DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/20/2025, 08/11/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are considered by examiner.
Claim Objections
Claims 1, 12 are objected to because of the following informalities: In Claims 1 and 12 the punctuation for the list of steps performed by the computing device is currently shown as a comma and shou. Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 11-13, 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Short et al (US 2024/0288995).
Regarding Claim 1, Short et al teach a computing device (server 130; Fig 1-3 and ¶ [0031]-[0033]) comprising: a memory storing computer readable instructions (memory 220-2 that stores instructions; ¶ [0031]); and processing circuitry configured to execute the computer readable instructions (processor 212-2 executes instructions; ¶ [0031-[0032]) to cause the computing device to,
obtain at least one image associated with at least one user (the receiving module 310 receives a video input from a user; Fig 3 and ¶ [0034]),
recognize at least one object included in the at least one image using image analysis (the identifying module 320 identifies objects in the image data; Fig 3 and ¶ [0035]-[0036]),
determine at least one theme from a plurality of themes based on the recognized at least one object (the identifying module 320 is a CNN model that performs image classification to identify content, which context determination module 340 further refines to determine context, such as themes; Fig 3 and ¶ [0036]-[0038], [0043]), and
provide at least one recommendation to the at least one user based on the at least one theme (the recommendation module 350 generates a recommendation aligned with the contextual information of the object from the image, input by the user; Fig 3 and ¶ [0045]-[0046]).
Regarding Claim 2, Short et al teach the computing device of claim 1 (as described above), wherein the processing circuitry is further configured to execute the computer readable instructions (processor 212-2 executes instructions; ¶ [0031-[0032]) to cause the computing device to:
obtain a plurality of images from a source of images associated with the at least one user, the plurality of images including the at least one image (input from user may be video (plurality of images); ¶ [0034]); and
receive at least one user input from the user, the at least one user input selecting the at least one image from the plurality of images (user may interact with objects, through interaction and is interpreted as selecting data as a creator; ¶ [0034]-[0035]).
Regarding Claim 11, Short et al teach the computing device of claim 1 (as described above), wherein the processing circuitry is further configured to execute the computer readable instructions (processor 212-2 executes instructions; ¶ [0031-[0032]) to cause the computing device to: receive a user input indicating creation of a new theme from the user (the user may make selections for recommendations for similar content (to the input); ¶ [0052]); provide a plurality of themes to the user (the display module 370 may display a plurality of recommendations of themes to the user; ¶ [0053]]); receive a selection of at least two themes of the plurality of themes in response to the providing of the plurality of themes (the user may select multiple theme recommendations based on the displayed recommendations; ¶ [0053]-[0054]); and generate a new theme based on the selection of the at least two themes (based on the selected themes, new objects may be generated and recommended to user based on selections; ¶ [0053]-[0054]).
Regarding Claim 12, Short et al teach a method of operating a computing device (processor 212-2 executes instructions to perform image analysis; ¶ [0031-[0032]) comprising: steps identical to claim 1 (as described above).
Regarding Claim 13, Short et al teach the method of claim 12 (as described above) with further limitations claimed in parallel to claim 2 (as described above).
Regarding Claim 22, Short et al teach the method of claim 12 (as described above) with further limitations claimed in parallel to claim 11 (as described above).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Short et al (US 2024/0288995) in view of Price et al (US 2020/0380285).
Regarding Claim 3, Short et al teach the computing device of claim 1 (as described above), including the processing circuitry to execute the computer readable instructions (processor 212-2 executes instructions; ¶ [0031-[0032]) to recognize the at least one object included in the at least one image by, for each image of the at least one image, identifying each object included in the respective image (the identifying module 320 identifies objects in the image data; Fig 3 and ¶ [0035]-[0036])
Short et al does not teach the identifying including calculating a confidence value associated with each identified object; and for each identified object, determining an object name corresponding to the respective identified object in response to the confidence value associated with the respective identified object satisfying a desired threshold confidence value, and associating the respective identified object with the respective image as the recognized at least one object associated with the respective image.
