumable item by {Hadad: [0051] The diet calculator relies on a multitude of inputs: [0053] User culinary preferences [0058] Nutrition Database [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0063] The algorithm combines all constraints and creates a constraints matrix A, and constraints vector b.}:
applying a first statistical model to the descriptive string to determine a first set of probabilities that the consumable item has each of the plurality of possible tastes{Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated},
applying a second statistical model to the nutritional data to determine a second set of probabilities that the consumable item has each of the plurality of possible tastes{Hadad: [0067] The final phase of the algorithm includes analyzing and clustering the platform's users. Different clustering methods, K-NN for example, are used to find the different "preference type casts" and associate each user to the one is closest to him. Examiner notes that a KNN algorithm outputs probabilities, which are then used for the preference type casts.}; and
determining the one of the plurality of possible tastes for the consumable item based on the first set of probabilities and the second set of probabilities {Hadad: [0066] Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated. The data acquired is used to improve the suggestions to the user, and increase its value; [0067] A user can be offered meals which another user with similar tastes enjoyed, thereby improving the initial user input process. Examiner interprets that both the crowdsourced user clustering (i.e., second set of probabilities) and individual user preference probabilities (i.e., first set) can be used to determine potential foods.};
updating a database in order to associate the determined taste with the data record in the database {Hadad: [0049] gathers all the required information-- different data from a nutrition database; [0066] the probability of the user's affection for a certain food item is calculated from the changes he made to the diet prescribed for him by the algorithm. The data acquired is used to improve the suggestions to the user; [0068] an analysis of the general population--trends and insights. Users are clustered, enabling users to get similar recommendations to those of other users in their cluster. This creates “nutrition crowdsourcing.”};
performing the receiving, the determining, and the associating with respect to each of a plurality of data records stored in the database such that each of the plurality of data records is associated with a taste {Hadad: [0049] The nutrition scheduler gathers all the required information about the user, including different data from a nutrition database, i.e., nutritional content of each food item; [0067] A user can be offered meals which another user with similar tastes enjoyed, thereby improving the initial user input process, and providing a unique nutritional crowd sourcing platform.}; and
enabling a health tracking device associated with a user to access the database and search the plurality of data records having the tastes associated therewith {Hadad: fig 5, #520 diet profiler; [0007] installing the application on computing devices belonging to a plurality of users and creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm; [0045] the application queries the user about culinary preferences. The application tries to infer the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen; [0082] eating habits 514 are input to the diet profiler 520; [0049] nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--the user's activity profile (training activities schedule), medical profile (the user's medical condition), different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences. Using this data, the first module creates a nutritional schedule; [0066] Create a module that will be able to query and analyze the user's inputs efficiently, generating value to both the algorithm and the user. Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated}.
Hadad does not disclose:
wherein the plurality of possible tastes includes a plurality of fundamental flavors.
Hadad does disclose a plurality of tastes and determining a user’s taste profile using culinary preferences (Hadad: [0045], [0066], [0082]).
However, Pickelsimer teaches:
wherein the plurality of possible tastes includes a plurality of fundamental flavors {Pickelsimer: [0083] Taste information is received through sensory organs known as taste buds, which are concentrated on the upper surface of the tongue. The sensation of taste is often categorized into four basic tastes: sweetness, acidity (i.e., tartness), bitterness, and saltiness. Some food scientists include a fifth category called umami or savoriness. Examiner interprets acidity/tartness as sour.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included determining a user profile based on salty, sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claim 25:
Hadad and Pickelsimer teach the method of claim 21. Hadad further discloses:
wherein at least one of the first statistical model and the second statistical model comprises a weighted k-nearest neighbor algorithm {Hadad: [0067] The final phase of the algorithm includes analyzing and clustering the platform's users. Different clustering methods, K-NN for example, are used to find the different "preference type casts" and associate each user to the one is closest to him. Examiner notes that a KNN algorithm outputs probabilities, which are then used for the preference type casts.}.
Claim 26:
Hadad and Pickelsimer teach the method of claim 21. Hadad further discloses:
wherein the applying the second statistical model to the descriptive string further comprises{Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated}:
vectorizing the descriptive string {Hadad: [0063] The algorithm combines all constraints listed and creates a constraints vector b.}; and
applying the second statistical model to the vectorized descriptive string {Hadad: [0064] The solution vector x will contain the actual amounts of each food item the user is required to consume. Additionally, the problem of what to eat and how much is solved using an optimization function; [0066] Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships}.
Claim 29:
Hadad and Pickelsimer teach the method of claim 21. Hadad further discloses:
determining a taste profile specific to the user, said taste defined by a probability of each of the plurality of flavors{Hadad: fig 5, #520 diet profiler; [0045] the application queries the user about culinary preferences. The application tries to infer the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen; [0082] eating habits 514 are input to the diet profiler 520; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated};
generating a list of recommended ones of a plurality of consumable items for the user based at least in part on the determined taste profile specific to the user and the tastes associated with the plurality of data records {Hadad: fig 5, #530 diet generator; [0015] The Diet Generator, which creates a list of recommended dishes for each day. The diet is calculated automatically using a selected optimization technique to find the diet with the highest ranking for each specific user, namely, this is the diet with the best value for the user based on his/her preference. Thus, the optimization algorithm finds the optimal diet that satisfies the constraints given by the Diet Profiler; [0051] The diet calculator relies on a multitude of inputs: User culinary preferences, Nutrition Database, Nutritional values of each food item (i.e., nutritional data)}; and
transmitting the generated list of recommended ones of the plurality of consumable items to the health tracking device associated with the user {Hadad: [0038] platform is interfaced through multiple mobile and web clients; [0045] On installation of the application onto a PC, iPhone or tablet, the user is asked several questions to determine his eating habits; [0049] Once the nutrition schedule is computed, it is provided to the user. Examiner interprets the user’s PC, iPhone, or tablet as the health tracking device because that is the device that is used to receive the food, medical, and workout information from the user.}.
