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 Claims
This is a nonfinal rejection in response to amendments/remarks filed on 04/15/2026. Claims 1, and 11 are currently amended. Claims 7-9, and 17-19 are cancelled. Claims 21 and 22 are newly added. Claims 1-6, 10-16, and 20-22 are pending and are considered herein.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/15/2026 has been entered.
Priority
The effective filing date of the claims is the filing date of the provisional application #63/355,934 filed on 06/27/2022.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6, 11-14, and 16 are rejected under 35 U.S.C. 103 as being obvious over Vi Dinh Chau (US 11943074 B2) hereinafter Chau, in view of Krupa et al. (US 20160210568 A1) hereinafter Krupa.
Regarding Claim 1:
Chau discloses a real-time recommendation generator which provides suggestive actions for the speaker based on a positive or negative engagement level generated by the sentiment types detected from reactions on video. Chau teaches:
- arranging a voice sensor and image sensor in a way to capture voice signals and face images of participants during the team experience; (Chau [Col. 9 Line 58 – Col. 10 Line 8] The conferencing software 314 enables audio, video, and/or other forms of conferences between multiple participants, such as to facilitate a conference between those participants....In some cases, the participants may all be remote, in which the conferencing software 314 may facilitate a conference between the participants using different clients for the participants. [Col. 11 Lines 23-41] The reaction recognition software 408 determines a reaction of an audience participant in response to speaker actions or presentations. In an example, the reaction recognition software 408 monitors video of the audience participants during the conference implemented by the conferencing software 404 to determine video data-based reactions of audience participants in response to speaker participant actions or presentations... For example, the reaction recognition software 408 can use facial recognition and movement detection to determine facial expressions, gestures, head positions, and movement with respect to an audience device of the audience participant. In another example, the reaction recognition software 408 may supplement and/or confirm the video-based determinations by using a real-time transcription of the conference, to detect audible or verbal reactions of audience participants in response to speaker actions or presentations.)
- automatically determining, by the online experience-building platform, sentiment of the participants during the team experience by performing voice analysis on the captured voice signals and facial expression analysis on the captured face images; and(Chau [Col. 22 Lines 26-39] The real-time transcription is generated in real-time with a conversation occurring within a conference call attended by multiple participants including a speaker participant. The transcribed content including the audience participation reactions are evaluated using a contextual machine learning model to recognize reactions. The audio content is also evaluated using an audio-based contextual machine learning model to recognize sound reactions. The video content is processed using facial recognition and movement detection techniques to recognize visual reactions. The sentiment types are determined for each recognized reaction. The sentiment type processing includes determining a context of the speaker presentation associated with the audience participation reaction.)
- providing, via the second GUI, based on the determined sentiment of the participants, real-time action cues to the facilitator, wherein providing the real-time action cues comprises: (Chau [Col. 22 Lines 44-47] At 1004, an engagement level is based on the sentiment types. The sentiment types for the audience participation reactions for the audience participants are aggregated, accumulated, counted, or tracked. [Col. 22 Line 63 - Col. 23 Line 8] A real-time recommendation output is then determined based on the engagement level. In addition, the recommendation output processing can account for speaker participant presentation behavior as described herein. The real-time recommendation output can provide suggestions including, for example, maintain a present presentation topic and behavior due to a positive engagement level, change a present presentation topic due to a negative engagement level, change a present presentation behavior due to a negative engagement level, change a present presentation topic and a presentation behavior due to a negative engagement level, and/or pause a presentation due to a question. )
- recording actions performed by the facilitator responsive to the real-time action cues; and(Chau [Col. 13 Lines 34-46] The post-presentation analytics software 412 can aggregate conference sessions including, but not limited to, reaction detections, sentiment types, engagement levels, real-time recommendation outputs, and associated timestamps. The conference sessions can be of the speaker and other speakers. The aggregated conference sessions can indicate which real-time recommendation outputs were effective, what topics were interesting based on the engagement levels, what presentation behaviors were effective, trends, the impact of real-time recommendation outputs, and audience reaction to different speaker actions or behaviors. This can identify patterns with respect to audience reactions and different speaker actions or behaviors.) “Speaker actions” which include the behaviors in response to the real-time recommendation outputs, satisfy the limitation.
- activating the voice and image sensor, to capture additional voice signals and face images of the participants subsequent to the facilitator performing the actions; (Chau [Col. 11 Lines 23-41] The reaction recognition software 408 determines a reaction of an audience participant in response to speaker actions or presentations. In an example, the reaction recognition software 408 monitors video of the audience participants during the conference implemented by the conferencing software 404 to determine video data-based reactions of audience participants in response to speaker participant actions or presentations. The video can be from, for example, tiles or similar video windows, which show videos of the audience participants in the conference. For example, the reaction recognition software 408 can use facial recognition and movement detection to determine facial expressions, gestures, head positions, and movement with respect to an audience device of the audience participant. In another example, the reaction recognition software 408 may supplement and/or confirm the video-based determinations by using a real-time transcription of the conference, to detect audible or verbal reactions of audience participants in response to speaker actions or presentations.)