Price et al is analogous art pertinent to the technological problem addressed in the current application and teaches calculating a confidence value associated with each identified object (the object identified in the image 501 is assigned a confidence value 510 associated with the identification 520; Fig 5 and ¶ [0049], [0058]); and
for each identified object, determining an object name corresponding to the respective identified object in response to the confidence value associated with the respective identified object satisfying a desired threshold confidence value (the confidence value is associated with an identity (name) of the object based on the identified object attributes and may use a threshold 535 is determining the identity; ¶ [0049], [0058]-[0060]), and
associating the respective identified object with the respective image as the recognized at least one object associated with the respective image (the data may also be based on historical results for a particular user (associating a given object and identification with a given image of a given user); ¶ [0063]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Short et al with Price et al including calculating a confidence value associated with each identified object; and for each identified object, determining an object name corresponding to the respective identified object in response to the confidence value associated with the respective identified object satisfying a desired threshold confidence value, and associating the respective identified object with the respective image as the recognized at least one object associated with the respective image. By using confidence values and thresholds to the determination of the object, object identification is improved through enhanced accuracy while optimizing computing resources, as recognized by Price et al (¶ [0004]-[0005]).
Regarding Claim 14, Short et al teach the method of claim 12 (as described above) with further limitations claimed in parallel to claim 3 (as described above).
Claims 10, 21 are rejected under 35 U.S.C. 103 as being unpatentable over Short et al (US 2024/0288995) in view of Manggala (US 2021/0034682).
Regarding Claim 10, Short et al teach the computing device of claim 1 (as described above), including the processing circuitry to execute the computer readable instructions (processor 212-2 executes instructions; ¶ [0031-[0032]) to recognize the at least one object included in the at least one image by, for each image of the at least one image, identifying each object included in the respective image (the identifying module 320 identifies objects in the image data; Fig 3 and ¶ [0035]-[0036])
Short et al does not teach to obtain at least one second image associated with at least one second user, recognize at least one second object included in the at least second one image using image analysis, determine the at least one theme from the plurality of themes based on the recognized at least one object and the recognized at least one second object, and provide the at least one recommendation to the at least one user and the at least one second user based on the at least one theme.
Manggala is analogous art pertinent to the technological problem addressed in the current application and teaches to obtain at least one second image associated with at least one second user (a second user can select an image from an online store 138; ¶ [0047]),
recognize at least one second object included in the at least second one image using image analysis (the image may contain a theme and objects which is then identified by the image processing model 304; ¶[0047]-[0048]),
determine the at least one theme from the plurality of themes based on the recognized at least one object and the recognized at least one second object (a theme may be identified based on the analysis of the attributes of the image; ¶ [0047]-[0048]), and
provide the at least one recommendation to the at least one user and the at least one second user based on the at least one theme (the recommendation engine 302 may provide a recommendation to the given first or second user and the theme may contain a characteristic similarity between the two users based on analyzed metadata; ¶ [0046]-[0047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Short et al with Manggala including obtain at least one second image associated with at least one second user, recognize at least one second object included in the at least second one image using image analysis, determine the at least one theme from the plurality of themes based on the recognized at least one object and the recognized at least one second object, and provide the at least one recommendation to the at least one user and the at least one second user based on the at least one theme. By providing a second user an analysis of image data for theme content based on the image content, a topical interest between users may be identified for a given theme, thereby improving potential recommendations and associating themes with image data, as recognized by Manggala (¶ [0003]).
Regarding Claim 21, Short et al teach the method of claim 12 (as described above) with further limitations claimed in parallel to claim 10 (as described above).
Allowable Subject Matter
Claims 4-9, 15-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding Claim 4, 15, the following limitations in combination with the claims in which it depends on were not readily identified to be taught, suggested or provided motivation to combine the prior art in a non-obvious manner (claim 4 cited below and claimed in parallel for claim 15):
The computing device of claim 3, wherein the plurality of themes are each associated with a set of keywords; and the processing circuitry is further configured to execute the computer readable instructions to cause the computing device to determine the at least one theme based on the recognized at least one object by:
determining a relevance score using a natural language processing model between each recognized object associated with the at least one image and each theme of the plurality of themes based on the determined object name of the respective recognized object and the keywords associated with the respective theme; and
determining the at least one theme associated with the at least one image based on the determined relevance scores.
Claims 5-9 are dependent on claim 4 and therefore allowable for similar reasons.
Claims 16-20 are dependent on claim 15 and therefore allowable for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Freund et al (US 2015/0058079) teach a method and system for detecting trends from images of users uploaded to a social network including detecting a subject and objects within the image and determining a trend associated with the relevant time period of the images.
Cunico et al (US 2016/0350332) teach a method and system for individualized on-demand image acquisition including identifying object data within an image and associating the data with a user and preferences of the user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHLEEN M BROUGHTON whose telephone number is (571)270-7380. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
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