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 final rejection in response to amendments/remarks filed on 12/16/2025. Claims 1, and 11 are currently amended. Claims 7-9, and 17-19 are cancelled. Claims 1-6, 10-16, and 20 are pending and are considered herein.
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 10-16, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a Process, Machine, Manufacture, or Composition of Matter?
Claim 1 and its dependent claims recite a method which falls under the potentially eligible subject matter category of a Process. Claim 11 and its dependent claims recite a computer apparatus, therefore it is directed to at least one potentially eligible subject matter category of at least one of: a process, a product, or a machine. Therefore the claims are to be further analyzed under step 2 of the 2 step eligibility analysis.
Step 2a Prong 1: Is the claim directed to a Judicial Exception (A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claims 1, and 11 are marked up, isolating the abstract idea from additional elements, wherein the abstract idea is set in bold and the additional elements have been italicized as follows:
Claim 1 – 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;
accessing an experience database to determine one or more team experiences to be recommended to the team manager based on the input information;
displaying the determined team experiences on the first GUI;
receiving, via the first GUI from the team manager, at least a selection of a team experience;
providing, via a second 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;
coordinating, via a third GUI of the online experience-building platform, with an experience facilitator to facilitate the team experience;
activating a voice sensor and image sensor to record voices and facial expressions of the participants during the team experience;
analyzing the recorded voices and facial expressions to determine sentiment of the participants during the team experience;
providing, via the third GUI, real-time feedback to the experience facilitator based on the determined sentiment of the participants;
wherein the real-time feedback comprises real-time action cues for the facilitator to perform one or more actions to improve engagement of the participants;
recording the one or more actions performed by the facilitator and
obtaining, via the voice and image sensor, impacts of the one or more actions on the participants;
training a first machine-learning model based on the impacts of the recorded actions,
continuously updating the real-time action cues displayed on the third GUI based on outputs of the first machine learning model and training the first machine-learning model during the team experience to optimize the engagement of the participants
receiving, via the first GUI from the team manager, post-experience survey data; and
continuously training a second machine-learning model based on the team experience and the post-experience survey data, wherein the second machine-learning model has been previously trained based on historical data collected from a plurality of team managers and is used to recommend the one or more team experiences.
Claim 11 – 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;
accessing an experience database to determine one or more team experiences to be recommended to the team manager based on the input information;
displaying the recommended team experiences on the first GUI;
providing, via a second 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;
coordinating, via a third GUI of the online experience-building platform, with an experience facilitator to facilitate the team experience;
activating a voice sensor and image sensor to record voices and facial expressions of the participants during the team experience;
analyzing the recorded voices and facial expressions to determine sentiment of the participants during the team experience;
providing, via the third GUI, real-time feedback to the facilitator based on the determined sentiment of the participants, wherein the real-time feedback comprises real-time action cues for the facilitator to perform one or more actions to improve engagement of the participants;
recording the one or more actions performed by the facilitator and obtaining, via the voice sensor and image sensors, impacts of the one or more actions on the participants;
training a first machine-learning model based on the impacts of the recorded actions,
continuously updating the real-time action cues displayed on the third GUI based on outputs of the first machine-learning model and training the first machine-learning model during the team experience to optimize the engagement of the participants;
receiving, via the first GUI from the team manager, post-experience survey data; and
continuously training a second machine-learning model based on the team experience and the post-experience survey data, wherein the second machine-learning model has been previously trained based on historical data collected from a plurality of team managers and is used to recommend the one or more team experiences.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claims 1, and 11 recite the abstract idea category of certain methods of organizing human activity. This abstract idea grouping found in MPEP 2106.04(a)(2)(II) includes claims to “managing personal behavior or relationships or interactions between people.” The invention is directed to this subcategory which includes social activities, teaching, and following rules or instructions, which is supported by the background of the specification. The invention aims to solve the solve the problem found in the specification, “[0004] For example, remote or hybrid work can lead to the reduction of workplace belonging and engagement among team members.” This problem is typically attempted to be solved by a Manager who “[0006] have been trying to address these challenges by organizing virtual team-building activities. For example, a manager can find a local barista who can ship coffee samples to team members and then run a team
coffee-tasting session via video conferencing. Organizing a virtual team-building event can involve a significant amount of effort and time as the market of team- building providers is highly fragmented, the quality of services is inconsistent, and services are typically limited to specific geographic regions and cannot cover globally distributed teams.”
