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 .
Response to Arguments
Applicant's arguments filed with respect to rejections under 35 USC 101 have been fully considered but they are not persuasive. Applicant merely submits that the amendments to the claims overcome the rejection. Examiner disagrees for reasons set forth in the rejection below.
Applicant’s arguments with respect to claim(s) previously rejected over Sorensen in view of Ramaswamy and Zhuk have been considered but are not persuasive. Examiner notes that Sorenson describes training the machine learning model based on historical collaboration data [0093-0094]. While the response includes only a general allegation that the references do not teach the bolded features of the reproduced claim, the interview summary indicated Applicant suggested the combination of references does not disclose sentiment analysis being associated with the training dataset. Examiner point out that the machine learning algorithm training in Sorenson is based on collaboration data metrics. This equates to the training the machine learning model based on a training dataset. Sorenson is silent with respect to any sentiment analysis applied to the collaboration data to generate the collaboration metrics. Zhuk et al was brought in to show how sentiment analysis is used to analyze collaboration data. Therefore, it would have been obvious to replace Sorenson’s methods of generating the collaboration data metrics with Zhuk et al’s sentiment analysis to generate collaboration data metrics. The previous rejection of the claims is upheld, but updated to reflect the most recent amendments directed to the display of the insights, as claimed.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-20 is/are directed to a method, system, and computer program product. Thus, all the claims are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test.
As per Prong One of Step 2A of the §101 eligibility analysis provided in the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), the Examiner notes that the claims recite mental processes and certain methods of organizing human activity. More specifically, the steps of:
access external collaborative work data from a plurality of external collaboration platforms; [mental process – observation/evaluation and certain methods of organizing human activity – step one would follow in the instructions to train and configure a team health improvement machine learning model]
access internal collaborative work data from a plurality of internal collaboration platforms; [mental process – observation/evaluation and certain methods of organizing human activity – step one would follow in the instructions to train and configure a team health improvement machine learning model]
generate a team health training dataset based on the external collaborative work data and the internal collaborative work data, wherein the team health training dataset if generated based on apply sentiment analysis operations to the external collaborative work data and the internal collaborative work data; [mental process – observation/evaluation and certain methods of organizing human activity – step one would follow in the instructions to train and configure a team health improvement machine learning model]
train a machine learning model based on the team health training dataset to generate a trained team health improvement machine learning model; [mental process – observation/evaluation and certain methods of organizing human activity – a step one would follow in the instructions to train and configure a team health improvement machine learning model]
configure the contextually triggered team improvement engine based on the trained team health improvement machine learning model, wherein the contextually triggered team improvement components; [mental process – observation/evaluation and certain methods of organizing human activity – step one would follow in the instructions to train and configure a team health improvement machine learning model]
generate,
output, by transmitting to a team member
recite mental processes that can be practically performed by a human using pen and paper and recites certain methods of human activities.
The nominal recitation of a computer processor, engine, interface, memory and program code do not indicate the claimed invention is not an abstract idea as evidenced by the analysis at Prong 2 of Step 2A.
Regarding Prong Two of Step 2A, a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. Independent claim 1 recites “an apparatus for configuring a contextually triggered team improvement engine for operation in a collaborative enterprise platform, the apparatus comprising at least one processor, and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor…” and also, the engine monitors user engagement and displays insight. Independent claim 10 recites “A computer-implemented method for configuring a contextually triggered team improvement engine for operation in a collaborative enterprise platform” and wherein data is gathered from external and internal platforms and also, the engine monitors user engagement and displays insight. Independent claim 19 recites “A non-transitory computer-readable storage medium for configuring a contextually triggered team improvement engine for operation in a collaborative enterprise platform, the non-transitory computer-readable storage medium including instructions that when executed by at least one processor, cause the at least one processor to” and wherein data is gathered from external and internal platforms and also, the engine monitors user engagement and displays insight. Each of the independent claims recite rending a graphical interface element on a display which amounts to using a computer as a tool to perform an abstract idea. Further the machine learning model amounts to an algorithm implemented by a computer. The above listed computer elements amount to using a computer as a tool to perform the abstract idea. There is no integration into a practical application.
The dependent claims further limit the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application.
Claims 2, 11 and 20 recite generating a dataset by applying collaboration graph identification. These steps are abstract mental processes and certain methods of organizing human activity. Using a computer to apply collaboration graph information to generate a dataset amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 3 and 12 recite collecting collaboration data and training a machine learning model based on the collaboration data (work graph). These steps are abstract mental processes and certain methods of organizing human activity. Using a computer to apply gather collaboration information to generate a dataset and training a learning model amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 4 and 13 recite the collaborative work data comprises survey data. Collecting survey data is an abstract mental process and certain methods of organizing human activity. Implementation by computer amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 5 and 14 recite comparing health metrics over a time period with a historical team health metric over the time period. These steps are abstract mental processes and certain methods of organizing human activity. Using a computer to gather data from another computer system and outputting to an interface amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 6 and 15 recite monitoring engagement and generating an intervention component for output. These steps are abstract mental processes and certain methods of organizing human activity. Using a computer to gather data from another computer system and outputting to an interface amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 7 and 16 recite the intervention is a survey and rendering on a display. These steps are abstract mental processes and certain methods of organizing human activity. Using a computer to present a survey and outputting to an interface amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 8 and 17 recite the intervention is a workflow and rendering on a display. These steps are abstract mental processes and certain methods of organizing human activity. Using a computer to present a workflow and outputting to an interface amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 9 and 18 recite comparing metrics to threshold and output on a dashboard. These steps are abstract mental processes and certain methods of organizing human activity. Using a computer to compare metrics and outputting to an interface amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually, the system and software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense.
