DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on March 23, 2026, for the application with serial number 18/949,481.
Claims 21, 22, 28, and 36 are amended.
Claims 21-40 are pending.
Interview
The Examiner acknowledges the interview conducted on March 20, 2026, in which proposed amendments were discussed with respect to the outstanding rejections.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the claims are characterized at an impermissibly high level of abstraction in the rejection. See Remarks p. 9. In response, the Examiner points to the rejection, below, which considers each and every limitation of exemplary independent claim 21 in arriving at a conclusion of ineligibility. Virtually all machine learning could be said to include hyperparameter tuning as the machine learning algorithm “learns” from previous outcomes and adjusts weights or constants in the algorithm. As indicated in the rejection, below, the newly recited step for standardizing data formats amounts to insignificant extra-solution activity.
The Applicant further submits that the claims are subject matter eligible because the recited steps cannot be performed in the human mind. See Remarks p. 10. The Examiner points out that some of the subject matter is analogous to conversational comprehension by human beings, which suggests otherwise. However, the rejection, below, does not conclude that the claims fall withing the mental processes category of abstract ideas. Therefore, the Applicant’s arguments with respect to the mental processes category are moot. A human being could classify data according to rules set by thresholds expressed by hyperparameters, contrary to the Applicant’s assertions. Classifying conversations is not rooted in computer technology.
The Applicant further submits that the subject matter eligibility analysis fails to evaluate additional elements as a whole. See Remarks p. 11. The Examiner respectfully disagrees. As indicated in the rejection, below, the additional elements amount to generic computer hardware operating in a machine learning environment; or insignificant extra-solution activity. Merely applying the judicial exception with generic computer hardware in a machine learning environment does not provide an inventive concept. The Applicant appears to rely heavily on “hyperparameter tuning” as evidence of subject matter eligibility. See Remarks p. 11. The Examiner reiterates that this could merely amount to adjusting variables or constants in an algorithm. At best, “hyperparameter tuning” is a mathematical concept. Mathematical concepts are ineligible abstract ideas. See MPEP §2106.04(a). It is self-evident that an abstract idea cannot provide significantly more than an abstract idea. Contrary to the Applicant’s assertions, the claims do not provide any apparent improvement in machine learning. Standardizing formats is well-understood, routine, and conventional; as evidenced by the newly cited passage of the McCord reference.
The Applicant additionally contends that the claims provide significantly more than the recited abstract idea. See Remarks p. 12. For essentially the same reasons discussed, above, the Examiner disagrees. Standardizing data formats, taken as a highly generalized concept, does not provide significantly more than the recited abstract idea. Standardizing data formats is well-understood, routine, and conventional.
The Applicant additionally refers to “policy trends” at the USPTO. See Remarks p. 14. The rejection, below, relies on current Office policy.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the McCord reference, cited in the rejection of the independent claims, below. The Applicant’s arguments with respect to the standardization of data formats are moot in light of the newly cited reference.
The Applicant additionally submits that the Brown reference does not teach a classification based model. See Remarks p. 16. In response, the Examiner points to cited ¶0090]-[0091] of Brown, which explicitly teaches classifying outcomes in conversations. Brown also teaches hyperparameter tuning by teach metrics derived from conversations in cited ¶[0019], [0065], and [0096].
The Applicant further contends that the motivation to combine Brown and Raanani is improper. See Remarks p. 17. The Examiner respectfully disagrees. The references are combinable to arrive at the claimed invention with no unpredictable results. Contrary to the Applicant’s assertions, the cited references teach the elements to which they are mapped.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 21-40 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Under Step 1 of the subject matter eligibility analysis, claims(s) 21-40 are all directed to one of the four statutory categories of invention. However, under step 2A, prong one, the claims recite a judicial exception: generating data indicative of a prediction associated with content of a communication session (as evidenced by exemplary independent claim 21; “generating, . . . outcome data indicative of a prediction associated with content of the communication session”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 21 include: “obtaining . . . first analytics data;” “obtaining . . . second analytics data;” “ingesting into [a] data repository . . . the second analytics data and updated customer relations management [ ] data;” “generating . . . outcome data indicative of a prediction associated with content of [a] communication session;” and “outputting the outcome data.” The steps are all steps for managing personal behavior related to the abstract idea of generating data indicative of a prediction associated with content of a communication session that, when considered alone and in combination, are part of the abstract idea of generating data indicative of a prediction associated with content of a communication session. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of generating data indicative of a prediction associated with content of a communication session. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes making a prediction based on conversations that occur during conferences.
