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 .
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 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chang et al. (US 20260119514 A1), hereinafter referenced as Chang, in view of Menges et al. (US 20250086865 A1), hereinafter referenced as Menges.
Regarding Claim 1, Chang discloses an information handling system (Chang, [0093]: teaches computing resource 600 <read on information handling system>) comprising:
a memory (Chang, [0093]: teaches computing resource 600 including memory 605); and
a processor communicatively coupled to the memory, and configured to (Chang, [0093]: teaches computing resource 600 including "a CPU <read on processor> for processing data and computer-readable instructions"):
receive an input asset from a user (Chang, [0043]: teaches retrieving visual content items <read on input asset> that are responsive to user queries);
[[apply one or more comprehension models to the input asset to]] determine one or more characteristics associated with the input asset (Chang, [0049]: teaches a generated decision tree associated with content item 250 <read on input asset>, where each child node of the decision tree is associated with "particular feature, aspect, characteristic, etc. of the parent node to which it is directly connected");
based on the one or more characteristics, place the input asset into one or more virtual mood boards (Chang, [0060]: teaches determining "content items <read on input asset> from a corpus of content items to be presented at content item positions 324 <read on virtual mood boards>," which are based on queries related to retrieved visual content items);
receive and aggregate contextual information regarding the user (Chang, [0047]: teaches text-based user summary 254 <read on contextual information of user> includes "information extracted and aggregated from user information maintained by an online service"); and
apply a language model to a combination of the one or more virtual mood boards and the contextual information to create one or more generative artificial intelligence prompts (Chang, [0046]: teaches generating a generative model prompt 256 <read on generative AI prompt> based on content item 250 for generative model 260 <read on language model> to process, where content item 250 includes categories or labels associated with it, which are from text-based information 252 <read on contextual information>; [0064]: teaches "based on the user's responses to the questions of the questionnaire, quiz, survey, poll, etc., the conclusion summary, and/or inference relating to the user's response may be determined and presented to the user on client device 330 via user interface 332-5" as shown in FIG. 3C, which shows a combination of both images <read on virtual mood boards> and text information of the user based on the user's response).
PNG
media_image1.png
406
538
media_image1.png
Greyscale
However, Chang does not expressly disclose
apply one or more comprehension models to the input asset to determine one or more characteristics associated with the input asset.
Menges discloses
apply one or more comprehension models to the input asset to determine one or more characteristics associated with the input asset (Menges, [0113]: teaches a generative design system 300 <read on comprehension models> that "identifies a set of characteristics shared by the set of seed images <read on input asset>," such as "the set of seed images have a similar subject matter or theme, have a similar color palette, have a similar setting, have a similar artistic style, or share any other characteristic, trait, or property (e.g., the seed images share one or more “positive signals”)").
Menges is analogous art with respect to Chang because they are from the same field of endeavor, namely using generative models for image generation. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a generative system that automatically determines and categorizes sets of images as taught by Menges into the teaching of Chang. The suggestion for doing so would allow for more accurate categorized images that can be used in parallel with a generative multimodal system, such as presenting sets of relevant images to the user, thereby yielding predictable results. Therefore, it would have been obvious to combine Menges with Chang.
Regarding Claim 7, it recites the limitations that are similar in scope to Claim 1, but in a method. As shown in the rejection, the combination of Chang and Menges discloses the limitations of Claim 1. Additionally, Chang discloses a method (Chang, [0099]: teaches a computer method) comprising:…
Thus, Claim 7 is met by Chang according to the mapping presented in the rejection of Claim 1, given the information handling system corresponds to a method.
Regarding Claim 13, it recites the limitations that are similar in scope to Claim 1, but in an article of manufacture. As shown in the rejection, the combination of Chang and Menges discloses the limitations of Claim 1. Additionally, Chang discloses an article of manufacture (Chang, [0099]: teaches an article of manufacture) comprising:
a non-transitory computer-readable medium (Chang, [0099]: teaches a non-transitory computer-readable storage medium); and
computer-executable instructions carried on the computer-readable medium (Chang, [0099]: teaches "the computer-readable storage medium may be readable by a computer and may comprise instructions <read on computer-executable instructions> for causing a computer or other device to perform processes"),
the instructions readable by a processor (Chang, [0099]: teaches "the computer-readable storage medium may be readable by a computer <read on processor> and may comprise instructions for causing a computer or other device to perform processes"),
the instructions, when read and executed, for causing the processor to (Chang, [0099]: teaches "the computer-readable storage medium may be readable by a computer and may comprise instructions for causing a computer or other device to perform processes"):…
Thus, Claim 13 is met by Chang according to the mapping presented in the rejection of Claim 1, given the information handling system corresponds to an article of manufacture.