Although Hadad discloses finding a probability that a user will enjoy a certain food, Hadad does not disclose:
wherein the taste profile is defined by each of the fundamental flavors.
However, Pickelsimer teaches:
wherein the taste profile is defined by each of the fundamental flavors {Pickelsimer: [0016] The step of determining an individual taste profile may further comprise defining a set of individual taste profile preferences comprising one or more of salty, sweet, tart, and bitter, wherein the set of individual taste profile preferences is substantially correlated to the flavor characteristics. Examiner interprets tart as sour.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included food preferences based on sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claim 30:
Hadad and Pickelsimer teach the method of claim 29. Hadad further discloses:
receiving a list of consumable items, the received list being associated with a user profile stored in the database, the received list containing a plurality of entries corresponding to individual ones of the data records selected by the user {Hadad: [0013] 1. The User Profiler, which employs the users' feedback to learn their habits and preferences. [0014] 2. The Diet Profiler, which creates a diet profile based on the user's profile. It generates a list of constraints that each diet for the user must satisfy; [0049] The nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences.}; and
associating the determined taste profile to the user profile in the database, wherein the determined taste profile specific to the user profile is based on one or more patterns determined from the tastes associated to each of the data records corresponding to the plurality of entries in the received list {Hadad: [0015] 3. The Diet Generator, which creates a list of recommended dishes for each day. The diet is calculated automatically using a selected optimization technique, to find the diet with the highest ranking for each specific user, namely, this is the diet with the best value for the user based on his/her preference. Thus, the optimization algorithm finds the optimal diet that satisfies the constraints given by the Diet Profiler; [0049] The nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences. Using this data, the first module creates a nutritional schedule--specifying which food items are to be consumed at each meal and at what quantity for the next week, with several options for substitution for each meal are provided.}.
Claim 32:
Hadad and Pickelsimer teach the method of claim 29. Hadad further discloses:
wherein the taste profile is defined by a user probability for flavors {Hadad: fig 5, #520 diet profiler; [0045] the application queries the user about culinary preferences. The application tries to infer the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen; [0082] eating habits 514 are input to the diet profiler 520; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated}.
Although Hadad discloses finding a probability that a user will enjoy a certain food, Hadad does not disclose:
wherein the taste profile is defined by each of the fundamental flavors.
However, Pickelsimer teaches:
wherein the taste profile is defined by each of the fundamental flavors {Pickelsimer: [0016] The step of determining an individual taste profile may further comprise defining a set of individual taste profile preferences comprising one or more of salty, sweet, tart, and bitter, wherein the set of individual taste profile preferences is substantially correlated to the flavor characteristics. Examiner interprets tart as sour.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included food preferences based on sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claim 33:
Hadad and Pickelsimer teach the method of claim 21.
Hadad does not disclose:
wherein the plurality of fundamental flavors include two or more of sweet, salty, umami, sour, spicy and bitter.
However, Pickelsimer teaches:
wherein the plurality of fundamental flavors include two or more of sweet, salty, umami, sour, spicy and bitter {Pickelsimer: [0016] The step of determining an individual taste profile may further comprise defining a set of individual taste profile preferences comprising one or more of salty, sweet, tart, and bitter, wherein the set of individual taste profile preferences is substantially correlated to the flavor characteristics. Examiner interprets tart as sour.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included food preferences based on sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claims 22-23, 27, 31, 34-37, and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Hadad, in view of Pickelsimer, in view of Wilson et. al. (US 20140280226 A1, herein referred to as Wilson).
Claim 22:
Hadad and Pickelsimer teach the method of claim 21. Hadad does disclose determining the one of the plurality of possible tastes for the consumable item, an output of the first statistical model, and an output of the second statistical model (Hadad: [0049], [0063]-[0064], [0066]-[0067]).
Hadad does not disclose:
wherein the determining further comprises: weighting an output of the first, and weighting an output of the second.
However, Wilson teaches:
wherein the determining further comprises: weighting an output of the first, and weighting an output of the second {Wilson: [0100] resulting recommendations are weighted based on empirical observations concerning the predictiveness or accuracy; [0113] the recommendation is a blending of the content-based link strength 901, collaborative link strength 903, and content-collaborative link strength 905. Each link strength is assigned a distinct weighting factor. The weighting factors may be set to vary according to the predictiveness or accuracy of each type of link. Examiner interprets each link strength as an output.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included weighting based on accuracy evaluation as taught by Wilson in the taste profile method of Hadad in order to indicate an optimal decision recommendation (Wilson: [0053]).