Under step 2A, prong two, of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a processing engine and client device in independent claim 21; a system with a processing engine and client device in independent claim 28; and a computer readable medium executable by a processor in independent claim 36). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of a trained machine learning algorithm, but the abstract idea of generating data indicative of a prediction associated with content of a communication sessions is generally linked to a machine learning environment for implementation. The Examiner notes that the use of machine learning implies the use of a trained machine learning algorithm to “learn” from previous iterations. Therefore, the machine learning merely amounts to a technological environment that does not provide a practical application or significantly more than the abstract idea. Newly amended language recites the standardization of data formats, but the step is a well-known step that is tangential to the inventive concept, and the step if necessary for data gathering and processing. Therefore, the step amounts to insignificant extra-solution activity. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). Under step 2B of the subject matter eligibility analysis, the claims do not integrate the abstract idea into a judicial exception. Referring to the additional elements provided in the analysis in step one, above, the generic computer hardware does not provide significantly more than the recited abstract idea. See MPEP §2106.05(f).
Furthermore: an element that is found to amount to insignificant extra-solution activity in step 2A of the subject matter eligibility analysis must be evaluated in step 2B to determine whether the step is well-understood, routine, and conventional. Standardizing formats, as recited in the independent claims, was found to amount to insignificant extra-solution activity in step 2A. That step is well-understood, routine, and conventional; as evidenced by ¶[0043] and Fig. 2 of US 20170255945 A1 to McCord et al. That passage teaches that data from disparate sources may be required to be normalized to be useful for further processing, including CRM and conversational data. Further processing also includes the use of machine learning, as taught in that passage. The standardization of data formats is well-understood, routine, and conventional.
For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 21, 25, 26, 28, 32, 36, and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210258424 A1 to Brown et al. (hereinafter ‘BROWN’) in view of US 20170255945 A1 to McCord et al. (hereinafter ‘MCCORD’) and US 20180096271 A1 to Raanani et al. (hereinafter ‘RAANANI’).
Claim 21 (Currently Amended)
BROWN discloses a method, comprising: training, using a training data set derived from a data repository internal to a software- based communication platform (see ¶[0053]; a conversation system trained to determine sentiment based on conversation metrics) and using hyperparameter tuning (see ¶[0019] , [0065] and [0096]; generate a modality for behavior based on metrics derived from interactions. Achieve an outcome based on an objective of the conversation. Dynamically generate a plan to achieve an outcome based on variables), a classification-based machine learning model to predict outcomes of communication sessions facilitated by the software-based communication platform (see ¶[0090]-[0091]; process may include classifying the outcome of each completed conversation. See also ¶[0017]; artificial intelligence and machine learning);
obtaining, by a processing engine of the software-based communication platform (see ¶[0137]-[0138] and Fig. 11; a device with a processor), first analytics data based on a communication session facilitated by the software-based communication platform (see ¶[0031]; use historical information from past conversations. Generate a modality to include insights an state information);
obtaining, by the processing engine, second analytics data derived from the communication session and one or more other communication sessions facilitated by the software-based communication platform (see again ¶[0031]; use historical information from past conversations. Generate a modality to include insights and state information); and
ingesting into the data repository, by the processing engine, the second analytics data and updated customer relations management (CRM) data derived from an instance of a CRM platform accessible to the software-based communication platform (see ¶[0030]-[0031] and [0090]; the individuals’ information may be obtained from a Customer Relationship Management System (CRM) or database).