- determining impacts of the actions based on the additional voice signals and face images; (Chau [Col. 12 Lines 26-34] In an example, the audience engagement software 406 can determine, at or near a time of the reaction detection, a performance behavior of the speaker participant based on analyzing video data obtained from a device of the speaker and/or some or all of a real-time transcription of a presentation of the speaker using one or more contextual machine learning models. For example, the performance behavior can include, but are not limited to, monotonic speaking patterns, waving arms, no eye contact, and talking too fast. [Col. 12 Lines 1-14] The determined reaction can have multiple meanings depending on the context. For example, an audience participant nodding his or her head may have multiple meanings depending on the context of the speaker presentation. For example, if the context is a sales presentation, then the nodding can indicate a positive reaction. In another example, if the context is a customer service conversation, then the nodding can indicate a negative reaction. The sentiment types can include, but is not limited to, a positive reaction, negative reaction, questioning reaction, surprised reaction, neutral, or blank face reaction. The sentiment analysis software 410 aggregates the sentiment types to determine an engagement level or type.)
- training a first machine-learning model based on the impacts of the recorded actions, (Chau [Col. 13 Lines 11-17] The audience engagement software 406 can provide recommendation outputs for reaction detections in the historical conference sessions. These recommendation outputs can be used for training and education purposes. In yet another example, the recommendation outputs can be used to train a machine learning model specific to a speaker participant.)
- wherein training the first machine-learning model comprises correlating the actions performed by the facilitator with the impacts and updating parameters of the first machine-learning model based on the correlation; and(Chau [Col. 19 Line 55 – Col. 20 Line 14] In particular, the sentiment-based recommendation tool 804 determines the context of the conference at or near the time of the detected reaction by evaluating content of a real-time transcription of the conference using a contextual machine learning model. The determined context and the recognized reactions from a reaction detection software such as reaction detection software 702 as shown in FIG. 7 are input processed using a learning model 806 to determine a sentiment type associated with the participant of the conference. The learning model 806 may be or include a neural network (e.g., a convolutional neural network, recurrent neural network, or other neural network), decision tree, vector machine, Bayesian network, genetic algorithm, deep learning system separate from a neural network, or another machine learning model. The learning model 806 is trained to recognize context and reaction patterns. For example, the learning model 806 may be a contextual learning model which is trained to evaluate the recognized reaction in view of the determined context. For example, if the recognized reaction is a frown after an explanation by the speaker, then the sentiment type can be one of confusion. In another example, if the recognized reaction is a “yay” after a sales presentation, then the sentiment type can be one of elation. The learning model 806 evaluates the context and reaction against historical communication records 808 to determine when and which context and reaction pairs correspond different sentiment types. [Col. 17 Lines 13-25] In another example, sentiment types can be assigned a numerical value such as 10 for smiling and 0 for bored. The assigned values can change depending on the determined context. The engagement level can then be determined by averaging the numbers. A high value can be highly engaged and a low value can be not engaged. Alternatively, the engagement status determination tool 606 may use output of a learning model trained for contextual content processing to determine the engagement level. For example, the learning model, which may be a contextual machine learning model, may evaluate the sentiment types and a quantitative value for each sentiment type to produce output.) The “context” includes the actions performed by the facilitator, and the “reactions” fall within the scope of “impacts.” Col. 17 provides examples of “quantitative values” being updated based on the contexts.
- continuously updating the real-time action cues displayed on the second GUI based on outputs of the first machine learning model (Chau [Col. 23 Line 46- Col. 24 Line 8] In one or more implementations, the method may include evaluating content of a real-time transcription of the conference using a contextual machine learning model to identify the real-time recommendation output. In one or more implementations, the method may include determining a performance characterization of the speaker participant corresponding to the reaction detection, wherein the real-time recommendation output indicates, when the engagement level is positive, to continue discussing a current topic and to continue a speaker participant behavior. In one or more implementations, the method may include determining a performance characterization of the speaker participant corresponding to the reaction detection, wherein the real-time recommendation output indicates, when the engagement level is negative, to change at least one of a topic or a speaker participant behavior. In one or more implementations, the method may include maintaining engagement levels over a course of the conference to determine trends and determining an impact of real-time recommendation outputs on the engagement levels over the course of the conference. In one or more implementations, the real-time recommendation output indicates, when the engagement level is positive, to continue discussing a current topic. In one or more implementations, the real-time recommendation output indicates, when the engagement level is negative, to change to a new topic determined by a contextual machine learning model. In one or more implementations, the real-time recommendation output indicates, when the engagement level is neutral, to pause the conference for questions.)