Claim 23:
Hadad, Pickelsimer, and Wilson teach the method of claim 22. Hadad does disclose a first statistical model and a second statistical model (Hadad: [0063]-[0064], [0066]-[0067]).
Hadad does not disclose:
wherein a value by which the output of the first is weighted and a value by which the output of the second is weighted are dependent on a value of the output of the first.
However, Wilson teaches:
wherein a value by which the output of the first is weighted and a value by which the output of the second is weighted are dependent on a value of the output of the first {Wilson: [0113] Each link strength is assigned a distinct weighting factor, and the blending equation may be a second order equation; [0114] Second order relationships could be included in the link matrices used to calculate overall link strength. Restaurant 5 could be assigned a direct +0.25 link to Restaurant 8 based on this second order relationship. That link could operate in the matrix independently of the nodes.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included second-order weighting as taught by Wilson in the taste profile method of Hadad in order to indicate an optimal decision recommendation (Wilson: [0053]).
Claim 27:
Hadad and Pickelsimer teach the method of claim 21. Hadad does disclose wherein the determining the one of the plurality of possible tastes for the consumable item further comprises: evaluating the nutritional data of the data record; the nutritional data; the respective consumable item (Hadad: [0063]-[0064], [0066]-[0067]); and the applying the second statistical model to the descriptive string (Hadad: [0049]; [0067]).
Hadad does not disclose:
evaluating to determine an accuracy of the data with respect to the item, wherein the applying is performed only in response to determining that the data is accurate.
Hadad does disclose determining tastes based on nutritional data of consumable items (Hadad: [0045], [0049]).
However, Wilson teaches:
evaluating to determine an accuracy of the data with respect to the item, wherein the applying is performed only in response to determining that the data is accurate {Wilson: [0185] the recommendation engine 112 will dynamically harvest data items from multiple source sites and resolve any differences between these data items. The recommendation engine 112 can resolve these differences by indicating certain web sites as containing inaccurate information with respect to a venue; [0053] analyzing a user's affinity for a particular entity when determining an individual's taste profile (i.e., a user personality matrix) may include creating and utilizing a unified taste profile (i.e., a combined personality matrix derived from aggregated user personality matrices); [0186] a sliding scale resonance threshold may be applied to determine at what point information can be deemed accurate as opposed to outlying with respect to other harvested information. The system 100 may determine that the 45 web sites indicating Restaurant 1 as expensive and formal are outliers and will not take their information into account. Examiner interprets that if the data overall is below an accuracy threshold (i.e., determined to be inaccurate), then the system excludes outlying information.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included an accuracy evaluation as taught by Wilson in the taste profile method of Hadad in order to indicate an optimal decision recommendation (Wilson: [0053]).
Claim 31:
Hadad and Wilson teach the method of claim 30. Hadad further discloses:
selecting individual ones of a plurality stored in the database based on the determined taste profile {Hadad: fig 5, #530 diet generator; [0015] The Diet Generator, which creates a list of recommended dishes for each day. The diet is calculated automatically using a selected optimization technique to find the diet with the highest ranking for each specific user, namely, this is the diet with the best value for the user based on his/her preference. Thus, the optimization algorithm finds the optimal diet that satisfies the constraints given by the Diet Profiler; [0051] The diet calculator relies on a multitude of inputs: User culinary preferences, Nutrition Database, Nutritional values of each food item (i.e., nutritional data)}; and
transmitting the selected individual ones of the plurality to the health tracking device {Hadad: [0038] platform is interfaced through multiple mobile and web clients; [0045] On installation of the application onto a PC, iPhone or tablet, the user is asked several questions to determine his eating habits; [0049] Once the nutrition schedule is computed, it is provided to the user. Examiner interprets the user’s PC, iPhone, or tablet as the health tracking device because that is the device that is used to receive the food, medical, and workout information from the user.}.
Although Hadad discloses selecting and transmitting one or more recommendations to a user based on their user profile, Hadad does not disclose:
select individual ones of a plurality of advertisements; and
transmit the selected individual ones of the plurality of advertisements.
However, Wilson teaches:
select individual ones of a plurality of advertisements {Wilson: [0097] system 100 may access user profiles to collect data for each user; [0246] determine advertisement information based on previous customer activity.}; and
transmit the selected individual ones of the plurality of advertisements {Wilson: fig 1A, #108 users; fig 14; [0121] user interface for deployment at a client device; [0246] determine advertisement information based on previous customer activity.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included advertisements as taught by Wilson in the taste profile method of Hadad in order to indicate an optimal decision recommendation (Wilson: [0053]).
