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 .
DETAILED ACTION
Claims 1-25 have been examined.
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.
Independent Claims 1, 10, 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are in a statutory category of invention. However, the claims recite receiving, , a base object description and parameter values for one or more targeting parameters; obtaining, , based on the base object description and the parameter values, a natural language prompt for generate content from natural language prompts; storing, the natural language prompt; processing, , the natural language prompt to generate content; causing, the content to be served for a period of time targeted according to the parameter values; and after the period of time, causing, , the content to be deleted. This is considered in the Abstract Idea grouping of certain methods of organizing human activity - advertising, marketing or sales activities or behaviors. This judicial exception is not integrated into a practical application because the claim is directed to an abstract idea with additional generic computer elements. The additional elements are considered by one or more processors, a generative model trained to, in one or more storage devices in communication with the one or more processors, digital content, to one or more computing devices. These are considered generic. Also, the machine learning is considered generic as it operates at a high and generic level without technical specifics. The generically recited computer elements do not add a practical application or meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations only perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d). Also, the additional hardware elements are: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions. Viewed separately or as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amounts to significantly more than the abstract idea itself. The claim does not provide significantly more than the identified abstract idea, in that there is no improvement to another technology or technical field, no improvement to the functioning of a computer, no application with, or by use of a particular machine, no transformation or reduction of a particular article to a different state or thing, no specific limitation other than what is well-understood, routing and conventional in the field, no unconventional step that confines the claim to a particular useful application, or meaningful limitations that amount to more than generally linking the use of the abstract idea to a particular technological environment. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Dependent claims 2-9, 11-18, 20 are not considered directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above. The machine learning is considered generic as it operates at a high and generic level without technical specifics. The dependent claims are not presently found to provide details to go past generic machine learning. While these descriptive elements may provide further helpful description for the claimed invention, these elements do not confer subject matter eligibility to the invention since their individual and combined significance is still not more than the abstract concepts identified in the claimed invention. Hence, these dependent claims are also rejected under 101.
Please see the 35 USC 101 section at the Examination Guidance and Training Materials page on the USPTO website.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vakil (20240370898).
Claims 1, 10, 19. Vakil discloses a method for serving digital content, comprising:
receiving, by one or more processors, a base object description and parameter values for one or more targeting parameters (see target and campaign and [46, 47], note product at [54]);
obtaining, by the one or more processors, based on the base object description and the parameter values, a natural language prompt for a generative model trained to generate content from natural language prompts (see generate and campaign and natural language prompt at [46, 45]);
storing, by the one or more processors, the natural language prompt in one or more storage devices in communication with the one or more processors (see generate and campaign and natural language prompt at [46]);
processing, by the one or more processors, the natural language prompt through the generative model to generate digital content (see generate and campaign and natural language prompt at [46]);
causing, by the one or more processors, the digital content to be served for a period of time to one or more computing devices targeted according to the parameter values (see generate and campaign and natural language prompt at [46] and for period of time see “[46]… The generative AI module 520 can then collectively generate contextually relevant, shareable taglines, textual content, and images representing specific promotional content for an offer that is to be shared via various preferred channels throughout the campaign's duration.”). Vakil does not explicitly disclose and after the period of time, causing, by the one or more processors, the digital content from the one or more storage devices to be deleted. However, Vakil discloses the content is only provided for a duration or limited period (“[46]… The generative AI module 520 can then collectively generate contextually relevant, shareable taglines, textual content, and images representing specific promotional content for an offer that is to be shared via various preferred channels throughout the campaign's duration.”). And, Vakil also discloses that data can only be stored for short periods [67] and removing no longer needed data/content in order to optimize performance (see remove/removing at [39, 41]) and also “[55]… reducing cumbersome processes and enhancing efficiency for both an organization and its clients”. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Vakil’s data stored for short periods and removing data/content to Vakil’s content displayed for a duration so that no longer needed content can be deleted/removed. One would have been motivated to do this in order to optimize performance [41] and for “[55]… reducing cumbersome processes and enhancing efficiency for both an organization and its clients”.
Claim 2, 11, 20. Vakil further discloses the method of claim 1, wherein receiving the natural language prompt comprises: determining, by the one or more processors, whether the one or more storage devices store a natural language prompt generated from a respective base object description and respective parameter values within a threshold measure of similarity to the base object description and the parameter values; and in response to determining that the one or more storage devices store the natural language prompt generated from the respective base object description and the respective parameter values, retrieving the natural language prompt from the one or more storage devices (see generate content based on previous campaign and natural language prompt at [46]; also see similar and product at [22] also see threshold values for rules at [15, 32] and similar campaigns at [32]).
Claim 3, 12. Vakil further discloses the method of claim 1, further comprising identifying, by the one or more processors, differences between (i) the base object description and the parameter values and (ii) the respective base object description and the respective parameter values used in generating the stored natural language prompt (see curated the historical campaign data at [46]).
Claim 4, 13. Vakil further discloses the method of claim 3, further comprising modifying, by the one or more processors, the retrieved natural language prompt in accordance with the identified differences between the received base object description and the parameter values and the respective base object description and parameter values used to generate the received natural language prompt (see natural language prompt and curate the historical campaign data at [46]).
Claim 5, 14. Vakil further discloses the method of claim 1 wherein receiving the natural language prompt comprises: determining, by the one or more processors, whether the one or more storage devices store a natural language prompt generated from a respective base object description and respective parameter values within a threshold measure of similarity to the base object description and the parameter values (see generate content based on previous campaign and natural language prompt at [46]; also see similar and product at [22] also see threshold values for rules at [15, 32] and similar campaigns at [32]). Vakil does not explicitly disclose in response to determining that the one or more storage devices do not store the natural language prompt generated from the respective base object description and the respective parameter values, generating the natural language prompt from the base object description and the parameter values. However, Vakil discloses using natural language prompts [46, 45] and guided generative Ai with guide input for AI generation [29]. Therefore, it would have been obvious to one having ordinary skill in the art at the time the invention was made to add Vakil’s natural language prompts to Vakil’s guided inputs for AI generation so that natural language prompts can be used to guide the AI generation. One would have been motivated to do this in order to better “[29]… tailored and hyper-targeted for the given scenario/customer to best sustain the customer's engagement.”.
Claim 6, 15. Vakil further discloses the method of claim1, wherein the generative model is trained to generate the same output in response to the same input prompts (see predefined rules and constraints at [15]).
Claim 7,16. Vakil further discloses the method of claim 1, wherein the base object description comprises at least one of a name of the base object, a natural language description of the base object, or data modeling characteristics of the base object (see [46] and historical campaign).
Claim 8, 17. Vakil further discloses the method of claim 7, wherein the base objection description comprises data corresponding to one or more modalities, the one or more modalities comprising at least one of video, audio, image, text, or multi-dimensional model (see any type of graphic element at [47], see asset and content and video or audio at [65]; see different presentation modes at [54, 56]).
Claim 9, 18. Vakil further discloses the method of claim 8, wherein the generative model comprises one or more modality-specific encoders for encoding data comprising multiple modalities (see encoder at [46] and different presentation modes at [54, 56]).
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a) West [40], Lohiya disclose natural language prompts and generating content for campaigns.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARTHUR DURAN whose telephone number is (571)272-6718. The examiner can normally be reached Mon-Thurs, 7-5pm.
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, Ilana Spar can be reached at (571) 270-7537. 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.
/ARTHUR DURAN/Primary Examiner, Art Unit 3622 3/9/26