- and updating the parameters of the first machine-learning model during the team experience to optimize engagement of the participants in the team experiences.(Chau [Col. 13 Lines 14-26] These recommendation outputs can be used for training and education purposes. In yet another example, the recommendation outputs can be used to train a machine learning model specific to a speaker participant. The trained machine learning model can then be used to provide real-time recommendations to the speaker participant when presenting during conferences, webinars, and other conferencing arrangements. In still another example, the post-presentation analytics software 412 can analyze the reaction detections, the sentiment types, the engagement levels, and the real-time recommendation outputs to determine effectiveness of speakers with respect to one or more presentations. [Col. 13 Lines 39-47] The aggregated conference sessions can indicate which real-time recommendation outputs were effective, what topics were interesting based on the engagement levels, what presentation behaviors were effective, trends, the impact of real-time recommendation outputs, and audience reaction to different speaker actions or behaviors. This can identify patterns with respect to audience reactions and different speaker actions or behaviors. This, in turn, can be used for training or education purposes. )
However, Chau fails to teach:
- A method for facilitating team experiences, the method comprising: receiving, via a first graphic user interface (GUI) of an online experience-building platform, from a team manager, input information associated with a team;
- receiving, a selection of a team experience based on the input information;
- coordinating, via a second GUI of the online experience-building platform, with an experience facilitator to facilitate the team experience;
Krupa teaches:
- A method for facilitating team experiences, the method comprising: receiving, via a first graphic user interface (GUI) of an online experience-building platform, from a team manager, input information associated with a team;(Krupa [0071] Thus, the illustrative embodiments provide a method and apparatus for identification of employee preferences and participation statistics among various activities to be performed as part of an employee wellness management system. Activity recommendations can then be based on that identification. These recommended activities are for people in an organization. In particular, the people may be employees in an organization. In one example, a process for managing wellness of employees is presented. [0110] Wellness management system 102 may receive user input selecting the display information in graphical user interface 134. Wellness management system 102 may also receive user input through graphical user interface 134 recommending, scheduling, monitoring and analyzing various activities 110 to be performed by employees 104, or a portion of employees 130, at one of locations 120. [0112] In this illustrative example, graphical user interface 134 includes administrator interface 148. Administrator interface 148 is an interface through which the administrators of wellness management system 102 or designated ones of employees 104 can interact with wellness management system 102. Wellness management system 102 can display information such as,... aggregate activity preferences 128, [0113] Administrator interface 148 is an interface through which administrators of wellness management system 102 or designated ones of employees 104 can receive input for recommending, scheduling, monitoring and analyzing various activities 110 to be performed by employees 104, or a portion of employees 130, at one of locations 120.)
- receiving, a selection of a team experience based on the input information; (Krupa [0083] Activity preferences 118 can include an indication of a general activity. The general activity may include, for example but not limited to, outdoor activities, indoor activities, team activities, [0222] From aggregate activity preferences 2012 displayed in FIG. 20B, administrator 2012 can easily identify that a majority of employees 104 are included in portion of employees 130 indicating activity preferences 118 for tennis. Because tennis is an activity preferred by a large portion of employees 130, wellness management system 102 can preferentially make recommendation 132 for recommended activity 136 of tennis to portion of employees 130 in order to maximize participation in recommended activity 136.)
- coordinating, via a second GUI of the online experience-building platform, with an experience facilitator to facilitate the team experience; (Krupa [0105] Wellness manager 129 can send recommendation 132 to portion of employees 130, and additionally send recommendation 132 to those other employees in order to, for example, introduce those other employees to additional activities, introduce those other employees to portion of employees 130, or to facilitate the other employees attaining desired level of wellness 112. [0109] In this illustrative example, display system 142 includes graphical user interface 134. In this illustrative example, wellness management system 102 can display information such as for example, at least one of user identification, current activities, historic monitoring information, employee wellness rankings, employer-sponsored incentives, employer-sponsored campaigns, recommendation 132, participation statistics 144, or other suitable information in graphical user interface 134.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Chau by implementing the sentiment analysis steps of Chau on a team experience platform such as Krupa’s, which enables the selection of a team experience based on input information from an arrangement of GUI’s. One would have reasonably arrived at the claimed limitations by performing the combination as it merely implements the teachings of Chau in the context of team experiences. One of ordinary skill in the art would have been motivated to perform the combination by Krupa’s benefit of improving employee wellness among various activities. (Krupa [0068] The illustrative embodiments implement and integrate the basic building blocks of managing employee wellness into something significantly more by applying the basic building blocks in a meaningful way to improve managing employee wellness beyond that provided by current uses of these basic building blocks. For example, the illustrative embodiments expand upon and integrate the basic building blocks of managing employee wellness into something significantly more by enabling identification of employee preferences and participation statistics among various activities as part of an employee wellness management system.)
Regarding Claim 11:
Chau teaches:
arranging a voice sensor and image sensor in a way to capture voice signals and face images of the participants during the team experience; (Chau [Col. 9 Line 58 – Col. 10 Line 8] The conferencing software 314 enables audio, video, and/or other forms of conferences between multiple participants, such as to facilitate a conference between those participants....In some cases, the participants may all be remote, in which the conferencing software 314 may facilitate a conference between the participants using different clients for the participants. [Col. 11 Lines 23-41] The reaction recognition software 408 determines a reaction of an audience participant in response to speaker actions or presentations. In an example, the reaction recognition software 408 monitors video of the audience participants during the conference implemented by the conferencing software 404 to determine video data-based reactions of audience participants in response to speaker participant actions or presentations... For example, the reaction recognition software 408 can use facial recognition and movement detection to determine facial expressions, gestures, head positions, and movement with respect to an audience device of the audience participant. In another example, the reaction recognition software 408 may supplement and/or confirm the video-based determinations by using a real-time transcription of the conference, to detect audible or verbal reactions of audience participants in response to speaker actions or presentations.)
automatically determining sentiment of the participants during the team experience by performing voice analysis on the captured voice signals and facial expression analysis on the face images; and(Chau [Col. 22 Lines 26-39] The real-time transcription is generated in real-time with a conversation occurring within a conference call attended by multiple participants including a speaker participant. The transcribed content including the audience participation reactions are evaluated using a contextual machine learning model to recognize reactions. The audio content is also evaluated using an audio-based contextual machine learning model to recognize sound reactions. The video content is processed using facial recognition and movement detection techniques to recognize visual reactions. The sentiment types are determined for each recognized reaction. The sentiment type processing includes determining a context of the speaker presentation associated with the audience participation reaction.)