BROWN does not specifically disclose, but MCCORD discloses, wherein the second analytics data and the updated CRM data are standardized into a common format in the data repository such that the trained classification-based machine learning model uses the standardized second analytics data and the standardized CRM data together to generate the outcome data (see ¶[0043] and Fig. 2; sales data may be retrieved from external sources such as a company’s CRM and transcripts from conversations. Such data may require formalization to remove unusable records and normalize data format to a point [sic] usefulness). Teach the machine learning module to recognize stage markers in sales processes.
BROWN further discloses generating, by the trained classification-based machine learning model, outcome data indicative of a prediction associated with content of the communication session (see ¶[0017] and [10105]; predict outcomes for the conversations).
BROWN does not explicitly disclose, but RAANANI discloses, outputting the outcome data for access by one or more client devices associated with the software-based communication platform (see ¶[0026]; notify the consumer user regarding the deals at risk via an email or alert on a display of a user device).
BROWN discloses a system and method for dynamically controlling conversations and workflows based on conversation monitoring that includes participants in a deal (see [0085], [0090], and [0100]). MCCORD discloses intelligent sales management that includes normalizing CRM data and conversational transcripts for be used for further analysis, including machine learning purposes. It would have been obvious for one of ordinary skill in the art at the time of invention to include the normalization of data as taught by MCCORD in the system executing the method of BROWN with the motivation to control conversations and workflows pertaining to deals and sales.
BROWN discloses a system and method for dynamically controlling conversations and workflows based on conversation monitoring that includes participants in a deal (see [0085], [0090], and [0100]). RAANANI discloses analyzing conversations to automatically identify deals at risk. It would have been obvious to identify deals at risk as taught by RAANANI in the system executing the method of BROWN with the motivation to control deal conversations and workflows.
Claim 25 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the method as set forth in claim 21.
BROWN further discloses wherein the communication session is a video communication session (see ¶[0020]; video conferencing streams or sessions).
Claim 26 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the method as set forth in claim 21.
BROWN does not explicitly disclose, but RAANANI discloses, further comprising: connecting the one or more client devices within a virtual communication room (see claims 1 and 26; at least one of the recordings includes a recording of a virtual reality-based conversation between one of the customers and one of the representatives).
BROWN discloses a system and method for dynamically controlling conversations and workflows based on conversation monitoring that includes participants in a deal (see [0085], [0090], and [0100]). RAANANI discloses analyzing conversations to automatically identify deals at risk, where the conversations include recordings of virtual-reality based conversations. It would have been obvious to include the virtual-reality conversations as taught by RAANANI in the system executing the method of BROWN with the motivation to control deal conversations and workflows.
Claim 28 (Currently Amended)
BROWN discloses a system, comprising: a processing engine (see ¶[0137]-[0138] and Fig. 11; a device with a processor) configured to: train, via a training data set derived from a data repository internal to a software- based communication platform (see ¶[0053]; a conversation system trained to determine sentiment based on conversation metrics) and using hyperparameter tuning (see ¶[0019] , [0065] and [0096]; generate a modality for behavior based on metrics derived from interactions. Achieve an outcome based on an objective of the conversation. Dynamically generate a plan to achieve an outcome based on variables), a classification-based machine learning model to predict outcomes of communication sessions facilitated by the software-based communication platform (see ¶[0090]-[0091]; process may include classifying the outcome of each completed conversation. See also ¶[0017]; artificial intelligence and machine learning);
obtain first analytics data based on a communication session facilitated by the software-based communication platform (see ¶[0031]; use historical information from past conversations. Generate a modality to include insights an state information);
obtain second analytics data derived from the communication session and one or more other communication sessions facilitated by the software-based communication platform (see again ¶[0031]; use historical information from past conversations. Generate a modality to include insights an state information);
ingest into the data repository, the second analytics data and updated customer relations management (CRM) data derived from an instance of a CRM platform accessible to the software-based communication platform (see ¶[0030]-[0031] and [0090]; the individuals’ information may be obtained from a Customer Relationship Management System (CRM) or database).
