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
This responds to Applicant’s Arguments/Remarks filed 06/19/2026. Claims 1, 3-4, 27 and 30 have been amended. Claims 2 and 5 have been cancelled. Claims 1, 3-4, 6-30 are now pending in this Application.
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 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) 1, 3-4, 6-15, 18-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guggenmos et al (U.S. Patent No. 12,254,030), and Codella et al (U.S. Pub No. 2019/0163814 A1), and further in view of Huda et al (U.S. Pub No. 2024/0114392 A1)
As per claim 1, Guggenmos discloses a system for enhancing data fields in exchangeable structured data files of property listings, the system comprising a server in operable communication with a property management system across a wide area network, the server comprising a processor executing computer program code instruction controllers comprising:
a data extraction and parsing controller configured to (Col 10 lines 53-67 and claim 4):
receive a structured data file for a property listing from the property management system, the structured data file comprising populated data fields and free text (Col 1 lines 25-55); and
identify incomplete data fields; a data enhancement controller comprising (Col 6 lines 14-22, col 9 lines 6-48):
determine, by the natural language processing sub-controller, whether a descriptor associated with a missing data field is recognized in contextual data obtained from the free text (Col 10 lines 53-67, col 23 lines 6-36);
responsive to the natural language processing sub-controller recognising the descriptor, a data pass-back controller configured to transmit the enhanced structured data file to the property management system.
Guggenmos does not explicitly disclose responsive to the natural language processing sub-controller recognising the descriptor, generate, by the natural language processing sub-controller, a query according to the missing data field and the contextual data, wherein the query is generated according to a data type of the missing data field; query the large language model sub-controller with the query generated by the natural language processing sub-controller; update the missing data field in the structured data file with a response received from the natural language processing sub-controller to generate an enhanced structured data file; and a data pass-back controller configured to transmit the enhanced structured data file to the property management system.
However, Codella discloses responsive to the natural language processing sub-controller recognising the descriptor, generate, by the natural language processing sub-controller, a query according to the missing data field and the contextual data, wherein the query is generated according to a data type of the missing data field; query the large language model sub-controller with the query generated by the natural language processing sub-controller (par [0021, 0033]);
update the missing data field in the structured data file with a response received from the natural language processing sub-controller to generate an enhanced structured data file (par [0033]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Codella into the teachings of Guggenmos in order to improve the technical field of system (Par [0021]).
Guggenmos and Codella disclose controller and sub-engines. Guggenmos and Codella do not explicitly disclose a sub-controller; and a large language model sub-controller, wherein the data enhancement controller is configured to.
However, Huda discloses a sub-controller; and a large language model sub-controller, wherein the data enhancement controller is configured to (Par [0016]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Huda into the teachings of Guggenmos as modified by Codella in order to improve the network performance (Par [0002]).
As per claim 3, Guggenmos discloses the system as claimed in claim 2, wherein the descriptor is associated with at least one data field (Col 5 lines 20-67]).
As per claim 4, Guggenmos discloses the system as claimed in claim 2, wherein the natural language processing sub-controller is configured to identify semantic equivalents of the descriptor in the contextual data (Col 10 lines 53-67, col 23 lines 6-36).
As per claim 6, Codella discloses the system as claimed in claim 1, wherein the natural language processing sub-controller is configured to generate the query according to multiple incomplete data fields to query the large language model sub-controller simultaneously for the multiple incomplete data fields (Par [0021, 0033]).
Guggenmos and Codella disclose controller and sub-engines. Guggenmos and Codella do not explicitly disclose a large language model sub-controller.
However, Huda discloses a large language model sub-controller (Par [0016]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Huda into the teachings of Guggenmos as modified by Codella in order to improve the network performance (Par [0002]).
As per claim 7, Codella disclose the system as claimed in claim 1, wherein the data enhancement controller further comprises a machine learning sub-controller and wherein the data enhancement controller is further configured to: query the machine learning sub-controller with a further missing data field and the contextual data; and update the further missing data field with a response received from the machine learning sub-controller to generate the enhanced structured data file (Par [0021, 0033]).
Guggenmos and Codella disclose controller and sub-engines. Guggenmos and Codella do not explicitly disclose a large language model sub-controller.
However, Huda discloses a large language model sub-controller (Par [0016]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Huda into the teachings of Guggenmos as modified by Codella in order to improve the network performance (Par [0002]).
