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 .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements 08/23/2023, 10/27/2023, 08/01/2024, 01/28/2025, 06/18/2025, 09/16/2025, 12/09/2025, and 03/09/2026, which comply with 37 CFR 1.97. As such, the information disclosure statements have been placed in the application file and the information referred to therein has been considered by the examiner.
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)(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 1-7 and 13-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Catalano et al, U.S. PG PUB (US 20240291779 A1), published August 29, 2024, having priority to provisional application 63/486,824 filed February 24, 2023 and provisional application 63/496877 filed April 18, 2023.
With regard to independent claim 1,
Catalano teaches “A computer-implemented method for providing information via a machine learning (ML) chatbot emulating traits of a person,” (paragraph 0148 and 0156; EN: This denotes a method where a chatbot can imitate a person based on a randomly generated persona or a user generated persona; Provisional support found in application 63/486,824 at paragraph 0103 and 0105). “the method comprising: receiving, by one or more processors from the user via a user device, a request;” (paragraph 0080; EN: This denotes receiving a prompt from a user device; Provisional support found in application 63/486,824 at paragraph 0064). “providing, by the one or more processors, the request to an ML chatbot,” (paragraph 0080; EN: This denotes that the user prompt is sent to the chatbot; Provisional support found in application 63/486,824 at paragraph 0064). “wherein: the ML chatbot is trained to generate a response,” (paragraph 0088 and 0105; EN: This denotes that various models associated with the chatbot are constantly being retrained or finetuned based on user interaction; Provisional support found in application 63/486,824 at paragraph 0072 and 0087). “the response being provided in a style of communication emulating the traits of the person;” (paragraph 0096-0097; EN: This denotes that the chatbot can take on a persona that the user gives it or determine a personality tone to use when chatting with a user; Provisional support found in application 63/486,824 at paragraph 0080-0081). “and the ML chatbot is trained using historical training data indicative of the traits of the person;” (paragraph 0088; EN: This denotes that the chatbot can be trained to utilize various data gained from the user, such as engagement with ads, services provided to the user, and responses to the chatbot itself; Provisional support found in application 63/486,824 at paragraph 0072). “obtaining, by the one or more processors, an output of the ML chatbot that is responsive to the request;” (paragraph 0076-0077; EN: This denotes that the chatbot can generate a response to the user prompt; Provisional support found in application 63/486,824 at paragraph 0061-0062). “generating, by the one or more processors, content based upon the output;” (paragraph 0108; EN: This denotes that the chatbot can generate a response to the user prompt utilizing various forms of communication and media; Provisional support found in application 63/486,824 at paragraph 0090). “and providing, by the one or more processors, the content to the user device” (paragraph 0108; EN: This denotes that the chatbot can display content to the user system; Provisional support found in application 63/486,824 at paragraph 0090).
With regard to dependent claim 2,
Catalano teaches “The computer-implemented method of claim 1, wherein the ML chatbot is based upon a fine-tuned ML model,” (paragraph 0105; EN: This denotes a generative ai model, which is usually employed by the chatbot, being fined-tuned through the use of user data; Provisional support found in application 63/486,824 at paragraph 0087). “the method further comprising: training, by the one or more processors, a base ML model using historical base model training data;” (paragraph 0105-0107; EN: This denotes that once the system receives a response from the user it’ll store the response in a database and use it to continuously retrain or fine tune the various models and components associated with the ML chatbot. It’ll also extract the user’s intent from conversations and use that to match the content related to the generated response; Provisional support found in application 63/486,824 at paragraph 0087-0089). “fine-tuning, by the one or more processors, the base ML model based upon a plurality of training data associated with a plurality of persons having associated traits to generate a plurality of fine-tuned ML models associated with the respective plurality of persons;” (paragraph 0105-0106; EN: This denotes that various models and components of the chatbot system are finetuned or retrained based on user interactions or conversations between users and other chatbots; Provisional support found in application 63/486,824 at paragraph 0087-0088). “and storing, by the one or more processors, the plurality of fine-tuned ML models” (paragraph 0094; EN: This denotes that the generative ai model and the chatbot system are stored and hosted on server systems; Provisional support found in application 63/486,824 at paragraph 0078).
