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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 19 is directed to: A method for providing diverse visual contents based on prompts, the method comprising: the steps of receiving, obtaining, obtaining and causing, which are nothing more than software instructions. Software instructions are non-statutory under 35 U.S.C. 101.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 8 and 19-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by NEAL (US2024/0112394A1).
Regarding claim 1, NEAL teaches:
1. A non-transitory computer readable medium storing a software program comprising data and computer implementable instructions that when executed by at least one processor cause the at least one processor to perform operations for providing diverse visual contents based on prompts, the operations comprising (NEAL: par. 67):
receiving a textual input in a natural language indicative of a desire of an individual to receive at least one visual content of an inanimate object of a particular category (NEAL: fig. 1 see pars. 29-30);
obtaining a demographic requirement (NEAL: par. 29; note: the examiner is interpreting “race” as a demographic requirement);
obtaining a visual content based on the demographic requirement and the textual input, the visual content includes a depiction of at least one inanimate object of the particular category and a depiction of one or more persons matching the demographic requirement (NEAL: fig. 1 see pars. 29-30 and abstract); and
causing a presentation of the visual content to the individual (NEAL: fig. 1 see pars. 29-30 and abstract).
Regarding claim 2, NEAL teaches:
2. The non-transitory computer readable medium of claim 1, wherein the operations further comprise using a machine learning model to analyze the textual input to determine the demographic requirement (NEAL: fig. 1, 104 see par. 30).
Regarding claim 8, NEAL teaches:
8. The non-transitory computer readable medium of claim 1, wherein the operations further comprise using an inference model to generate the visual content based on the demographic requirement and the textual input (NEAL: par. 30; note: the examiner is interpreting the GAN as an interence model).
Claim 19 is analogous to claim 1 and is therefore rejected using the same rationale.
Claim further requires a different preamble, also taught by NEAL: 19. A method for providing diverse visual contents based on prompts, the method comprising (NEAL: par. 11).
Claim 20 is analogous to claim 1 and is therefore rejected using the same rationale.
Claim 20 further requires a different preamble, also taught by NEAL: 20. A system for providing diverse visual contents based on prompts, the system comprising (NEAL: par. 11): at least one processor configured to perform the operations of: (NEAL: fig. 4, 402 see par. 67).
Claim Rejections - 35 USC § 103
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 factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over NEAL in view of WOLFE et al., “American == White in Multimodal Language-and-Image AI,” AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (July 2022) hps://doi.org/10.1145/3514094.3534136
ISBN: 9781450392471.
Regarding claim 4, doesn’t teach however the analogous prior art WOLFE teaches:
4. The non-transitory computer readable medium of claim 1, wherein the textual input is indicative of a geographical region, and the determination of the demographic requirement is based on the geographical region (WOLFE: see pg. 801, par.: (1) Visual Semantic…, left col.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine wherein the textual input is indicative of a geographical region, and the determination of the demographic requirement is based on the geographical region as shown in WOLFE with NEAL for the benefit of exposing the biases that equate American identity with being White being learned by language-and-image AI, to prevent propagation to downstream applications of such models (Abstract, pg. 800, left col., last three lines).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over NEAL in view of PATEL (US2023/0034089A1).
Regarding claim 12, NEAL doesn’t teach however the analogous prior art PATEL teaches:
12. The non-transitory computer readable medium of claim 1, wherein the demographic requirement comply with a diversity requirement (PATEL: par. 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine wherein the demographic requirement comply with a diversity requirement as shown in PATEL with NEAL for the benefit of addressing a shortcoming in the prior art in that while prior art methods exist for quantifying diversity, these methods fall short of providing meaningful and actionable insights to brand managers or content creators. In addition, there is a need for a scalable system that can score and visualize diversity among large and ever-growing catalogs of UGVs hosted on platforms such as YouTube [5].
Allowable Subject Matter
Claims 3, 5-7, 9-11 and 13-18 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 3, 5-7, 9-11 and 13-18 the prior art doesn’t teach:
3. The non-transitory computer readable medium of claim 1, wherein the operations further comprise:analyzing the textual input to identify a first mathematical object in a mathematical space,
wherein the first mathematical object corresponds to a first word of the textual input;
analyzing the textual input to identify a second mathematical object in a mathematical space,
wherein the second mathematical object corresponds to a second word of the textual input;
calculating a function of the first mathematical object and the second mathematical object to identify a third mathematical object in the mathematical space; and
determining the demographic requirement based on the third mathematical object.
5. The non-transitory computer readable medium of claim 1, wherein the textual input is indicative of a specific category different from the particular category, wherein the visual content further includes a depiction of at least one inanimate object of the specific category, and wherein the determination of the demographic requirement is based on the specific category.
6. The non-transitory computer readable medium of claim 1, wherein the textual input includes a noun and an adjective adjacent to the noun, wherein the determination of the demographic requirement is based on the adjective, and wherein the particular category is based on the noun.
