Overseas Food Ingredient Allergy Decoder

3.80

Derivation Chain

Step 1 Rapid growth of interest in Rakuten overseas direct purchase

→

Step 2 Increase in direct purchase of health foods among 50s

→

Step 3 Risk of allergies and drug interactions due to inability to read Japanese ingredient labels

Problem

When people in their 50s directly purchase health supplements and functional foods from Rakuten and others, they cannot accurately decipher Japanese ingredient labels. In particular, those in their 50s taking medication for chronic conditions may be exposed to health risks because certain ingredients (St. John's Wort, Coenzyme Q10, glucosamine, etc.) can interact with their medications, and they consume them unknowingly. The ingredient names of Japanese health foods differ from Korean notation, making accurate matching difficult even with translation tools.

Solution

Entering a Japanese health food product URL or a photo of the ingredient label translates the ingredients into Korean and cross-references them with the user's registered medication and allergy information, highlighting cautionary or contraindicated ingredients. It also indicates whether the ingredient is recognized as a health functional food by the MFDS.

Target: Ages 48-60, taking medication for chronic conditions (hypertension, diabetes, hyperlipidemia), with experience in direct purchasing Japanese health foods.

Revenue Model: Free basic (5 decodes per month), Premium Plan at 3,900 KRW/month (approx. $2.93) with unlimited decodes, family medication profile sharing, and ingredient change alerts.

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

4.0/5

M Market

3.0/5

R Realizability

4.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (64%)

Tech Complexity

24.0/40

Data Availability

20.0/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (53/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

7.5/15

Solo Buildability

7.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Frontend [medium] Backend [medium] AI/ML [medium]

Dashboard