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
The following is a final office action.
Claims [1, 3-8, 10-17, and 19] are currently pending and have been examined on their merits.
Claims 1, 3, 10, and 19 are newly amended see REMARKS December 29, 2025.
Claims 2, 9, 18, and 20 are newly canceled see REMARKS December 29, 2025.
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, 3-8, 10-17, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception that is an abstract idea without a practical application or significantly more.
Step 1: Claims 1, 3-8 recite a method, claims 10-17 recite an apparatus, claim 19 recite one or more non-transitory, computer readable media and therefore each claim falls within one of the four statutory categories.
Step 2A prong 1 (Is a judicial exception recited?):
The representative claims 1 and 19 recite: A method for monitoring communications channels and determining actions, the method comprising; based on, at least in part, a plurality of global key-values pairs representing at least contracts and agreements between users of the collaborative system, generating ownership and license agreements, and transforming the ownership and license agreements into an ownership-agreement tree; generating, transcripts of the intercepted data streams; analyzing, the transcripts to identify characteristics indicating potential violations of constraints defined in an ownership-agreement tree; generating, one or more triggers based on the identified characteristics indicating the potential violations; determining, one or more actions to address the potential violations based on the generated triggers, wherein the actions are determined by referencing an actions database that maps triggers to corresponding actions; executing, the determined actions to address the potential violations, wherein the actions include at least one of terminating the collaboration session, removing a user from the session, sending notifications to users, and filtering or removing portions of the communications.
Claim 10 recites: transform ownership and license agreements into an ownership-agreement tree; evaluate the ownership-agreement tree with respect to a plurality of role-based communications channels to determine whether any communications violate constraints; determine triggers corresponding to violations of the constraints; determine actions to address the violations based on the triggers; generate transcripts of the intercepted communications; analyze the transcripts to identify characteristics that trigger violations of the constraints define in the ownership-agreement tree; assist in determining actions to address the violations; a trigger generator configured to: emit triggers when communications violate constraints specified in the ownership-agreement tree; store user profiles, product data definitions, global-key-values, and attribution trees; store mappings between triggers and actions; monitor the communications channels, determine triggers, and execute actions to address violations of constraints.
The claims recite a certain method of organizing human activity. The claims recite a certain method of organizing human activity as the disclosure is directed to managing personal behavior or relationships or interactions between people. The claims merely recite a method for monitoring the communications between individuals such as speech and gestures and determining if the communications violate any policies or agreements which would result in an action to address the violations. Merely observing a user’s actions or speech and determining if they are in violation of an agreement and triggering a response is a method of managing interactions between people as this is a standard practice in most social interactions.
Alternatively, the claims recite a mental process. The examiner finds the claims to be similar to a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis." The claims merely recite a method for monitoring communications of users such as their speech, generating a transcript of the speech, and analyzing the content of the transcript to determine if the content is in violation of any constraints specified in an agreement, and determining a trigger to execute an action in response to the violation. Therefore, the examiner finds the claims to be similar to examples the courts have identified as reciting a mental process including observations, evaluations, judgements, and opinions. As the method of observing a user’s communications and evaluating the content to determine if the user has violated a policy or agreement and executing a response can be performed in the mind of an individual such as a moderator of a communication session.
Therefore, the examiner finds the claims to be directed to an abstract idea.
Step 2A Prong 2 (Is the exception integrated into a practical application?): The claims additionally recite;
Claim 1: a computer, a role-based collaborative system, intercepting, by a monitoring system, a plurality of data streams communicated between user devices during a collaboration session, wherein the data streams include at least one of video streams, audio streams, and chat streams, a trigger generator, and a collaboration server, wherein the monitoring system comprises a natural language processing module configured to enhance an accuracy of the generated transcripts; wherein the collaboration server comprises a machine learning module configured to improve the determination of actions to address the potential violations.
Claim 10: An apparatus for monitoring communications channels and determining actions in a role-based collaborative system, comprising: a collaboration server configured to: a monitoring system configured to: intercept communications exchanged between users via the role-based communications channels; the collaboration server; one or more databases; an actions database; one or more user devices configured to: facilitate communications between users and the collaboration server; wherein the collaboration server, monitoring system, and trigger generator cooperate, which comprises a machine learning module configured to improve the determination of actions to address the potential violations, wherein the monitoring system is further configured to utilize natural language processing techniques to improve an accuracy of transcript analysis.