Claim 34:
Hadad discloses:
A health tracking system comprising {Hadad: fig 5}:
a crowd-sourced 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 to which the data record corresponds{Hadad: [0049] data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships, and previously inputted user culinary preferences; [0068] an analysis of the general population--trends and insights. Users are clustered, enabling users to get similar recommendations to those of other users in their cluster. This creates “nutrition crowdsourcing.”},
the plurality of data records of the crowd-sourced database being accessible and searchable by a health tracking device associated with a user {Hadad: fig 5, #520 diet profiler; [0007] installing the application on computing devices belonging to a plurality of users and creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm; [0045] the application queries the user about culinary preferences. The application tries to infer the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen; [0082] eating habits 514 are input to the diet profiler 520; [0049] nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--the user's activity profile (training activities schedule), medical profile (the user's medical condition), different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences. Using this data, the first module creates a nutritional schedule; [0066] Create a module that will be able to query and analyze the user's inputs efficiently, generating value to both the algorithm and the user. Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated}; and
a data processor in communication with the crowd-sourced database, the data processor being configured to, for each respective data record of the plurality of data records {Hadad: [0039] algorithm can be developed using many different programming languages on various servers; [0007] installing the application on computing devices belonging to a plurality of users and creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm; see also Fig. 1},
(i) determine one of a plurality of possible tastes associated to the respective consumable item {Hadad: [0051] The diet calculator relies on a multitude of inputs: [0053] User culinary preferences [0058] Nutrition Database [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0063] The algorithm combines all constraints and creates a constraints matrix A, and constraints vector b.} and
(ii) store the determined taste in the crowd-sourced database in association with the respective data record, the one of the plurality of possible tastes for the respective consumable item being determined by {Hadad: [0051] The diet calculator relies on a multitude of inputs: [0053] User culinary preferences [0058] Nutrition Database [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0063] The algorithm combines all constraints and creates a constraints matrix A, and constraints vector b; [0049] gathers all the required information-- different data from a nutrition database; [0066] the probability of the user's affection for a certain food item is calculated from the changes he made to the diet prescribed for him by the algorithm. The data acquired is used to improve the suggestions to the user; [0068] an analysis of the general population--trends and insights. Users are clustered, enabling users to get similar recommendations to those of other users in their cluster. This creates “nutrition crowdsourcing.”}:
evaluating the nutritional data of the respective data record with respect to the respective consumable item {Hadad: [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0064] algorithm reduces the problem of finding the proper diet to a linear problem},
(i) applying a first statistical model to the descriptive string to determine a first set of probabilities that the respective consumable item has each of the plurality of possible tastes {Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated},
(ii) applying a second statistical model to the nutritional data to determine a second set of probabilities that the respective consumable item has each of the plurality of possible tastes {Hadad: [0067] The final phase of the algorithm includes analyzing and clustering the platform's users. Different clustering methods, K-NN for example, are used to find the different "preference type casts" and associate each user to the one is closest to him. Examiner notes that a KNN algorithm outputs probabilities, which are then used for the preference type casts.}, and
(iii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities and the second set of probabilities{Hadad: [0066] Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated. The data acquired is used to improve the suggestions to the user, and increase its value; [0067] A user can be offered meals which another user with similar tastes enjoyed, thereby improving the initial user input process. Examiner interprets that both the crowdsourced user clustering (i.e., second set of probabilities) and individual user preference probabilities (i.e., first set) can be used to determine potential foods.}; and
(i) applying the first statistical model to the descriptive string to determine the first set of probabilities that the respective consumable item has each of the plurality of possible tastes and (ii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities{Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated; [0065] the stochastic algorithm is elaborated on herein. When the user receives a meal recommendation, he may decide to change it. In such an occasion, the system finds a set of replacements that fits the users' preferences and habits while satisfying the constraints. Examiner notes that the stochastic algorithm can make recommendations with only the user’s preferences.}.
Although Hadad discloses a nutrition analysis and diet recommendation algorithm, Hadad does not disclose:
evaluating to determine an accuracy of the nutritional data;
in response to determining that the nutritional data is accurate, applying;
wherein the plurality of possible tastes includes a plurality of fundamental flavors; and
in response to determining that the nutritional data is not accurate, applying.
Hadad does disclosed nutritional data and a plurality of data records (Hadad: [0045], [0049]).
However, Wilson teaches:
evaluating to determine an accuracy of the nutritional data {Wilson: [0185] the recommendation engine 112 will dynamically harvest data items from multiple source sites and resolve any differences between these data items. The recommendation engine 112 can resolve these differences by indicating certain web sites as containing inaccurate information with respect to a venue; [0186] a sliding scale resonance threshold may be applied to determine at what point information can be deemed accurate as opposed to outlying with respect to other harvested information.};
in response to determining that the nutritional data is accurate, applying {Wilson: [0053] analyzing a user's affinity for a particular entity when determining an individual's taste profile; [0186] a sliding scale resonance threshold may be applied to determine at what point information can be deemed accurate as opposed to outlying with respect to other harvested information. The system 100 may determine that the 45 web sites indicating Restaurant 1 as expensive and formal are outliers. The system 100 may opt to use the information contained within the web sites but provide a negative weighting to this information so that it does not have too much of an effect. Examiner interprets that if the data overall is above an accuracy threshold (i.e., determined to be accurate), then the system uses the information from both the accurate and outlying information.}; and
in response to determining that the nutritional data is not accurate, applying {Wilson: [0053] analyzing a user's affinity for a particular entity when determining an individual's taste profile; [0186] a sliding scale resonance threshold may be applied to determine at what point information can be deemed accurate as opposed to outlying with respect to other harvested information. The system 100 may determine that the 45 web sites indicating Restaurant 1 as expensive and formal are outliers and will not take their information into account. Examiner interprets that if the data overall is below an accuracy threshold (i.e., determined to be inaccurate), then the system excludes outlying information.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included an accuracy evaluation and processor as taught by Wilson in the taste profile system of Hadad in order to indicate an optimal decision recommendation (Wilson: [0053]).
