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 .
Status of the Claims
Claims 1-20 are currently pending in the present application, with claims 1, 8, and 15 being independent.
Response to Amendments / Arguments
Applicant’s arguments, see Pg. 14-16, filed 06/11/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art.
Regarding the remaining arguments: Applicant argues with respect to the amended claim language, which is fully addressed in the prior art rejections set forth below.
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) 1-6, 8-13, and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jarvela et al. "Augmented virtual reality meditation: Shared dyadic biofeedback increases social presence via respiratory synchrony." ACM Transactions on Social Computing 4, no. 2 (2021): 1-19, hereinafter referred to as “Jarvela”, in view of Suto et al. (US 20240193820 A1), hereinafter referred to as “Suto”, in further view of Muller (US 20190201786), and in further view of Chamola et al. "Beyond reality: The pivotal role of generative ai in the metaverse." arXiv preprint arXiv:2308.06272 (2023), hereinafter referred to as “Chamola”.
Regarding claim 1, Jarvela discloses a computer-implemented method for managing a virtual environment context (DYNECOM), the method comprising:
analyzing, by a computing device (Section 3.1 Hardware and Setup…), a plurality of avatars associated with the virtual environment (Fig. 1 and Section 2.2; DYNECOM incorporates EEG-based neurofeedback but adds social dynamics to the environment by having multiple simultaneous users sharing the same VR space),
wherein grouping comprises determining, (Section 3.3, Pg. 6:7; When the measured EEG states of both users reach the limit of synchrony (within the same 1/10th of their individual current range)…as the system highlights dyadic synchrony, it is visualized even in low approach motivation (suggesting low empathy) cases…The adaptivity results in a system where users showing synchrony of color, meaning them being currently at the same respective percentage of their individual ranges at this moment in the session, even if the raw FA-values (frontal symmetry) differ…Section 4.1; …instructed to concentrate on empathetic, warm, and compassionate feelings and direct them at the statue representing their pair…Section 4.5; synchrony index within each dyad…); and
modifying, by the computing device (Section 3.1 Hardware and Setup…), the virtual environment for the subset of the plurality of avatars in accordance with the virtual environment context based on the analysis of the plurality of avatars (Fig. 1-2 and Section 3.2; Depending on the test condition, the bridge, the scene lights and the aura-like ring surrounding active statues show various visual effects or cues to inform the user of their current state. Scene layout is shown in Figure 1…Section 3.3, Pg. 6:7; In the environment different colors are used to represent the amount of empathy related approach motivation measured with the EEG…Colors are present in all of the lights in the scene…When the measured EEG states of both users reach the limit of synchrony (within the same 1/10th of their individual current range) the glowing effect visualizing this is activated. The glowing effect raises the intensity and brightness of the color…Section 4.1; the different visualizations and color coding in the environment mean (e.g., "When the EEG adaptation is turned on, the color of the bridge and the halo around the statue will change from green (= a little) to pink (= a lot) according to how strongly you are directing the feelings you are aiming to conjure in the exercise towards the opposite statue.") and instructed to concentrate on empathetic, warm, and compassionate feelings and direct them at the statue representing their pair. They were also encouraged to use the information provided by the VR environment to enhance their exercise);
wherein modifying comprises transitioning from a first virtual environment scene to a second virtual environment scene (Fig. 1-2 and Section 4.2; experiment consisted of eight different conditions, each of which included a baseline measure, meditation, and a questionnaire about the meditation experience. The baseline measurement was conducted in a VR-room with an "X" on the wall…After two minutes the VR switched to the meditation environment. Condition scenarios differed based on which adaptions were being used (respiration, EEG, both, or no-biofeedback scenarios)…If the adaptions were active in the scenario, the environment started adapting to the participants' neurophysiological responses after the first 30 seconds. Section 3.2; This immersive setting provides several suitable aspects: It is a relaxed social situation with a shared activity, where nature provides a relaxing background, and the built-in elements balance the wilderness with familiarity. Each session starts by showing a minimalistic room for recording the participant’s baseline neurophysiological activation. It is followed by the meditation environment consisting of six stone statues sitting in a ring on a small shrine-like platform. The platform is surrounded by a short wall and a forest background lit by a cloudy evening sky. Dusk was chosen as scene lighting, as a dark ambience acts as a contrasting background in the visual hierarchy, guiding attention towards the neurofeedback cues and making them easily readable…)
Jarvela does not disclose wherein analyzing the plurality of avatars comprises utilizing electromyography to ascertain a plurality of attributes;