Claim 19: one or more non-transitory, computer-readable media storing one or more computer instructions, when executed by one or more computer processors, cause the one or more computer processors to perform, a role-based collaborative system, intercepting, by a monitoring system, a plurality of data streams communicated between user devices during a collaboration session, wherein the data streams include at least one of video streams, audio streams, and chat streams, a trigger generator, and a collaboration server, wherein the monitoring system comprises a natural language processing module configured to enhance an accuracy of the generated transcripts; wherein the collaboration server comprises a machine learning module configured to improve the determination of actions to address the potential violations.
The additional element of using generic computer elements to perform the abstract idea are directed to merely applying the known use of a computer to receive, store, and analyze information to perform the method in the recited claim limitations. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. As the claims are merely directed to utilizing a generic computer to perform the abstract idea of monitoring a communication session and determining if a user has violated an agreement and triggering a response to address the violation.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?): As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The claims merely recite using generic computer elements to perform the abstract idea of receiving and analyzing information to determine a response. Therefore, the additional elements do not amount to significantly more as they do not recite any improvements to a technology or technical field.
Dependent claims 3-8 and 11-17, further narrow the abstract idea of monitoring communication sessions between individuals by converting communications into a transcript, determining if a person violates an agreement with their speech or actions during a communication session, and executing an action in response to a violation.
The dependent claims recite the following additional elements:
Claim 3: a screen sharing stream.
Claim 11: generate different types of notifications, including email alerts, SMS messages, and in-app notifications.
Claim 16: integrate with external analytics tools.
However, the additional elements are directed to merely “apply it” or applying generic computer elements to perform the abstract idea.
Therefore, claims 1, 3-8, 10-17, and 19 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 3-8, 10-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Panattoni (US 2019/0052471) in view of Kennedy (US 2011/0209194) further in view of Jung (US 2019/0392837) even further in view of Borden (US 2004/0111479).
Claims 1 and 19: Panattoni discloses (Claim 1) a computer-implemented method for monitoring communications channels and determining actions in a role-based collaborative system, the method comprising: (Claim 19) One or more non-transitory, computer-readable storage media storing one or more computer instructions which, when executed by one or more computer processors, cause the one or more computer processors to perform: intercepting, by a monitoring system, a plurality of data streams communicated between user devices during a collaboration session, wherein the data streams include at least one of video streams, audio streams, and chat streams (Paragraph [0003-0004]; [0033]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior);
analyzing, by the monitoring system, the transcripts to identify characteristics indicating potential violations of constraints defined in an ownership-agreement tree (Paragraph [0003-0006]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive);
generating, by a trigger generator, one or more triggers based on the identified characteristics indicating the potential violations (Paragraph [0021]; [0047-0048]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data);
determining, by a collaboration server, one or more actions to address the potential violations based on the generated triggers, wherein the actions are determined by referencing an actions database that maps triggers to corresponding actions (Paragraph [0021]; [0047-0048]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways);
wherein the collaboration server comprises a machine learning module configured to improve the determination of actions to address the potential violations(Paragraph [0003-0006]; [0021]; [0064]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, using verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive. The machine learning engine may build a toxicity prediction model and update and/or revise the toxicity prediction model as data evolves over time. Stated alternatively, the offending participant may have their communications privileges disabled for a predetermined period of time (e.g., for the current session, for one hour, for one week, for one year, etc.) in response to exhibiting the predetermined toxic behavior);
executing, by the collaboration server, the determined actions to address the potential violations, wherein the actions include at least one of terminating the collaboration session, removing a user from the session, sending notifications to users, and filtering or removing portions of the communications (Paragraph [0021]; [0047-0049]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways. Exemplary reprimands includes pausing an offending participant’s ability to participate in the communication session for a period of time, modifying aspects of the offending participant’s user profile, and/or any other suitable reprimand. The one or more repercussions may be an audible warning message played through the offending participant’s devices).