As per claim 8, Codella discloses the system as claimed in claim 7, wherein the machine learning sub-controller interfaces with a trained model optimised using historical contextual data and historical structured data files (par [0043-0044]).
Guggenmos and Codella disclose controller and sub-engines. Guggenmos and Codella do not explicitly disclose the machine learning sub-controller interfaces with a trained model.
However, Huda discloses the machine learning sub-controller interfaces with a trained model (Par [0016]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Huda into the teachings of Guggenmos as modified by Codella in order to improve the network performance (Par [0002]).
As per claim 9, Guggenmos discloses the system as claimed in claim 1, wherein the data enhancement controller further comprises a computer vision sub-controller which is configured to determine a missing data field value by computer vision analysis of at least one of image and video data within the contextual data (Col 10 lines 53-67, col 23 lines 6-36).
As per claim 10, Guggenmos discloses the system as claimed in claim 9, wherein the computer vision analysis employs object detection to recognise objects within an image or video frame (Col 23 lines 6-36).
As per claim 11, Huda discloses the system as claimed in claim 10, wherein the data enhancement controller is configured to query the large language model sub-controller to validate a recognised object (Par [0028-0031]).
As per claim 12, Huda discloses the system as claimed in claim 11, wherein the data enhancement controller is configured to only update a data field if receiving validation of the recognised object from the large language model sub-controller (Par [0016, 0119]).
As per claim 13, Guggenmos discloses the system as claimed in claim 9, wherein the computer vision analysis employs image classification to assign labels or categories to images (Col 23 lines 6-36).
As per claim 14, Guggenmos discloses the system as claimed in claim 9, wherein the computer vision analysis employs scene understanding to infer context and elements present in a scene captured by an image or video (Col 23 lines 6-36).
As per claim 15, Guggenmos discloses the system as claimed in claim 9, wherein the computer vision sub-controller is firstly configured to employ scene understanding to infer a context and then object identification to identify an object within the context (Col 23 lines 6-36).
As per claim 18, Codella discloses the system as claimed in claim 1, wherein the system is configured to recognise additional data fields from structured data file data fields (Par [0050, 0056]).
As per claim 19, Codella discloses the system as claimed in claim 18, wherein the large language model sub-controller is configured to analyse text data fields within structured data files to recognise the additional data fields (par [0050, 0056]).
As per claim 20, Guggenmos discloses the system as claimed in claim 18, wherein the data enhancement controller further comprises a computer vision sub-controller which is configured to recognise the additional data fields by computer vision analysis of at least one of image and video data (Col 23 lines 6-36]).
Guggenmos does not explicitly disclose additional data fields. However, Codella discloses additional data fields (Par [0050, 0056]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Codella into the teachings of Guggenmos in order to improve the technical field of system (Par [0021]).
As per claim 21, Guggenmos discloses the system as claimed in claim 20, wherein the computer vision analysis is configured to analyse at least one of image and video data to detect scene elements (Col 23 lines 6-36).
As per claim 22, Codella discloses the system as claimed in claim 21, wherein the additional data fields are generated according to the detected scene elements (par [0050, 0056]).
As per claim 23, Codella discloses the system as claimed in claim 21, wherein the additional data fields are generated according to the frequency of detection of the detected scene elements (par [0050, 0056]).
As per claim 24, Guggenmos discloses the system as claimed in claim 21, wherein the computer vision analysis subsequently analyses at least one of image and video data to detect the scene elements according to data fields relating to the detected scene elements (Col 6 lines 6-36).
As per claim 25, Guggenmos discloses the system as claimed in claim 24, wherein the detected scene elements are related and wherein the computer vision analysis is configured to detect a scene element dependent on the detection of another related scene element (col 23 lines 6-36).
Claim(s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guggenmos et al, Codella et al, and Huda et al, and further in view of Eathakota (U.S. Pub No. 2021/0056607 A1).
As per claim 16, Guggenmos, Codella and Huda do not explicitly disclose the system as claimed in claim 1, wherein the controllers further comprise a search controller configured to: receive a search term; interface with the natural language processing sub-controller to identify matching data fields according to the search term; and search a database of enhanced structured data files to identify matching property listings using the matching data fields.
However, Eathakota discloses receive a search term; interface with the natural language processing sub-controller to identify matching data fields according to the search term; and search a database of enhanced structured data files to identify matching property listings using the matching data fields (par [0060]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Eathakota into the teachings of Guggenmos as modified by Codella and Huda in order to improve the system (Par [0007]).
