IP Library › Granted Patent US 12,602,147
Granted Patent B2
US 12,602,147 · App. 17/664,710 · Granted Apr 14, 2026

Zoom action based image presentation

Inventors: Tushar Agrawal (West Fargo, ND); Jeremy R. Fox (Georgetown, TX); Martin G. Keen (Cary, NC); Sarbajit K. Rakshit (Kolkata, IN)
Assignee: International Business Machines Corporation
G06F3/04845G06F3/0488G06F2203/04806
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Quick Facts
Patent No.
US 12,602,147
App. No.
17/664,710
Granted
Apr 14, 2026
Kind
B2
Abstract

Aspects of the present disclosure relate to zoom action based image presentation. A zoom action on a first image can be received on a user device, the zoom action defined by a set of zoom action parameters. The first image can be analyzed to determine image properties of the first image. A second image depicting a different structural level of the first image can be searched for based on the zoom action parameters of the zoom action and the image properties of the first image. The second image can be presented to the user as a response to the received zoom action.

Claims (40)

1 . A method comprising:

receiving, on a user device, a zoom action on a first image, the zoom action defined by a set of zoom action parameters;

analyzing, responsive to receiving the zoom action, the first image to determine image properties of the first image;

searching, within an image datastore, for a second image depicting a different structural level of the first image based on the zoom action parameters of the zoom action and the image properties of the first image;

modifying the second image to generate a third image based on the image properties of the first image; and

presenting the third image to the user as a response to the received zoom action.

2 . The method of claim 1 , wherein the second image is selected from a plurality of candidate images within the image datastore.

3 . The method of claim 1 , wherein the second image is modified to generate the third image using a generative adversarial network (GAN).

4 . The method of claim 1 , wherein the zoom action parameters include a type of zoom action, a magnitude of zoom action, and a location of zoom action within the first image.

5 . The method of claim 4 , wherein determining image properties of the first image includes:

determining a set of objects within the first image; and

determining color data associated with the first image.

6 . The method of claim 5 , wherein the second image is searched for using an object of the set of objects within the first image where the zoom action was executed as indicated by the location of the zoom action.

7 . A system comprising:

one or more processors; and

one or more computer-readable storage media collectively storing program instructions which, when executed by the one or more processors, are configured to cause the one or more processors to perform a method comprising:

receiving, on a user device, a zoom action on a first image, the zoom action defined by a set of zoom action parameters;

analyzing, responsive to receiving the zoom action, the first image to determine image properties of the first image;

searching, within an image datastore, for a second image depicting a different structural level of the first image based on the zoom action parameters of the zoom action and the image properties of the first image;

modifying the second image to generate a third image based on the image properties of the first image; and

presenting the third image to the user as a response to the received zoom action.

8 . The system of claim 7 , wherein the second image is selected from a plurality of candidate images within the image datastore.

9 . The system of claim 7 , wherein the second image is modified to generate the third image using a generative adversarial network (GAN).

10 . The system of claim 7 , wherein the zoom action parameters include a type of zoom action, a magnitude of zoom action, and a location of zoom action within the first image.

11 . The system of claim 7 , wherein determining image properties of the first image includes:

determining a set of objects within the first image; and

determining color data associated with the first image.

12 . The system of claim 11 , wherein the second image is searched for using an object of the set of objects within the first image where the zoom action was executed as indicated by a location of the zoom action.

13 . A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors to perform a method comprising:

receiving, on a user device, a zoom action on a first image, the zoom action defined by a set of zoom action parameters;

analyzing, responsive to receiving the zoom action, the first image to determine image properties of the first image;

searching, within an image datastore, for a second image depicting a different structural level of the first image based on the zoom action parameters of the zoom action and the image properties of the first image;

modifying the second image to generate a third image based on the image properties of the first image; and

presenting the third image to the user as a response to the received zoom action.

14 . The computer program product of claim 13 , wherein the second image is selected from a plurality of candidate images within the image datastore.

15 . The computer program product of claim 13 , wherein the second image is modified to generate the third image using a generative adversarial network (GAN).

16 . The computer program product of claim 13 , wherein determining image properties of the first image includes:

determining a set of objects within the first image; and

determining color data associated with the first image.