Panattoni discloses a system of monitoring communications and triggering an action if a user violates a regulation. However, Panattoni does not disclose the following claim limitations: based on, at least in part, a plurality of global key-values pairs representing at least contracts and agreements between users of the collaborative system, generating ownership and license agreement, and transforming the ownership license agreement into an ownership-agreement tree; generating, by the monitoring system, transcripts of the intercepted data streams; wherein the monitoring system comprises a natural language processing module configured to enhance an accuracy the generated transcripts.
In the same field of endeavor of monitoring a communication session Kennedy teaches based on, at least in part, a plurality of global key-values pairs representing at least contracts and agreements between users of the collaborative system, generating ownership and license agreement, and transforming the ownership license agreement into an ownership-agreement tree (Paragraph [0014-0018]; [0119-0120]; [0162]; [0186]; Fig. 7, in one embodiment, an enterprise network has a plurality of subscribers, a plurality of nodes, and a policy enforcement server to enforce policies and/or rules of the enterprise corresponding to the enterprise network. Each node includes a policy agent to monitor or track behavior of the subscriber. In one configuration, the behavior instance of the subscriber intending to make one or more of a selected communication and content accessible to one or more parties. In response the policy enforcement server performs: receiving a policy tag respecting one or more of the communication content; determining an applicable policy; and determining a policy measure. Exemplary policies regard agreement compliance (e.g. compliance with the terms and conditions of the agreement). Exemplary policies include limits on presentation of communication and/or content based on the user, user role, level, and/or particular communication node currently in use by the user. The policy enforcement server considers whether the audience is permitted to receive the communications or content. This can be done by a simple mapping operation in which the recipient is mapped against a permitted list of accessors. The control module maps the pertinent policies and rules to identified policy factors in the policy tags to determine proposed policy measures.
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating transcripts of communications and determining a user’s actions during a communication session as disclosed by modified Panattoni with the system of based on, at least in part, a plurality of global key-values pairs representing at least contracts and agreements between users of the collaborative system, generating ownership and license agreement, and transforming the ownership license agreement into an ownership-agreement tree as taught by Kennedy (Kennedy [0120]). With the motivation of helping to map and enforce policies and agreements to participants of a collaborative network (Kennedy [0007]).
In the same field of endeavor of monitoring a communication session Jung teaches generating, by the monitoring system, transcripts of the intercepted data streams (Paragraph [0002-0004] the techniques disclosed herein improve the way in which a transcript is generate and displayed sot that the context of a conversation taking place during a meeting or another type of collaboration event can be understood by a person that reviews the transcript. The speech of the user is converted to text for the transcript).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of monitoring communications during a collaboration session as disclosed by Panattoni with the system of generating, by the monitoring system, transcripts of the intercepted data streams as taught by Jung (Jung [0002]). With the motivation of helping to generate content of the communications to be monitored and reviewed (Jung [0001]).
In the same field of endeavor of monitoring communications in online activities to detect undesired actions Borden teaches wherein the monitoring system comprises a natural language processing module configured to enhance an accuracy the generated transcripts (Paragraph [0034]; [0043] the present invention can employ a set of known pattern recognition techniques to generate multiple predictions of the intention of a chat room participant. Such known pattern recognition techniques can include for example natural language processing. The text is parsed using a number of pattern recognizers such as natural language query searching and/or natural language processing searching).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating transcripts of communications and determining a user’s actions during a communication session as disclosed by modified Panattoni with the system of wherein the monitoring system further comprises a natural language processing module configured to enhance an accuracy of the generated transcripts as taught by Borden (Borden [0034]). With the motivation of helping to successfully monitor the communications of online users to determine any inappropriate actions (Borden [0006]).
Claim 3: Modified Panattoni discloses the method as per claim 2. Panattoni further discloses wherein the data streams further include screen sharing streams (Paragraph [0003-0006]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive).
Claim 4: Modified Panattoni discloses the method as per claim 3. Panattoni further discloses wherein the characteristics indicating potential violations include the use of prohibited words or phrases (Paragraph [0003-0006]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive).