wherein utilizing the electromyography comprises analyzing, using one or more machine learning models, a plurality of facial muscles derived from at least one computer-mediated reality device associated with the virtual environment and utilizing one or more machine learning models to ascertain the virtual environment context;
ranking, by the computing device, each avatar of the plurality of avatars based on the analysis;
grouping, by the computing device, at least a subset of the avatars based on a scoring of each avatar associated with the ranking;
In the same art of multi-user virtual environment context, Suto discloses wherein analyzing the plurality of avatars comprises utilizing electromyography to ascertain a plurality of attributes (Par. 0047; muscle activity data (electromyography) collected by computing device 230 or any other applicable computing device associated with user 220 (e.g. mobile device, wearable device, etc.))
wherein utilizing the electromyography comprises analyzing, using one or more machine learning models, a plurality of facial muscles derived from at least one computer-mediated reality device associated with the virtual environment (Par. 0047; The machine learning models may further generate predictions pertaining to user 220, such as sentiments of user 220 based on statements, expressions, and/or movements of user 220 derived from audio data, biological data, muscle activity data (electromyography) collected by computing device 230 or any other applicable computing device associated with user 220 (e.g. mobile device, wearable device, etc.), in which the predictions pertaining to user 220 may be correlated and/or mapped to predictions pertaining to inanimate object 250. Machine learning module 310 is configured to utilize a machine learning model to predict the sentiment of user 220 and map the sentiment to a detected status or indicator of inanimate object 250) and utilizing one or more machine learning models to ascertain the virtual environment context (Par. 0006; generating an augmented reality based visualization associated with the inanimate object including at least one indicator of the status; generating a feedback associated with the inanimate object including the at least one indicator for a user; and transmitting the feedback to a wearable device of the user. Par. 0047; For example, the predicted sentiment of user 220 may be anger in response to a scratch/dent on the external surface of inanimate object 250 detected by inanimate object module 270. The output of the machine learning module representing the sentiment of anger is mapped to the detected scratch/dent allowing machine learning module 310 to predict and/or inanimate object module 270 to determine a related status of inanimate object 250 resulting in AR module 350 generating AR content including an angry or sad emoji/emoticon to be included in the AR visualization depicted through computing device 230 to user 220),
(Par. 0047; The machine learning models may further generate predictions pertaining to user 220, such as sentiments of user 220 based on statements, expressions, and/or movements of user 220 derived from audio data, biological data, muscle activity data (electromyography) collected by computing device 230 or any other applicable computing device associated with user 220 (e.g. mobile device, wearable device, etc.), in which the predictions pertaining to user 220 may be correlated and/or mapped to predictions pertaining to inanimate object 250. Machine learning module 310 is configured to utilize a machine learning model to predict the sentiment of user 220 and map the sentiment to a detected status or indicator of inanimate object 250),
comprising a theme and a plurality of virtual objects reflecting the virtual environment context and the affinity derived from one or more outputs of the one or more machine learning models (Par. 0047; The machine learning models may further generate predictions pertaining to user 220, such as sentiments of user 220 based on statements, expressions, and/or movements of user 220 derived from audio data, biological data, muscle activity data (electromyography) collected by computing device 230 or any other applicable computing device associated with user 220 (e.g. mobile device, wearable device, etc.), in which the predictions pertaining to user 220 may be correlated and/or mapped to predictions pertaining to inanimate object 250. Machine learning module 310 is configured to utilize a machine learning model to predict the sentiment of user 220 and map the sentiment to a detected status or indicator of inanimate object 250…).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Suto’s EMG-based machine-learning analysis into Jarvela’s biofeedback-responsive multi-user VR system. Doing so provides an additional physiological input capable of automatically determining user sentiment and corresponding environmental context, thereby enabling Jarvela’s adaptive virtual environment to respond more accurately to the users’ physiological and emotional states. Overall, yielding predictable results in improved accuracy of user attribute detection, enhancing user experience, and allowing more context-aware modification of multi-user virtual environments
Jarvela in view of Suto does not disclose ranking, by the computing device, each avatar of the plurality of avatars based on the analysis, and grouping, by the computing device, at least a subset of the avatars based on a scoring of each avatar associated with the ranking.
