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 § 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 of this title, 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 1-3, 6-10, 13-17 and 20, are rejected under 35 U.S.C. 103 as being unpatentable by Ingel et al. (U.S. 2025/0006182 A1) in view of Crabtree et al. (U.S. 2025/0258685 A1).
Regarding Claim 1, Ingel discloses a computer-based method of generative AI-based questionnaire and transcript generation for virtual reality (VR) environments (Ingel, [0006] “methods for generating and operating artificial entities are provided” and [0039] “AI module 108—designed to generate text, images, and video as well as create an artificial entity (e.g., a digital clone) associated with a source individual” and [0037] “The artificial entity service host 130 may receive input data 102 from source individual 100, the received information may include answers to questionnaires 322 (e.g., responses to surveys or questionnaires), which can cover various topics” and [0043] “the computing such as a virtual reality (VR) device” Ingel teaches a computer-based method of an AI model includes generating questionnaire, transcripts (generated text) for a virtual reality environment (VR device), the method comprising:
generating, by a generative adversarial network (GAN), a video of a virtual interaction session between the one or more virtual avatars and the user in the VR environment based on the personalized questionnaire and the transcript (Ingel, Fig. 1, [0039] “AI module 108 may utilize generative adversarial networks (GANs), AI module 108 is capable of generating artificial entity 110. AI module 108 may learn from the person's behavior in videos to replicate their gestures, movements, and body language” and [0059] “the processing device 210 may receive data reflecting an interaction with the artificial entity (step 378), the received data include text input: providing a question is one of the simplest triggers, prompting the artificial entity to generate a response based on the text input it receives and [0089] “FIG. 4B, in step 454, the system generates an artificial entity for acting as a surrogate to the source individual based on the received information. This artificial entity may be a digital avatar, a virtual assistant, or a robotic interface designed to interact with users in a natural and engaging manner” Ingel teaches generating, by a GAN, a video of a virtual interaction session (e.g., user question, gestures, movements) between a virtual avatar and user in the VR environment based on the user’s question and the transcript (text input).
identifying first one or more interactions of the user with the one or more virtual avatars (Ingel, Fig. 4A, [0063] “interactions 410 may include reporting to source individual 100 about the response 408 that artificial entity 110 provided to reference individual 116C and feedback from source individual 100 on whether response 408 was appropriate” Ingel teaches identifying first one or more interactions (410) of the user (source individual 100, reference individual 116C) with a virtual avatars (artificial entity 110), Fig. 4A.
determining whether an answer can be derived above a threshold confidence level for one or more questions presented by the one or more virtual avatars in accordance with the transcript based on the first one or more interactions (Ingel, [0178] “the second situation faced by the artificial entity may be different from the first situations encountered by the source individual. An example of this could be a question asked by someone other than the source individual, and the artificial entity generates an answer based on the determined prompts indicative of personal bias, cultural influence, level of interest, expertise” and [0230] “Process 1100 further includes a step 1106 of identifying from the determined interest levels at least one topic of interest of the source individual. A topic of interest may be one where the determined interest level is greater than a threshold” Ingel teaches determining whether an answer can be derived greater than a threshold of interest level for the question presented by a virtual avatar (the artificial entity); and
based on determining the answer can be derived, adapting, by the GAN, the video of the virtual interaction session (Ingel, Fig. 1, [0039] “AI module 108 may utilize generative adversarial networks (GANs), AI module 108 is capable of generating artificial entity 110. AI module 108 may learn from the person's behavior in videos” and [0219] “the software would be valuable to identify (e.g., user just wrote this in a way that is single threaded-did you intend to? Can I optimize this for a specific hardware platform for you?). This could be returned as text-to-voice or generate a video to explain it to the user with an avatar” Ingel teaches based on determining the answer can be derived, adapting, by the GAN, the video of the virtual interaction session.