providing, via the second GUI, based on the determined sentiment of the participants, real-time action cues to the facilitator wherein providing the real-time action cues comprises: (Chau [Col. 22 Lines 44-47] At 1004, an engagement level is based on the sentiment types. The sentiment types for the audience participation reactions for the audience participants are aggregated, accumulated, counted, or tracked. [Col. 22 Line 63 - Col. 23 Line 8] A real-time recommendation output is then determined based on the engagement level. In addition, the recommendation output processing can account for speaker participant presentation behavior as described herein. The real-time recommendation output can provide suggestions including, for example, maintain a present presentation topic and behavior due to a positive engagement level, change a present presentation topic due to a negative engagement level, change a present presentation behavior due to a negative engagement level, change a present presentation topic and a presentation behavior due to a negative engagement level, and/or pause a presentation due to a question. )
recording actions performed by the facilitator responsive to the real-time action cues; (Chau [Col. 13 Lines 34-46] The post-presentation analytics software 412 can aggregate conference sessions including, but not limited to, reaction detections, sentiment types, engagement levels, real-time recommendation outputs, and associated timestamps. The conference sessions can be of the speaker and other speakers. The aggregated conference sessions can indicate which real-time recommendation outputs were effective, what topics were interesting based on the engagement levels, what presentation behaviors were effective, trends, the impact of real-time recommendation outputs, and audience reaction to different speaker actions or behaviors. This can identify patterns with respect to audience reactions and different speaker actions or behaviors.) “Speaker actions” which include the behaviors in response to the real-time recommendation outputs, satisfy the limitation.
activating the voice sensor and image sensors, to capture additional voice signals and face images of the participants subsequent to the facilitator performing the actions; (Chau [Col. 11 Lines 23-41] The reaction recognition software 408 determines a reaction of an audience participant in response to speaker actions or presentations. In an example, the reaction recognition software 408 monitors video of the audience participants during the conference implemented by the conferencing software 404 to determine video data-based reactions of audience participants in response to speaker participant actions or presentations. The video can be from, for example, tiles or similar video windows, which show videos of the audience participants in the conference. For example, the reaction recognition software 408 can use facial recognition and movement detection to determine facial expressions, gestures, head positions, and movement with respect to an audience device of the audience participant. In another example, the reaction recognition software 408 may supplement and/or confirm the video-based determinations by using a real-time transcription of the conference, to detect audible or verbal reactions of audience participants in response to speaker actions or presentations.)
determining impacts of the actions based on the additional voice signals and face images; (Chau [Col. 12 Lines 26-34] In an example, the audience engagement software 406 can determine, at or near a time of the reaction detection, a performance behavior of the speaker participant based on analyzing video data obtained from a device of the speaker and/or some or all of a real-time transcription of a presentation of the speaker using one or more contextual machine learning models. For example, the performance behavior can include, but are not limited to, monotonic speaking patterns, waving arms, no eye contact, and talking too fast. [Col. 12 Lines 1-14] The determined reaction can have multiple meanings depending on the context. For example, an audience participant nodding his or her head may have multiple meanings depending on the context of the speaker presentation. For example, if the context is a sales presentation, then the nodding can indicate a positive reaction. In another example, if the context is a customer service conversation, then the nodding can indicate a negative reaction. The sentiment types can include, but is not limited to, a positive reaction, negative reaction, questioning reaction, surprised reaction, neutral, or blank face reaction. The sentiment analysis software 410 aggregates the sentiment types to determine an engagement level or type.)
- training a first machine-learning model based on the impacts of the recorded actions, (Chau [Col. 13 Lines 11-17] The audience engagement software 406 can provide recommendation outputs for reaction detections in the historical conference sessions. These recommendation outputs can be used for training and education purposes. In yet another example, the recommendation outputs can be used to train a machine learning model specific to a speaker participant.)
- wherein training the first machine-learning model comprises correlating the actions performed by the facilitator with the impacts and updating parameters of the first machine-learning model based on the correlation; and(Chau [Col. 19 Line 55 – Col. 20 Line 14] In particular, the sentiment-based recommendation tool 804 determines the context of the conference at or near the time of the detected reaction by evaluating content of a real-time transcription of the conference using a contextual machine learning model. The determined context and the recognized reactions from a reaction detection software such as reaction detection software 702 as shown in FIG. 7 are input processed using a learning model 806 to determine a sentiment type associated with the participant of the conference. The learning model 806 may be or include a neural network (e.g., a convolutional neural network, recurrent neural network, or other neural network), decision tree, vector machine, Bayesian network, genetic algorithm, deep learning system separate from a neural network, or another machine learning model. The learning model 806 is trained to recognize context and reaction patterns. For example, the learning model 806 may be a contextual learning model which is trained to evaluate the recognized reaction in view of the determined context. For example, if the recognized reaction is a frown after an explanation by the speaker, then the sentiment type can be one of confusion. In another example, if the recognized reaction is a “yay” after a sales presentation, then the sentiment type can be one of elation. The learning model 806 evaluates the context and reaction against historical communication records 808 to determine when and which context and reaction pairs correspond different sentiment types. [Col. 17 Lines 13-25] In another example, sentiment types can be assigned a numerical value such as 10 for smiling and 0 for bored. The assigned values can change depending on the determined context. The engagement level can then be determined by averaging the numbers. A high value can be highly engaged and a low value can be not engaged. Alternatively, the engagement status determination tool 606 may use output of a learning model trained for contextual content processing to determine the engagement level. For example, the learning model, which may be a contextual machine learning model, may evaluate the sentiment types and a quantitative value for each sentiment type to produce output.) The “context” includes the actions performed by the facilitator, and the “reactions” fall within the scope of “impacts.” Col. 17 provides examples of “quantitative values” being updated based on the contexts.