With regard to dependent claim 3,
Catalano teaches “The computer-implemented method of claim 2, further comprising: obtaining, by the one or more processors, an indication of a person;” (paragraph 0082-0083; EN: This denotes that the chatbot is able to allow various uses to interact with it through various apps, while also being able to determine a user’s intent and mapping various conversations to the user based on keywords and concepts; Provisional support found in application 63/486,824 at paragraph 0066-0067). “identifying, by the one or more processors, a fine-tuned ML model of the plurality of fine-tuned ML models associated with the indicated person;” (paragraph 0082-0083 and 0105-0106; EN: This denotes that various components and models associated with the chatbot, which are continuously retrained or finetuned based on user interaction, can determine a user’s intent and map keywords and concepts to their conversations. It can also use and store conversations between users and other chatbots in an analytics database; Provisional support found in application 63/486,824 at paragraph 0066-0067 and 0087-0088). “and loading, by the one or more processors, the identified fine-tuned ML model for the ML chatbot into one or more memories for use as the ML chatbot” (paragraph 0105-0106 and 0173; EN: This denotes that various components and models associated with the chatbot, which are continuously retrained or finetuned based on user interaction, can load a chatbot persona for a user that originally mentions a chatbot in a group chat session; Provisional support found in application 63/496877 at paragraph 0093-0094, and 0151).
With regard to dependent claim 4,
Catalano teaches “The computer-implemented method of claim 3, wherein obtaining the indication of the person comprises: generating, by the one or more processors, a model selection interface,” (paragraph 0043; EN: This denotes the use of an API server that allows a user to create an account or login to an account through various interfaces; Provisional support found in application 63/486,824 at paragraph 0030, 0055and, 0094). “the model selection interface providing a selection element for selecting persons associated with fine-tuned ML models;” (paragraph 0043, 0070, 0106; EN: This denotes that the interaction client, which is utilized by the API, will allow users to decide what user data an external resource, such as other chatbots, can access. Once the external resources have access to the user data, the interaction client will provide another GUI that provides the various functions and features of the external resource, such as account creation and login, to the user; Provisional support found in application 63/486,824 at paragraph 0030). “providing, by the one or more processors, the model selection interface to the user device;” (paragraph 0046-0047; EN: This denotes that the interaction client can launch and display both external resources and locally installed applications on the user system; Provisional support found in application 63/486,824 at paragraph 0033-0034). “and detecting, via the model selection interface, the indication of the person” (paragraph 0050 and 0066-0067; EN: This denotes that the interactive server, which is housed inside the interaction platform and alongside the interaction client platform, can provide external resources access to specific user data of the interaction client; Provisional support found in application 63/486,824 at paragraph 0037 and 0051-0052).
With regard to dependent claim 5,
Catalano teaches “The computer-implemented method of claim 1, wherein the style of communication includes one or more of vocabulary, phrasing, accent, tone, sentiment, conciseness, humor, and/or depth of knowledge” (paragraph 0096; EN: This denotes that the chatbot can adopt a different personality or tone based on the demographic of the user or the way they text or talk in conversations; Provisional support found in application 63/486,824 at paragraph 0080).
With regard to dependent claim 6,
Catalano teaches “The computer-implemented method of claim 1, wherein the historical training data includes personal content created by the person” (paragraph 0088 and 0115; EN: This denotes that the chatbot can be trained to utilize various data gained from the user, such as engagement with ads, services provided to the user, responses to the chatbot itself, user stories, and other types of media; Provisional support found in application 63/486,824 at paragraph 0072 and 0092).
With regard to dependent claim 7,
Catalano teaches “The computer-implemented method of claim 6, wherein the personal content includes written content, audio content, image content, and/or video content” (paragraph 0115; EN: This denotes that the use of various data gained from the user, such as user stories, audio media, and other types of media; Provisional support found in application 63/486,824 at paragraph 0092).
With regard to independent claim 13,
Catalano teaches “A system for providing information via a machine learning (ML) chatbot emulating traits of a person,” (paragraph 0148 and 0156; EN: This denotes a method where a chatbot can imitate a person based on a randomly generated persona or a user generated persona; Provisional support found in application 63/486,824 at paragraph 0103 and 0105). “the system comprising: one or more processors;” (paragraph 0326; EN: This denotes the use of a processor; Provisional support found in application 63/486,824 at paragraph 0148). “and one or more non-transitory memories storing processor-executable instructions that, when executed by the one or more processors, cause the system to: receive a request from the user via a user device;” (paragraph 0341; EN: This denotes that the memory storing processor can execute instructions that cause the system to receive a request from a user via the user system; Provisional support found in application 63/486,824 at paragraph 0159). The rest of this claim is similar in scope to claim 1 and is rejected under a similar rationale.