Claim 5: Modified Panattoni discloses the method as per claim 4. Panattoni further discloses wherein the actions further include temporarily muting a user in the collaboration session (Paragraph [0021]; [0047-0049]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways. Exemplary reprimands includes pausing an offending participant’s ability to participate in the communication session for a period of time, modifying aspects of the offending participant’s user profile, and/or any other suitable reprimand. The one or more repercussions may be an audible warning message played through the offending participant’s devices).
Claim 6: Modified Panattoni discloses the method as per claim 5. Panattoni further discloses wherein the actions further include logging the potential violations for future reference (Paragraph [0045]; [0047-0049]; Fig. 5, in some implementations, one or more of the systems may report the instance of toxic behavior back to the virtual environment services so that it can be logged with respect to the second participant).
Claim 7: Modified Panattoni discloses the method as per claim 6. Panattoni further discloses wherein the monitoring system is configured to operate in real-time during the collaboration session (Paragraph [0021]; [0047-0048]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data).
Claim 8: Modified Panattoni discloses the method as per claim 7. Panattoni further discloses wherein the actions database is updated dynamically based on outcomes of previous actions (Paragraph [0003-0006]; [0064]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive. The machine learning engine may build a toxicity prediction model and update and/or revise the toxicity prediction model as data evolves over time).
Claim 10: Panattoni discloses An apparatus for monitoring communications channels and determining actions in a role-based collaborative system, comprising: a collaboration server configured to: evaluate the ownership-agreement tree with respect to a plurality of role-based communications channels to determine whether any communications violate constraints (Paragraph [0003-0004]; [0033]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior);
determine triggers corresponding to violations of the constraints defined in the ownership-agreement tree (Paragraph [0021]; [0047-0048]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data);
determine actions to address the violations based on the triggers (Paragraph [0021]; [0047-0048]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data);
a monitoring system configured to: intercept communications exchanged between users via the role-based communications channels (Paragraph [0003-0006]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive);
analyze the transcripts to identify characteristics that trigger violations of the constraints (Paragraph [0021]; [0047-0048]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways);
assist the collaboration server, which comprises a machine learning module configured to improve the determination of actions to address the potential violations, in determining actions to address the violations (Paragraph [0021]; [0047-0049]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways. Exemplary reprimands includes pausing an offending participant’s ability to participate in the communication session for a period of time, modifying aspects of the offending participant’s user profile, and/or any other suitable reprimand. The one or more repercussions may be an audible warning message played through the offending participant’s devices);
a trigger generator configured to: emit triggers when communications violate constraints specified in the ownership-agreement tree; one or more databases configured to: store user profiles, product data definitions, global-key-values, and attribution trees (Paragraph [0021]; [0047-0049]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways. Exemplary reprimands includes pausing an offending participant’s ability to participate in the communication session for a period of time, modifying aspects of the offending participant’s user profile, and/or any other suitable reprimand. The one or more repercussions may be an audible warning message played through the offending participant’s devices);
store mappings between triggers and actions in an actions database (Paragraph [0003-0006]; [0045]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive. In some implementations the TSMs may report the instance of toxic behavior back to the virtual environment so that it can be logged with respect to the participant);
one or more user devices configured to: facilitate communications between users and the collaboration server (Paragraph [0003-0004]; [0033]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior);
wherein the collaboration server, monitoring system, and trigger generator cooperate to monitor the communications channels, determine triggers, and execute actions to address violations of constraints (Paragraph [0021]; [0047-0049]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. The system may identify the particular instance of the predetermine toxic behavior shortly after it is spoken and prevent the offending participant from transmitting future communications. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. In some implementations the system may prevent an instance of toxic behavior from being transmitted in the communication session whatsoever in response to a determinations that at least one participant of the environment is intolerant of the instance of toxic behavior. The system may identify and ultimately prevent a portion of the communication data. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways. Exemplary reprimands includes pausing an offending participant’s ability to participate in the communication session for a period of time, modifying aspects of the offending participant’s user profile, and/or any other suitable reprimand. The one or more repercussions may be an audible warning message played through the offending participant’s devices).
