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 § 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.
(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) 1-7 and 9-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hudetz et al. (US 20240370479 A1).
Regarding claims 1, 13, and 20, Hudetz discloses a computer-implemented artificial intelligence (AI) system comprising: one or more processors; and one or more storage devices storing instructions that, when executed by the one or more processors (fig. 1, [0057]), cause the one or more processors to perform operations comprising:
generating, by an artificial intelligence (AI) system, a digital component using a first generative model ([0050] and [0085]);
generating, by the AI system, a summary of the digital component using a second generative model, the summary of the digital component indicating contents comprised in the digital component ([0051] and [0052], [0063], [0084] and [0085]);
generating, by the AI system, an evaluation result of the digital component using the second generative model, the evaluation result of the digital component indicating one or more suggestions for improving the digital component ([0088] and [0089]); and
refining, by the AI system and using the first generative model, the digital component based on the summary of the digital component and the evaluation result of the digital component ([0147] and [0217]).
Regarding claim 20, see analysis in claims 1 and 13.
Regarding claims 2 and 14, Hudetz discloses the computer-implemented method of claim 1s and 13, comprising:
generating, by the AI system, a policy review result of the digital component using the second generative model, the policy review result of the digital component indicating whether the digital component includes restricted content ([0100] a well-defined set of rules), wherein refining the digital component comprises:
refining, by the AI system and using the first generative model, the digital component based on the summary of the digital component, the evaluation result of the digital component, and the policy review result of the digital component ([0104]) .
Regarding claims 3 and 15, Hudetz discloses the computer-implemented method of claims 1 and 13, comprising:
determining, by the AI system and using the second generative model, one or more entity attributes of an entity associated with the digital component ([0147] The context information 734 may also comprise metadata for the electronic document 706 (e.g., signatures, STME, marker elements, document length, document type, etc.), the user generating the search query 144 (e.g., demographics, location, interests, business entity, etc.), a device used to generate the search query 144 (e.g., capabilities, compute resources, memory resources, I/O devices, screen size, interfaces, etc.), sensors (e.g., temperature, accelerometers, altitude, proximity, etc.), and any other context information 734 that may be suitable for further refining the search query 144 (e.g., using search term expansion techniques));
determining, by the AI system and using the second generative model, one or more digital component attributes of the digital component ([0054] the agent 508 can assess whether essential content (e.g., action items, core discussion topics, and/or the like) are included in the summarized content 506 and non-essential content (e.g., off-topic conversations, personal remarks, redundant comments) are omitted (e.g., truncated)); and
generating, by the AI system and using the second generative model, an attribute review result of the digital component based on comparing the one or more entity attributes of the entity and the one or more digital component attributes of the digital component ([0147] The context information 734 may allow the user to build search queries in an iterative manner, drilling down on more specific search questions in follow-up to reviewing previous search results 146. The context information 734 may also comprise metadata for the electronic document 706 (e.g., signatures, STME, marker elements, document length, document type, etc.), the user generating the search query 144 (e.g., demographics, location, interests, business entity, etc.), a device used to generate the search query 144 (e.g., capabilities, compute resources, memory resources, I/O devices, screen size, interfaces, etc.), sensors (e.g., temperature, accelerometers, altitude, proximity, etc.), and any other context information 734 that may be suitable for further refining the search query 144 (e.g., using search term expansion techniques)) wherein refining the digital component comprises:
refining, by the AI system, the digital component based on the summary of the digital component, the evaluation result of the digital component, and the attribute review result of the digital component ([0147] The context information 734 may allow the user to build search queries in an iterative manner, drilling down on more specific search questions in follow-up to reviewing previous search results 146. The context information 734 may also comprise metadata for the electronic document 706 (e.g., signatures, STME, marker elements, document length, document type, etc.), the user generating the search query 144 (e.g., demographics, location, interests, business entity, etc.), a device used to generate the search query 144 (e.g., capabilities, compute resources, memory resources, I/O devices, screen size, interfaces, etc.), sensors (e.g., temperature, accelerometers, altitude, proximity, etc.), and any other context information 734 that may be suitable for further refining the search query 144 (e.g., using search term expansion techniques)).
Regarding claims 4 and 16, Hudetz discloses the computer-implemented method of claims 1 and 13, comprising:
generating, by the AI system and using the second generative model, a performance evaluation result of the digital component ([0103] and [0104]), wherein refining the digital component comprises:
refining, by the AI system, the digital component based on the summary of the digital component, the evaluation result of the digital component, and the performance evaluation result of the digital component ([0104] The feedback 320 may include model performance feedback information, which may be used for monitoring and improving performance of the ML model 312).
Regarding claims 5 and 17, Hudetz discloses the computer-implemented method of claims 4and 16, wherein the performance evaluation result of the digital component comprises at least one of a predicted clickthrough rate (CTR) or a predicted conversion rate (CVR) ([0105] predictions of a given task, [0114] prediction outcome, [0117] to make prediction about future outcomes).
Regarding claims 6 and 18, Hudetz discloses the computer-implemented method of claims 1 and 13, wherein a refined digital component is generated based on refining the digital component, and wherein the computer-implemented method comprises: determining, by the AI system and using the second generative model, whether the refined digital component satisfies one or more conditions ([0070] and [0100] satisfy a pre-defined rules).
Regarding claims 7 and 19, Hudetz discloses the computer-implemented method of claims 6 and 18, comprising: in response to determining that the refined digital component satisfies the one or more conditions, outputting, by the AI system, the refined digital component ([0070] and [0100]).
Regarding claim 9, Hudetz discloses the computer-implemented method of claim 1, comprising:
generating, by the AI system, training data comprising a training digital component and one or more suggestions for improving the training digital component ([0102], [0105]); and
training, by the AI system, the second generative model using the training data ([0109]).
Regarding claim 10, Hudetz discloses the computer-implemented method of claim 1, comprising:
generating, by the AI system, training data comprising the digital component and the one or more suggestions for improving the digital component ([0092] and [0102]); and
refining, by the AI system, the first generative model using the training data ([0102] and [0105], [0109]).
Regarding claim 11, Hudetz discloses the computer-implemented method of claim 1, comprising: displaying, by the AI system, one or more pointers pointing to one or more regions of the digital component, the one or more regions of the digital component associated with the one or more suggestions for improving the digital component ([0082] and [0096]).
Regarding claim 12, Hudetz discloses the computer-implemented method of claim 1, wherein the one or more suggestions for improving the digital component comprise identifications of pixels to be improved ([0048], [0052] and [0062] and [0082]).
Allowable Subject Matter
Claim 8 is 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
McDevitt (US 20260127231 A1) discloses the system and method provide a means to introduce the fake digital content into a process to recognize and analyze digital content in order to train that process to identify fake digital content.
Sankar et al. (US 20250322294 A1) discloses the contextual content generation system evaluates whether the generated digital content items adhere to the context-specific guidelines of the digital guideline document and refines the content generation machine learning model utilizing reinforcement learning in response to the fine-tuned model(s) generating digital content that does not adhere to the context-specific guidelines.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUNG T VO whose telephone number is (571)272-7340. The examiner can normally be reached Monday-Friday 6:30 AM - 5:00 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, Brian Pendleton can be reached at 571-272-7527. 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.
TUNG T. VO
Primary Examiner
Art Unit 2425
/TUNG T VO/Primary Examiner, Art Unit 2425
Prosecution Insights