BROWN does not specifically disclose, but MCCORD discloses, wherein the second analytics data and the updated CRM data are standardized into a common format in the data repository such that the trained classification-based machine learning model uses the standardized second analytics data and the standardized CRM data together to generate the outcome data (see ¶[0043] and Fig. 2; sales data may be retrieved from external sources such as a company’s CRM and transcripts from conversations. Such data may require formalization to remove unusable records and normalize data format to a point [sic] usefulness). Teach the machine learning module to recognize stage markers in sales processes.
BROWN further discloses generate, via the trained classification-based machine learning model, outcome data indicative of a prediction associated with content of the communication session (see ¶[0017] and [10105]; predict outcomes for the conversations).
BROWN does not explicitly disclose, but RAANANI discloses, output the outcome data for access by one or more client devices associated with the software-based communication platform (see ¶[0026]; notify the consumer user regarding the deals at risk via an email or alert on a display of a user device).
BROWN discloses a system and method for dynamically controlling conversations and workflows based on conversation monitoring that includes participants in a deal (see [0085], [0090], and [0100]). MCCORD discloses intelligent sales management that includes normalizing CRM data and conversational transcripts for be used for further analysis, including machine learning purposes. It would have been obvious for one of ordinary skill in the art at the time of invention to include the normalization of data as taught by MCCORD in the system executing the method of BROWN with the motivation to control conversations and workflows pertaining to deals and sales.
BROWN discloses a system and method for dynamically controlling conversations and workflows based on conversation monitoring that includes participants in a deal (see [0085], [0090], and [0100]). RAANANI discloses analyzing conversations to automatically identify deals at risk. It would have been obvious to identify deals at risk as taught by RAANANI in the system executing the method of BROWN with the motivation to control deal conversations and workflows.
Claim 35 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the system as set forth in claim 28.
BROWN further discloses wherein the communication session is a video communication session (see ¶[0020]; video conferencing streams or sessions).
Claim 36 (Currently Amended)
BROWN discloses a non-transitory computer-readable medium comprising instructions (see ¶[0141]; a device with a computer readable medium. Software instructions read into memory), that when executed by one or more processors (see ¶[0137]-[0138] and Fig. 11; a device with a processor), causes the one or more processors to perform operations comprising: training, using a training data set derived from a data repository internal to a software- based communication platform (see ¶[0053]; a conversation system trained to determine sentiment based on conversation metrics) and using hyperparameter tuning (see ¶[0019] , [0065] and [0096]; generate a modality for behavior based on metrics derived from interactions. Achieve an outcome based on an objective of the conversation. Dynamically generate a plan to achieve an outcome based on variables), a classification-based machine learning model to predict outcomes of communication sessions facilitated by the software-based communication platform (see ¶[0090]-[0091]; process may include classifying the outcome of each completed conversation. See also ¶[0017]; artificial intelligence and machine learning);
obtaining, by a processing engine of the software-based communication platform (see ¶[0137]-[0138] and Fig. 11; a device with a processor), first analytics data based on a communication session facilitated by the software-based communication platform (see ¶[0031]; use historical information from past conversations. Generate a modality to include insights an state information);
obtaining, by the processing engine, second analytics data derived from the communication session and one or more other communication sessions facilitated by the software-based communication platform (see again ¶[0031]; use historical information from past conversations. Generate a modality to include insights an state information);
ingesting into the data repository, by the processing engine, the second analytics data and updated customer relations management (CRM) data derived from an instance of a CRM platform accessible to the software-based communication platform (see ¶[0030]-[0031] and [0090]; the individuals’ information may be obtained from a Customer Relationship Management System (CRM) or database).
BROWN does not specifically disclose, but MCCORD discloses, wherein the second analytics data and the updated CRM data are standardized into a common format in the data repository such that the trained classification-based machine learning model uses the standardized second analytics data and the standardized CRM data together to generate the outcome data (see ¶[0043] and Fig. 2; sales data may be retrieved from external sources such as a company’s CRM and transcripts from conversations. Such data may require formalization to remove unusable records and normalize data format to a point [sic] usefulness). Teach the machine learning module to recognize stage markers in sales processes.