However, Ingel does not explicitly teach receiving real-time and historical data from one or more sources in a VR environment;
generating, by a first generative artificial intelligence (AI) model, a personalized questionnaire for a user regarding a VR experience based on the real-time and the historical data;
generating, by a second generative Al model, a transcript for one or more virtual avatars to interact with the user in the VR environment based on the personalized questionnaire;
Crabtree teaches receiving real-time and historical data from one or more sources in a VR environment (Crabtree, Fig. 2, [0083] “A generated UI/UX environment 240 represents the output of the user experience curation system 100. It is a dynamic, personalized, and context-aware interface that combines the outputs from the various AI subsystems. This environment adapts in real-time based on user interactions, preferences” and Fig. 22, [0217] “an AI system may be used to catalogue and suggest historical templatized interfaces and an ongoing “generative content” catalogue which may be stored in design catalogue database 2236” Crabtree teaches receiving real-time and historical data (via historical templatized interfaces) from a source (database 2236) in a VR environment (UI/UX environment 240) of AI system.
generating, by a first generative artificial intelligence (AI) model, a personalized questionnaire for a user regarding a VR experience based on the real-time and the historical data (Crabtree, [0071] “FIG.1. The user experience curation system 100 interacts with multiple components of the AI platform to gather relevant information and generate tailored user experiences, enabling the creation of user-specific profiles” and [0083] “A generated UI/UX environment 240 represents the output of the user experience curation system 100. This environment adapts in real-time based on user interactions, preferences” and [0178] “the AI model inference can generate a response, which could be an answer to a question, newly generated text” and [0028] “the use case directed to curating a user’s experience with the Internet and render content on a mobile app render, an AR/VR environment” Crabtree teaches generating by a first AI model a user profile’s question/answer for user experience in VR environment based on the real-time and the historical data (user interactions, preferences).
generating, by a second generative Al model, a transcript for one or more virtual avatars to interact with the user in the VR environment based on the personalized questionnaire (Crabtree, [0216] “one or more AI systems may be configured to analyze these websites/applications to identify common design patterns, elements, and layouts that can be used as templates” and [0219] “the software would be valuable to identify (e.g., user just wrote this in a way that is single threaded-did you intend to? Can I optimize this for a specific hardware platform for you?). This could be returned as text-to-voice or generate a video to explain it to the user with an avatar” Crabtree teaches generating (analyze application/software), by a 2nd AI model (AI system) to identify a transcript (text-to-voice or generate a video to explain it) for an interaction between the user with an avatar based on the personalized questionnaire e.g., Can I optimize this for a specific hardware platform for you?.
Ingel and Crabtree are combinable because they are from the same field of endeavor, system and method for image processing and try to solve similar problems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made for modifying the method of Ingel to combine with receiving real-time and historical data from one or more sources in a VR environment (as taught by Crabtree) in order to receive real-time and historical data from one or more sources in a VR environment because Crabtree can provide receiving real-time and historical data (via historical templatized interfaces) from a source (database 2236) in a VR environment (UI/UX environment 240) of AI system (Crabtree, Fig. 2, [0083], Fig. 22, [0217]). Doing so, it may allow the interface or experience to be tailored to a particular user, and collect a plurality of user feedback data which may input into an artificial intelligence training live or training system to better tailor an interface (Crabtree, [0011]).
Regarding Claim 2, a combination of Ingel and Crabtree discloses the computer-based method of claim 1, further comprising:
based on determining the answer cannot be derived, iterating, until the answer can be derived (Ingel, [0059] “if the artificial entity detects that the user is looking for recommendations. Multi-turn conversation: engaging in a back-and-forth conversation prompts the artificial entity to continue generating responses in a conversational manner. Sentiment analysis: the artificial entity detects the sentiment of the user's input and generates responses that match the emotional tone detected” Ingel teaches based on determining the answer cannot be derived, iterating (continuing), until the answer can be derived (the matching answer).