As per claim 17, Eathakota discloses the system as claimed in claim 1, wherein the controllers further comprise a conversational controller configured to: receive a query string; interface with the natural language processing sub-controller to identify matching data fields according to the query string; and interface with the natural language processing sub-controller to generate a natural language response according to values of the matching data fields and the search term (par [0060]).
Claim(s) 26, 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guggenmos et al, Codella et al, and Huda et al, and further in view of Kasaba (U.S. Pub No. 2022/0385700 A1).
As per claim 26, Guggenmos, Codella and Huda do not explicitly disclose the system as claimed in claim 1, wherein the data enhancement controller further comprises a data creation module configured to interface with the large language model sub-controller to extract and refine video scripts from the structured data files, removing acronyms and complex terms to ensure compatibility with synthetic video production.
However, Kasaba discloses wherein the data enhancement controller further comprises a data creation module configured to interface with the large language model sub-controller to extract and refine video scripts from the structured data files, removing acronyms and complex terms to ensure compatibility with synthetic video production (par [0058, 0090, 0172]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Kasaba into the teachings of Guggenmos as modified by Codella and Huda in order to improve the personalization or customization of the interaction between the user and the digital avatar (Par [0074]).
As per claim 28, Kasaba discloses the system as claimed in claim 27, wherein the data creation module further comprises a video creation component configured to adjust an avatar's movements and speech to align with the selected background image, thereby enhancing the realism of the synthetic video (par [0058, 0090, 0172]).
Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guggenmos et al, Codella et al, and Huda et al, and further in view of Benedetto et al (U.S. Pub No. 2024/0226750 A1).
As per claim 27, Kasaba discloses the system as claimed in claim 26, wherein the data creation module is further configured to transmit the refined video script to an application programming interface (API) for video and voice creation, wherein the API integrates the refined video script with a selected background image, producing a synthetic video that simulates an on-location recording (Par [0063, 0082, 0151, 0172]).
Guggenmos, Codella and Huda, Kasaba do not explicitly disclose an application programming interface (API).
However, Benedetto discloses an application programming interface (API) (par [0036, 0038]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Benedetto into the teachings of Guggenmos as modified by Codella and Huda in order to improve the ability of the image generation AI (Par [0043]).
Claim(s) 29-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guggenmos et al, Codella et al, and Huda et al, and further in view of Lin et al (U.S. Pub No. 2024/0073372 A1).
As per claim 29, Huda discloses the system as claimed in claim 1, wherein the data enhancement controller further comprises a multi-modal large language model sub-controller configured to operate the multi-modal large language model sub-controller, wherein the multi-modal large language model sub-controller interfaces with a vision analysis sub-controller (par [0016])
Guggenmos, Codella and Huda do not explicitly disclose in real-time during property walkthroughs, capturing and processing video and visual data to generate an automated property description, the vision analysis sub-controller configured to process live video feeds during the property walkthrough, generating descriptions of the property's features, architecture, and layout and wherein the vision analysis sub-controller is further configured to dynamically update the property description based on the live video feed, thereby providing real-time context-aware information.
However, Lin discloses in real-time during property walkthroughs, capturing and processing video and visual data to generate an automated property description, the vision analysis sub-controller configured to process live video feeds during the property walkthrough, generating descriptions of the property's features, architecture, and layout and wherein the vision analysis sub-controller is further configured to dynamically update the property description based on the live video feed, thereby providing real-time context-aware information (Par [0075-0076, 0079]).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention was made to incorporate the features as disclosed in Lin into the teachings of Guggenmos as modified by Codella and Huda in order to improve the system (Par [0018]).
As per claim 30, Lin discloses the system as claimed in claim 1, wherein the data enhancement controller further comprises an interactive open home feature, the interactive open home feature configured to allow attendees to engage with the property via a mobile device application, the mobile device application being triggered by scanning a quick response (QR) code and enabling natural language input through text or voice, wherein the interactive open home feature is further configured to capture video feed data corresponding to the attendee's location within the property, and wherein the data enhancement controller is configured to process the video feed data to provide contextual information in response to queries and wherein the data enhancement controller is further configured to utilize pre-processed data from videos, photos, and extensible markup language (XML) files to enhance interaction and deliver contextually relevant information based on the attendee's location (Par [0075-0076, 0079]).
Conclusion
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July 17, 2026
/THU N NGUYEN/Examiner, Art Unit 2154
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