BROWN further discloses generating, by the trained classification-based machine learning model, outcome data indicative of a prediction associated with content of the communication session (see ¶[0017] and [10105]; predict outcomes for the conversations).
BROWN does not explicitly disclose, but RAANANI discloses, outputting the outcome data for access by one or more client devices associated with the software-based communication platform (see ¶[0026]; notify the consumer user regarding the deals at risk via an email or alert on a display of a user device).
BROWN discloses a system and method for dynamically controlling conversations and workflows based on conversation monitoring that includes participants in a deal (see [0085], [0090], and [0100]). MCCORD discloses intelligent sales management that includes normalizing CRM data and conversational transcripts for be used for further analysis, including machine learning purposes. It would have been obvious for one of ordinary skill in the art at the time of invention to include the normalization of data as taught by MCCORD in the system executing the method of BROWN with the motivation to control conversations and workflows pertaining to deals and sales.
BROWN discloses a system and method for dynamically controlling conversations and workflows based on conversation monitoring that includes participants in a deal (see [0085], [0090], and [0100]). RAANANI discloses analyzing conversations to automatically identify deals at risk. It would have been obvious to identify deals at risk as taught by RAANANI in the system executing the method of BROWN with the motivation to control deal conversations and workflows.
Claim 40 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the non-transitory computer-readable medium as set forth in claim 36.
BROWN further discloses wherein the communication session is a video communication session (see ¶[0020]; video conferencing streams or sessions).
Claim(s) 22, 23, 32, 33, 37, and 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210258424 A1 to BROWN et al. in view of US 20170255945 A1 to MCCORD et al. and US 20180096271 A1 to RAANANI et al. as applied to claim 21 above, and further in view of US 20190122030 A1 to Raudies et al. (hereinafter ‘RAUDIES’).
Claim 22 (Currently Amended)
The combination of BROWN, MCCORD, and RAANANI discloses the method as set forth in claim 21.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but RAUDIES discloses, wherein the second analytics data is derived using machine learning and is computed via exponentially weighting averaging of analytics features across the communication session and the one or more other communication sessions, with recent communication sessions weighted more heavily than communication sessions that are not recent. (see ¶[0021]; a meeting participant interacts with the video conference system 106 via a client-side application installed on the client devices 102 and 104. In some embodiments, the client-side application includes a video conference system specific component. For example, the component is a stand-alone application, one or more application plug-ins, and/or a browser extension).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). RAUDIES discloses video conferencing that includes a browser extension. It would have been obvious for one of ordinary skill in the art at the time of invention to include the browser extension as taught by RAUDIES in the system executing the method of BROWN with the motivation to enable a video conferencing application.
Claim 23 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the method as set forth in claim 21.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but RAUDIES discloses, wherein the processing engine is a client application (see ¶[0021]; a meeting participant interacts with the video conference system 106 via a client-side application installed on the client devices 102 and 104. In some embodiments, the client-side application includes a video conference system specific component. For example, the component is a stand-alone application, one or more application plug-ins, and/or a browser extension).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). RAUDIES discloses video conferencing that includes a client-side application. It would have been obvious for one of ordinary skill in the art at the time of invention to include the client-side application as taught by RAUDIES in the system executing the method of BROWN with the motivation to enable a video conferencing application.
Claim 32 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the system as set forth in claim 28.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but RAUDIES discloses, wherein the processing engine is a browser extension (see ¶[0021]; a meeting participant interacts with the video conference system 106 via a client-side application installed on the client devices 102 and 104. In some embodiments, the client-side application includes a video conference system specific component. For example, the component is a stand-alone application, one or more application plug-ins, and/or a browser extension).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). RAUDIES discloses video conferencing that includes a browser extension. It would have been obvious for one of ordinary skill in the art at the time of invention to include the browser extension as taught by RAUDIES in the system executing the method of BROWN with the motivation to enable a video conferencing application.
