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 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.
Claims 1-18 and 20 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Demiralp et al. (US 2017/0309046).
Regarding Claim 1, Demiralp discloses a computer-implemented method, comprising:
establishing a bi-directional connection between two heterogeneous content creation applications for one or more feature values corresponding to an object in a virtual scene of synthetically generated graphical data maintained in a distributed content creation platform (Fig. 1, [0060], Demiralp);
receiving, via the bi-directional connection, an indication that a modification was performed to a first feature value of the one or more feature values at a first selected address location (Fig. 1, [0060], Demiralp);
determining that the modification to the first feature value exceeds a threshold (Fig. 1, [0060], “if any of the one or more interaction statistics calculated from the currently retrieved entries of the interaction log 140 is different from the corresponding one or more interaction statistics calculated from the previously retrieved entries of the interaction log 140 by the predefined statistic threshold, the interaction statistics module 134 can transmit the one or more interaction statistics calculated from the currently retrieved entries to the content provider devices 115A-N, the content publisher devices 120A-N, the client devices 125A-N, and the content administrator device 145 associated with the request for interaction statistics,” Demiralp);
identifying a second selected address location associated with the first feature value (Fig. 1, [0060], “the interaction statistics module 134 can transmit the one or more interaction statistics calculated from the currently retrieved entries to the content provider devices 115A-N, the content publisher devices 120A-N, the client devices 125A-N, and the content administrator device 145 associated with the request for interaction statistics,” Demiralp);
updating a second feature value at the second selected address location based on the modification ([0065], “Receipt of the interaction statistics visualization script can cause the respective content provider device 115A-N or the content publisher device 120A-N to insert the interaction statistics visualization script into the information resource or the content element and transmit the information resource or the content element with the interaction statistics visualization script to any one of the client devices 125A-N or the content administrator device 145,” Demiralp); and
updating a representation of an object corresponding to the second feature value based on the modification ([0065], “Receipt of the interaction statistics visualization script can cause the respective content provider device 115A-N or the content publisher device 120A-N to insert the interaction statistics visualization script into the information resource or the content element and transmit the information resource or the content element with the interaction statistics visualization script to any one of the client devices 125A-N or the content administrator device 145,” Demiralp).
Regarding Claim 2, Demiralp discloses a computer-implemented method of claim 1, further comprising:
receiving a notification corresponding to the modification via the bi-directional connection ([0099]-[0100], Demiralp); and
determining a modified first feature value corresponds to a modification type ([0099]-[0100], Demiralp).
Regarding Claim 3, Demiralp discloses a computer-implemented method of claim 1, wherein the first selected address location is associated with a first data source (Fig. 1, [0049], [0060], and [0067], Demiralp) and the second selected address location is associated with a second data source (Fig. 1, [0049], [0060], and [0067], Demiralp).
Regarding Claim 4, Demiralp discloses a computer-implemented method of claim 3, wherein the second data source provides a three-dimensional representation of the first feature value ([0074] and [0110], Demiralp).
Regarding Claim 5, Demiralp discloses a computer-implemented method of claim 1, wherein the establishing a bi-directional connection comprises:
generating a listener between the first selected address location and the second selected address location ([0072], Demiralp).
Regarding Claim 6, Demiralp discloses a computer-implemented method of claim 1, further comprising:
selecting, from a first data source, one or more addresses corresponding to a selected feature ([0049], Demiralp).
Regarding Claim 7, Demiralp discloses a computer-implemented method of claim 1, wherein the threshold corresponds to at least one of a minimum value, a maximum value, a percentage, or a duration of time ([0057], Demiralp).
Regarding Claim 8, Demiralp discloses a computer-implemented method of claim 7, further comprising:
determining a first output format of a first data source associated with the first address location ([0116] and [0128], Demiralp);
determining a second output format of a second data source associated with the second address location ([0116] and [0128], Demiralp);
determining one or more common features between the first data source and the second data source ([0116] and [0128], Demiralp); and
determining at least one of the first address location or the second address location based on the determined one or more common features ([0116] and [0128], Demiralp).
Regarding Claim 9, Demiralp discloses a processor, comprising:
one or more circuits to (Fig. 1, 100, Demiralp):
identify a first address corresponding to a selected feature in a first data source, the selected feature corresponding to an object in a virtual scene of synthetically generated graphical data maintained in a distributed content creation platform (Fig. 1, [0060], Demiralp);
identify a second address corresponding to the selected feature in a second data source (Fig. 1, [0060], “the interaction statistics module 134 can transmit the one or more interaction statistics calculated from the currently retrieved entries to the content provider devices 115A-N, the content publisher devices 120A-N, the client devices 125A-N, and the content administrator device 145 associated with the request for interaction statistics,” Demiralp);
generate a bi-directional connection between the first address and the second address, the first address corresponding to a first content creation application, and the second address corresponding to a second content creation application, the first and second content creation applications comprising heterogeneous applications (Fig. 1, [0060], Demiralp);
determine a modification to a first value for at least one of the first address or the second address ([0065], “Receipt of the interaction statistics visualization script can cause the respective content provider device 115A-N or the content publisher device 120A-N to insert the interaction statistics visualization script into the information resource or the content element and transmit the information resource or the content element with the interaction statistics visualization script to any one of the client devices 125A-N or the content administrator device 145,” Demiralp); and
modify, based at least on the modification, a second value for the other of the first address or the second address ([0065], “Receipt of the interaction statistics visualization script can cause the respective content provider device 115A-N or the content publisher device 120A-N to insert the interaction statistics visualization script into the information resource or the content element and transmit the information resource or the content element with the interaction statistics visualization script to any one of the client devices 125A-N or the content administrator device 145,” Demiralp).