- continuously updating the real-time action cues displayed on the second GUI based on outputs of the first machine-learning model and updating the parameters of the first machine-learning model during the team experience to optimize the engagement of the participants; (Chau [Col. 13 Lines 14-26] These recommendation outputs can be used for training and education purposes. In yet another example, the recommendation outputs can be used to train a machine learning model specific to a speaker participant. The trained machine learning model can then be used to provide real-time recommendations to the speaker participant when presenting during conferences, webinars, and other conferencing arrangements. In still another example, the post-presentation analytics software 412 can analyze the reaction detections, the sentiment types, the engagement levels, and the real-time recommendation outputs to determine effectiveness of speakers with respect to one or more presentations. [Col. 13 Lines 39-47] The aggregated conference sessions can indicate which real-time recommendation outputs were effective, what topics were interesting based on the engagement levels, what presentation behaviors were effective, trends, the impact of real-time recommendation outputs, and audience reaction to different speaker actions or behaviors. This can identify patterns with respect to audience reactions and different speaker actions or behaviors. This, in turn, can be used for training or education purposes. )
However, Chau fails to teach:
A computer system implementing an experience-building platform for facilitating team experiences, the computer system comprising:
A processor; and
A storage device coupled to the processor and storing instructions, which when executed by the processor cause the processor to perform a method, the method comprising:
receiving, via a first graphic user interface (GUI) of an online experience-building platform from a team manager, input information associated with a team;
receiving a selection of a team experience based on the input information;
coordinating, via a second GUI of the online experience-building platform, with an experience facilitator to facilitate the team experience;
Alternatively, Krupa teaches:
- A computer system implementing an experience-building platform for facilitating team experiences, the computer system comprising: (Krupa [0071] Thus, the illustrative embodiments provide a method and apparatus for identification of employee preferences and participation statistics among various activities to be performed as part of an employee wellness management system. Activity recommendations can then be based on that identification. These recommended activities are for people in an organization. In particular, the people may be employees in an organization. In one example, a process for managing wellness of employees is presented. [0110] Wellness management system 102 may receive user input selecting the display information in graphical user interface 134. Wellness management system 102 may also receive user input through graphical user interface 134 recommending, scheduling, monitoring and analyzing various activities 110 to be performed by employees 104, or a portion of employees 130, at one of locations 120. [0112] In this illustrative example, graphical user interface 134 includes administrator interface 148. Administrator interface 148 is an interface through which the administrators of wellness management system 102 or designated ones of employees 104 can interact with wellness management system 102. Wellness management system 102 can display information such as,... aggregate activity preferences 128, [0113] Administrator interface 148 is an interface through which administrators of wellness management system 102 or designated ones of employees 104 can receive input for recommending, scheduling, monitoring and analyzing various activities 110 to be performed by employees 104, or a portion of employees 130, at one of locations 120. [0073] Wellness management system 102 can be implemented in computer system 101, where the computer system is a hardware system includes one or more data processing systems.)
- A processor; and (Krupa [0073] The data processing systems may be selected from at least one of a computer, a workstation, a server computer, a tablet computer, a laptop computer, a mobile phone, a personal digital assistant (PDA), or some other suitable data processing system.—then we can say that the steps may be distributed to different data processing systems in the computer system—then we have a data processing system diagram that shows a processor unit—that has one or more processors—i.e. chips with one or more cores on each chip.)
- A storage device coupled to the processor and storing instructions, which when executed by the processor cause the processor to perform a method, the method comprising:(Krupa [0277] Memory 2506 and persistent storage 2508 are examples of storage devices 2516. A storage device is any piece of hardware that is capable of storing information, such as, for example, without limitation, at least one of data, program code in functional form, or other suitable information either on a temporary basis, a permanent basis, or both on a temporary basis and a permanent basis. Storage devices 2516 may also be referred to as computer readable storage devices in these illustrative examples. Memory 2506, in these examples, may be, for example, a random access memory or any other suitable volatile or non-volatile storage device. Persistent storage 2508 may take various forms, depending on the particular implementation.)
- receiving, via a first graphic user interface (GUI) of an online experience-building platform from a team manager, input information associated with a team; (Krupa [0083] Activity preferences 118 can include an indication of a general activity. The general activity may include, for example but not limited to, outdoor activities, indoor activities, team activities, [0222] From aggregate activity preferences 2012 displayed in FIG. 20B, administrator 2012 can easily identify that a majority of employees 104 are included in portion of employees 130 indicating activity preferences 118 for tennis. Because tennis is an activity preferred by a large portion of employees 130, wellness management system 102 can preferentially make recommendation 132 for recommended activity 136 of tennis to portion of employees 130 in order to maximize participation in recommended activity 136.)
- receiving a selection of a team experience based on the input information; (Krupa [0083] Activity preferences 118 can include an indication of a general activity. The general activity may include, for example but not limited to, outdoor activities, indoor activities, team activities, [0222] From aggregate activity preferences 2012 displayed in FIG. 20B, administrator 2012 can easily identify that a majority of employees 104 are included in portion of employees 130 indicating activity preferences 118 for tennis. Because tennis is an activity preferred by a large portion of employees 130, wellness management system 102 can preferentially make recommendation 132 for recommended activity 136 of tennis to portion of employees 130 in order to maximize participation in recommended activity 136.)