17 . The computer program product of claim 16 , wherein the second image is searched for using an object of the set of objects within the first image where the zoom action was executed as indicated by a location of the zoom action.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 24, 2022
From: AGRAWAL, TUSHAR; FOX, JEREMY R.; KEEN, MARTIN G.; RAKSHIT, SARBAJIT K.
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 059998/0782 →
Continuity (1)
Related Publication 20230384917A1 · Nov 30, 2023
References Cited (28)
US 9342862B2 · Cristescu · 2016 [cited by examiner]
US 10628931B1 · Ramos · 2020 [cited by applicant]
US 10922793B2 · Baek · 2021 [cited by applicant]
US 11222415B2 · Ozcan · 2022 [cited by applicant]
US 20190138030A1 · Wu · 2019 [cited by examiner]
US 20190156120A1 · Lorenzo · 2019 [cited by examiner]
US 20190327413A1 · Lorenzo · 2019 [cited by examiner]
US 20210166807A1 · Quennesson · 2021 [cited by examiner]
US 20220343834A1 · Fischer · 2022 [cited by examiner]
CN 110415194A · 2019 [cited by applicant]
CN 111091151 · 2021 [cited by applicant]
Ehrenfeucht A, Rozenberg G. Standard and ordered zoom structures. Theoretical Computer Science. Dec. 10, 2015;608:4-15. [cited by examiner]
De Luca F, Hossain I, Gray K, Kobourov S, Borner K. Multi-level tree based approach for interactive graph visualization with semantic zoom. arXiv preprint arXiv:1906.05996. Jun. 14, 2019. [cited by examiner]
Martins R, Lourenco N, Horta N. Laygen II—Automatic layout generation of analog integrated circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. Oct. 16, 2013;32(11):1641-54. [cited by examiner]
Chen L, Fang Z, Fu Y. Consistency-Aware Map Generation at Multiple Zoom Levels Using Aerial Image. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Apr. 27, 2022;15:5953-66. [cited by examiner]
Kuperstein I, Cohen DP, Pook S, Viara E, Calzone L, Barillot E, Zinovyev A. NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps. BMC systems biology. Dec. 2013;… [cited by examiner]
Gibin M, Singleton A, Milton R, Mateos P, Longley P. An exploratory cartographic visualisation of London through the Google Maps API. Applied Spatial Analysis and Policy. Jul. 2008;1:85-97. [cited by examiner]
Kuperstein I, Bonnet E, Nguyen HA, Cohen D, Viara E, Grieco L, Fourquet S, Calzone L, Russo C, Kondratova M, Dutreix M. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer d… [cited by examiner]
Hudson-Smith A, Batty M, Crooks A, Milton R. Mapping for the masses: Accessing Web 2.0 through crowdsourcing. Social science computer review. Nov. 2009;27(4):524-38. [cited by examiner]
Jahanian A, Chai L, Isola P. On the“steerability” of generative adversarial networks. InInternational Conference on Learning Representations Sep. 25, 2019. [cited by examiner]
Alicioglu G, Sun B. A survey of visual analytics for explainable artificial intelligence methods. Computers & Graphics. Feb. 1, 2022; 102:502-20. [cited by examiner]
Lukac R, Martin K, Platanoitis KN. Digital camera zooming based on unified CFA image processing steps. IEEE Transactions on Consumer Electronics. Feb. 2004;50(1):15-24. [cited by examiner]
Fatima N. AI in photography: scrutinizing implementation of super-resolution techniques in photo-editors. In2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) Nov. 25, 2020 (pp. 1-6). I… [cited by examiner]
Greg Heinrich, Photo Editing with Generative Adversarial Networks (Parts 1 & 2), .2017. https://developer.nvidia.com/blog/photo-editing-generative-adversarial-networks-1/ and https://developer.nvidia.com/blog/photo-edit… [cited by examiner]
BMW Group IBM, “Gaining Control Over Software Quality With IBM Software”, Downloaded Mar. 29, 2022, 5 PGS, <https://www.ibm.com/case-studies/d421912w54680d62>. [cited by applicant]
Lei.,“Generative Adversarial Network Technology: AI Goes Mainstream”, Servers & Storage, Downloaded Mar. 29, 2022, 7 PGS,, <https://www.ibm.com/blogs/systems/generative adversarial-network-technology-ai-goes-mainstream/… [cited by applicant]
Mell et al., “The NIST Definition of Cloud Computing”, National Institute of Standards and Technology, Special Publication 800-145, Sep. 2011, 7 pages. [cited by applicant]
Mitra et al., “A Generative Approach to Visualizing Satellite Data,” 2021 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, 2021, DOI: 10.1109/Cluster 48925.2021.00079, 2 PGS. [cited by applicant]