generating, by the second generative Al model, a modified transcript for one or more virtual avatars to interact with the user in the VR environment based on the personalized questionnaire (Ingel, Fig. 3B, [0059] “If it is not the first time, the processing device 210 updates the artificial entity 110 (step 376B) using the collected data and the received personalization parameters. Examples of the received data include text input: providing a prompt or a question is one of the simplest triggers, prompting the artificial entity to generate a response based on the text input it receives” Ingel teaches generating, by 2nd AI model, a modified transcript (text input) for a virtual avatar to interact with user based on the personalized parameters (questions);
generating, by the GAN, a video of a modified virtual interaction session between the one or more virtual avatars and the user in the VR environment based on the personalized questionnaire and the modified transcript (Ingel, Fig. 1, [0039] “AI module 108 may utilize generative adversarial networks (GANs), AI module 108 is capable of generating artificial entity 110” and Fig. 3B, [0059] “If it is not the first time, the processing device 210 updates the artificial entity 110 (step 376B) using the collected data and the received personalization parameters. Examples of the received data include text input: providing a prompt or a question is one of the simplest triggers, prompting the artificial entity to generate a response based on the text input it receives” Ingel teaches generating, by GAN, a modified transcript (text input) for a virtual avatar to interact with user based on the personalized parameters (questions); and
identifying subsequent one or more interactions of the user with the one or more virtual avatars (Ingel, [0150] “causing the artificial entity to control the at least one player character during a second time period subsequent to the first time period, wherein a second manner by which the artificial entity plays the online game is based on the first manner by which the source individual played the online game” and Fig. 6A, [0125] “the artificial assistant may schedule meetings, send messages, or manage other administrative tasks for John. As illustrated in FIG. 6A, artificial entity 110 (Emily) may be generated as John's artificial assistant” Ingel teaches identifying subsequent interactions of the user (John) with a virtual avatar (artificial entity 110), Fig. 6A.
Regarding Claim 3, a combination of Ingel and Crabtree discloses the computer-based method of claim 1, further comprising:
identifying additional one or more interactions of the user with the one or more virtual avatars in the adapted video (Ingel, Fig. 5A, [0094] “request 502 may be an invitation for a digital activity 504, such as a video conference that includes a number of human participants 506” and [0095] “utterance 508 may include a question asked by a human participant named Bill. In other cases, utterance 508 may be received by another artificial entity during the digital activity” and [0097] “the artificial entity could be programmed to deliver a presentation, respond to queries, or perform administrative tasks, the artificial entity 110 may generate a response 510 to address utterance 508 based on the determined manner” Ingel teaches identifying additional interactions (the utterance question 508, the response 510, Fig. 5A) of the user (506) with the avatar (artificial entity 110) in the adapted video (video conference for digital activity 504) and
obtaining one or more responses to the personalized questionnaire based on the additional one or more interactions (Ingel, [0095] “utterance 508 may include a question asked by a human participant named Bill. In other cases, utterance 508 may be received by another artificial entity during the digital activity” and [0097] “the artificial entity could be programmed to deliver a presentation, respond to queries, or perform administrative tasks, the artificial entity 110 may generate a response 510 to address utterance 508 based on the determined manner” Ingel teaches obtaining a response 510 to address a utterance question 508 based on the determined manner in the adapted video (video conference for digital activity 504), Fig. 5A.
Regarding Claim 6, a combination of Ingel and Crabtree discloses the computer-based method of claim 1, wherein adapting the video of the virtual interaction session further comprises:
adjusting, by the GAN, a placement of one or more items in the VR environment based on the first one or more interactions of the user (Ingel, Fig. 1, [0039] “AI module 108 may utilize generative adversarial networks (GANs), AI module 108 is capable of generating artificial entity 110” and [0034] “the inferred output may include an inferred value for an item depicted in the image (such as an estimated property of the item, such as size, volume, age of a person depicted in the image” Ingel teaches adjusting, by GAN, a placement (the inferred output) of one item(s) e.g., size, volume, age of a person based on interaction of user.
Regarding Claim 7, a combination of Ingel and Crabtree discloses the computer-based method of claim 1, where the first one or more interactions are selected from a group consisting of verbal feedback, facial expressions, and bodily gestures (Ingel, [0135] “This can include verbal feedback” and [0232] “The behavior patterns of the source individual can include both verbal and non-verbal parameters, such as body language and facial expressions” Ingel teaches user feedback, facial expression and body language.
