Music-search-specific UI that surfaces related artist, album, and song links alongside the primary music search result, supporting discovery of adjacent musical content without forcing the user to issue follow-up queries.
Patent Overview
- Inventor
- Jeromy William Henry, others
- Assignee
- Google LLC
- Filed
- 2014-07-29
- Granted
- 2018-06-05
- Application Number
- US 14/445,961
The Challenge
The Challenge
Music queries are inherently relational. Searching for a song implies interest in the artist, the album, similar songs. A flat result list misses the relational structure of the music graph. The system needs music-specific UI surfacing the relevant adjacencies.
- Music Queries Imply Adjacency — A query for a song typically implies the user might also want the artist's other songs, the album it came from, similar artists. Standard results miss these adjacencies.
- Music Entities Form A Rich Graph — Artists, albums, songs, genres, collaborators all connect in a dense graph. Music search benefits from surfacing graph neighbors prominently.
- Follow-Up Queries Are Friction — Forcing users to re-query for each adjacency adds friction. Inline secondary links reduce the cost of exploration.
- Secondary Links Must Be Useful — Random adjacent links produce noise. The selected secondary links must be relevant, high-quality, and genuinely useful to typical music-search users.
- Layout Must Not Crowd Primary Result — The primary music result (the song, the artist, the album) must remain prominent. Secondary links support without overshadowing.
Innovation
How The System Works
The system identifies the primary music entity from the query, walks the music graph to find relevant adjacencies (artist, album, related songs, similar artists), scores each candidate for usefulness, selects top secondary links, and renders them in a designated secondary-link region alongside the primary result.
- Identify Primary Music Entity — Music-aware entity resolution maps the query to the primary entity: a song, artist, album, or genre. Confidence-gated; ambiguous cases fall back to standard search.
- Walk Music Graph For Adjacencies — From the primary entity, walk the music-graph edges: artist of song, songs on album, albums by artist, similar artists, related genres. Candidate adjacencies emerge.
- Score Candidate Links — Per adjacency, score on relevance, popularity, and historical user click patterns. High-engagement adjacencies score high.
- Filter For Diversity — Selected secondary links must cover diverse adjacency types. The user sees variety (artist + album + similar) rather than monotone repetition.
- Compose Secondary Link Region — The secondary-link UI region is composed: typically a row or grid of clickable cards each representing one adjacency. Layout adapts to surface.
- Render Alongside Primary — Primary music result remains the focal element. Secondary links render in a designated region without crowding the primary.
- Capture Engagement — Clicks on secondary links log per primary entity. Engagement patterns refine the scoring model continuously.
Music Graph As Discovery Surface
The patent's load-bearing idea is to use the rich relational structure of music entities to power inline discovery. Each music query becomes a launching point for exploring the surrounding musical neighborhood.
Adjacency Is Music's Native Pattern
Music users think relationally: artist to album to song to similar artist. The secondary-link UI matches this native pattern rather than treating each music entity as isolated.
- Music Graph Walk — Per primary entity, the system walks the music graph for relevant adjacencies. The graph is dense; many candidates emerge.
- Engagement-Scored Selection — Adjacencies score on engagement patterns. Historical user behavior tells the system which secondary links matter for which primaries.
- Diversity Enforcement — Selected links cover diverse adjacency types. Users see breadth, not redundancy.
Technical Foundation
Technical Foundation
The patent specifies the music entity resolver, the music graph store, the adjacency scorer, the diversity filter, the layout composer, and the engagement-feedback pipeline.
- Music Entity Resolver — Music-aware variant of standard entity resolution. Recognizes songs, artists, albums, genres with confidence thresholds.
- Music Graph Store — Dense graph database of music entities and their relationships. Walk queries return candidate adjacencies quickly.
- Adjacency Scorer — Per adjacency, scores on relevance, popularity, and click patterns. Learned model trained on engagement data.
- Diversity Filter — Ensures selected links cover diverse adjacency types: artist, album, songs, similar artists. Prevents monotone selection.
- Layout Composer — Composes the secondary-link region: card row, grid, or other layouts depending on surface and number of links.
- Engagement Pipeline — Click patterns on secondary links feed back into the scorer. Continuous learning improves selection over time.