Other than the steps of “training...”, all of the steps above in bold recite data input, processing, or output steps towards performing “certain methods of organizing human activity.” More specifically, this is an abstract idea is defined by “recommending potential teambuilding activities based on employee’s input, providing the materials and personnel to facilitate the activity, observing the sentiment during the activity, providing feedback of the sentiment of the activity, and optimizing the selection of team experiences.” The amended limitations are merely aimed at improving engagement of participants by providing real-time action cues. This is a form of “following rules or instructions” to facilitate the interactions between individuals. Despite the use of models to optimize these outputs, the claims are recited with such generality that they are merely “managing personal behavior,” because they still rely on an individual to facilitate the interactions. The amended limitations of “to be recommended to the team manager,” “continuously updating the real-time action cues displayed based on outputs of the first model”, “wherein the second model has been previously trained based on historical data collected from a plurality of team managers and is used to recommend the one or more team experiences” are merely steps in which instructions are provided to an individual in order to facilitate a social activity. The fact that the recommendations are generated based on outputs of the models does not preclude the claims from being categorized under “managing personal behavior” within “certain methods of organizing human activity.” Given that the models are merely recited as black box models producing the intended outcome without specifically reciting the algorithmic steps that arrive at the outcome, then the models are still part of the abstract idea.
Furthermore, the amended limitation “analyzing the recorded voices and facial expressions to determine sentiment of the participants during the team experience;” is another example claiming analyzing wherein the inputs and outputs are recited, but lack specific steps in how the outcome is generated. Therefore, the limitation is broadly claimed, and can thereby encapsulate instructions to a person to perform the analysis to determine the sentiment. Thus when considering all of the bolded elements with the exception of the training of the model, the claims recite “certain methods of organizing human activity” particularly “managing personal behavior, interactions, or relationships.”
Furthermore, the steps of training the first machine learning model and second machine learning model, when given their broadest reasonable interpretation in view of the specification, represents the creation of mathematical interrelationships between data. The MPEP 2106.04(a)(2)(I) clearly states that mathematical concepts, including mathematical relationship, which are relationships between variables or numbers that may be expressed in words or using mathematical symbols, which falls under abstract ideas. The present disclosure states in [0050], “The machine-learning model can be continuously trained and improved as more teams are going through the experiences and the number of data points about these teams grows.” At the level of breadth of the claims, this is merely an abstract idea under “mathematical relationships.” As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The amended limitations specifying that the training is “during the team experience”, “continuously trained,” and “has been previously trained based on historical data collected from a plurality of team managers” is still part of the mathematical concepts category of abstract idea because it is merely reciting the timing in which the mathematical process is performed, or the type of data it is trained on, and are therefore still part of the abstract idea. Furthermore, the fact that the models are “continuously” trained is still part of mathematical concepts idea because constantly creating new mathematical relationships would still fall under mathematical concepts. Therefore, the claims recite an abstract idea under both “certain methods of organizing human activity, and “mathematical concepts.” Since the mathematical concepts are merely used to carry out another abstract idea “managing personal behavior, interactions, or relationships” then even when considering all of the claims in bold, the claims recite an abstract idea and are therefore to be further analyzed under Step 2A Prong 2.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 1 and 11 recite the following additional elements: (a)-first/second/third graphical user interface (claims 1 and 11)
(b)-online experience building platform (claims 1 and 11)
(c)- voice and image sensors (claims 1, 11)
(d)-machine learning (claims 1, 11)
(e)-computer system (claims 1, 11)
(f)-processor (claims 1, 11)
(g)-storage device (claims 1, 11)
The additional elements are no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on a computer on its ordinary capacity. In this case, the abstract idea of “recommending potential teambuilding activities based on employee’s input, providing the materials and personnel to facilitate the activity, observing the sentiment during the activity, providing feedback of the sentiment of the activity, and training a model to optimize the selection of team experiences” is merely instructed to be performed on generic computing devices such as user interfaces, online platforms, machine, computer, system, processor, and storage devices, or utilizing devices in their ordinary capacity to perform economic tasks such as data collection(voice and image sensor capturing auditory and visual data). The fact that the experience building platform is “online” is no more than an indication to merely applying the abstract idea on a generic computer.