In the same art of avatar analysis, Muller discloses ranking, by the computing device (Fig. 1B), each avatar of the plurality of avatars based on the analysis (Par. 0142; the system may track character attributes of other characters as well and generate one or more rankings for the characters based on the character attributes. Par. 0230; the system develops rankings of characterizations for players and stores such data…),
grouping, by the computing device (Fig. 1B), at least a subset of the avatars (Fig. 3; group 325. Par. 0142; the system may attempt to identify complementary, for example similar or like-minded characters and/or players or characters or players that otherwise likely enhance the collective gaming experience) based on a scoring of each avatar associated with the ranking (see Par. 0281 on “bonding metric” and Par. 0132; The threshold can represent a statistically determined dividing line between an average among all players, and those players within a predefined category or characterization of players who are statistically more likely than the rest of the players to perform certain actions) based on the plurality of attributes (Par. 0058; the system identifies, determines, analyzes, and/or performs calculations regarding various items of information and/or data associated with users and user characters including characteristics, metrics, criteria, classifications, attributes, etc.).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Muller’s ranking and grouping techniques into the combined system of Jarvela and Suto. Doing so enables structured differentiation and organization of multiple avatars based on their analyzed attributes, yielding predictable results in improved tailoring to virtual environment modifications to specific subsets of users, therefore, providing more efficient and contextually relevant multi-user interactions within the virtual environment.
Jarvela in view of Suto and in further view of Muller does not disclose wherein modifying comprises transitioning from a first virtual environment scene to a second virtual environment scene generated by a generative adversarial network (GAN).
Chamola discloses wherein modifying comprises transitioning from a first virtual environment scene to a second virtual environment scene generated by a generative adversarial networks (GAN) (Table I, Fig. 3, and Pg. 6; NightCafe utilize GANs to generate high-quality landscape and scenery images…MidJourney utilize GANs to generate intermediate steps between given images, allowing for smooth transitions…during scene changes…) comprising a theme and a plurality of virtual objects (Table I; NightCafe…Generates high-quality landscape and scenery images based on user input or predefined themes. Table I; 3D Object Generation…).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Chamola’s GAN-based virtual environment generation into the combined adaptive VR system of Jarvela, Suto, and Muller. Doing so allows dynamic generation of virtual scenes corresponding to the determined context and characteristics of the grouped users. Chamola expressly recognizes generative AI as advantageous for creating personalized and dynamic virtual worlds, and teaches GAN-based generation of landscapes, scenery, and virtual environments based on user input or predefined themes (Pg. 5-6, Section IV; By leveraging advanced machine learning techniques, such as GANs (Generative Adversarial Networks) and variational autoencoders, generative AI models offer promising avenues for creating and enriching visual content within the virtual environment…). Therefore, employing Chamola’s known generative technique to generate the environment already adapted according to the users’ determined context would have predictably provided a more personalized, diversified, and immersive virtual environments in multi-user virtual experiences.