- coordinating, via a second GUI of the online experience-building platform, with an experience facilitator to facilitate the team experience; (Krupa [0105] Wellness manager 129 can send recommendation 132 to portion of employees 130, and additionally send recommendation 132 to those other employees in order to, for example, introduce those other employees to additional activities, introduce those other employees to portion of employees 130, or to facilitate the other employees attaining desired level of wellness 112. [0109] In this illustrative example, display system 142 includes graphical user interface 134. In this illustrative example, wellness management system 102 can display information such as for example, at least one of user identification, current activities, historic monitoring information, employee wellness rankings, employer-sponsored incentives, employer-sponsored campaigns, recommendation 132, participation statistics 144, or other suitable information in graphical user interface 134.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Chau by implementing the sentiment analysis steps of Chau on a team experience platform such as Krupa’s, which enables the selection of a team experience based on input information from an arrangement of GUI’s. One would have reasonably arrived at the claimed limitations by performing the combination as it merely implements the teachings of Chau in the context of team experiences. One of ordinary skill in the art would have been motivated to perform the combination by Krupa’s benefit of improving employee wellness among various activities. (Krupa [0068] The illustrative embodiments implement and integrate the basic building blocks of managing employee wellness into something significantly more by applying the basic building blocks in a meaningful way to improve managing employee wellness beyond that provided by current uses of these basic building blocks. For example, the illustrative embodiments expand upon and integrate the basic building blocks of managing employee wellness into something significantly more by enabling identification of employee preferences and participation statistics among various activities as part of an employee wellness management system.)
Regarding Claims 2 and 12:
The combination of Chau and Krupa teaches The method of claim 1/The computer system of claim 11
However, Chau fails to teach:
-wherein the input information comprises one or more of:
-a profile of the team;
-an occasion for the team experience; and
-team goal.
Alternatively, Krupa teaches:
-wherein the input information comprises one or more of: a profile of the team; an occasion for the team experience; and team goal.(Krupa [0104] Recommended time 138 is a time at which or during which recommended activity 136 occurs. According to an illustrative embodiment, wellness management system 102 can identify recommended time 138 based on availability information parsed from calendar applications for employees 104. In this manner, wellness management system 102 can account for scheduled vacation days, personal days, sick days, times during which business activities or meetings are scheduled for employees 104, times during which employees 104 are geographically remote from recommended location 140, times during which others of activities 110 are scheduled for employees 104 [0077] Characteristics 108 can include desired level of wellness 112. Desired level of wellness 112 can be at least one of a target standard or goal for various health related parameters... Desired level of wellness 112 can include one-time standards or goals, or also periodic repeating standards or goals, such as daily goals, weekly goals, yearly goals, or some other suitable types of goals.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Chau by implementing the sentiment analysis steps of Chau on a team experience platform such as Krupa’s, which enables the selection of a team experience based on input information from an arrangement of GUI’s. One would have reasonably arrived at the claimed limitations by performing the combination as it merely implements the teachings of Chau in the context of team experiences. One of ordinary skill in the art would have been motivated to perform the combination by Krupa’s benefit of improving employee wellness among various activities. (Krupa [0068] The illustrative embodiments implement and integrate the basic building blocks of managing employee wellness into something significantly more by applying the basic building blocks in a meaningful way to improve managing employee wellness beyond that provided by current uses of these basic building blocks. For example, the illustrative embodiments expand upon and integrate the basic building blocks of managing employee wellness into something significantly more by enabling identification of employee preferences and participation statistics among various activities as part of an employee wellness management system.)
Regarding Claims 3 and 13:
The combination of Chau and Krupa teaches The method of claim 2/The computer system of claim 12,
However, Chau fails to teach:
-wherein the profile of the team comprises one or more of:
-size; geographic distribution; industry; history of the team; and state of members of the team.
Alternatively, Krupa teaches: -wherein the profile of the team comprises one or more of:
-size; geographic distribution; industry; history of the team; and state of members of the team. (Krupa [0104] In this manner, wellness management system 102 can account for scheduled vacation days, personal days, sick days, times during which business activities or meetings are scheduled for employees 104, times during which employees 104 are geographically remote from recommended location 140, times during which others of activities 110 are scheduled for employees 104, or other conflicting engagements that might impede employees 104 or portion of employees 130 from participating in recommended activity 136. [0109] In this illustrative example, display system 142 includes graphical user interface 134. In this illustrative example, wellness management system 102 can display information such as for example, at least one of user identification, current activities, historic monitoring information, [0115] By displaying the information in polar charts 150, wellness management system 102, administrators utilizing administrator interface 148 can more quickly determine at least one of normalities, similarities, or outliers among employees 104, activities 110, locations 120, aggregate activity preferences 128, and participation statistics 144. )
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Chau by implementing the sentiment analysis steps of Chau on a team experience platform such as Krupa’s, which enables the selection of a team experience based on input information from an arrangement of GUI’s. One would have reasonably arrived at the claimed limitations by performing the combination as it merely implements the teachings of Chau in the context of team experiences. One of ordinary skill in the art would have been motivated to perform the combination by Krupa’s benefit of improving employee wellness among various activities. (Krupa [0068] The illustrative embodiments implement and integrate the basic building blocks of managing employee wellness into something significantly more by applying the basic building blocks in a meaningful way to improve managing employee wellness beyond that provided by current uses of these basic building blocks. For example, the illustrative embodiments expand upon and integrate the basic building blocks of managing employee wellness into something significantly more by enabling identification of employee preferences and participation statistics among various activities as part of an employee wellness management system.)