The additional elements “graphical user interfaces,” “online,” and “machine learning” are also examples of generally linking the abstract idea to a technological environment or field of use as outlined in MPEP 2106.05(h). The fact that there are three GUIs interacting with the system does not purport an improvement to the field of “graphical user interface” technology because it does not provide any functionalities beyond what graphical user interfaces are inherently capable of doing, and the specification would not make it apparent to one of ordinary skill that a technical improvement is reflected in the scope of the claims.
Regarding the first and second machine learning models, merely reciting the types of data used to train the model generically and at a high level (impacts of the recorded actions), and the intended outcome of the machine learning model (continuously update the real-time action cues to optimize the engagement) is merely a general link to machine learning because it simply limits the functions of the abstract idea to be performed on a machine learning model. The “impacts of the recorded actions” are forms of personal behavior being assessed, and the result of the model are a set of instructions to help manage interactions between individuals. Similarly, the second machine learning model merely provides the experience feedback as an input, with the intended outcome of determining the team experiences. These are merely the abstract idea of “managing personal behavior or interactions or relationships between people” as outlined in MPEP 2106.04(a)(2)(II)(C) instructed to be performed on a generic machine learning model without meaningfully limiting its use on the claims. Using generic machine learning in a “black box” manner,(i.e. only reciting the inputs and outputs without specific algorithms to arrive at the outputs), where the inputs and outputs are part of the abstract idea, does not provide integration into a practical application. Furthermore, though both machine-learning models are continuously updating, no improvements to the field of machine learning are purported and it would not be apparent to one of ordinary skill in the art that a technical improvement is reflected in the scope of the claims.
In addition, the claims do not provide any improvements to a computer or to the technological environment, therefore they do fall under the consideration found in MPEP 2106.05(a). Even when considering the additional elements individually or as an ordered combination, the additional elements do not integrate the abstract idea into a practical application. Even the combination of multiple GUIs and machine learning algorithms within the online system is still recited so broadly that even the combination of elements do provide a technical improvement. Though there may be an improvement in the business process itself, or though the training of the model may cause higher accuracy in the intended outcome, both of these would be improvements to the abstract idea. MPEP 2106.05(a) states, “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Therefore, the claims are directed to an abstract idea without integration into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 1 and 11 recite the following additional elements: (a)-first/second/third graphical user interface (claims 1 and 11)
(b)-online experience building platform (claims 1 and 11)
(c)- voice and image sensors (claims 1, 11)
(d)-machine learning (claims 1, 11)
(e)-computer system (claims 1, 11)
(f)-processor (claims 1, 11)
(g)-storage device (claims 1, 11)
The additional elements have also not been found to include significantly more in order to consider it an inventive concept for the same reasons set forth in Prong 2. The additional elements (a), (b), (c), (d), (e), (f) and (g) are no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on a computer on its ordinary capacity as outlined in MPEP 2106.05(f). The voice and/or image sensor of claims 1 and 11 are generic devices performing the function of recording data, and the rest of the additional elements are generally applying the abstract idea to a generic computing device. Furthermore, the additional elements (a), (b), and (d) are examples of generally linking the use of a judicial exception to a particular technological environment or field of use as outlined by MPEP 2106.05(h). These elements do not provide significantly more, because they do not meaningfully limit the use of the field on the abstract idea. Furthermore, improvements to the technology or technical field have not been purported. Please review MPEP 2106.05(a) for more information regarding improvements to computing devices(Section I), or technological fields(Section II). Therefore, the claims do not include additional elements that provide significantly more in order to be considered as an inventive concept. Even when considering the claims as a whole, nothing in the claims provides significantly more than the abstract idea, therefore the claims are directed to an abstract idea without an inventive concept.