7. The non-transitory computer readable medium of claim 1, wherein the textual input includes a verb and an adverb adjacent to the verb, wherein the determination of the demographic requirement is based on the adverb.
9. The non-transitory computer readable medium of claim 1, wherein the operations further comprise selecting the visual content of a plurality of alternative visual contents based on the demographic requirement and the textual input.
10. The non-transitory computer readable medium of claim 1, wherein the operations further comprise:identifying a first mathematical object in a mathematical space, wherein the first mathematical object corresponds to a word of the textual input;
identifying a second mathematical object in the mathematical space, wherein the second mathematical object corresponds to the demographic requirement;
calculating a function of the first mathematical object and the second mathematical object to identify a third mathematical object in the mathematical space; and
using the third mathematical object to generate the visual content.
11. The non-transitory computer readable medium of claim 1, wherein the visual content includes a depiction of a specific person matching the demographic requirement performing an action associated with a specific inanimate object of the particular category, and wherein the action is selected based on the demographic requirement.
13. The non-transitory computer readable medium of claim 1, wherein a characteristic of the at least one inanimate object of the particular category in the visual content is selected based on the demographic requirement.
14. The non-transitory computer readable medium of claim 13, wherein the characteristic is a quantity of the at least one inanimate object.
15. The non-transitory computer readable medium of claim 1, wherein the demographic requirement is indicative of an age group, and a size of the at least one inanimate object in the visual content is selected based on the age group.
16. The non-transitory computer readable medium of claim 1, wherein the at least one inanimate object includes an amulet, and a color of the amulet in the visual content is selected based on the demographic requirement.
17. The non-transitory computer readable medium of claim 1, wherein a spatial relation between the at least one inanimate object and the one or more persons in the visual content is selected based on the demographic requirement.
18. The non-transitory computer readable medium of claim 1, wherein the visual content includes a depiction of a first person matching the demographic requirement performing a first action associated with an inanimate object of the particular category, and a depiction of a second person matching the demographic requirement performing a second action associated with the inanimate object of the particular category, and wherein a temporal relation between the first action and the second action in the visual content is selected based on the demographic requirement.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PARASNIS (US2025/0200114A1) discloses methods, systems, and computer programs are presented for generating personalized content. One method includes an operation for identifying audience parameter values that indicate which users are members of an audience. The method further includes operations for determining attribute values for attributes used to generate items based on the audience parameter values, and generating items for the audience based on the attribute values. Generating each item comprises creating a prompt based on a type of the item, the attribute values, and the audience parameter values; selecting a generative artificial intelligence (GAI) tool to generate the item based on the type of item; and providing the created prompt to the selected GAI tool. The method further includes causing presentation of the generated items in a user interface (UI), receiving on the UI a selection of one of the items; and transmitting the selected item to one or more members of the audience; ACKERMAN (US2024/0242428A1) discloses a system that supports generation of media content based on textual inputs. The system is designed to receive text and other forms of content as input. The text input may be amplified using one or more artificial intelligence techniques to produce modified text content. The text content is then processed using an artificial intelligence algorithm configured to perform text-to-image processing to produce image content. The amplification of the text content and the generation of image content based on the text content may be performed iteratively, with changes to the text content in each iteration resulting in a new image that potentially comes closer to the user's desired result for the image content. 3D data may be extracted from the final image and used to generate a 3D model that may be integrated with or used by external systems, platforms, or devices; ZHANG (US2024/0362830A1) discloses a computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene; ALKALAY (US2024/0144565A1) discloses methods and apparatuses are described for generating customized, context-specific visual artifacts using artificial intelligence (AI). A server computing device captures input data from one or more remote computing devices, the input data associated with one or more users. The server computing device creates one or more visual artifacts based upon the input data, each of the one or more visual artifacts associated with one or more of the users. The server computing device integrates the visual artifacts into a communication session associated with the remote computing devices; PRASAD (US2022/0060438A1) discloses an actionable content generation system (ACGS) and a method for generating and rendering intent-based actionable content in real time are provided. The ACGS, integrated within an input interface, for example, a keyboard, detects and simultaneously analyzes one or more messages being entered by a user in an input field of a user application using the input interface in real time. The ACGS generates tokens from the message(s) and determines intent with a confidence element from the tokens in real time based on a stored mapping and a confidence computation. The ACGS generates actionable content based on the intent using campaign data. The ACGS renders at least one element containing the actionable content on a graphical user interface and/or the input interface for interaction by the user and performance of one or more recommended actions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAURICE L MCDOWELL, JR whose telephone number is (571)270-3707. The examiner can normally be reached Mon-Fri: 2pm-10pm.
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, Said A. Broome can be reached at 571-272-2931. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MAURICE L. MCDOWELL, JR/Primary Examiner, Art Unit 2612