Neither Hadad nor Wilson disclose:
wherein the plurality of possible tastes includes a plurality of fundamental flavors.
However, Pickelsimer teaches:
wherein the plurality of possible tastes includes a plurality of fundamental flavors {Pickelsimer: Fig 15; [0092] the wine classification system 10 in particular embodiments includes consideration of six human-detectable wine flavor characteristics 20: (1) sweetness, (2) acidity, (3) bitterness (e.g., tannin), (4) body, (5) oak, and (6) earthiness. Examiner interprets acidity as sour.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included determining a user profile based on salty, sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claim 35:
Hadad, Wilson, and Pickelsimer teach the system of claim 34. Hadad further discloses:
wherein the data processor is further configured to (i) receive one or more of the plurality of data records from the crowd- sourced database {Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences; [0051] The diet calculator relies on a multitude of inputs: User culinary preferences, Nutrition Database, Nutritional values of each food item; [0039] algorithm can be developed using many different programming languages on various servers; [0007] installing the application on computing devices belonging to a plurality of users and creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm},
(ii) determine a taste for the consumable item to which each of the received one or more of the plurality of data records corresponds, the determination being based on an evaluation of at least one of the descriptive string and the nutritional data, wherein the taste defines probabilities for flavors {Hadad: [0051] The diet calculator relies on a multitude of inputs: [0053] User culinary preferences [0058] Nutrition Database [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0063] The algorithm combines all constraints and creates a constraints matrix A, and a constraints vector b; [0064] algorithm reduces the problem of finding the proper diet to a linear problem; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated},
(iii) generate a list of recommended ones of the plurality of data records based at least in part on the determined taste corresponding to the plurality of data records {Hadad: fig 5, #530 diet generator; [0015] The Diet Generator, which creates a list of recommended dishes for each day. The diet is calculated automatically using a selected optimization technique to find the diet with the highest ranking for each specific user, namely, this is the diet with the best value for the user based on his/her preference. Thus, the optimization algorithm finds the optimal diet that satisfies the constraints given by the Diet Profiler; [0051] The diet calculator relies on a multitude of inputs: User culinary preferences, Nutrition Database, Nutritional values of each food item (i.e., nutritional data)}, and
(iv) send the generated list of recommended ones of the plurality of data records to the health tracking device associated with the user {Hadad: [0038] platform is interfaced through multiple mobile and web clients; [0045] On installation of the application onto a PC, iPhone or tablet, the user is asked several questions to determine his eating habits; [0049] Once the nutrition schedule is computed, it is provided to the user. Examiner interprets the user’s PC, iPhone, or tablet as the health tracking device because that is the device that is used to receive the food, medical, and workout information from the user.}.
Although Hadad discloses finding a probability that a user will enjoy a certain food, Hadad does not disclose:
wherein the taste is defined by each of the plurality of fundamental flavors.
However, Pickelsimer teaches:
wherein the taste is defined by each of the plurality of fundamental flavors {Pickelsimer: [0016] The step of determining an individual taste profile may further comprise defining a set of individual taste profile preferences comprising one or more of salty, sweet, tart, and bitter, wherein the set of individual taste profile preferences is substantially correlated to the flavor characteristics. Examiner interprets tart as sour.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included food preferences based on sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claim 36:
Hadad, Wilson, and Pickelsimer teach the system of claim 35. Hadad further discloses:
wherein the data processor is further configured to: receive a list of consumable items, the received list being associated with a user profile stored in the database, the received list containing a plurality of entries corresponding to individual ones of the plurality of data records {Hadad: [0013] 1. The User Profiler, which employs the users' feedback to learn their habits and preferences. [0014] 2. The Diet Profiler, which creates a diet profile based on the user's profile. It generates a list of constraints that each diet for the user must satisfy; [0049] The nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences.}; and
associate the determined taste to the user profile in the database, wherein the determined taste specific to the user profile is based on one or more patterns determined from the tastes associated to each of the data records corresponding to the plurality of entries in the received list {Hadad: [0015] 3. The Diet Generator, which creates a list of recommended dishes for each day. The diet is calculated automatically using a selected optimization technique, to find the diet with the highest ranking for each specific user, namely, this is the diet with the best value for the user based on his/her preference. Thus, the optimization algorithm finds the optimal diet that satisfies the constraints given by the Diet Profiler; [0049] The nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences. Using this data, the first module creates a nutritional schedule--specifying which food items are to be consumed at each meal and at what quantity for the next week, with several options for substitution for each meal are provided.}.
Claim 37:
Hadad, Wilson, and Pickelsimer teach the system of claim 36. Hadad further discloses:
select individual ones of a plurality stored in the database based on the user profile {Hadad: fig 5, #530 diet generator; [0015] The Diet Generator, which creates a list of recommended dishes for each day. The diet is calculated automatically using a selected optimization technique to find the diet with the highest ranking for each specific user, namely, this is the diet with the best value for the user based on his/her preference. Thus, the optimization algorithm finds the optimal diet that satisfies the constraints given by the Diet Profiler; [0051] The diet calculator relies on a multitude of inputs: User culinary preferences, Nutrition Database, Nutritional values of each food item (i.e., nutritional data)}; and
transmit the selected individual ones of the plurality to the health tracking device {Hadad: [0038] platform is interfaced through multiple mobile and web clients; [0045] On installation of the application onto a PC, iPhone or tablet, the user is asked several questions to determine his eating habits; [0049] Once the nutrition schedule is computed, it is provided to the user. Examiner interprets the user’s PC, iPhone, or tablet as the health tracking device because that is the device that is used to receive the food, medical, and workout information from the user.}.
