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
Art Unit – Location
The Art Unit location of your application in the USPTO may have changed. To aid in correlating any papers for this application, all further correspondence regarding this application should be directed to Art Unit 2682.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because:
The claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) mathematical concepts, a mental process, or certain methods of organizing human activity. This judicial exception is not integrated into a practical application because the abstract idea is implemented using generic machine learning.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use of machine learning is generic and lacks detailed improvements to machine learning technology.
Analysis:
Step 1. The claims are directed to a Process, Machine, and Article of Manufacture.
Step 2A. Prong 1. The invention comprises an Abstract Idea having a Mental Process.
A Mental Process of: Receiving a target presentation slide; obtaining a theme; and modifying the slide based on a recommendation is routinely performed by a person. MPEP 2106.04(a)(2) III B. e.g. “A Claim That Encompasses a Human Performing the Step(s) Mentally With or Without a Physical Aid Recites a Mental Process”. Furthermore, receiving a target presentation slide; obtaining a theme; modifying the slide based on a recommendation can be performed by a person to modify or customize a slide based on their or their organizations preference. E.g. “following rules or instructions” MPEP 2106.04(a)(2) II C. The addition of machine learning to the task of slide preparation lacks specific details as to how the machine learning model is unique, or how the machine learning model is modified to uniquely improve the task of the slide modification.
Step 2A Prong 2. There is no technical improvement to the claimed learning-machine 2106.04(d)(1), 2106.05.
Step 2B. Are there additional elements in the claims that amount to significantly more?
The claims cite a process which can be performed by a human using a mental processes, and following known human activity; where the inclusion of a learning machine as a substitute for a human is generic and lacks details for a significant technical improvement or an inventive concept. MPEP 2106.05.
The learning machine is merely a generic machine; which is well-understood, routine, conventional, and not specific to the task of slide presentation. The additional claim limitation elements in combination with the learning machine do not amount to significantly more because: In combination the well-understood, routine, conventional functions do not improve the function of learning machine. There appears to be no specific meaningful result or output from the learning machine related to the task of slide preparation.
Furthermore, dependent claims of generating a new slide, modifying the slide according to a rule, generating rules based on a plurality of slides, modifying the slide and training a machine learning model on the modified slide, and removing data from a slide are processes that a human can perform using their own mind; and in addition to the reasons cited above do not amount to significantly more.
Claim Rejections - 35 USC § 102
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 1-8, 10-17 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al. (US 2022/0147702 A1).
10. A computing system ("FIG. 1A illustrates an exemplary system diagram of components interfacing to enable automatic generation of transformations of formatted templates" [0007]) , comprising: a memory ("Storage system 403 may comprise … memory" [0100]) configured to store computer instructions ("capable of storing software 405" [0100]); and a processor ("processing system 402 may comprise processor…that retrieves and executes software 405 from storage system 403" [0099]) configured to execute the computer instructions to: employ a machine learning mechanism on a plurality of training slides to generate a trained machine learning model (feature data, that is analyzed in formatted templates and further used to train deep learning modeling" [0014]) ; receive a target presentation slide ("a representation of the feature data and feature data from one or more other formatted templates" [0013]) ; obtain a theme for the target presentation slide; ("feature data pertaining to a presentation theme of a formatted template (or set of formatted templates) may be utilized to enhance transformation of a formatted template" [0015]) employ a trained machine learning model on the target presentation slide based on the theme to generate at least one recommended modification to the target presentation slide ("a transformed template from the representation shown in 302" [0091] by the learning model.) ; and modify the target presentation slide based on the at least one recommended modification ("In transforming a formatted template, the trained AI processing not only generates feature transformation of objects thereof but may also provide style transformations where attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates." [0013]) .
11. The computing system of claim 10, wherein the processor modifies the target presentation slide by further executing the computer instructions to: generate a new slide to include at least one object based on the at least one recommended modification ("the trained AI processing not only generates feature transformation of objects" [0013]).
12. The computing system of claim 10, wherein the processor further executes the computer instructions to: define a plurality of rule-based operations; select at least one rule-based operation from the plurality of rule-based operations based on the selected theme; and perform at least one selected rule-based operation to automatically modify the target presentation slide ("AI processing may further be tailored for working with formatted templates through the application of formatting rules specific to the type of formatted template that is being transformed." [0013]) .
13. The computing system of claim 12, wherein the processor defines the plurality of rule-based operations by further executing the computer instructions to: receive a plurality of training slides; identify slide attributes from the plurality of training slides; and generate the plurality of rule-based operations based on the identified slide attributes ("the trained AI processing not only generates feature transformation of objects thereof but may also provide style transformations where attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates" and “formatting rules” [0013].) .
14. The computing system of claim 12, wherein the processor defines the plurality of rule-based operations by further executing the computer instructions to: receive a plurality of training slides; identify object attributes from each object on the plurality of training slides; and generate the plurality of rule-based operations based on the identified object attributes ("formatting rules may specify whether to add/remove object types based on identified shape position information." [0045]. The shape position information is an attribute; e.g. “object type” [0061]) .