Regarding claim 2, Jarvela in view of Suto, in further view of Muller, and in further view of Chamola discloses the computer-implemented method of claim 1, and Jarvela further discloses wherein analyzing the plurality of avatars comprises: determining, (Jarvela Section 3.3, Pg. 6:7; In the environment different colors are used to represent the amount of empathy related approach motivation measured with the EEG…Individual minimum and maximum values of the monitored bio signals are tracked during the session and are used to define the individual's range…Section 4.1; EEG, electrocardiogram (ECG), EDA, and respiration were measured…The experiment consisted of baseline measurement session, meditation activity, and self-reporting. Fig. 3-4 and Section 4.4; To assess their subjective experiences, the participants also rated the three-dimensional emotion scale Valence (how positive or negative the experience was). Arousal (how intense the experience was), and Dominance (how in control they felt) using Self-Assessment Manakins (SAM)…These bi-directional scores were used to calculate empathic accuracy scores, and intersubjective symmetries (ISS) from Social Presence scales); and the first environment scene (Fig. 1-2 and Section 3.2; This immersive setting provides several suitable aspects: It is a relaxed social situation with a shared activity, where nature provides a relaxing background, and the built-in elements balance the wilderness with familiarity. Each session starts by showing a minimalistic room for recording the participant’s baseline neurophysiological activation. It is followed by the meditation environment consisting of six stone statues sitting in a ring on a small shrine-like platform. The platform is surrounded by a short wall and a forest background lit by a cloudy evening sky. Dusk was chosen as scene lighting, as a dark ambience acts as a contrasting background in the visual hierarchy, guiding attention towards the neurofeedback cues and making them easily readable…); and
scoring, by the computing device, each avatar of the plurality of avatars based on a determined compatibility associated with the virtual environment context (Jarvela Section 3.3, Pg. 6:7; When the measured EEG states of both users reach the limit of synchrony (within the same 1/10th of their individual current range) the glowing effect visualizing this is activated. The glowing effect raises the intensity and brightness of the color…as the system highlights dyadic synchrony, it is visualized even in low approach motivation (suggesting low empathy) cases…The adaptivity results in a system where users showing synchrony of color, meaning them being currently at the same respective percentage of their individual ranges at this moment in the session, even if the raw FA-values (frontal symmetry) differ…Section 4.1; the different visualizations and color coding in the environment mean (e.g., "When the EEG adaptation is turned on, the color of the bridge and the halo around the statue will change from green (= a little) to pink (= a lot) according to how strongly you are directing the feelings you are aiming to conjure in the exercise towards the opposite statue.") and instructed to concentrate on empathetic, warm, and compassionate feelings and direct them at the statue representing their pair. They were also encouraged to use the information provided by the VR environment to enhance their exercise…Section 4.5; synchrony index within each dyad…).
Jarvela does not disclose utilizing the one or more machine learning models.
Suto discloses determining, utilizing the one or more machine learning models, a sentiment of each avatar of the plurality of avatars (Par. 0047; The machine learning models may further generate predictions pertaining to user 220, such as sentiments of user 220 based on statements, expressions, and/or movements of user 220 derived from audio data, biological data, muscle activity data (electromyography) collected by computing device 230 or any other applicable computing device associated with user 220 (e.g. mobile device, wearable device, etc.), in which the predictions pertaining to user 220 may be correlated and/or mapped to predictions pertaining to inanimate object 250. Machine learning module 310 is configured to utilize a machine learning model to predict the sentiment of user 220 and map the sentiment to a detected status or indicator of inanimate object 250…For example, the predicted sentiment of user 220 may be anger in response to a scratch/dent on the external surface of inanimate object 250 detected by inanimate object module 270. The output of the machine learning module representing the sentiment of anger is mapped to the detected scratch/dent allowing machine learning module 310 to predict and/or inanimate object module 270 to determine a related status of inanimate object 250 resulting in AR module 350 generating AR content including an angry or sad emoji/emoticon to be included in the AR visualization depicted through computing device 230 to user 220).