Although Hadad discloses selecting and transmitting one or more recommendations to a user based on their user profile, Hadad does not disclose:
select individual ones of a plurality of advertisements; and
transmit the selected individual ones of the plurality of advertisements.
However, Wilson teaches:
select individual ones of a plurality of advertisements {Wilson: [0097] system 100 may access user profiles to collect data for each user; [0246] determine advertisement information based on previous customer activity.}; and
transmit the selected individual ones of the plurality of advertisements {Wilson: fig 1A, #108 users; fig 14; [0121] user interface for deployment at a client device; [0246] determine advertisement information based on previous customer activity.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included advertisements as taught by Wilson in the taste profile method of Hadad in order to indicate an optimal decision recommendation (Wilson: [0053]).
Claim 39:
Hadad discloses:
A non-transitory computer-readable medium for operating a health tracking system, the computer-readable medium having a plurality of instructions stored thereon that, when executed by a processor, cause the processor to{Hadad: [0049]; [0039]; [0007]; and fig 5}:
access a crowd-sourced 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 to which the data record corresponds {Hadad: [0049] data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships, and previously inputted user culinary preferences; [0068] an analysis of the general population--trends and insights. Users are clustered, enabling users to get similar recommendations to those of other users in their cluster. This creates “nutrition crowdsourcing.”},
the plurality of data records of the crowd-sourced database also being accessible and searchable by a health tracking device associated with a user {Hadad: fig 5, #520 diet profiler; [0007] installing the application on computing devices belonging to a plurality of users and creating a personal nutritional schedule based on a set of constraints which are solved using an optimization algorithm; [0045] the application queries the user about culinary preferences. The application tries to infer the user's tastes and habits according to a game where the user iteratively chooses one of two options on the screen; [0082] eating habits 514 are input to the diet profiler 520; [0049] nutrition scheduler gathers all the required information about the user needed to create a nutrition schedule--the user's activity profile (training activities schedule), medical profile (the user's medical condition), different data from a nutrition database, i.e., nutritional content of each food item and their relationships, and previously inputted user culinary preferences. Using this data, the first module creates a nutritional schedule; [0066] Create a module that will be able to query and analyze the user's inputs efficiently, generating value to both the algorithm and the user. Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated};
determine, for each respective data record of the plurality of data records, one of a plurality of possible tastes associated to the respective consumable item by {Hadad: [0051] The diet calculator relies on a multitude of inputs: [0053] User culinary preferences [0058] Nutrition Database [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0063] The algorithm combines all constraints and creates a constraints matrix A, and constraints vector b.}:
evaluating the nutritional data of the data record to determine the nutritional data with respect to the consumable item {Hadad: [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0064] algorithm reduces the problem of finding the proper diet to a linear problem},
(i) applying a first statistical model to the descriptive string to determine a first set of probabilities that the respective consumable item has each of the plurality of possible tastes {Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated},
(ii) applying a second statistical model to the nutritional data to determine a second set of probabilities that the respective consumable item has each of the plurality of possible tastes {Hadad: [0067] The final phase of the algorithm includes analyzing and clustering the platform's users. Different clustering methods, K-NN for example, are used to find the different "preference type casts" and associate each user to the one is closest to him. Examiner notes that a KNN algorithm outputs probabilities, which are then used for the preference type casts.}, and
(iii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities and the second set of probabilities {Hadad: [0066] Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated. The data acquired is used to improve the suggestions to the user, and increase its value; [0067] A user can be offered meals which another user with similar tastes enjoyed, thereby improving the initial user input process. Examiner interprets that both the crowdsourced user clustering (i.e., second set of probabilities) and individual user preference probabilities (i.e., first set) can be used to determine potential foods.}; and
(i) applying the first statistical model to the descriptive string to determine the first set of probabilities that the respective consumable item has each of the plurality of possible tastes and (ii) determining the one of the plurality of possible tastes for the respective consumable item based on the first set of probabilities {Hadad: [0049] The nutrition scheduler gathers all the required information, including different data from a nutrition database, i.e., nutritional content of each food item (i.e., descriptive string) and their relationships; [0066] Single User Analyzer: Stochastic models extrapolate the user's preferences given the patterns in their behavior using the different food item's relationships, then the probability of the user's affection for a certain food item is calculated; [0065] the stochastic algorithm is elaborated on herein. When the user receives a meal recommendation, he may decide to change it. In such an occasion, the system finds a set of replacements that fits the users' preferences and habits while satisfying the constraints. Examiner notes that the stochastic algorithm can make recommendations with only the user’s preferences.}; and
store, for each respective data record of the plurality of data records, the determined taste in the crowd-sourced database in association with the respective data record {Hadad: [0051] The diet calculator relies on a multitude of inputs: [0053] User culinary preferences [0058] Nutrition Database [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0063] The algorithm combines all constraints and creates a constraints matrix A, and constraints vector b; [0049] gathers all the required information-- different data from a nutrition database; [0066] the probability of the user's affection for a certain food item is calculated from the changes he made to the diet prescribed for him by the algorithm. The data acquired is used to improve the suggestions to the user; [0068] an analysis of the general population--trends and insights. Users are clustered, enabling users to get similar recommendations to those of other users in their cluster. This creates “nutrition crowdsourcing.”};
Although Hadad discloses a nutrition analysis and diet recommendation algorithm, Hadad does not disclose:
evaluating to determine an accuracy of the nutritional data;
in response to determining that the nutritional data is accurate, applying;
wherein the plurality of possible tastes includes a plurality of fundamental flavors; and
in response to determining that the nutritional data is not accurate, applying.