Panattoni discloses a system of monitoring communications and triggering an action if a user violates a regulation. However, Panattoni does not disclose the following claim limitations: based on, at least in part, a plurality of global key-values pairs representing at least contracts and agreements between users of the collaborative system, generating ownership and license agreement, and transforming, and transform the ownership and license agreements into an ownership-agreement tree; generate transcripts of the intercepted communications; wherein the monitoring system is further configured to utilize natural language processing (NLP) techniques to improve an accuracy of transcript analysis.
In the same field of endeavor of monitoring a communication session Kennedy teaches based on, at least in part, a plurality of global key-values pairs representing at least contracts and agreements between users of the collaborative system, generating ownership and license agreement, and transforming, and transform the ownership and license agreements into an ownership-agreement tree (Paragraph [0014-0018]; [0119-0120]; [0162]; [0186]; Fig. 7, in one embodiment, an enterprise network has a plurality of subscribers, a plurality of nodes, and a policy enforcement server to enforce policies and/or rules of the enterprise corresponding to the enterprise network. Each node includes a policy agent to monitor or track behavior of the subscriber. In one configuration, the behavior instance of the subscriber intending to make one or more of a selected communication and content accessible to one or more parties. In response the policy enforcement server performs: receiving a policy tag respecting one or more of the communication content; determining an applicable policy; and determining a policy measure. Exemplary policies regard agreement compliance (e.g. compliance with the terms and conditions of the agreement). Exemplary policies include limits on presentation of communication and/or content based on the user, user role, level, and/or particular communication node currently in use by the user. The policy enforcement server considers whether the audience is permitted to receive the communications or content. This can be done by a simple mapping operation in which the recipient is mapped against a permitted list of accessors. The control module maps the pertinent policies and rules to identified policy factors in the policy tags to determine proposed policy measures.
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating transcripts of communications and determining a user’s actions during a communication session as disclosed by modified Panattoni with the system of based on, at least in part, a plurality of global key-values pairs representing at least contracts and agreements between users of the collaborative system, generating ownership and license agreement, and transforming, and transform the ownership and license agreements into an ownership-agreement tree as taught by Kennedy (Kennedy [0120]). With the motivation of helping to map and enforce policies and agreements to participants of a collaborative network (Kennedy [0007]).
In the same field of endeavor of monitoring a communication session Jung teaches generate transcripts of the intercepted communications (Paragraph [0002-0004] the techniques disclosed herein improve the way in which a transcript is generate and displayed sot that the context of a conversation taking place during a meeting or another type of collaboration event can be understood by a person that reviews the transcript. The speech of the user is converted to text for the transcript).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of monitoring communications during a collaboration session as disclosed by Panattoni with the system of generate transcripts of the intercepted communications as taught by Jung (Jung [0002]). With the motivation of helping to generate content of the communications to be monitored and reviewed (Jung [0001]).
In the same field of endeavor of monitoring communications in online activities to detect undesired actions Borden teaches wherein the monitoring system is further configured to utilize natural language processing (NLP) techniques to improve an accuracy of transcript analysis (Paragraph [0034]; [0043] the present invention can employ a set of known pattern recognition techniques to generate multiple predictions of the intention of a chat room participant. Such known pattern recognition techniques can include for example natural language processing. The text is parsed using a number of pattern recognizers such as natural language query searching and/or natural language processing searching).
Before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify the system of generating transcripts of communications and determining a user’s actions during a communication session as disclosed by modified Panattoni with the system of wherein the monitoring system further comprises a natural language processing module configured to enhance an accuracy of the generated transcripts as taught by Borden (Borden [0034]). With the motivation of helping to successfully monitor the communications of online users to determine any inappropriate actions (Borden [0006]).
Claim 11: Modified Panattoni disclose the apparatus as per claim 10. Panattoni further discloses wherein the collaboration server is further configured to generate different types of notifications, including email alerts, SMS messages, and in-app notifications (Paragraph [0021]; [0038]; [0047-0049]; Fig. 5, the system may proactively prevent individual participants from being exposed to the predetermined toxic behavior in real-time. The system may immediately identify a particular instance of the particular expletive as it is spoken by an offending participant and prevent that particular instance from being exposed to at least some other participants of the virtual environment. The system may reactively prevent the individual participants from being exposed to future instances of the predetermined toxic behavior from the offending participant in response to the particular instance being spoken by the offending participant. For example, in response to the participant directing the expletive towards the first participant the system may reprimand the second participant in various ways. Exemplary reprimands includes pausing an offending participant’s ability to participate in the communication session for a period of time, modifying aspects of the offending participant’s user profile, and/or any other suitable reprimand. The one or more repercussions may be an audible warning message played through the offending participant’s devices. Once identified the system may take various mitigation functionality such as transmitting a warning message to one or more participants indicating a consequence of toxic behavior).