Jarvela, Suto, Muller, and Chamola are combined for the reasons set forth above with respect to claim 1.
Regarding claim 3, Jarvela in view of Suto, in further view of Muller, and in further view of Chamola discloses the computer-implemented method of claim 2, and further discloses further comprising: generating, (Jarvela Section 3.3, Pg. 6:7; In the environment different colors are used to represent the amount of empathy related approach motivation measured with the EEG…Individual minimum and maximum values of the monitored bio signals are tracked during the session and are used to define the individual's range…Section 4.1; EEG, electrocardiogram (ECG), EDA, and respiration were measured…The experiment consisted of baseline measurement session, meditation activity, and self-reporting. Fig. 3-4 and Section 4.4; …These bi-directional scores were used to calculate empathic accuracy scores, and intersubjective symmetries (ISS) from Social Presence scales).
Jarvela does not disclose utilizing the one or more machine learning models.
Suto discloses generating, utilizing the one or more machine learning models, a visualized theme for the virtual environment (Par. 0006; generating an augmented reality based visualization associated with the inanimate object including at least one indicator of the status; generating a feedback associated with the inanimate object including the at least one indicator for a user; and transmitting the feedback to a wearable device of the user. Par. 0047; …For example, the predicted sentiment of user 220 may be anger in response to a scratch/dent on the external surface of inanimate object 250 detected by inanimate object module 270. The output of the machine learning module representing the sentiment of anger is mapped to the detected scratch/dent allowing machine learning module 310 to predict and/or inanimate object module 270 to determine a related status of inanimate object 250 resulting in AR module 350 generating AR content including an angry or sad emoji/emoticon to be included in the AR visualization depicted through computing device 230 to user 220.)
Jarvela, Suto, Muller, and Chamola are combined for the reasons set forth above with respect to claim 2.
Regarding claim 4, Jarvela in view of Suto, in further view of Muller, and in further view of Chamola discloses the computer-implemented method of claim 1, but Jarvela in view of Suto does not disclose wherein the scoring comprises a threshold correlated to the ranking of each avatar of the plurality of avatars, and the grouping is determined based on a score exceeding the threshold;
wherein the grouping is further based on a user profile of a plurality of users associated with the plurality of avatars.
In the same art of avatar analysis, Muller discloses wherein the scoring comprises a threshold correlated to the ranking of each avatar of the plurality of avatars (Muller Par. 0132; The threshold can represent a statistically determined dividing line between an average among all players, and those players within a predefined category or characterization of players who are statistically more likely than the rest of the players to perform certain actions…) and the grouping is determined based on a score exceeding the threshold (Muller Par. 0281; analyze which fellow players a given player frequently plays with, the number of communications with particular players, and so forth, to thereby determine whether the given player likes to play only with a certain group of other players or that their play with a certain group of players exceeds a given threshold, communicate with those players beyond a threshold, and so forth…);
wherein the grouping is further based on a user profile of a plurality of users associated with the plurality of avatars (Fig. 7 and Par. 0140; the system collects data related to game interactions, aggregates the data, and analyzes the data to identify characteristics of players and/or characters and develop a profile for players and/or characters…Par. 0148; When looking for a group or an activity partner or other similar individual(s), the player may make their information available, and matching software, either at the local client or through one or more other third party applications hosted on the network, analyzes this information to determine other suitable matches based on similar information provided by other users…Par. 0157; determines a character profile for the user based on that analysis. For example, the system may track the number of separate social interactions, such as conversations, that the player has over a particular period of time. The system could aggregate and analyze the collected information and classify players as “social” or “non-social” and notify the players of any players who are similarly interested in the social aspects of the game).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Muller’s threshold-based scoring and user profile grouping into the system Jarvela and Suto. Doing so provides a clear and objective criterion for distinguishing between avatars when performing grouping operations. Utilizing a threshold to allow the system to efficiently identify whether objects exceed a defined level is a common technique in the art, therefore using a known technique in the context of identifying subsets of avatars scores and profile databases enables consistent and scalable grouping decisions based on quantitative measures of user attributes. Such implementation yields predictable results in improved system’s ability to selectively group avatars in a multi-user virtual environment and apply necessary post calculation context-specific modifications.