Regarding Claim 8, a combination of Ingel and Crabtree discloses a computer system (Ingel, [0036] “System 150 may be computer-based and may include at least some computer system components”, the computer system comprising:
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories (Ingel, [0011] “a processing device 210 may include at least one processor configured to execute computer programs” and [0048] “Processing device 210 and/or memory device 220 may also include machine-readable media for storing software” Ingel teaches a processor is configured to execute computer program, a memory includes a machine-readable media for storing software, wherein the computer system is capable of performing a method comprising:
receiving real-time and historical data from one or more sources in a VR environment;
generating, by a first generative artificial intelligence (AI) model, a personalized questionnaire for a user regarding a VR experience based on the real-time and the historical data;
generating, by a second generative Al model, a transcript for one or more virtual avatars to interact with the user in the VR environment based on the personalized questionnaire;
generating, by a generative adversarial network (GAN), a video of a virtual interaction session between the one or more virtual avatars and the user in the VR environment based on the personalized questionnaire and the transcript;
identifying first one or more interactions of the user with the one or more virtual avatars;
determining whether an answer can be derived above a threshold confidence level for one or more questions presented by the one or more virtual avatars in accordance with the transcript based on the first one or more interactions; and
based on determining the answer can be derived, adapting, by the GAN, the video of the virtual interaction session.
Claim 8 is substantially similar to claim 1 is rejected based on similar analyses.
Regarding Claim 9, a combination of Ingel and Crabtree discloses the computer system of claim 8, the method further comprising:
based on determining the answer cannot be derived, iterating, until the answer can be derived:
generating, by the second generative Al model, a modified transcript for one or more virtual avatars to interact with the user in the VR environment based on the personalized questionnaire;
generating, by the GAN, a video of a modified virtual interaction session between the one or more virtual avatars and the user in the VR environment based on the personalized questionnaire and the modified transcript; and
identifying subsequent one or more interactions of the user with the one or more virtual avatars.
Claim 9 is substantially similar to claim 2 is rejected based on similar analyses.
Regarding Claim 10, a combination of Ingel and Crabtree discloses the computer system of claim 8, the method further comprising:
identifying additional one or more interactions of the user with the one or more virtual avatars in the adapted video; and
obtaining one or more responses to the personalized questionnaire based on the additional one or more interactions.
Claim 10 is substantially similar to claim 3 is rejected based on similar analyses.
Regarding Claim 13, a combination of Ingel and Crabtree discloses the computer system of claim 8, wherein adapting the video of the virtual interaction session further comprises:
adjusting, by the GAN, a placement of one or more items in the VR environment based on the first one or more interactions of the user.
Claim 13 is substantially similar to claim 6 is rejected based on similar analyses.
Regarding Claim 14, a combination of Ingel and Crabtree discloses discloses the computer system of claim 8, wherein the first one or more interactions are selected from a group consisting of verbal feedback, facial expressions, and bodily gestures.
Claim 14 is substantially similar to claim 7 is rejected based on similar analyses.
Regarding Claim 15, a combination of Ingel and Crabtree discloses a computer program product (Ingel, [0235] “a computer program”), the computer program product comprising:
one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more computer-readable tangible storage medium, the program instructions executable by a processor (Ingel, [0028] “a non-transitory computer-readable medium containing instructions that, when executed by at least one processor” capable of performing a method, the method comprising:
receiving real-time and historical data from one or more sources in a VR environment;
generating, by a first generative artificial intelligence (AI) model, a personalized questionnaire for a user regarding a VR experience based on the real-time and the historical data;
generating, by a second generative Al model, a transcript for one or more virtual avatars to interact with the user in the VR environment based on the personalized questionnaire;
generating, by a generative adversarial network (GAN), a video of a virtual interaction session between the one or more virtual avatars and the user in the VR environment based on the personalized questionnaire and the transcript;
identifying first one or more interactions of the user with the one or more virtual avatars;
determining whether an answer can be derived above a threshold confidence level for one or more questions presented by the one or more virtual avatars in accordance with the transcript based on the first one or more interactions; and
based on determining the answer can be derived, adapting, by the GAN, the video of the virtual interaction session.
Claim 15 is substantially similar to claim 1 is rejected based on similar analyses.
