PATENT CLAIM ANALYSIS

Application Number: 16258426
Application Type: Utility
Filing Date: 2019-01
Publication Date: 2019-07
Patent Classification: ["705", "026700"]

Abstract:
A method for improving food-related personalized for a user including determining food-related preferences associated with a plurality of users to generate a user food preferences database; collecting dietary inputs from a subject matter expert (SME) at an SME interface associated with the user food preferences database; determining personalized food parameters for the user based on the user food-related preferences and the dietary inputs; receiving feedback associated with the personalized food parameters from the user; and updating the user food preferences database based on the feedback.

Claim (Index 8):
The method of  claim 1 , wherein mapping food-related preferences to food parameters comprises:\n providing the food-related preferences to a trained neural network model as input vectors, wherein the trained neural network model comprises a plurality of neuronal layers that each transform the input vector received from the preceding neuronal layer of the plurality of neuronal layers into an intermediate vector provided as an input vector to a subsequent neuronal layer, wherein the food parameters define a vector in the recipe vector space equivalent to an intermediate vector transformed by one of the plurality of neuronal layers.

Metadata:
- Claim Count in Document: 21.0
- Percentile: 99.0
- Lexical Diversity: 2.13953
- Patent Class: 705.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['12117820', '15859062', '09583886', '15087857', '10797284']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1134078431342515
- 35 USC 102 Novelty (BERT): 0.4865245746546522
- Combined Prediction Score: 0.1507195162862916
- Mean Citation Score: 151.890462
- Max Citation Score: 155.25896
- Similarity Product: 98.65124662164212

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test