Apple’s patent describes classifying each app‑store search into types (e.g., navigational vs. functional vs. browse) and then switching the search/ranking technique accordingly. The system blends textual signals (exact matches in titles/metadata) with popularity/behavioral signals (clicks, downloads, purchases, ratings), and it may re‑weight these signals by query type. A core element is analyzing empirical distributions of which apps users downloaded after similar queries to decide the query type and influence ranking.
What the patent says (key points)
- Query categorization
The system analyzes the query terms, sometimes a preliminary search, and historical behavior for similar queries to assign a category—notably navigational, functional, or browse. Indicators include capitalization/quotes, shortness of query (navigational), and empirical patterns of downloads after similar queries. Categories can also include location‑based and price‑based in some embodiments. - Empirical data and LUTs
The backend maintains an empirical query database and a search‑term look‑up table (LUT) that store frequencies, CTR/BR, and distributions of downloads/purchases after prior searches. These stores are updated over time (e.g., down‑weight old data). - How categories are decided (example flow)
The patent details a process (Fig. 7) that:- counts unique apps downloaded after the query,
- checks whether the top app’s share exceeds a threshold,
- computes cumulative proportions and checks a threshold at the n‑th app,
to determine whether a query behaves more like navigational (tight distribution) or functional (long tail).
- Switching the search technique
Once categorized, the system chooses a search technique. For navigational queries, it emphasizes exact textual matches (e.g., multiple hits, title matches). For functional/browse, it emphasizes semantic similarity and popularity. The patent even gives an example weighting: textual signals might carry ~0.8 vs. 0.2 for others in navigational searches, but ~0.4 vs. 0.6 in functional searches. - Ranking signals the patent names
- Textual: counts of query terms in title/metadata, exact‑term matches.
- Popularity/behavioral: “clicks” (viewing more info), downloads, purchases, user reviews, avg/max rating—with their importance varying by category.
- CTR/BR‑style summaries (e.g., top/cumulative/exact‑term): stored per term in the LUT.
A ranking score can be generated from textual comparisons and then adjusted by other features (e.g., popularity).
- End‑to‑end flow (Fig. 5)
Receive query → analyze terms → assign category → choose search technique → search → return results. - Claim language that ties it together
The claims explicitly state that different categories map to different search techniques, that a “first technique emphasizes exact textual matches more than a second,” and that empirical data (e.g., other users’ downloads) is used in the analysis; the query’s capitalization/length/punctuation can also be considered.
What this implies for App Store search
While a patent isn’t a product spec, this document was filed by Apple and targets app‑search specifically, so it gives a strong architectural picture of how App Store search could operate:
- Two (or more) search modes under the hood. Entering “TikTok” likely triggers a navigational mode where exact title/metadata matches dominate ranking; entering “photo editor” likely triggers a functional mode where popularity/behavioral signals and semantic matches carry more weight. The patent’s own words: “Searches estimated to be for a specific app can return … exact‑term matches,” while functional searches return results “having same or similar terms … and high user popularity.”
- Behavior feeds the classifier and the ranker. The system observes what users actually download/click after similar queries to both classify the query type and inform ranking (via CTR/BR‑like statistics and popularity scores).
- Dynamic weighting. The patent explicitly contemplates re‑weighting textual vs. non‑textual (e.g., ratings, synonym detection) components by query type (e.g., 0.8/0.2 vs. 0.4/0.6). This is consistent with an App Store that feels “name‑precise” on branded queries but “reputation/fit‑heavy” on generic ones.
Practical ASO takeaways (inferred from the patent)
- Optimize for both “modes.”
- For navigational searches, ensure exact brand/product naming in the app title and key metadata (the patent credits repeated term occurrences and title matches).
- For functional/browse queries, build popularity and quality signals (better ratings, more reviews, actual downloads/purchases, good engagement), since these are emphasized more in that mode.
- Behavior matters. Improvements that lift CTR to product pages and conversion (downloads/purchases) on generic queries can shift the empirical distribution in your favor (and, per the patent, those empirical distributions are explicitly analyzed).
- Query features influence categorization. Capitalization/length/“versioning” words (e.g., pro, lite) and “browse” words (best, top) are treated as signals, so aligning how users mention your brand/features externally can influence how queries about you are classified.
Limitations to keep in mind
- This is a 2013 application describing an approach Apple can use; Apple may have evolved the system. Still, the classification‑plus‑reweighting architecture and reliance on empirical behavior are clearly and repeatedly claimed for app search, making it a reasonable lens to interpret App Store search behavior.
Short direct quote (illustrative)
“Search strategies can differentially weight and/or use text‑based strategies.”
The analysis “utilizes empirical data … [and] a first search technique emphasizes exact textual matches more than a second.”
These two lines capture the essence: learn from user behavior, classify the query, then re‑weight ranking signals accordingly.
If you’d like, I can turn this into a blog‑style explainer with diagrams of the Fig. 5 / Fig. 7 flows and an ASO checklist mapped to each signal the patent names.
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