Regarding Claim 16, a combination of Ingel and Crabtree discloses the computer program product of claim 15, the method further comprising:
based on determining the answer cannot be derived, iterating, until the answer can be derived:
generating, by the second generative Al model, a modified transcript for one or more virtual avatars to interact with the user in the VR environment based on the personalized questionnaire;
generating, by the GAN, a video of a modified virtual interaction session between the one or more virtual avatars and the user in the VR environment based on the personalized questionnaire and the modified transcript; and
identifying subsequent one or more interactions of the user with the one or more virtual avatars.
Claim 16 is substantially similar to claim 2 is rejected based on similar analyses.
Regarding Claim 17, a combination of Ingel and Crabtree discloses the computer program product of claim 15, the method further comprising:
identifying additional one or more interactions of the user with the one or more virtual avatars in the adapted video; and
obtaining one or more responses to the personalized questionnaire based on the additional one or more interactions.
Claim 17 is substantially similar to claim 3 is rejected based on similar analyses.
Regarding Claim 20, a combination of Ingel and Crabtree discloses the computer program product of claim 15, wherein adapting the video of the virtual interaction session further comprises:
adjusting, by the GAN, a placement of one or more items in the VR environment based on the first one or more interactions of the user.
Claim 20 is substantially similar to claim 6 is rejected based on similar analyses.
Claims 4-5, 11-12, 18-19 are rejected under 35 U.S.C. 103 as being unpatentable by Ingel et al. (U.S. 2025/0006182 A1) in view of Crabtree et al. (U.S. 2025/0258685 A1) and further in view of Armstrong et al. (U.S. 2024/0394788 A1).
Regarding Claim 4, the computer-based method of claim 1, a combination of Ingel and Crabtree does not explicitly teach wherein generating the personalized questionnaire for the user further comprises:
identifying one or more items with which the user is currently engaged in the VR environment and one or more preferences of the user regarding the VR experience; and tailoring the personalized questionnaire to the user based on the identified one or more items and the one or more preferences.
However, Amstrong teaches identifying one or more items with which the user is currently engaged in the VR environment and one or more preferences of the user regarding the VR experience (Amstrong, [0045] “the virtual marketplace platform may enable companies, entities, businesses, etc. to pay for advertising, a virtual avatar holding an item (e.g., sign) that includes the identity of the company and/or an offer to purchase an item (e.g., food, beverage) offered by that company” and [0046] At any time, while a customer virtual avatar is navigating a virtual store, the user associated with the customer virtual avatar may select a graphical element on the user interface to request to speak to the entity virtual avatar” Amstrong teaches identifying purchase an items (e.g., food, beverage) offered by that company, while a customer virtual avatar is navigating a virtual store, the user associated with the customer virtual avatar may select a graphical element on the user interface to request to speak to the entity virtual avatar regarding the VR experience.
tailoring the personalized questionnaire to the user based on the identified one or more items and the one or more preferences (Amstrong, [0100] “These models can be trained to receive input from a customer virtual avatar, where the input comprises, questions posed by users within a virtual marketplace environment. The machine learning models can process these inputs to determine the appropriate output, which is an answer tailored to the user's inquiry” Amstrong teaches tailoring the user’s questions within a virtual marketplace environment as inputs to determine the appropriate output, which is an answer tailored to the user's inquiry.
Ingel, Crabtree and Amstrong are combinable because they are from the same field of endeavor, system and method for image processing and try to solve similar problems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made for modifying the method of Crabtree to combine with identifying item(s) with which the user is currently engaged in the VR environment (as taught by Amstrong) in order to identify item(s) with which the user is currently engaged in the VR environment because Amstrong can provide identifying purchase an items (e.g., food, beverage) offered by that company, while a customer virtual avatar is navigating a virtual store, the user associated with the customer virtual avatar may select a graphical element on the user interface to request to speak to the entity virtual avatar regarding the VR experience (Amstrong, [0045], [0046]). Doing so, it may allow the transfer and sharing of this accumulated expertise among avatars, businesses can optimize their customer engagement strategies utilizing artificial intelligence (Amstrong, [0009]).