The Process
The Process
The pipeline runs in the music-search query path. Latency overhead is small because graph walks are fast and scoring uses precomputed signals.
- Receive Music Query — Query identified as music-intent via classifier. Music-search pipeline activates.
- Resolve Primary Entity — Music entity resolver outputs the primary entity with confidence. Above-threshold cases proceed.
- Walk Graph — From the primary entity, the graph walker returns candidate adjacencies across the music graph.
- Score Candidates — Adjacency scorer outputs per-candidate scores. Sorts by score.
- Filter Diversity — Diversity filter ensures the selected set covers multiple adjacency types.
- Compose And Render — Secondary-link region renders alongside the primary music result. Layout adapts to surface.
- Track Engagement — Per-link clicks log. Engagement feeds scoring refinement.
Quality Control
Quality Control
Wrong secondary links degrade music search UX. The patent specifies safeguards.
- Entity Resolution Confidence — Music entity resolution must clear confidence threshold. Ambiguous cases fall back to standard search rather than displaying wrong secondary links.
- Source Authority — Music entity data comes from authoritative sources (music metadata services, verified artist profiles). Low-authority data is excluded.
- Diversity Enforcement — Selected links cover diverse adjacency types. Redundant selections are filtered.
- Engagement Validation — Selected links are validated against historical engagement. Poorly-performing patterns trigger scoring adjustment.
- Content Policy Compliance — Music content with policy violations is excluded from secondary links. Compliance is enforced at composition time.
Real-World Application
Music-specific secondary links appear in Google music-search results, YouTube Music surfaces, and the music sections in Knowledge Panels. The patent's primitives shape how Google supports musical discovery beyond the literal query.
- Graph-driven Selection Source — Secondary links come from music-graph adjacencies. The graph structure is the substrate for discovery.
- Engagement-scored Ranking Method — Adjacencies score on historical engagement. Real user behavior informs selection.
- Diversity-filtered Layout Coverage — Selected links cover diverse adjacency types. Users see breadth across the music graph neighborhood.
Why Verified Music Data Compounds Visibility
Music entities with verified, authoritative data (Schema.org MusicAlbum, MusicGroup, MusicRecording markup; verified profiles) feed clean signal to the secondary-link pipeline. Verified content surfaces more reliably in adjacency selections.
Why Music-Domain Sites Benefit From Schema Coverage
Sites covering music (lyrics, reviews, artist profiles) earn visibility on music secondary-link surfaces by feeding the music graph through structured markup. Coverage breadth across the music adjacency types compounds.
<\/section>What This Means for SEO
What This Means for SEO
The patent surfaces related artist, album, and song links beside the primary music result by walking the music graph. SEO implication: verified, well-marked-up music data feeds the adjacency pipeline, so structured coverage across music entity types compounds visibility on these discovery surfaces.
- Verified Music Data Compounds — Music entities with verified, authoritative data and Schema.org MusicGroup, MusicAlbum, and MusicRecording markup feed clean signal to the secondary-link pipeline. Verified, marked-up content surfaces more reliably in adjacency selections.
- Adjacency Coverage Breadth Wins — The system walks the music graph for artist, album, related songs, and similar artists. Sites covering breadth across these adjacency types (not just one) earn placement across more secondary-link surfaces. Comprehensive music coverage compounds.
- Music Is Inherently Relational — Music users think relationally from song to album to artist to similar artist. Structuring your music content to express these relationships explicitly aligns it with the secondary-link UI's native pattern.
- Clean Entity Resolution Is Required — The system must identify the primary music entity from the query before walking the graph. Unambiguous, consistent naming and verified profiles ensure your entities resolve correctly and become eligible for adjacency surfacing.
- Discovery Without Re-Querying — Secondary links let users explore the musical neighborhood without follow-up queries. Owning the adjacent entities to a popular one captures discovery traffic flowing from that focal entity's results.
- Schema Markup Is The Entry Ticket — Lyrics, reviews, and artist-profile sites earn visibility by feeding the music graph through structured markup. Without the markup, your music content is hard for the pipeline to select even when relevant.
- Usefulness Scoring Filters Candidates — Candidate secondary links are scored for usefulness before selection. Genuinely relevant, high-quality related content beats thin or tangential pages in the selection, so depth on each adjacency matters more than mere presence.