The dependent claims 2-6, 10, 12-16, and 20 are also given the full two-part analysis, individually and in combination with the claims they depend on, in the following analysis:
Claims 2, 3, 4, 12, 13, and 14 further limit the type of data included within a limitation of the claims they depend on; therefore they are more of the same abstract idea. For example, claims 2 and 12 further limit input information to comprise “one or more of: a profile of the team; an occasion for the team experience; and a team goal.” Claims 3 and 13 further limit the profile of the team to comprise, “one or more of: size; geographic distribution; industry; history of the team; and state of members of the team.” Claims 4 and 14 further limit the team goal to comprise, “wherein the team goal comprises one or more of: improving trust; improving collaboration; and improving communication.” Since the further limitations fall within the scope of the representative claims 1 and 11, the abstract idea is still directed to “recommending potential teambuilding activities based on employee’s inputs, providing the materials and personnel to facilitate the activity, observing the sentiment during the activity, and providing feedback of the sentiment of the activity” which has been categorized as certain methods of organizing human activity outline in MPEP 2106.04(a)(2)(II). Furthermore, no additional elements have been recited, therefore the claims have not integrated the abstract idea into a practical application and they do not include significantly more.
Claims 5 and 15 add the additional step, “wherein determining the team experiences comprises applying the trained machine learning model to recommend one or more experiences based at least on the profile of the team and the team goal.” The bolded claims are directed to certain methods of organizing human activity because they involve the selection of the teambuilding activity, and only further recite the type of data being used to determine that selection. This is encompassed within the scope of the abstract idea of, “recommending potential teambuilding activities based on employee’s inputs, providing the materials and personnel to facilitate the activity, observing the sentiment during the activity, and providing feedback of the sentiment of the activity.” Furthermore, “machine learning” is an additional element that is merely an example of generally linking the abstract idea to a particular technological environment or field of use. In this case the abstract idea of “determining teambuilding activities” is generally applied to the field of machine learning, because the steps generally recite using machine learning on certain data sources to determine a team experience, which does not provide enough details that would meaningful limit how the field of use is limited. Please read MPEP 2106.05(h) for more information on Technological environments and fields of use, and review MPEP 2106.05(a) for more information on improvements to technology. For the reasons above, and due to the fact that an improvement to machine learning has not been purported, the claims have not been integrated to a practical application and they have not been found to include significantly more.
Claims 6 and 16 adds the additional step, “displaying the recommended experiences to the team manager to allow the team manager to select at least one experience from the recommended experiences.” The bolded claims are directed to the abstract idea category of certain methods of organizing human activity, further categorized within “managing personal behavior or relationships or interactions between people” because the step above includes a set of rules for the team manager to follow in order manage personal behavior and relationships between people(in the form of selecting the experience for them to partake in). In addition there are no further additional elements to consider therefore the claims are directed to an abstract idea without integration into a practical application or significantly more.
Claims 10 and 20 add the additional step, “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.” The bolded claim is directed to the abstract idea category “certain methods of organizing human behavior,” because they provide instructions to manage a social activity, where the social activity is logistics to deliver materials to conduct a group experience. Therefore the combined abstract idea is now, “recommending potential teambuilding activities based on employee’s inputs, contacting the participants to gather delivery information, providing the materials and personnel to facilitate the activity, observing the sentiment during the activity, and providing feedback of the sentiment of the activity.” Furthermore, there are no additional elements to consider therefore the abstract idea has not been integrated into a practical application and it does not provide significantly more.
Subject Matter Free of Prior Art
Claims 1-6, 10-16, and 20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action.
The following is a statement of reasons for the indication of subject matter free over prior art:
- Claims 1-6, 9-16, and 19-20 were previously rejected under 35 USC 103 as being unpatentable over Krupa (US 20160210568 A1), Gross (US 8744900 B2), McGarvey (US 20200020454 A1), and Dejoux (US 20230325944 A1). The applicant’s arguments that Krupa does not disclose providing real-time action cues, Dejoux does not disclose training a machine-learning model based on the impacts..., and that Gross and McGarvey fail to remedy these discrepancies. Upon a review of the prior art references, these arguments are persuasive, and the limitations of “wherein the real-time feedback comprises real-time action cues for the facilitator to perform one or more actions to improve engagement of the participants; recording the one or more actions performed by the facilitator and obtaining, via the voice and image sensors, impacts of the one or more actions on the participants; training a first machine-learning model based on the impacts of the recorded actions, continuously updating the real-time action cues displayed on the third GUI based on outputs of the first machine-learning model and training the first-machine model during the team experience to optimize the engagement of the participants;”
Even, after performing an updated search, the examiner has been able to identify similar features in which machine learning models are used to provide instructions to increase engagement (ABU-GHAZALEH (US 20190319813 A1)). However, the examiner has not found a prior art reference or a combination of references that discloses all of the specific claim limitations of claims 1, and 11 which require a specific set of interfaces, accessing experience databases, delivery of kits, sentiment monitoring, real-time feedback, action cues for the event facilitator, training of machine learning model based on the impacts of the real time cues to optimize engagement, and receiving of survey data to determine one or more team experiences. The specific combination of these features has not been taught or suggested in the prior art and are therefore free of prior art. Dependents claims 2-6, 10, 12-16, and 20 are also free of prior art by virtue of their dependency on claims 1, and 11 respectively, if rewritten or amended to overcome the rejections under 35 USC 101.