15. The computing system of claim 10, wherein the processor further executes the computer instructions to: receive at least one user-modification to the modified target presentation slide; and retrain the trained machine learning model based on the at least one user-modification ("determinations as to when to update trained AI processing (e.g., re-train deep learning modeling with respect to specific features of formatted templates)." [0026]) .
16. The computing system of claim 10, wherein the processor further executes the computer instructions to: receive a plurality of training slides; identify slide attributes from the plurality of training slides; and generate the trained machine learning model based on the identified slide attributes ("general application of trained AI processing including creation, training and update of generative deep learning modeling" [0041]; “Feature data pertaining to one or more formatted templates and/or presentation themes may be a component of exemplary training data that is used to train exemplary modeling of the trained AI processing”. [0042]. “attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates." [Abstract])) .
17. The computing system of claim 10, wherein the processor further executes the computer instructions to: receive a plurality of training slides; identify object attributes from each object on the plurality of training slides; and generate the trained machine learning model based on the identified object attributes ("the trained AI processing not only generates feature transformation of objects thereof but may also provide style transformations where attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates." [Abstract]) .
1-8. The method of claims 1-8 have been analyzed in view of the method of Li (The method of claim 1) and further in view of claims 10-17 respectively. Claims 1-8 are rejected in a similar manner to claims 10-17.
19. A non-transitory computer-readable storage medium ("Storage system 403 may comprise … memory" [0100]) that stores instructions that, when executed by a processor in a computing system, cause the processor to perform actions ("processing system 402 may comprise processor…that retrieves and executes software 405 from storage system 403" [0099]), the actions comprising: defining a plurality of rule-based operations ("AI processing may further be tailored for working with formatted templates through the application of formatting rules specific to the type of formatted template that is being transformed." [0013]); selecting a plurality of target presentation slides ("a representation of the feature data and feature data from one or more other formatted templates" [0013]); selecting a theme for the plurality of target presentation slides ("feature data pertaining to a presentation theme of a formatted template (or set of formatted templates) may be utilized to enhance transformation of a formatted template " [0015]) ; determining whether at least one rule-based operation or a trained machine learning model is to be employed on the plurality of target presentation slides ("formatting rules may specify whether to add/remove object types based on identified shape position information." [0045]. (feature data, that is analyzed in formatted templates and further used to train deep learning modeling " [0014]): in response to determining that the at least one rule-based operation is to be employed on the plurality of target presentation slides: selecting the at least one rule-based operation from the plurality of rule-based operations based on the selected theme (“add/remove object types based on identified shape position information." [0045]; and performing the at least one selected rule-based operation to automatically modify the target presentation slide (the object is added or removed [0045]; ; and in response to determining that the trained machine learning model is to be employed on the plurality of target presentation slides (feature data, that is analyzed in formatted templates and further used to train deep learning modeling" [0014]): employing the trained machine learning model on the target presentation slide based on the theme to generate at least one recommended modification to the target presentation slide ("a transformed template from the representation shown in 302" [0091])
; and modifying the target presentation slide based on the at least one recommended modification ("the trained AI processing not only generates feature transformation of objects thereof but may also provide style transformations where attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates." [Abstract]) .
.
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.
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.
Claims 9, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 2022/0147702 A1) “Li” in view of Hannart et al. (US 2023/0041867 A1) “Hannart”.
18. Li teaches: The computing system of claim 10, wherein the processor further executes the computer instructions to: receive a plurality of training slides
Li does not explicitly teach to: remove private data from the plurality of training slides; and employ a machine learning mechanism on the plurality of training slides to generate the trained machine learning model.
However, Hannart teaches to: remove private data from the plurality of training slides; and employ a machine learning mechanism on the plurality of training slides to generate the trained machine learning model ("The input slide presentation can be anonymized to remove any private or client-sensitive material." [0115]) .
The training slides of Li can be modified by Hannart to remove private data from the training slides.
The motivation for the combination is to secure confidential information and allow the machine learning model to generalize based on the training of slides.
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
9. The method of claim 9 has been analyzed in view of the method of Li (The method of claim 1) and further in view of claim 18. Claim 9 is rejected in a similar manner to claim 18.
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
20. The non-transitory computer-readable storage medium of claim 20 has been analyzed in view of the “Storage system 403” [0100] and further in view of claims 6, 9, and 18. Claim 20 is rejected in a similar manner.
Therefore, the Applicant’s claimed invention would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention and the claim is rejected.
Relevant Prior Art
Drost (US 2022/0083606 A1)
Abstract
The systems and methods of creating document templates for a collection of documents, comprising: presenting a collection of documents for input processing; analyzing the collection of documents using an artificial intelligence processor; grouping documents for standardization to create one or more document templates; anonymizing the documents for General Data Protection Regulation (GDPR) compliance; creating a flow chart connecting parts for each of the document template wherein the document template includes: a header section, a footer section, and one or more body text sections; and wherein each section includes: one or more variables; one or more data fields; formatting style; and font style.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TED W BARNES whose telephone number is (571) 270-1785. The examiner can normally be reached Mon-Fri. 8:00-5:00.
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, Benny Tieu can be reached at 571-272-7490. 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.
/TED W. BARNES/ Ph.D. Electrical Engineering
Primary Examiner
Art Unit 2682
/TED W BARNES/Primary Examiner, Art Unit 2682
Prosecution Insights