US 2023/0095944 A1 is aimed specifically at Apple Pay/Wallet, but its core pattern—tag data on-device → index it locally → surface instant suggestions—closely resembles two App Store search features:
Trending searches (the 8–10 keywords that appear when you first open the Search tab)
“Search Hints” that autocomplete while you type
Apple never publishes a full search-ranking spec, yet several public clues point to a very similar architecture across Wallet, Mail, and the App Store:
| Source | Key take-aways |
|---|---|
| Apple Developer “App Store search” page | Mentions on-device models, natural-language queries, and app-level tags |
| iOS 18.1 (Oct 2024) release notes | “Search the way you talk” slogan—full-sentence queries now work |
| 2021 roll-out of “search suggestions” | Auto-completes refine “food” → “delivery / recipes / games,” etc. |
| 2024 ML paper (“On-Device Query Auto-Completion…”) | Confirms on-device neural autocomplete (example given for Mail) |
| Apple Privacy Report | Uses Differential Privacy to collect anonymized App Store metrics |
Putting the pieces together, a likely flow is:
- User starts typing
- The first characters are tokenized locally and run through an on-device autocomplete model, producing ultra-fast, private suggestions.
- Server-side re-ranking
- Simultaneously, popularity data, Search Ads insights, and daily “trending” lists arrive from Apple’s servers. Stats are sanitized via Differential Privacy before being used.
- Tag & LLM layer
- Each app now carries multi-word tags (“fitness • time-tracker • widget-support”) generated by Apple’s LLMs. These tags map natural-language queries to relevant apps and filter suggestions.
- Result merging
- The device merges local candidates with server candidates, scores them on-device, and shows the list—without sending the full, raw query off the phone until the user hits Search.
So while the patent is not a one-to-one blueprint, the “on-device tagging + private, low-latency suggestions” principle is clearly reused in App Store search.
How App Store Search Suggestions Work (ASO View)
| Stage | What actually happens? | Why it matters for ASO |
|---|---|---|
| Trending keywords | Region-specific list refreshed ~every 24 h | Sudden visibility spikes; rank if your app matches a hot term |
| Autocomplete / Search Hints | ML model combines typed prefix with high-probability multi-word phrases | Long-tail keywords are born here—exact matches in title/subtitle win |
| Natural-language intent | LLM parses full sentences (“apps that help me learn Spanish”) into search tokens | Descriptions with clear, semantically rich sentences feed the model |
| LLM-generated app tags | Apple derives tags from your metadata and surfaces them on the product page | Ensure your metadata forms a coherent theme, or irrelevant tags hurt CTR |
| Ranking signals | Text match + user behavior (install, retention, ratings) + core quality metrics | Retention and star ratings now influence even autocomplete ranking |
| Search Ads overlay | Paid ads can occupy a suggestion slot, labeled “Ad” | Plan organic + paid in tandem; Search Match piggybacks on the same model |
Practical Steps for Your App
Try deferred deep-links or promoted IAP: since iOS 18, search cards can show an IAP badge, lifting conversion by 5–8 %.
Place your two or three strongest keywords in the Title + Subtitle—autocomplete heavily weights the first ~28 characters.
In the 100-character keyword field, balance trending head terms with long-tail variants; log new suggestions often.
Write your Description in natural language (“Linearity Curve lets you create vector illustrations in seconds”) to reinforce LLM tags.
Optimize rating prompts; higher ★★★★☆–★★★★★ ratios boost your presence even inside autocomplete.
Track regional trends: in Turkey, weekend searches skew toward entertainment, weekdays toward productivity—adapt local ASA campaigns.
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