Response to Arguments
Applicant's arguments filed 12/16/2025 have been fully considered but they are not persuasive.
Regarding arguments over rejections under 35 U.S.C. 101, the examiner has acknowledged the applicant’s amendments, which aim to overcome the 35 U.S.C. 101 rejection by clarifying that the voice and image sensors record voices and facial expressions to determine the sentiment of the participants, that the system continuously trains a first machine-learning model and updates the real-time action cues displayed on the third GUI based on outputs of the first machine-learning model, and the system continuously trains a second machine-learning model based on the team experience and the post-experience survey data, wherein the second machine-learning model has been previously trained based on historical data collected from a plurality of team managers. However, the examiner respectfully disagrees that any of these amendments lead to eligibility over 101.
Regarding the applicant’s remarks over Step 2A on pages 9-10 of the applicant’s remarks, the applicant argues that the claims are directed to a specific technological solution implemented through a computer-based platform that integrates multiple hardware and software components to address a technical problem: real-time engagement monitoring and optimization in distributed team environments. However, the examiner does not find this argument persuasive for the following reasons (reiterated from the updated 101 rejection above), the claims result in recommendations provided to a facilitator, which are essentially instructions to help carry out the social activity. The computer based platform, through the various GUIs are merely using generic computers to perform the abstract idea by enabling interactions between remote individuals, and using voice/image sensors in their ordinary capacity to perform an economic task. The machine learning models and analysis of the image and voice data are claimed in a broad manner which treats the analysis as a black box, in which the only details are the types of data inputted and the type of output intended. Therefore, the applicant’s argument that the claimed invention is not a mere concept of organizing human activity; rather, it is rooted in computer technology and improves the functioning of an online experience-building platform is not persuasive because there are no technical improvements purported. The improvements to the functioning of the online experience-building platform would merely be creating more accurate recommendations to a user based on the machine learning outputs. At best, this is an improvement to the business process of carrying out the social activities, which is an improvement to the abstract idea, not a technical improvement. Nothing in claims makes it apparent to one of ordinary skill in the art that a technical improvement is reflected in the claim language. See MPEP 2106.05(a) for more information.
Furthermore, the applicant cites to McRO v. Bandai Namco Games America Inc., 837 F.3d 1299 (Fed. Cir. 2016), and alleges a nexus between McRO and the present application by providing a technological improvement to engagement optimization. However, the examiner disagrees that the technical improvements that made McRO eligible are also applicable to the present application. As stated in MPEP 2106.05(a), “For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).” McRO specifically explained how the particular rules enable automation of tasks that could not be performed by humans, and because the abstract idea was only under “mental processes” and not “certain methods of organizing human activities.” This is contrary to the present claims which carry out “certain methods of organizing human activities, are claimed broadly in a manner that encapsulates instructions to a person, and fail to explain, in the specification, how the particular rules provide an improvement to the functioning of a computer. Therefore, the applicant’s arguments are not persuasive.