Claim 12: Modified Panattoni disclose the apparatus as per claim 11. Panattoni further discloses wherein the monitoring system is further configured to include advanced filtering options to remove specific types of content, such as offensive language or confidential information (Paragraph [0003-0006]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive).
Claim 13: Modified Panattoni disclose the apparatus as per claim 12. Panattoni further discloses wherein the trigger generator is further configured to detect context-based triggers, including inappropriate behavior and unauthorized access attempts (Paragraph [0003-0006]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive).
Claim 14: Modified Panattoni disclose the apparatus as per claim 13. Panattoni further discloses wherein the actions database is further configured to include a wider range of actions, such as escalating issues to higher management and initiating automated corrective measures (Paragraph [0003-0006]; [0045]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive. In some implementations the TSMs may report the instance of toxic behavior back to the virtual environment so that it can be logged with respect to the participant);
Claim 15: Modified Panattoni disclose the apparatus as per claim 14. Panattoni further discloses wherein the user devices are further configured to include additional interfaces, such as voice recognition and gesture control, to facilitate user interactions (Paragraph [0003-0006]; [0026]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive. The offending participant may have their communications privileges disabled for a predetermined period of time in response to the toxic behavior. A client device includes a gestural input device).
Claim 16: Modified Panattoni disclose the apparatus as per claim 15. Panattoni further discloses wherein the monitoring system is further configured to integrate with external analytics tools to provide deeper insights into communication patterns and potential violations (Paragraph [0003-0006]; [0045]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive. In some implementations the TSMs may report the instance of toxic behavior back to the virtual environment so that it can be logged with respect to the participant).
Claim 17: Modified Panattoni disclose the apparatus as per claim 16. Panattoni further discloses wherein the collaboration server is further configured to maintain a detailed audit trail of all actions taken, including timestamps and user identifiers (Paragraph [0003-0006]; [0045]; Fig. 2, this disclosure describes systems and techniques that allow participants in a multiuser virtual environment that are intolerant of predetermined toxic behaviors (e.g. expletives and/or verbally abusive language) to shield themselves from such predetermined toxic behavior while conversing with other participants of the multiuser virtual environment. In some implementations the techniques can be implemented in association with an in-session voice based and/or text based “chat” component. The system may monitor communications data associated with the virtual environment to identify instances of the predetermined toxic behavior. A first participant provides a system with toxicity tolerance data indicating a tolerance for exposure to the predetermined toxic behavior. Exemplary predetermined toxic behaviors include, but are not limited to, using expletive language, suing verbally abusive language, speaking with a particular tone, producing one or more inadvertent but undesirable sound, and/or any other suitable behavior that can be readability identified that may negatively impact user experiences. Based on the toxicity tolerance data, the system may determine that a first participant is intolerant of the instance of the second participant using the particular expletive. In some implementations the TSMs may report the instance of toxic behavior back to the virtual environment so that it can be logged with respect to the participant).
Therefore, claims 1, 3-8, 10-17, and 19 are rejected under 35 U.S.C. 103.
Response to arguments
Applicant’s arguments, see REMARKS, filed December 29, 2025, with respect to the rejections of Claim(s) 1, 3-8, 10-17, and 19 is/are rejected under 35 U.S.C. 101 are considered and not persuasive.