With regard to dependent claim 14,
This claim is similar in scope to claim 2 and is rejected under a similar rationale.
With regard to dependent claim 15,
This claim is similar in scope to claim 3 and is rejected under a similar rationale.
With regard to dependent claim 16,
This claim is similar in scope to claim 4 and is rejected under a similar rationale.
With regard to dependent claim 17,
This claim is similar in scope to claim 5 and is rejected under a similar rationale.
With regard to dependent claim 18,
This claim is similar in scope to claim 6 and is rejected under a similar rationale.
With regard to dependent claim 19,
This claim is similar in scope to claim 7 and is rejected under a similar rationale.
With regard to independent claim 20,
Catalano teaches “A non-transitory computer-readable medium storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to: receive a request from the user via a user device;” (paragraph 0327; EN: This denotes the use of a machine-readable medium that can receive a prompt from a user system; Provisional support found in application 63/486,824 at paragraph 0149). The rest of this claim is similar in scope to claim 1 and is rejected under a similar rationale.
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 8-12 are rejected under 35 U.S.C. 103 as being unpatentable over Catalano et al, U.S. PG PUB (US 20220397411 A1), published August 29, 2024, having priority to provisional application 63/486,824 filed February 24, 2023 and provisional application 63/496877 filed April 18, 2023 in view of Brown et al, U.S. PG PUB (US 20220397411 A1), published December 15, 2022.
With regard to dependent claim 8,
Catalano fails to explicitly teach “The computer-implemented method of claim 1, wherein the content is associated with a tour”.
Brown teaches “The computer-implemented method of claim 1, wherein the content is associated with a tour” (paragraph 0037-0038; EN: This denotes the use of a tour module, which can process, track, or maintain data regarding tours).
Catalano and Brown are considered to be analogous to the claimed invention due to the fact that they are each generally related to utilizing software applications to deliver various forms of media to a user’s device. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the human emulating chatbot of Catalano with the delivery of content to a user’s device based on geolocation and software applications of Brown. One would be motivated to do so to in order to allow a chatbot to be able to generate AR and VR content such as tours based on the user’s location and what the user was looking at in real time.
With regard to dependent claim 9,
Catalano teaches “The computer-implemented method of claim 8, wherein: the user device is a viewer device, the content is virtual reality content,” (paragraph 0287-0288; EN: This denotes using the device sensors, such as cameras, on a client device to display AR, VR, and MR content; Provisional support found in application 63/486,824 at paragraph 0121-0122).“and the method further comprises: obtaining, by the one or more processors, a virtual model…” (paragraph 0287-0288; EN: This denotes the use of generating a virtual model for objects or objects viewed in VR; Provisional support found in application 63/486,824 at paragraph 0121-0122). “generating, by the one or more processors, a virtual configuration…” (paragraph 0287-0288; EN: This denotes the use of generating a virtual model for objects or objects viewed in VR; Provisional support found in application 63/486,824 at paragraph 0121-0122). “and presenting, via the display of the viewer device, the virtual configuration” (paragraph 0287-0288; EN: This denotes that the various transformations, modifications, and models can be shown in real time; Provisional support found in application 63/486,824 at paragraph 0121-0122).
However, Catalano fails to explicitly disclose “…and the tour is a virtual reality (VR) tour,”, “…associated with the VR tour;”, and “based upon the VR tour;”.
Brown teaches “and the tour is a virtual reality (VR) tour,” (paragraph 0006, 0047; EN: This denotes that the user device can display content related to VR and VR tours). “associated with the VR tour;” (paragraph 0006, 0047; EN: This denotes the creation of a VR tour). “based upon the VR tour;” (paragraph 0006, 0047; EN: This denotes the creation of a VR tour).