Hadad does disclosed nutritional data and a plurality of data records (Hadad: [0045], [0049]).
However, Wilson teaches:
evaluating to determine an accuracy of the nutritional data {Wilson: [0185] the recommendation engine 112 will dynamically harvest data items from multiple source sites and resolve any differences between these data items. The recommendation engine 112 can resolve these differences by indicating certain web sites as containing inaccurate information with respect to a venue; [0186] a sliding scale resonance threshold may be applied to determine at what point information can be deemed accurate as opposed to outlying with respect to other harvested information.};
in response to determining that the nutritional data is accurate, applying {Wilson: [0053] analyzing a user's affinity for a particular entity when determining an individual's taste profile; [0186] a sliding scale resonance threshold may be applied to determine at what point information can be deemed accurate as opposed to outlying with respect to other harvested information. The system 100 may determine that the 45 web sites indicating Restaurant 1 as expensive and formal are outliers. The system 100 may opt to use the information contained within the web sites but provide a negative weighting to this information so that it does not have too much of an effect. Examiner interprets that if the data overall is above an accuracy threshold (i.e., determined to be accurate), then the system uses the information from both the accurate and outlying information.}; and
in response to determining that the nutritional data is not accurate, applying {Wilson: [0053] analyzing a user's affinity for a particular entity when determining an individual's taste profile; [0186] a sliding scale resonance threshold may be applied to determine at what point information can be deemed accurate as opposed to outlying with respect to other harvested information. The system 100 may determine that the 45 web sites indicating Restaurant 1 as expensive and formal are outliers and will not take their information into account. Examiner interprets that if the data overall is below an accuracy threshold (i.e., determined to be inaccurate), then the system excludes outlying information.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included an accuracy evaluation and processor as taught by Wilson in the taste profile system of Hadad in order to indicate an optimal decision recommendation (Wilson: [0053]).
Neither Hadad nor Wilson disclose:
wherein the plurality of possible tastes includes a plurality of fundamental flavors.
However, Pickelsimer teaches:
wherein the plurality of possible tastes includes a plurality of fundamental flavors {Pickelsimer: Fig 15; [0092] the wine classification system 10 in particular embodiments includes consideration of six human-detectable wine flavor characteristics 20: (1) sweetness, (2) acidity, (3) bitterness (e.g., tannin), (4) body, (5) oak, and (6) earthiness. Examiner interprets acidity as sour.}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included determining a user profile based on salty, sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claim 40:
Hadad, Wilson, and Pickelsimer teach the medium of claim 39. Hadad further discloses:
wherein the plurality of instructions, when executed by the processor, further cause the processor to: determining a taste profile specific to the user, said taste defined by a probability of each of the plurality of flavors {Hadad: [0051] The diet calculator relies on a multitude of inputs: [0053] User culinary preferences [0058] Nutrition Database [0060] Hierarchical association of each food item to a set of food groups [0061] Association of each food item to a set of meals and meal types [0062] Metric system between food items and food groups--distances between food items; [0063] The algorithm combines all constraints and creates a constraints matrix A, and constraints vector b};
generating a list of recommended ones of a plurality of consumable items for the user based at least in part on the determined taste profile specific to the user and the tastes associated with the plurality of data record {Hadad: fig 5, #530 diet generator; [0015] The Diet Generator, which creates a list of recommended dishes for each day. The diet is calculated automatically using a selected optimization technique to find the diet with the highest ranking for each specific user, namely, this is the diet with the best value for the user based on his/her preference. Thus, the optimization algorithm finds the optimal diet that satisfies the constraints given by the Diet Profiler; [0051] The diet calculator relies on a multitude of inputs: User culinary preferences, Nutrition Database, Nutritional values of each food item (i.e., nutritional data)}; and
transmitting the generated list of recommended ones of the plurality of consumable items to the health tracking device associated with the user {Hadad: [0038] platform is interfaced through multiple mobile and web clients; [0045] On installation of the application onto a PC, iPhone or tablet, the user is asked several questions to determine his eating habits; [0049] Once the nutrition schedule is computed, it is provided to the user. Examiner interprets the user’s PC, iPhone, or tablet as the health tracking device because that is the device that is used to receive the food, medical, and workout information from the user.}.
Although Hadad discloses a nutrition analysis and diet recommendation algorithm, Hadad does not disclose:
each of the plurality of fundamental flavors.
Hadad does disclose a plurality of tastes and determining a user’s taste profile using culinary preferences (Hadad: [0045], [0066], [0082]).