Regarding claim 5, Jarvela in view of Suto, in further view of Muller, and in further view of Chamola discloses the computer-implemented method of claim 3, and further discloses wherein modifying the virtual environment comprises: transitioning, by the computing device, from a first virtual environment scene to a second virtual environment scene based on the analysis (Jarvela Fig. 1-2 and Section 4.2; experiment consisted of eight different conditions, each of which included a baseline measure, meditation, and a questionnaire about the meditation experience. The baseline measurement was conducted in a VR-room with an "X" on the wall…After two minutes the VR switched to the meditation environment. Condition scenarios differed based on which adaptions were being used (respiration, EEG, both, or no-biofeedback scenarios) …If the adaptions were active in the scenario, the environment started adapting to the participants' neurophysiological responses after the first 30 seconds).
wherein the second virtual environment scene is rendered based on a detected change to the sentiment or the affinity associated with the subset (Jarvela Fig. 1-2 and Section 4.1; the different visualizations and color coding in the environment mean (e.g., "When the EEG adaptation is turned on, the color of the bridge and the halo around the statue will change from green (= a little) to pink (= a lot) according to how strongly you are directing the feelings you are aiming to conjure in the exercise towards the opposite statue.") and instructed to concentrate on empathetic, warm, and compassionate feelings and direct them at the statue representing their pair. Section 4.2; Condition scenarios differed based on which adaptions were being used (respiration, EEG, both, or no-biofeedback scenarios) …If the adaptions were active in the scenario, the environment started adapting to the participants' neurophysiological responses after the first 30 seconds)
Jarvela, Suto, Muller, and Chamola are combined for the reasons set forth above with respect to claim 1.
Regarding claim 6, Jarvela in view of Suto, in further view of Muller, and in further view of Chamola discloses the computer-implemented method of claim 5, and further discloses wherein the second virtual environment scene comprises a shared theme for the avatars of the subset of the plurality of avatars in accordance with the affinity (Jarvela Section 4.1; the different visualizations and color coding in the environment mean (e.g., "When the EEG adaptation is turned on, the color of the bridge and the halo around the statue will change from green (= a little) to pink (= a lot) according to how strongly you are directing the feelings you are aiming to conjure in the exercise towards the opposite statue.") and instructed to concentrate on empathetic, warm, and compassionate feelings and direct them at the statue representing their pair. They were also encouraged to use the information provided by the VR environment to enhance their exercise. Examiner's note: shared theme is the meditation environment color visualization).
Jarvela, Suto, Muller, and Chamola are combined for the reasons set forth above with respect to claim 5.
Regarding claims 8 and 15, claim 8 is the CRM claim (Jarvela Section 3.1 Hardware and Setup…) and claim 15 is the system claim (Jarvela Section 3.1 Hardware and Setup…) of method claim 1, and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1.
Regarding claims 9 and 16, claims 9 and 16 has similar limitations as of claim 2, except claim 9 is the computer program product comprising a CRM claim (Jarvela Section 3.1 Hardware and Setup…) and claim 16 is the system claim (Jarvela Section 3.1 Hardware and Setup…) to the method claim 2, therefore it is rejected under the same rationale as claim 2.
Regarding claims 11 and 18, claims 11 and 18 has similar limitations as of claim 4, except claim 11 is the computer program product comprising a CRM claim (Jarvela Section 3.1 Hardware and Setup…) and claim 18 is the system claim (Jarvela Section 3.1 Hardware and Setup…) to the method claim 4, therefore it is rejected under the same rationale as claim 4.
