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US Patent 11348664 Machine learning driven chemical compound replacement technology

Patent 11348664 was granted and assigned to NotCo on May, 2022 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
NotCo
NotCo
Current Assignee
NotCo
NotCo
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11348664
Patent Inventor Names
Aadit Patel0
Karim Pichara0
Kavitakumari Solanki0
Kyohei Kaneko0
Nathan O'Hara0
Isadora Nun0
Date of Patent
May 31, 2022
Patent Application Number
17351069
Date Filed
June 17, 2021
Patent Citations
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US Patent 10915818 Latent space method of generating food formulas
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US Patent 10957424 Neural network method of generating food formulas
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US Patent 10962473 Protein secondary structure prediction
0
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US Patent 10984145 Using machine learning to explore formulations recipes with new ingredients
0
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US Patent 10970621 Methods to predict food color and recommend changes to achieve a target food color
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US Patent 10993465 Method of classifying flavors
0
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US Patent 11164069 Controllable formula generation
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US Patent 11164478 Systems and methods to mimic target food items using artificial intelligence
0
...
Patent Citations Received
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US Patent 11982661 Sensory transformer method of generating ingredients and formulas
0
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US Patent 11631034 Method of classifying flavors
0
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US Patent 11644416 Protein secondary structure prediction
0
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US Patent 11837327 System and method for protein selection
0
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US Patent 11922164 System and method for sensory characterization
0
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US Patent 11514350 Machine learning driven experimental design for food technology
Patent Primary Examiner
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Russell S Negin
CPC Code
‌
G16C 20/20
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G16C 20/50
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G06N 3/04
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G06N 3/08
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G16B 40/00
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G16B 99/00
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G16C 20/70
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G16C 20/40

Techniques to suggest alternative chemical compounds that can be used to recreate or mimic a target flavor using artificial intelligence are disclosed. A neural network based model is trained on source chemical compounds and their corresponding flavors and odors. The neural network-based model learns compound embeddings of the source chemical compounds and a target chemical compound of a food item. From the compound embeddings, one or more chemical compounds that are closest to the target chemical compound may be determined by a distance metric. Each suggested chemical compound is an alternative that can be used to recreate functional features of the target chemical compound.

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