Claim 33 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the system as set forth in claim 28.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but RAUDIES discloses, wherein the processing engine is a client application (see ¶[0021]; a meeting participant interacts with the video conference system 106 via a client-side application installed on the client devices 102 and 104. In some embodiments, the client-side application includes a video conference system specific component. For example, the component is a stand-alone application, one or more application plug-ins, and/or a browser extension).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). RAUDIES discloses video conferencing that includes a client-side application. It would have been obvious for one of ordinary skill in the art at the time of invention to include the client-side application as taught by RAUDIES in the system executing the method of BROWN with the motivation to enable a video conferencing application.
Claim 37 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the non-transitory computer-readable medium as set forth in claim 36.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but RAUDIES discloses, wherein the processing engine is a browser extension (see ¶[0021]; a meeting participant interacts with the video conference system 106 via a client-side application installed on the client devices 102 and 104. In some embodiments, the client-side application includes a video conference system specific component. For example, the component is a stand-alone application, one or more application plug-ins, and/or a browser extension).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). RAUDIES discloses video conferencing that includes a browser extension. It would have been obvious for one of ordinary skill in the art at the time of invention to include the browser extension as taught by RAUDIES in the system executing the method of BROWN with the motivation to enable a video conferencing application.
Claim 38 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the non-transitory computer-readable medium as set forth in claim 36.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but RAUDIES discloses, wherein the processing engine is a client application (see ¶[0021]; a meeting participant interacts with the video conference system 106 via a client-side application installed on the client devices 102 and 104. In some embodiments, the client-side application includes a video conference system specific component. For example, the component is a stand-alone application, one or more application plug-ins, and/or a browser extension).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). RAUDIES discloses video conferencing that includes a client-side application. It would have been obvious for one of ordinary skill in the art at the time of invention to include the client-side application as taught by RAUDIES in the system executing the method of BROWN with the motivation to enable a video conferencing application.
Claim(s) 24, 34, and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210258424 A1 to BROWN et al. in view of US 20170255945 A1 to MCCORD et al. and US 20180096271 A1 to RAANANI et al. as applied to claim 21 above, and further in view of US 20020087552 A1 to Applewhite et al. (hereinafter ‘APPLEWHITE’).
Claim 24 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the method as set forth in claim 21.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but APPLEWHITE discloses, wherein the outcome data is output via an application server (see ¶[0062]; also at processor 515 may be an applications server 540, preferably operating behind a firewall, in data communications with network server 538 and having a memory 542 that contains software used in the present invention, including an information server engine 544, for generating and processing forms, and a replica engine such as a title engine 546 and/or a drive engine 548 in data communications with applications server 540).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). APPLEWHITE discloses applications using an application server for processing information. It would have been obvious to include the application server as taught by APPLEWHITE in the system executing the method of BROWN with the motivation to provide a video conferencing application.
Claim 34 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the system as set forth in claim 28.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but APPLEWHITE discloses, wherein the processing engine is configured to output the outcome data via an application server (see ¶[0062]; also at processor 515 may be an applications server 540, preferably operating behind a firewall, in data communications with network server 538 and having a memory 542 that contains software used in the present invention, including an information server engine 544, for generating and processing forms, and a replica engine such as a title engine 546 and/or a drive engine 548 in data communications with applications server 540).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). APPLEWHITE discloses applications using an application server for processing information. It would have been obvious to include the application server as taught by APPLEWHITE in the system executing the method of BROWN with the motivation to provide a video conferencing application.
Claim 39 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the non-transitory computer-readable medium as set forth in claim 36.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but APPLEWHITE discloses, wherein the outcome data is output via an application server (see ¶[0062]; also at processor 515 may be an applications server 540, preferably operating behind a firewall, in data communications with network server 538 and having a memory 542 that contains software used in the present invention, including an information server engine 544, for generating and processing forms, and a replica engine such as a title engine 546 and/or a drive engine 548 in data communications with applications server 540).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). APPLEWHITE discloses applications using an application server for processing information. It would have been obvious to include the application server as taught by APPLEWHITE in the system executing the method of BROWN with the motivation to provide a video conferencing application.
Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210258424 A1 to BROWN et al. in view of US 20170255945 A1 to MCCORD et al. and US 20180096271 A1 to RAANANI et al. as applied to claims 21 and 26 above, and further in view of US 20070299710 A1 to Haveliwala (hereinafter ‘HAVELIWALA’).
Claim 27 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the method as set forth in claim 26.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but HAVELIWALA discloses wherein the virtual room is a breakout room (see abstract and ¶[0048] a virtual session and video conferencing with a breakout room).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). RAANANI discloses analyzing conversations to automatically identify deals at risk, where the conversations include recordings of virtual-reality based conversations. HAVELIWALA discloses breakout rooms for conferencing in a virtual video conferencing environment. It would have been obvious for one of ordinary skill in the art at the time of invention to include the breakout room as taught by HAVELIWALA in the system executing the method of BROWN and RAANANI with the motivation to provide sub-rooms for smaller subsets of a larger meeting (see HAVELIWALA abstract).
Claim(s) 29 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210258424 A1 to BROWN et al. in view of US 20170255945 A1 to MCCORD et al. and US 20180096271 A1 to RAANANI et al. as applied to claim 28 above, and further in view of US 20040186738 A1 to Reisman (hereinafter ‘REISMAN’).
Claim 29 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the system as set forth in claim 28.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but REISMAN discloses, wherein the processing engine is further configured to: perform synchronous messaging within the communication session (see ¶[0125]; current and emerging communications methods may include synchronous or real-time methods, including in-person, telephonic, teleconferencing, text, audio, and video chat or conferencing, collaborative virtual environments (CVEs).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). REISMAN discloses an idea adoption marketplace that includes synchronous and asynchronous communication methods. It would have been obvious for one of ordinary skill in the art at the time of invention to include the synchronous messaging as taught by REISMAN in the system executing the method of BROWN with the motivation to enable conversations using current and emerging methods.
Claim 30 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the system as set forth in claim 28.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose, but REISMAN discloses, wherein the processing engine is further configured to: perform asynchronous messaging within the communication session (see ¶[0125]; current and emerging communications methods may include asynchronous methods, including mail and physical publication, e-mail, SMS (Short Message Service), and other messaging, bulletin boards, discussion groups, newsgroups and similar threaded conferencing systems, Web sites, Weblogs, knowledgebases, databases, content management systems, visualization and virtual reality (VR) systems, Lifestreams, and related support structures, whether based on file structures, relational, object, or other databases, tuple spaces, or other communication and information technologies.)
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]). REISMAN discloses an idea adoption marketplace that includes synchronous and asynchronous communication methods. It would have been obvious for one of ordinary skill in the art at the time of invention to include the synchronous messaging as taught by REISMAN in the system executing the method of BROWN with the motivation to enable conversations using current and emerging methods.
Claim(s) 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210258424 A1 to BROWN et al. in view of US 20170255945 A1 to MCCORD et al. and US 20180096271 A1 to RAANANI et al. as applied to claim 28 above, and further in view of US 20190325605 A1 to Ye et al. (hereinafter ‘YE’).
Claim 31 (Previously Presented)
The combination of BROWN, MCCORD, and RAANANI discloses the system as set forth in claim 28.
The combination of BROWN, MCCORD, and RAANANI does not specifically disclose but YE discloses, wherein the processing engine is configured to use machine learning to derive the second analytics data (see ¶[0006] and [0054]; object detection in images that may be produced from video conferences using a neural network with an intermediate layer, classification layer, and regression layer. Dimensional data is derived in the intermediate layer).
BROWN discloses a system and method for dynamically controlling conversations and workflows in a video conferencing application (see ¶[0002]) that uses machine learning (see ¶[0017]). YE discloses detecting objects in images using a neural network with an intermediate layer to generate dimensional data prior to an output. It would have been obvious for one of ordinary skill in the art at the time of invention to include the neural network with the intermediate layer to generate dimensional data as taught by YE in the system executing the method of BROWN with the motivation to detect and classify objects in images obtained in video conferences.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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