Regarding Claim 5, the computer-based method of claim 1, a combination of Ingel and Crabtree does not explicitly teach wherein generating the transcript for the one or more virtual avatars further comprises:
associating a first virtual avatar of the one or more virtual avatars with a first segment of the VR environment and a second virtual avatar of the one or more virtual avatars with a second segment of the VR environment, wherein at least one question of the one or more questions presented by the first virtual avatar corresponds to the first segment of the VR environment and at least one question of the one or more questions presented by the second virtual avatar corresponds to the second segment of the VR environment.
However, Amstrong teaches associating a first virtual avatar of the one or more virtual avatars with a first segment of the VR environment and a second virtual avatar of the one or more virtual avatars with a second segment of the VR environment, wherein at least one question of the one or more questions presented by the first virtual avatar corresponds to the first segment of the VR environment and at least one question of the one or more questions presented by the second virtual avatar corresponds to the second segment of the VR environment (Amstrong, Fig. 49,[0101] At block 4904, to receive interaction data from a first virtual avatar. This interaction data can include the outputs, i.e., answers generated by the machine learning models in response to user inquiries within the first virtual marketplace environment and [0105] At block 4910, through the processing device, with the second virtual avatar updating the transferred knowledge base. This update enables the second avatar to provide one or more targeted responses to user inquiries based on the enriched knowledge base. The second avatar can now deliver personalized and informed interactions tailored to the specific needs of users in its virtual marketplace environment” Amstrong teaches associating a first virtual avatar with a first segment (block 4904, Fig. 49) and a second virtual avatar with a second segment (block 4910) in the VR environment, where question/answer presented by the first virtual avatar in response to user inquiries within the first virtual marketplace environment and the second avatar can now deliver personalized and informed interactions tailored to the specific needs of users in its virtual marketplace environment.
Ingel, Crabtree and Amstrong are combinable see rationale in claim 4.
Regarding Claim 11, a combination of Ingel, Crabtree and Amstrong discloses the computer system of claim 8, wherein generating the personalized questionnaire for the user further comprises:
identifying one or more items with which the user is currently engaged in the VR environment and one or more preferences of the user regarding the VR experience; and
tailoring the personalized questionnaire to the user based on the identified one or more items and the one or more preferences.
Claim 11 is substantially similar to claim 4 is rejected based on similar analyses.
Regarding Claim 12, a combination of Ingel, Crabtree and Amstrong discloses the computer system of claim 8, wherein generating the transcript for the one or more virtual avatars further comprises:
associating a first virtual avatar of the one or more virtual avatars with a first segment of the VR environment and a second virtual avatar of the one or more virtual avatars with a second segment of the VR environment, wherein at least one question of the one or more questions presented by the first virtual avatar corresponds to the first segment of the VR environment and at least one question of the one or more questions presented by the second virtual avatar corresponds to the second segment of the VR environment.
Claim 12 is substantially similar to claim 5 is rejected based on similar analyses.
Regarding Claim 18, a combination of Ingel, Crabtree and Amstrong discloses the computer program product of claim 15, wherein generating the personalized questionnaire for the user further comprises:
identifying one or more items with which the user is currently engaged in the VR environment and one or more preferences of the user regarding the VR experience; and
tailoring the personalized questionnaire to the user based on the identified one or more items and the one or more preferences.
Claim 18 is substantially similar to claim 4 is rejected based on similar analyses.
Regarding Claim 19, a combination of Ingel, Crabtree and Amstrong discloses the computer program product of claim 15, wherein generating the transcript for the one or more virtual avatars further comprises:
associating a first virtual avatar of the one or more virtual avatars with a first segment of the VR environment and a second virtual avatar of the one or more virtual avatars with a second segment of the VR environment, wherein at least one question of the one or more questions presented by the first virtual avatar corresponds to the first segment of the VR environment and at least one question of the one or more questions presented by the second virtual avatar corresponds to the second segment of the VR environment.
Claim 19 is substantially similar to claim 5 is rejected based on similar analyses.
Conclusion
The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure Fieldman et al. (U.S. 2026/0141103 A1) and Kaplan et al. (U.S. 2026/0228575 A1).
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/KHOA VU/Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
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