Regarding claims 10 and 17, claims 10 and 17 has similar limitations as of claim 3, except claim 10 is the computer program product comprising a CRM claim (Jarvela Section 3.1 Hardware and Setup…) and claim 17 is the system claim (Jarvela Section 3.1 Hardware and Setup…) to the method claim 3, therefore it is rejected under the same rationale as claim 3.
Regarding claims 12 and 19, claims 12 and 19 has similar limitations as of claim 5, except claim 12 is the computer program product comprising a CRM claim (Jarvela Section 3.1 Hardware and Setup…) and claim 19 is the system claim (Jarvela Section 3.1 Hardware and Setup…) to the method claim 5, therefore it is rejected under the same rationale as claim 5.
Regarding claim 13, claims 13 has similar limitations as of claim 6, except claim 13 is the computer program product comprising a CRM claim (Jarvela Section 3.1 Hardware and Setup…) to the method claim 6, therefore it is rejected under the same rationale as claim 6.
Claim(s) 7, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jarvela et al. "Augmented virtual reality meditation: Shared dyadic biofeedback increases social presence via respiratory synchrony." ACM Transactions on Social Computing 4, no. 2 (2021): 1-19, hereinafter referred to as “Jarvela”, in view of Suto et al. (US 20240193820 A1), hereinafter referred to as “Suto”, in further view of Muller (US 20190201786), in further view of Chamola et al. "Beyond reality: The pivotal role of generative ai in the metaverse." arXiv preprint arXiv:2308.06272 (2023), hereinafter referred to as “Chamola”, and in further view of Allen et. al. (US 9779327), hereinafter referred to as “Allen”.
Regarding claim 7, Jarvela in view of Suto, in further view of Muller, and in further view of Chamola discloses the computer-implemented method of claim 4, but does not disclose analyzing, by the computing device, a plurality of social media profiles of the plurality of users associated with the plurality of avatars, and extracting, by the computing device, social media characteristics of the plurality of users from the plurality of social media profiles.
In the same art of avatar similarity matching, Allen discloses analyzing, by the computing device (Computing devices 104), a plurality of social media profiles of the plurality of users associated with the plurality of avatars (Column 14, lines 28-35; The cognitive traits avatar system 150 generates a dynamically changing cognitive trait avatar which provides an intuitive and elegant visualization of a person's personality trait-balance versus personality trait-dominance. In one illustrative embodiment, this visualization is dynamically adjusted based on a user's social media presence as the user's social media presence changes over time) and
extracting, by the computing device (Computing devices 104), social media characteristics of the plurality of users from the plurality of social media profiles. (Column 15, lines 2-12; Through analysis of current interactions (i.e. within a defined moving window of text or number of interactions) and/or a history of interactions with a social networking website, or plurality of social networking websites, or other systems/services that provide electronic communication in a textual format, voice-to-text format, or the like, the cognitive traits avatar system 150 is configured to extract features from these interactions and correlate the features with cognitive traits to help define a representation of the user's personality, i.e. the collection of cognitive traits).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the social-media based avatar analysis, as taught by Allen, into the combined augmented reality system of Jarvela, Suto, Muller, and Chamola. The motivation lies in the advantage of providing relevant attributes to an avatar’s character for further improved ranking and grouping accuracy. Because social media presence and activity is very prevalent, integrating social media profile data into avatar systems yields predictable improvement in avatar personalization and scoring.
Regarding claims 14 and 20, claims 14 and 20 has similar limitations as of claim 7, except claim 14 is the computer program product comprising a CRM claim (Jarvela Section 3.1 Hardware and Setup…) and claim 20 is the system claim (Jarvela Section 3.1 Hardware and Setup…) to the method claim 7, therefore it is rejected under the same rationale as claim 7.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JENNY NGAN TRAN whose telephone number is (571) 272-6888. The examiner can normally be reached Mon-Thurs 8am-5pm.
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/JENNY N TRAN/Examiner, Art Unit 2615
/YANNA WU/Primary Examiner, Art Unit 2615
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