Regarding the applicant’s remarks over Step 2a Prong 2, on pages 10-11 of the applicant’s remarks. The applicant asserts that the amendments integrate the alleged abstract idea into a practical application by improving the technical functioning of an online platform for facilitating distributed team experiences. However, the examiner respectfully disagrees. The applicants cites to DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014), stating that like DDR Holdings, the claims are directed to overcome the problem of facilitating distributed team experiences and optimizing real-time engagement of participants, which cannot be provided by conventional platforms. However, this argument is not persuasive, because as stated by the applicant, DDR holdings claims are “necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of a computer network.” The present claims do not purport to solve a problem in the realm of computer technology or computer networks, and are instead providing abstract idea improvements to facilitating interactions and improving real-time engagement. Furthermore, in view of the additional elements, the applicant argues that the voice/image sensors, GUIs, and machine-learning models integrate the abstract idea into the practical application of optimizing real-time engagement of participants through real-time analysis of voice and facial expressions, continuous updates of action cues presented on a user interface, and continuous training of machine learning models during and after the experience. However, as stated previously, none of these features are technical improvements and are instead merely using generic computers and devices in their ordinary capacity to carry out the abstract idea. Even the combination of elements and the claim as a whole fails to impose meaningful limits on the abstract idea because they are equivalent to “apply it” or instructions to perform the abstract idea on a generic computer(MPEP 2106.05(f)) or a “general link” to machine learning (MPEP 2106.05(h)). Therefore, none of the applicant’s arguments are persuasive.
Regarding the applicant’s arguments over step 2B, the applicant alleges that the claims include unconventional steps that are not well-understood, routine, or conventional, including providing real-time action cues to facilitators based on sensor-derived sentiment, recording facilitator actions and correlating them with participant responses, and applying and continuously training machine-learning models to continuously update action cues during the experience. Furthermore, the applicant alleges that the combination of elements creates a closed-loop adaptive system that improves engagement in distributed team environments. However, these arguments are not persuasive because the rejection does not rely on a finding that the claimed elements or their combination are well-understood, routine or conventional, and instead solely relies on the fact that the combination of elements are no more than “apply it” level elements or a “general link” to machine learning. Furthermore, the applicant’s argument that the combination creates a closed-loop adaptive system does not lend itself towards eligibility because the claims inherently rely on a human facilitator to carry out the recommendations and rely on human behavior/interaction data as inputs into the machine learning models. Furthermore, the system merely describes the types inputs and the intended outputs of the models and analysis in a black box manner, but does not provide a specific technological method of translating the voices and facial expressions to sentiment, does not provide a specific improvement to improving the functionalities of the computer, and does not provide a specific technique to improve the machine learning training process. Furthermore, the reasons that made BASCOM Global Internet Services, Inc. v. AT&T mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016) do not apply to the present claims. MPEP 2106.05(f) states, “In BASCOM, the court determined that the claimed combination of limitations did not simply recite an instruction to apply the abstract idea of filtering content on the Internet. BASCOM Global Internet Servs. v. AT&T Mobility, LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016). Instead, the claim recited a "technology based solution" of filtering content on the Internet that overcome the disadvantages of prior art filtering systems. 827 F.3d at 1350-51, 119 USPQ2d at 1243.” Furthermore, in the November 2, 2016 Memorandum, “Recent Subject Matter Eligibility Decisions,” it states, “an inventive concept may be found in the non-conventional and non-generic arrangement of the additional elements, i.e., the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user.” While the examiner acknowledges the applicant’s comparison of BASCOM to the present claims, the examiner notes the following distinctions between BASCOM and the present claims. Firstly, in BASCOM, the claims were specifically limited to filtering content on the Internet as part of the abstract idea, which contrasts from the present claims which are clearly “managing personal behavior, interactions, or relationships between people.” Secondly, in Bascom the claims found a non-generic arrangement of the installation of the filtering tool at a specific location, remote from the end-users with customizable filtering features specific to each end user. However, the present claims provide broadly three separate GUIs and voice sensors without a specific arrangement (the GUIs being remote from one another or the specific location of the sensors). Therefore, even when considering the machine learning models, the GUIs, and the sensors, they are recited broadly enough that they merely describe the use of generic computers and ordinary devices to perform the certain methods of organizing human activity, but do not describe the arrangement of elements to a level of specificity that meaningfully limits its use on the abstract idea. Therefore, the applicant’s argument is not persuasive, and the 101 rejection stands.
After updated search and consideration, the claims still distinguish over the prior art and would be allowable if amended to overcome the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
-Margolin et al. (US 20220327954 A1) discloses automated control methodologies for creating individualized suggestions for altering daily patterns and dynamics of interpersonal relationships.
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 NICO LAUREN PADUA whose telephone number is (703)756-1978. The examiner can normally be reached Mon to Fri: 8:30 to 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICO L PADUA/ Junior Patent Examiner, Art Unit 3626
/JESSICA LEMIEUX/ Supervisory Patent Examiner, Art Unit 3626