However, Pickelsimer teaches:
each of the plurality of fundamental flavors {Pickelsimer: [0083] Taste information is received through sensory organs known as taste buds, which are concentrated on the upper surface of the tongue. The sensation of taste is often categorized into four basic tastes: sweetness, acidity (i.e., tartness), bitterness, and saltiness. Some food scientists include a fifth category called umami or savoriness. Examiner interprets acidity/tartness as sour.}; and
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included determining a user profile based on salty, sweet, tart, and bitter as taught by Pickelsimer in the taste profile method of Hadad in order to provide specific information about the flavor characteristics which constitute more reliable predictors of taste (Pickelsimer: [0306]).
Claims 24 is rejected under 35 U.S.C. 103 as being unpatentable over Hadad, in view of Pickelsimer, further view of Devries et. al. (US 20170249445 A1, herein referred to as Devries).
Claim 24:
Hadad and Pickelsimer teach the method of claim 21. Hadad further discloses:
wherein at least one of the first statistical model and the second statistical model comprises a machine learning model using a set of data records having tastes associated therewith{Hadad: [0007] analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insights and allowing improved performance by means of "machine-learning,"}.
Although disclosing that the algorithms utilize machine learning methods, Hadad does not disclose:
a model that has been previously trained using a training set of data records having known tastes.
Hadad does disclose that the data records are associated with taste aspects and that the mathematical models can be updated (Hadad: [0049], [0043]).
However, Devries teaches:
a model that has been previously trained using a training set of data records having known tastes {Devries: [0130] In obtaining datapoints for a given nutrition-related metric (e.g. for training of a regression model 206 used by processing circuitry 56 to predict that nutrition-related metric), known values can be used (from an existing database) for food items consumed in the collection of data}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included training a model using known values as taught by Devries in the taste prediction system of Hadad and Pickelsimer in order to monitor and calculate nutrition-related metrics (Devries: [0031]).
Claims 28 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Hadad, in view of Pickelsimer, in view of Wilson, in further view of Devries.
Claim 28:
Hadad, Pickelsimer, and Wilson teach the method of claim 27. Hadad nor Pickelsimer disclose:
wherein the nutritional data includes at least a total caloric content and respective amounts of a plurality of macronutrients of the consumable item, and the evaluating the nutritional data further comprises comparing the total caloric content to a caloric content representative of the respective amounts of the plurality of macronutrients.
Hadad does disclose the total caloric content in certain amounts of foods and making recommendations based on desired serving sizes (Hadad: [0103]).
However, Devries teaches:
wherein the nutritional data includes at least a total caloric content and respective amounts of a plurality of macronutrients of the consumable item {Devries: [0049] nutrition-related metrics can include: (a) calories categorized into the macronutrient type (for example, carbohydrates, proteins, and fats) (for example, by absolute calories and/or by caloric proportion; for example, a meal of 550 kcal, can be expressed as having 150 calories of carbohydrates, 100 calories of proteins, 300 calories of fats and/or can be expressed in caloric proportions as 27% from carbohydrates, 18% from proteins, 55% from fats}, and
the evaluating the nutritional data further comprises comparing the total caloric content to a caloric content representative of the respective amounts of the plurality of macronutrients {Devries: fig 21; [0124] regression models are trained to predict for a meal: mass of carbohydrates intake, mass of proteins intake, and mass of fats intake, from which the total caloric intake can be calculated; [0188] FIG. 21 depicts a histogram, showing the number of measurements (i.e. meals) occurring for a given amount of error in caloric intake prediction (in this case, error is the prediction minus the true value of a meal, in units of kcal). Examiner interprets determining the error as comparing the actual (i.e., total) caloric content with the predicted (i.e., representative) caloric content}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included total caloric information with respective amounts of macronutrients and comparing predicted amounts with actual amounts as taught by Devries in the taste prediction system of Hadad, Pickelsimer, and Wilson in order to monitor and calculate nutrition-related metrics (Devries: [0031]).
Claim 38:
Hadad, Wilson, and Pickelsimer teach the system of claim 34. Hadad further discloses:
wherein at least one of the first statistical model and the second statistical model comprises a machine learning model using a set of data records having tastes associated therewith {Hadad: [0007] analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insights and allowing improved performance by means of "machine-learning,"}.
Although disclosing that the algorithms utilize machine learning methods, Hadad does not disclose:
a model that has been previously trained using a training set of data records having known tastes.
Hadad does disclose that the data records are associated with taste aspects and that the mathematical models can be updated (Hadad: [0049], [0043]).
However, Devries teaches:
a model that has been previously trained using a training set of data records having known tastes {Devries: [0130] In obtaining datapoints for a given nutrition-related metric (e.g. for training of a regression model 206 used by processing circuitry 56 to predict that nutrition-related metric), known values can be used (from an existing database) for food items consumed in the collection of data}.
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included training a model using known values as taught by Devries in the taste prediction system of Hadad and Pickelsimer in order to monitor and calculate nutrition-related metrics (Devries: [0031]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
NPL reference U teaches a diet tracking platform. Recommendations regarding diet may be provided to individuals. Intake of various consumables may be monitored.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA MAE MITROS whose telephone number is (571)272-3969. The examiner can normally be reached Monday-Friday from 9:30-6.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
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