Regarding Claims 4 and 14:
The combination of Chau and Krupa teaches The method of claim 2/The computer system of claim 12,
Furthermore, Chau teaches:
- wherein the team goal comprises one or more of:
- improving trust; improving collaboration; and improving communication.(Chau [Col. 17 Lines 37-47] In another example, a change type recommendation output can include a recommended speaker presentation behavior. For example, when a current behavior is determined to be monotonic, the recommendation determination tool 608 can recommend change voice modulations to the speaker. In another example, when the speaker is mumbling, the recommendation determination tool 608 can recommend to the speaker to speak more clearly and/or loudly. The recommendation determination tool 608 can provide, for example, a combination of the topic and presentation behavior recommendations based on the engagement level.) Recommending the speaker to speak more clearly or loudly is an example of improving communication.
Regarding Claims 6 and 16:
The combination of Chau and Krupa teaches: The method of claim 5/The computer system of claim 15, further comprising
However, Chau fails to teach:
-displaying the recommended experiences to the team manager to allow the team manager to select the team experience from the recommended experiences.
Krupa teaches:
-displaying the recommended experiences to the team manager to allow the team manager to
select the team experience from the recommended experiences.(Krupa [0114] In this illustrative example, graphical user interface 134 can display at least one of aggregate activity preferences 128 or participation statistics 144 in polar charts 150. [0115] By displaying the information in polar charts 150, wellness management system 102, administrators utilizing administrator interface 148 can more quickly determine at least one of normalities, similarities, or outliers among employees 104, activities 110, locations 120, aggregate activity preferences 128, and participation statistics 144. [0225] From polar chart 2014 in FIGS. 20D and 20E, administrator 2012 can easily identify portion of employees 130 that has indicated a preference or dislike for each of the selected activity types 2010. Additionally, administrator 2012 can easily identify which of the selected activity types 2010 are preferred by a greater number of employees 104. Wellness management system 102 can therefore include a greater number of employees 104 in portion of employees 130 when making recommendation 132. [0178] Administrator 1210 can perform other administrative functions within wellness management system 102 by selecting appropriate icons within administrator identification 1200. As depicted, administrator 1210 can schedule one of activities 110 by selecting icon 1214;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify Chau by adding Krupa’s teachings of displaying recommended activities to an administrator to enable the administrator to select an activity. One of ordinary skill in the art would have been motivated by the benefit of Krupa’s system’s to allow an administrator to determine that a certain activity is preferable/logistically feasible for the most amount of employees. (Krupa [0220] Based on the selection of activity type 2010, wellness management system 102 updates aggregate activity preferences 2012 to graphically indicate aggregate activity preference 128 of employees 104 for tennis. From aggregate activity preferences 2012 displayed in FIG. 20B, administrator 2012 can easily identify that a majority of employees 104 are included in portion of employees 130 indicating activity preferences 118 for tennis. Because tennis is an activity preferred by a large portion of employees 130, wellness management system 102 can preferentially make recommendation 132 for recommended activity 136 of tennis to portion of employees 130 in order to maximize participation in recommended activity 136.)
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being obvious over Chau (US 11943074 B2), in view of Krupa (US 20160210568 A1), further in view of Gross (US 8744900 B2) hereinafter Gross.
Regarding Claims 10 and 20:
Chau and Krupa teaches: The method of claim 1/The computer system of claim 11
However, neither Chau nor Krupa teach or suggest:
-further comprising providing, via a third GUI of the online experience-building platform to a kit provider, delivery information for a number of participants of the team experience, thereby allowing the kit provider to send an experience kit to each participant;
-wherein providing the delivery information for the participants comprises:
-contacting each participant to request a delivery address and kit-configuration information; and
-allowing the kit provider to access the delivery address and kit-configuration.
Alternatively, Gross discloses of facilitating a group wine-tasting experience to increase collaboration while allowing remote individuals to experience the same sample kits of wine at the same time and provide their ratings and opinions. Gross teaches:
-further comprising providing, via a third GUI of the online experience-building platform to a kit provider, delivery information for a number of participants of the team experience, thereby allowing the kit provider to send an experience kit to each participant; (Gross[Col 11. Lines 1-2] Returning to FIG. 2, at step 210 the individual participant profiles are obtained. [Col 11. Lines 10-13] Again in FIG. 2, the participant data preferably includes such information as age, sex, residence address, and other similar demographic data. [Col. 11 Lines 32-36]Generally speaking, website 135 is enabled with a feature (see below in FIG. 4A) that allows a host to merely specify these parameters so that the wines are automatically selected, packaged and delivered to the event host prior to the event date. [Col. 21 Lines 19-21] For example, a host can decide whether they are going to run/manage the event on their own, or if they are going to rely on wine party website 135 instead. [Col. 21 Lines 31-34] In those cases where website is to supply the items, an option can then be elected for the bottles to be shipped to the event host with blanking labels to obscure their origin.[Col. 21 Line 64 – Col. 22 Line 5] Within the interface are also further options for configuring the event. For example at button/field 411 an additional menu/screen (not shown) is implemented in conventional fashion for capturing the participant names, emails, and other desired profile information as noted above in step 210. Field 413 allows the event host to select among and pick the different types of sampling kits that can be used for the event as noted above. The host's account information, including contact and billing information, can be selected from field 414.) In these citations, the kit provider can be mapped to the website that receives parameters which include address, to provide the kits to the event host. The kit provider sends the kits to the event host, which can also be mapped to the “each participant” limitation.
