Patent application EP16201474A (EP), filed 2016-11-30
Publication number: EP3255565A1
Title: METHOD AND SYSTEM FOR EVALUATING AND RANKING IMAGES WITH CONTENT BASED ON SIMILARITY SCORES IN RESPONSE TO A SEARCH QUERY
Abstract: According to one embodiment, a request is received for evaluating matching between a content item and an image that were identified and selected from a plurality of content items and images in response to a search query. First metadata associated with the content item, second metadata associated with the image, and third metadata associated with the search query are obtained. A first similarity score is determined based on the first metadata associated with the content item and the third metadata associated with the search query. A second similarity score is determined based on the second metadata associated with the image and the third metadata associated with the search query. An evaluation score is calculated for matching of the content item and the image in view of the search query. The evaluation score is utilized to identify subsequent content items and images in response to a subsequent search query.
IPC codes: G06F  17/    30            A I
Claims: number: 1
text: A computer-implemented method for evaluating matching of content items with images, the method comprising:
number: 2
text: The method of claim 1, wherein determining a first similarity score based on the first metadata associated with the content item and the third metadata associated with the search query comprises:
number: 3
text: The method of claim 1, wherein determining a second similarity score based on the second metadata associated with the image and the third metadata associated with the search query comprises:
number: 4
text: The method of claim 1, further comprising determining a third similarity score based on the first metadata associated with the content item and the second metadata associated with the image, wherein the evaluation score is calculated based on the first similarity score, the second similarity score, and the third similarity score, preferably, determining a third similarity score based on the first metadata associated with the content item and the second metadata associated with the image comprises:
number: 5
text: The method of claim 1, wherein the content item and the image were identified and selected using a predetermined selection method in response to the search query, and wherein the evaluation score is utilized to adjust the predetermined selection method for selecting the subsequent content items and images.
number: 6
text: A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of evaluating matching of content items with images, the operations comprising:
number: 7
text: The machine-readable medium of claim 6, wherein determining a first similarity score based on the first metadata associated with the content item and the third metadata associated with the search query comprises:
number: 8
text: The machine-readable medium of claim 6, wherein determining a second similarity score based on the second metadata associated with the image and the third metadata associated with the search query comprises:
number: 9
text: The machine-readable medium of claim 6, wherein the operations further comprise determining a third similarity score based on the first metadata associated with the content item and the second metadata associated with the image, wherein the evaluation score is calculated based on the first similarity score, the second similarity score, and the third similarity score,
preferably, determining a third similarity score based on the first metadata associated with the content item and the second metadata associated with the image comprises:
number: 10
text: The machine-readable medium of claim 6, wherein the content item and the image were identified and selected using a predetermined selection method in response to the search query, and wherein the evaluation score is utilized to adjust the predetermined selection method for selecting the subsequent content items and images.
number: 11
text: A data processing system, comprising:
number: 12
text: The system of claim 11, wherein determining a first similarity score based on the first metadata associated with the content item and the third metadata associated with the search query comprises:
number: 13
text: The system of claim 11, wherein determining a second similarity score based on the second metadata associated with the image and the third metadata associated with the search query comprises:
number: 14
text: A computer-implemented method for matching content items with images, the method comprising:
number: 15
text: The method of claim 14, wherein determining a first similarity score based on the first metadata associated with the content item and the third metadata associated with the search query comprises:
number: 16
text: The method of claim 14, wherein determining a second similarity score based on the second metadata associated with the image and the third metadata associated with the search query comprises:
number: 17
text: The method of claim 14, further comprising determining a third similarity score based on the first metadata associated with the content item and the second metadata associated with the image, wherein the ranking score is calculated based on the first similarity score, the second similarity score, and the third similarity score,
preferably, determining a third similarity score based on the first metadata associated with the content item and the second metadata associated with the image comprises:
Office action: search_report, dated 2017-12-13
Prior art cited: ref_id: US2002038299A1
ref_type: patent
severity: background
ref_id: US8775436B1
ref_type: patent
severity: background
ref_id: - "Text Mining: Applications and Theory", 3 May 2010, WILEY, ISBN: 978-0-470-74982-1, article LOULWAH W ALSUMAIT ET AL: "Embedding Semantics in LDA Topic Models", pages: 183 - 204, XP055358152
ref_type: npl
severity: background
ref_id: US2002038299
ref_type: patent
severity: background
ref_id: US8775436
ref_type: patent
severity: background
ref_id: - Loulwah W Alsumait ET AL, "Embedding Semantics in LDA Topic Models", Text Mining: Applications and Theory, Wiley, (20100503), pages 183 - 204, ISBN 978-0-470-74982-1, XP055358152 [A] 2-4,7-9,12-17 * page  185  - page  186 *
ref_type: npl
severity: background
Final disposition: pending (2017-12-13)