Claims 1, 10, and 19: Representative argues that the amended claims are directed to an improvement in the monitoring of communication channels to determine actions in a role-based collaborate system by using a natural language processing module configured to enhance an accuracy of a generated transcript and a machine learning module configured to improve the determinations of actions over time. However, the examiner respectfully disagrees. The claims recite a method for monitoring communications and determining actions by generating ownership and license agreements, intercepting a plurality of data streams, generating transcripts, analyzing the transcripts to identify characteristics indicating potential violations of constraints defined in the ownership-agreement, generating one or more triggers, determining one or more actions to address the potential violations, and executing the determined action. The examiner finds that the claims recite an abstract idea of managing personal behavior or interactions between people. As the claims merely recite a series of steps to monitor communications of users and based on a trigger, such as a violation of an agreement, perform a response. The claims merely recite a series of rules for monitoring the speech and actions of users in a group and determining if the users violate any terms of agreement before executing a response. For example, if a user uses crude language in their communications which violates the agreements to which they have agreed then a response is triggered such as warning the user about their violation. As such the claims recite an abstract idea.
The additional elements of a computer system, a natural language processing module, and machine learning model used to implement the abstract idea are directed to merely “apply it” or applying generic computer elements to perform the abstract idea. The claims do not recite an improvement in the technology or technical field of using natural language processing or a machine learning model but applying the elements to perform the abstract idea of monitoring and analyzing communication information to identify a trigger and determine a response. Merely using natural language processing to “enhance the accuracy of a generated transcript” and using a machine learning model to “improve the determination of actions to address the potential violations” is not an improvement to a technical field but applying generic computer elements to perform the abstract idea of receiving and analyzing the information to determine a result. Therefore, the examiner does not find the additional elements to direct the claims to a practical application and the claims do not amount to significantly more.
Therefore, the examiner maintains the current 101 rejection.
Claims 3-8, and 11-17 were dependent on claims 1, 10, and 19 Therefore, they are also rejected under the same rejection as above.
Applicant’s arguments, see REMARKS, filed December 29, 2025, with respect to the rejections of 1, 3-8, 10-17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Panattoni (US 2019/0052471) in view of Kennedy (US 2011/0209194) further in view of Jung (US 2019/0392837) even further in view of Borden (US 2004/0111479) are not persuasive as the claims were amended which required further search and consideration and new art was applied.
Claims 1, 10, and 19: Applicant argues that the current prior art does not disclose the newly amended claim limitations. However, upon further search and consideration the examiner finds that the combination of Panattoni, Kennedy, Jung, and Borden disclose the newly amended claim limitations. Panattoni discloses a system of monitoring communication channels in a collaborative network and using a natural language processing system to identify potential violations of rules and regulations for participants of the virtual environment. Additionally, Panattoni discloses a system of determining and enforcing an action in response to a user violating a rule such as attempting to send content or communications that are not allowed. The actions include responses such as removing privileges or access from users or censoring content. Panattoni can be further used in combination with Jung and Bordin which teach a system of monitoring data streams and communications in a virtual environment respectively and using models such as natural language processing trained to generate transcripts of the communications to be used in the determination of any violations. The current prior art can be further combined with Kennedy which teaches a system of monitoring users of a social network or virtual environment to determine any users violating policies or rules. Kennedy further teaches a system of mapping a user information such as roles and identities to agreements when determining potential violations of the agreements. The examiner finds that the broadest reasonable interpretation of an ownership and license agreements tree generated based on a plurality of key-value pairs representing contracts and agreements between users would include a mapping of agreements and policies tied to members of a network, their respective roles in the network, and corresponding agreements and regulations that are applied to the users. Therefore, the current combination of prior art teaches the newly amended claim limitations.
Therefore, claim 1, 10, and 19 are newly rejected under U.S.C. 103.
Claims 3-8 and 11-17 were dependent on claims 1, 10, and 19. Therefore, they are also newly rejected under the same rejection as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Williams (US 2020/0051189) System and methods for developing, monitoring, and enforcing agreements, understandings, and/or contracts.
Ioannou (US 2020/0036660) Conditional automatic social posts.
Dwyer (US 2019/0245974) Communication channel customer journey.
MacGillivray (US 2014/0278639) System and method for interface management.
Stolarz (US 2018/0097836) System and method for enterprise authorization for social partitions.
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 COREY RUSS whose telephone number is (571)270-5902. The examiner can normally be reached on M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on 5712726782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/COREY RUSS/Primary Examiner, Art Unit 3629
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