-wherein providing the delivery information for the participants comprises:
-contacting each participant to request a delivery address and kit-configuration information; and allowing the kit provider to access the delivery address and kit-configuration. (Gross[Col. 11 Line 1-13] Returning to FIG. 2, at step 210 the individual participant profiles are obtained. Again, as noted earlier, this may be secured from preexisting database records, from information gleaned from a participant's data collecting tool, etc. Alternatively it can be entered manually to compile an event participant list using an interface of event management website 135 as seen, for example in FIG. 4B. As seen therein a user can simply connect to an event in progress at the appropriate time. Again in FIG. 2, the participant data preferably includes such information as age, sex, residence address, and other similar demographic data.[Col. 22 Line 65-Col. 23 Line 3] In area 443 of the interface the participant can see predictions and recommendations for other wines/items based on correlations to other items. Negative correlations can also be accommodated if desired. Thus the participants can be given specific tailored suggestions on items that they are likely to enjoy given a positive/negative rating for a particular wine.) Gross’ participant’s data collecting tool which manually allows participants to input their information including address teaches the limitation above. Additionally, Gross’ allows the participants to configure the contents of the kit and even gives them recommendations.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of Krupa, and Chau by adding the feature of providing group tasting events with sampling kits as taught by Gross as a possible recommended group activity as taught by Krupa. One of ordinary skill in the art would have been motivated to make this combination because the inclusion of wine tasting parties would provide the benefit of being an entertaining and enjoyable activity and that can help provide a shared experience for people and build community. Further providing an ability to deliver the kit to the house and tailor the kit to each individual has the benefit of further providing these services remotely, giving more access to individuals wherever they are. (Gross [Col. 1 Lines 35-56])
Subject Matter Distinguished Over Prior Art
Claims 5, 15, 20, and 21 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding Claims 5 and 15:
The combination of Chau and Krupa teach The method of claim 2/The computer system of claim 12
However, neither Chau nor Krupa teach or suggest:
- further comprising accessing an experience database to determine one or more team experiences to be recommended to the team manager based on the input information;
- wherein determining the team experiences comprises applying a second machine-learning model previously trained based on historical data collected from a plurality of team managers to recommend the one or more experiences based at least on the profile of the team and the team goal.
The specific limitation of determining team experiences using a second machine-learning model previously trained based on historical data collected from a plurality of team managers to recommend the one or more experiences based at least on the profile of the team and the team goal recites a specific combination of inputs and outputs that would not have been obvious to one of ordinary skill in the art in view of the prior art of record. While Krupa teaches a selection of team experiences recommended to the administrator, these team experiences are selected based on activity statistics, feedback opinions, and the profile of the team but are not centered on a team goal. Krupa does not teach that machine learning is used to determine the experience. While Chau uses machine learning, Chau does not teach a second machine-learning model specifically for selecting a particular team experience. While Chau’s contextual machine learning model is used for determining the sentiment of a particular topic, it would not have been obvious to combine Krupa and Chau to arrive at the claimed limitation as neither are used to optimize a team goal. Furthermore, the remaining prior art yielded in a search does not remedy such a deficiency. Finally, by virtue of their dependency on claims 5 and 15, claims 20 and 21 respectively, also distinguish over the prior art.
Response to Arguments
Applicant's arguments filed 04/15/2026 have been fully considered.
Regarding the applicant’s remarks over rejections under 35 U.S.C. 101, the applicant asserts that the claims are directed to “specific technological solution that integrates multiple hardware and software components to solve the technical problem facing online meeting tools, as current tools lack the ability to automatically monitor and respond to participant sentiment without human observation and judgement.” The applicant’s arguments regarding 101 are persuasive, particularly in step 2B, where the combination of additional elements, particularly, the combination of voice and image sensors, the timing in which they are activated, the coordination between multiple graphical user interfaces, the training of the machine learning model by correlating the actions, recites a meaningful limitation of the abstract idea of facilitating team experiences, such that it is a technical improvement to technological systems. Thus, when viewed as a whole, the claims recite significantly more than the abstract idea, therefore, the rejection of the claims under 35 U.S.C. 101 has been withdrawn.
Regarding the applicant’s arguments over Step 2B, the applicant asserts that the claims are comparable to BASCOM, because they describe a specific closed-loop arrangement, that in combination allegedly impose a meaningful limitation. In BASCOM, the non-conventional and non-generic arrangement of known, conventional pieces led to an improvement in filtering internet content. Therefore, the applicant’s arguments that, “when viewed in combination, these additional elements impose a meaningful limitation (i.e., the feedback loop for continuous model training and action cue updating) that goes beyond merely applying the abstract idea on a generic computer” is persuasive.
The amendments to the claims have necessitated an updated search which have necessitated a new grounds of rejection in view of the references, therefore, the claims 1-4, 6, 11-14, and 16 are rejected under 35 U.S.C. 103 as being obvious over Vi Dinh Chau (US 11943074 B2) hereinafter Chau, in view of Krupa et al. (US 20160210568 A1) hereinafter Krupa. Claims 5, 15, 20, and 21 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Jain et al. (US 20220198949 A1) discloses a method for determining real-time engagement scores in interactive online learning sessions.
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/NICO L PADUA/Junior Patent Examiner, Art Unit 3626
/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
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