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 .
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.
Claim(s) 1, 3-8, 10-15, and 17-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO2018/081175 A1 to Grimmer et al. in view of U.S. Patent Application Publication 20210319887 to Derrick et al. in view of U.S. Patent Application Publication 2021/0104173 to Pauley et al.
As to claims 1, 8 and 15 Grimmer discloses a method of monitoring risk and recommending dietary intake for prediabetes via a personalized digital platform, the method comprising:
generating, by a computing device having one or more processors and via an application programming interface, an application for assessing a user-specific risk, wherein the application prompts entry of user attributes to assess the user-specific risk (Grimmer [0087] see “The user may be prompted to enter this data or may be given a web page with drop down menus to use to describe regular or one time activities.” and [0160] see risk factors);
receiving, by the computing device and from a user device associated with a user, one or more user attributes of the user to assess the user-specific risk for the user (Grimmer [0105] see blood samples provide insight);
generating, based on a vectorization of the one or more user attributes, a feature vector associated with the user (Grimmer [0155] see “The recommendations are essentially vectors that correlate relevant macronutrients or micronutrients with a level or range for each user. In the case of macronutrients, the user's vector includes values as shown for Carbohydrates, Fats and Protein.”);
performing, by a computing device, a clustering of a plurality of feature vectors comprising the feature vector associated with the user and a plurality of reference feature vectors (Grimmer [0229] see classifiers);
determining, based on the clustering, a risk level of the user (Grimmer [0229] see classifiers); and
generating, by the computing device and based on the risk level of the user and the one or more user attributes, a recommendation for a dietary intake for the user (Grimmer [0102] see “to some embodiments of the invention, a user's diet type and recommended meals and foods are based on an individualized determination of each user's macronutrient and micronutrient needs. Referring to Figure 10, these needs are determined by receiving vitals 1002, phenotype 1004 and genotype 1006 data from each user”).
filtering, by the computing device, a plurality of recipes stored in a recipe database based on the one or more user attributes including dietary preferences, sensitivities, or allergies (Grimmer [0093]-[0095] see filtering recipes by user attributes) ;
identifying, from among the filtered recipes, one or more recipes that satisfy nutritional rules comprising at least a caloric threshold, and fiber minimum (Grimmer [0094] and [0176]); and
assembling the one or more recipes into a personalized dietary intake recommendation (Grimmer [0182])
outputting, by the computing device, the dietary intake recommendation to the user device for display via the application (Grimmer [0182]).
However, Grimmer and Pauley do not explicitly teach
determining, based on a number of prediabetes risk levels, a number of clusters to be formed;
performing one or more iterations of: selecting, for each cluster of the number of clusters to be formed, a centroid feature vector among the plurality of feature vectors comprising the feature vector associated with the user and the plurality of reference feature vectors; and
determining the average distance of each of the plurality of feature vectors; identifying, based on a set of centroids that minimize the average distance, feature vectors belonging to each cluster of the number clusters;
determining a prediabetes risk level associated with each cluster;
assembling a treatment recommendation into a personalized recommendation associated with the prediabetes risk level of the user.
Derrick discloses
determining, based on a number of prediabetes risk levels, a number of clusters to be formed [0284]-[0286]);
performing one or more iterations of: selecting, for each cluster of the number of clusters to be formed, a centroid feature vector among the plurality of feature vectors comprising the feature vector associated with the user and the plurality of reference feature vectors (Derrick [0223]-[0227]); and
determining the average distance of each of the plurality of feature vectors (Derrick [0223]-[0227]);
identifying, based on a set of centroids that minimize the average distance, feature vectors belonging to each cluster of the number clusters (Derrick [0223]-[0227]);
determining a prediabetes risk level associated with each cluster (Derrick [0510]);
assembling a treatment recommendation into a personalized recommendation associated with the prediabetes risk level of the user (Derrick paragraphs [0284]-[0286]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to utilize a clustering algorithm as in Derrick in the system of Grimmer to improve the accuracy of the recommendations.
However, Derrick and Grimmer do not consider a glycemic load. Pauley discloses a determining, for each dietary intake option, a glycemic load (Pauley [0139]-[0141]); and comparing the glycemic load of each dietary intake option with a threshold glycemic load associated with the risk level of the user (Pauley [0139]-[0141]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to consider the effect of the glycemic load of foods as in Pauley in the system of Grimmer and Derrick to better treat a patient.
As to claim 3 and 10 see the discussion of claim 1, additionally, Grimmer discloses the method wherein the receiving the one or more user attributes comprises:
sending, to the user device via the application, a message requesting the user to input physiological data (Grimmer [0102]); and
receiving, from the user device, the physiological data (Grimmer [0102]).
As to claim 4, 11, and 17, see the discussion of claim 1, additionally, Grimmer discloses the method further comprising:
determining, based on the risk level of the user and the one or more user attributes, a set of user-specific products, wherein the generating the recommendation for the dietary intake comprises one or more user specific products from the set of user-specific products (Grimmer [0102]).
As to claim 5, 12, and 18, see the discussion of claim 4, additionally, Grimmer discloses the method further comprising:
sending, to the user device via the application, a message requesting the user to input one or more of a preferred diet (Grimmer [0015]), and
filtering, from the set of user-specific products, a user-specific product based on the one or more of the preferred diet (Grimmer [0015])
As to claim 6, 13, and 19, see the discussion of claim 1, additionally, Grimmer discloses the method further comprising:
generating, by the computing device based on the one or more user attributes of the user, dietary intake options for the user (Grimmer [0015]); and
filtering, based on the risk level of the user, the dietary intake options for the user to generate the recommendation for the dietary intake for the user (Grimmer [0015]).
As to claim 7, 14, and 20, see the discussion of claim 6, additionally, Pauley discloses the method wherein the filtering the dietary intake options comprises:
determining, for each dietary intake option, a glycemic load (Pauley [0139]-[0141]); and
comparing the glycemic load of each dietary intake option with a threshold glycemic load associated with the risk level of the user (Pauley [0139]-[0141]).
As to claims 21-23, see the discussion of claim 1, additionally, Grimmer discloses wherein user attributes consist of comorbidities (Grimmer [0010] and claim 16).
Response to Arguments
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Eliza Lam whose telephone number is (571)270-7052. The examiner can normally be reached Monday-Friday 8-4:30PST.
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/ELIZA A LAM/Primary Examiner, Art Unit 3681