{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3LHPAGQ5MD42NEXQRKEJKQJMOQ","short_pith_number":"pith:3LHPAGQ5","schema_version":"1.0","canonical_sha256":"dacef01a1d60f9a692f08a8895412c7409782e80fa04771c6aa9a989a8fe73a4","source":{"kind":"arxiv","id":"2508.15297","version":1},"attestation_state":"computed","paper":{"title":"DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Homaira Huda Shomee, Sathya N. Ravi, Sourav Medya, Zhu Wang","submitted_at":"2025-08-21T06:36:24Z","abstract_excerpt":"In the field of design patent analysis, traditional tasks such as patent classification and patent image retrieval heavily depend on the image data. However, patent images -- typically consisting of sketches with abstract and structural elements of an invention -- often fall short in conveying comprehensive visual context and semantic information. This inadequacy can lead to ambiguities in evaluation during prior art searches. Recent advancements in vision-language models, such as CLIP, offer promising opportunities for more reliable and accurate AI-driven patent analysis. In this work, we lev"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.15297","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-21T06:36:24Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eba78b9d2b1d8d1a6ac40cb926376b473552d765856889de98bc76654e5dcec8","abstract_canon_sha256":"038e777af4bd59c84ca5ca1bf29386789ecc27e83a5b303b1f34b8e47a4ba65e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:09.752423Z","signature_b64":"8gAoIDWwJD5vdYR018lIBQ/PvAT9X0dgx26sMK/CWOEFnkKDAJyJR8YDMCA3u9LfZWec/8e4yQwL93+tS9NaBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dacef01a1d60f9a692f08a8895412c7409782e80fa04771c6aa9a989a8fe73a4","last_reissued_at":"2026-07-05T11:57:09.751966Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:09.751966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Homaira Huda Shomee, Sathya N. Ravi, Sourav Medya, Zhu Wang","submitted_at":"2025-08-21T06:36:24Z","abstract_excerpt":"In the field of design patent analysis, traditional tasks such as patent classification and patent image retrieval heavily depend on the image data. However, patent images -- typically consisting of sketches with abstract and structural elements of an invention -- often fall short in conveying comprehensive visual context and semantic information. This inadequacy can lead to ambiguities in evaluation during prior art searches. Recent advancements in vision-language models, such as CLIP, offer promising opportunities for more reliable and accurate AI-driven patent analysis. In this work, we lev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.15297","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.15297/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.15297","created_at":"2026-07-05T11:57:09.752020+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.15297v1","created_at":"2026-07-05T11:57:09.752020+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.15297","created_at":"2026-07-05T11:57:09.752020+00:00"},{"alias_kind":"pith_short_12","alias_value":"3LHPAGQ5MD42","created_at":"2026-07-05T11:57:09.752020+00:00"},{"alias_kind":"pith_short_16","alias_value":"3LHPAGQ5MD42NEXQ","created_at":"2026-07-05T11:57:09.752020+00:00"},{"alias_kind":"pith_short_8","alias_value":"3LHPAGQ5","created_at":"2026-07-05T11:57:09.752020+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.15295","citing_title":"Contribution of Globular Clusters to Diffuse Gamma-ray Emission from Galactic Plane","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ","json":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ.json","graph_json":"https://pith.science/api/pith-number/3LHPAGQ5MD42NEXQRKEJKQJMOQ/graph.json","events_json":"https://pith.science/api/pith-number/3LHPAGQ5MD42NEXQRKEJKQJMOQ/events.json","paper":"https://pith.science/paper/3LHPAGQ5"},"agent_actions":{"view_html":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ","download_json":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ.json","view_paper":"https://pith.science/paper/3LHPAGQ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.15297&json=true","fetch_graph":"https://pith.science/api/pith-number/3LHPAGQ5MD42NEXQRKEJKQJMOQ/graph.json","fetch_events":"https://pith.science/api/pith-number/3LHPAGQ5MD42NEXQRKEJKQJMOQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ/action/storage_attestation","attest_author":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ/action/author_attestation","sign_citation":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ/action/citation_signature","submit_replication":"https://pith.science/pith/3LHPAGQ5MD42NEXQRKEJKQJMOQ/action/replication_record"}},"created_at":"2026-07-05T11:57:09.752020+00:00","updated_at":"2026-07-05T11:57:09.752020+00:00"}