With regard to dependent claim 10,
Catalano teaches “The computer-implemented method of claim 9, further comprising: detecting, via the one or more processors, that the output of the ML chatbot includes an indication of an object of interest not included in the virtual configuration;” (paragraph 0287-0288 and 0290-0291; EN: This denotes that the chatbot can track and detect particular modifications and content, which is to be transformed, if they are present in the frames of a video; Provisional support found in application 63/486,824 at paragraph 0121-0122 and 0124-0125). “and updating, via the one or more processors, the virtual configuration to include the object of interest” (paragraph 0287-0288 and 0290; EN: This denotes that when a particular modification along with the content that’s meant to be transformed enters the frame of the video, the chatbot will modify or transform the object of interest by applying mesh models or overlays; Provisional support found in application 63/486,824 at paragraph 0121-0122 and 0124).
With regard to dependent claim 11,
Catalano teaches “The computer-implemented method of claim 8, wherein: the user device is a viewer device, the content is augmented reality (AR) content” (paragraph 0287-0288; EN: This denotes using the device sensors, such as cameras, on a client device to display AR, VR, and MR content; Provisional support found in application 63/486,824 at paragraph 0121-0122). “and the method further comprises: determining, via the one or more processors, a field of view of the viewer device associated with a user;” (paragraph 0287-0288; EN: This denotes that the chatbot can capture and modify various types of content in real time using device sensors as the objects enter, leave, and move around in the sensors field of view; Provisional support found in application 63/486,824 at paragraph 0121-0122). “based upon the field of view, determining, by the one or more processors, a position of an object of interest relative to the user;” (paragraph 0287-0288; EN: This denotes that the chatbot can detect various objects and track when the objects enter, leave, and move around the field of view; Provisional support found in application 63/486,824 at paragraph 0121-0122). “identifying, by the one or more processors, the object of interest;” (paragraph 0287-0288 and 0290; EN: This denotes that the chatbot can detect objects within the field of view of the video or image frame; Provisional support found in application 63/486,824 at paragraph 0121-0122 and 0124). “responsive to identifying the object of interest, obtaining, by the one or more processors, a model associated with the object of interest;” (paragraph 0287-0288; EN: This denotes that the chatbot can apply a 2D or 3D model on an object or objects within the field of view of the video or image frame; Provisional support found in application 63/486,824 at paragraph 0121-0122). “and based upon the position of the object of interest, overlaying, via the one or more processors, the object of interest model onto the object of interest via a display of the viewer device to generate a virtual configuration of the object of interest model proximate the object of interest;” (paragraph 0287-0288 and 0290; EN: This denotes that the chatbot can overlay 2D or 3d models as well as other transformations and modifications on objects of interest and other objects related to the object of interest; Provisional support found in application 63/486,824 at paragraph 0121-0122 and 0124).
However, Catalano fails to explicitly disclose “and the tour is an AR tour,”.
Brown teaches “and the tour is an AR tour,” (paragraph 0006 and 0047; EN: This denotes that the user device can display content related to AR and AR tours).
With regard to dependent claim 12,
Catalano fails to explicitly disclose “The computer-implemented method of claim 8, wherein: the content is audio-guided content”, “and the tour is a location-based audio-guided tour,”, “and the method further comprises: identifying, by the one or more processors, a location of the user;”, “responsive to identifying a location of the user, outputting, via the one or more processors, at least a portion of the audio-guided content associated with the identified user location”.
Brown teaches “The computer-implemented method of claim 8, wherein: the content is audio-guided content” (paragraph 0047 and 00082-0084; EN: This denotes that audio content associated with POIs (points of interest), such as AR spatial audio experiences, may be displayed when the user device is close to possible AR content. These experiences can play virtual instruments, speakers, or other audio content). “and the tour is a location-based audio-guided tour,” (paragraph 0047 and 0084; EN: This denotes that the AR content associated with POIs can be based on the various location data related to the user device, such as geolocation data, motion data, or orientation data). “and the method further comprises: identifying, by the one or more processors, a location of the user;” (paragraph 0084; EN: This denotes that a software application can determine the proximity of the user device to AR content based on the location of the user device). “responsive to identifying a location of the user, outputting, via the one or more processors, at least a portion of the audio-guided content associated with the identified user location” (paragraph 0084; EN: This denotes that the software application can play audio content related to the AR content based on the location of the user device).
Conclusion
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/S.S./
Examiner, Art Unit 2141
/MATTHEW ELL/ Supervisory Patent Examiner, Art Unit 2141
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