Regarding Claims 2, 8, and 14, the combination of Chang and Menges discloses the information handling system, the method, and the article of manufacture of Claims 1, 7, and 13 respectively. Additionally, Chang further discloses wherein the processor is further configured to
apply a generative model to the one or more generative artificial intelligence prompts to generate one or more output assets based on the input asset (Chang, FIG. 2B teaches generative model 260 <read on generative model> generating model output 262 <read on output assets>, which determines which content items are relevant for display based on the generative model prompt 256).
PNG
media_image2.png
512
452
media_image2.png
Greyscale
Regarding Claims 3, 9, and 15, the combination of Chang and Menges discloses the information handling system, the method, and the article of manufacture of Claims 2, 8, and 14 respectively. Additionally, Chang further discloses wherein at least one of the one or more output assets comprises
an image or video (Chang, [0046]: teaches a model output 262 <read on output asset> being generated based on content item 250, where model output 262 includes generated content <read on image/video> that is to be presented on client device 230).
Regarding Claims 4, 10, and 16, the combination of Chang and Menges discloses the information handling system, the method, and the article of manufacture of Claims 1, 7, and 13 respectively. Chang does not expressly disclose the limitations of Claims 4, 10, and 16; however, Menges discloses wherein the processor is further configured to:
receive verbal instructions from the user regarding the input asset (Menges, [0116]: teaches a user request for the generative design system 300 to generate a mood based on seed images <read on input asset>; [0054]: teaches the user's request being "instructions in the form of text or speech <read on verbal>"); and
apply the one or more comprehension models to a combination of the input asset and the verbal instructions to determine one or more characteristics associated with the input asset (Menges, [0116]: teaches the generative design system 300 <read on comprehension models> generating a new mood based on a set of seed images <read on input asset>, which is done in response to a user's request <read on verbal instructions>; [0113]: teaches the generative design system 300 identifying a set of characteristics shared by the set of seed images).
Menges is analogous art with respect to Chang because they are from the same field of endeavor, namely using generative models for image generation. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement a generative system that automatically determines and categorizes sets of images as taught by Menges into the teaching of Chang. The suggestion for doing so would allow for more accurate categorized images that can be used in parallel with a generative multimodal system, such as presenting sets of relevant images to the user, thereby yielding predictable results. Therefore, it would have been obvious to combine Menges with Chang.
Regarding Claims 5, 11, and 17, the combination of Chang and Menges discloses the information handling system, the method, and the article of manufacture of Claims 1, 7, and 13 respectively. Additionally, Chang further discloses wherein the input asset comprises
an image or video (Chang, [0046]: teaches content item 254 <read on input asset> including non-textual content item, such as an image and video; Note: it should be noted that content item 254 is used as input for a generative model prompt 256).
Regarding Claims 6, 12, and 18, the combination of Chang and Menges discloses the information handling system, the method, and the article of manufacture of Claims 1, 7, and 13 respectively. Additionally, Chang further discloses wherein the one or more characteristics comprises one or more of
a type, a style, a layout, and a mood associated with the input asset (Chang, [0024]: teaches a collage of visual content items that represents aspects of the user's aesthetic <read on type>, taste <read on style>, vibe <read on mood>, preference, etc. in accordance with a template format/layout).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Agarwal et al. (US 20260044992 A1) discloses generating brand-aligned product images using context;
Borders et al. (US 20110096075 A1) discloses creating shape collages based on received image data that represents a plurality of images;
Gelfenbeyn et al. (US 20230351118 A1) discloses a system that determines a context of a dialogue between an AI character model and the user; and
Li et al. (US 20230051749 A1) discloses generating synthesized digital images using class-specific generators for objects of different classes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARL TRUONG whose telephone number is (703)756-5915. The examiner can normally be reached 10:30 AM - 7:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.D.T./Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
Prosecution Insights