Regarding Claim 10, Demiralp discloses a processor of claim 9, wherein the first data source stores a two-dimensional representation of the selected feature and the second data source stores a three-dimensional representation of the feature ([0074] and [0110], Demiralp).
Regarding Claim 11, Demiralp discloses a processor of claim 9, where the one or more circuits are further to:
determine the modification exceeds a threshold prior to modifying the second value (Fig. 1, [0060], “if any of the one or more interaction statistics calculated from the currently retrieved entries of the interaction log 140 is different from the corresponding one or more interaction statistics calculated from the previously retrieved entries of the interaction log 140 by the predefined statistic threshold, the interaction statistics module 134 can transmit the one or more interaction statistics calculated from the currently retrieved entries to the content provider devices 115A-N, the content publisher devices 120A-N, the client devices 125A-N, and the content administrator device 145 associated with the request for interaction statistics,” Demiralp).
Regarding Claim 12, Demiralp discloses a processor of claim 9, wherein the one or more circuits are further to:
identify a modification type for the first value ([0052], interaction type, Demiralp); and
determine the modification type corresponds to one or more selected modification types ([0052], interaction type, Demiralp).
Regarding Claim 13, Demiralp discloses a processor of claim 9, wherein the one or more circuits are further to:
determine a content type associated with the first address and the second address ([0052], interaction type, [0060], and [0099], Demiralp);
identify the selected feature from a list of features, wherein the selected feature has a corresponding value that is less than the value corresponding to each feature for the first data source and the second data source ([0052], interaction type, [0060], and [0099], Demiralp); and
provide a recommendation to generate the bi-directional connection ([0052], interaction type, [0060], and [0099], Demiralp).
Regarding Claim 14, Demiralp discloses a processor of claim 9, wherein the processor is comprised in at least one of:
a system for performing simulation operations;
a system for performing simulation operations to test or validate autonomous machine applications;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for rendering graphical output;
a system for performing deep learning operations;
a system implemented using an edge device;
a system for generating or presenting virtual reality (VR) content;
a system for generating or presenting augmented reality (AR) content;
a system for generating or presenting mixed reality (MR) content;
a system incorporating one or more Virtual Machines (VMs) ([0140], Demiralp);
a system for performing operations for a conversational AI application;
a system for performing operations for a generative AI application;
a system for performing operations using a language model;
a system for performing one or more generative content operations using a large language model (LLM);
a system for performing one or more generative content operations using a vision language model (VLM);
a system implemented at least partially in a data center ([0146], Demiralp);
a system for performing hardware testing using simulation;
a system for performing one or more generative content operations using a language model;
a system for synthetic data generation;
a collaborative content creation platform for 3D assets; or
a system implemented at least partially using cloud computing resources.
Regarding Claim 15, Demiralp discloses a system, comprising:
one or more processing units to identify a change to one or more feature values from a first data set, update one or more corresponding features values in a second data set based on the change (Fig. 1, [0060], Demiralp), and
update a three-dimensional representation of an object associated with the one or more feature values based on the one or more corresponding feature values in the second data set, wherein the object corresponds to a scene of synthetically generated graphical data maintained in a distributed content creation platform ([0065], Demiralp).
Regarding Claim 16, Demiralp discloses a system of claim 15, wherein the first data set is associated with a first file type and the second data set is associated with a second file type ([0141], Demiralp).
Regarding Claim 17, Demiralp discloses a system of claim 16, wherein the first file type includes three-dimensional (3D) geometric data and metadata for the object ([0074] and [0110], Demiralp).
Regarding Claim 18, Demiralp discloses a system of claim 17, wherein the second file type includes the metadata for the object ([0141], Demiralp).
Regarding Claim 20, Demiralp discloses a system of claim 15, wherein the system is one of:
a system for performing simulation operations;
a system for performing simulation operations to test or validate autonomous machine applications;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for rendering graphical output;
a system for performing deep learning operations;
a system implemented using an edge device;
a system for generating or presenting virtual reality (VR) content;
a system for generating or presenting augmented reality (AR) content;
a system for generating or presenting mixed reality (MR) content;
a system incorporating one or more Virtual Machines (VMs) ([0140], Demiralp);
a system for performing operations for a conversational AI application;
a system for performing operations for a generative AI application;
a system for performing operations using a language model;
a system for performing one or more generative content operations using a large language model (LLM);
a system for performing one or more generative content operations using a vision language model (VLM);
a system implemented at least partially in a data center ([0146], Demiralp);
a system for performing hardware testing using simulation;
a system for performing one or more generative content operations using a language model;
a system for synthetic data generation;
a collaborative content creation platform for 3D assets; or
a system implemented at least partially using cloud computing resources.
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 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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Demiralp et al. (US 2017/0309046) in view of St. Martin et al. (US 2025/0200680).
Regarding Claim 19, Demiralp discloses a system, wherein the system is further to identify the one or more feature values based on an evaluation of components associated with the first data set and the second data set. However, Demiralp does not expressly discloses a trained neural network evaluation. St. Martin discloses a trained neural network evaluation ([0205]-[0206], St. Martin). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Demiralp by incorporating the trained neural network evaluation, as disclosed by St. Martin, in order to predict outcomes even when data is incomplete. See: KSR International Co. v. Teleflex Inc., 82 USPQ 1385, 1396 (US 2007); MPEP § 2143.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIOVANNA B COLAN whose telephone number is (571)272-2752. The examiner can normally be reached on Mon - Fri 8:30-5:00.
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/GIOVANNA B COLAN/Primary Examiner, Art Unit 2165
May 20, 2026
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