Lastly and in accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5, 6, 8-12, 14, 15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sorensen, US 2024/0119417 in view of Ramaswamy et al, 2021/0209555, and Zhuk et al, US 2020/0287736.
As per claim 1, Sorensen teaches an apparatus for configurating a contextually triggered team improvement engine for operation in a collaborative enterprise platform, the apparatus comprising at least one processor, and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor [0154], cause the apparatus to at least:
access collaborative work data ([0011, 0029-0031, 0066, 0083] – collection of collaboration metrics and data);
generate a team health training dataset based on the collaborative work data ([0094] - the collaboration data and business outcome data for last year can be used to train a machine learning algorithm);
train a machine learning model based on the team health training dataset to generate a trained team health improvement machine learning model ([0094] - Once trained, the machine learning algorithm can be used to model what effect a change in the collaboration graph may have in the future);
configure the contextually triggered team improvement engine based on the trained team health improvement machine learning model ([0065-0068, 0093, 0094] - the system can use the collaboration metrics(s), model outputs, and/or business metrics, optionally with additional data (e.g., collaboration data, organizational data, business outcome data, and/or collaboration graph data) to generate and present a notification. The notification can be notice of an issue that is arising (e.g., a warning that a member or group has a collaboration score that is beyond a threshold, such as above a maximum threshold, below a minimum threshold, outside of a threshold range, or inside of a threshold range); a recommendation for an action),
wherein the contextually triggered team improvement engine is configured to monitor user engagement with the collaborative enterprise platform and to generate team improvement insight components for outputting to a team assessment interface for rendering on a team member client device ([0027-0028, 0068-0071] – a recommendation can be determined based on monitoring individuals and other members – an individual feeling siloed from others (low engagement) will trigger a recommendation to be provided; a recommendation to encourage collaboration between two members; or any other suitable notification. The notification can be presented to the user via any suitable technique, such as presentation on a display device (e.g., a monitor));
wherein the first team improvement insight component is configured to render as a graphical interface element on a display of the team member client device rendering the first team assessment interface ([0068, 0097-0099] – the insights are visualized using a GUI).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Sorensen and Ramaswamy et al discloses the training dataset based on external and internal collaborative work data but fails to explicitly disclose while Zhuk et al discloses wherein the team health training dataset is generated based on applying sentiment analysis operations to the external collaborative work data and the internal collaborative work data [0116, 0150]. Further, the combination fails to explicitly disclose while Zhuk et al discloses generate, by the contextually triggered team improvement engine and based on monitoring user engagement, a first team improvement insight component and output, by transmitting to a team member client device, the first team improvement insight component to a first team assessment interface [0156, 0194, 0207]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen and Ramaswamy et al the ability to apply sentiment analysis and generate and output insights as taught by Zhuk et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, Sorensen teaches the apparatus of claim 1, wherein the team health training dataset is generated based on additionally applying collaboration graph identification operations to the collaborative work data ([0029] – collaboration graphs created from collaboration data, etc. data can include content of collaborations (body of an email) and other metadata).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, Sorensen teaches the apparatus of claim 2, wherein the collaboration graph identification operations are configured to generate a collaboration work graph based on the collaborative work data, and the machine learning model is trained to generate the trained team health improvement machine learning model based at least in part on the collaboration work graph ([0029] – collaboration graphs created from collaboration data, etc. data can include content of collaborations (body of an email) and other metadata; [0063, 0065] – machine learning modeling to make predictions and facilitate assessing or improving metrics).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, Sorensen teaches the apparatus of claim 1, wherein outputting the team improvement insight components to the team assessment interface includes rendering one or more team improvement insight components that include at least one of a comparison of a first team health metric over a time period with a historical team health metric over the time period ([0093-0094] – time series analysis is performed to evaluation correlations between metrics and outcome metrics as well as analysis of historical metrics used to model what effect a change will have in the future such as effects on collaboration metrics).
As per claim 6, Sorensen teaches the apparatus of claim 1, wherein the contextually triggered team improvement engine is configured to monitor user engagement with the collaborative enterprise platform and to generate a team improvement intervention component for outputting to a team member client device of the collaborative enterprise platform ([0068-0071] – an intervention can be determined based on monitoring individuals and other members which will trigger a recommendation or intervention to be provided).
As per claim 8, Sorensen teaches the apparatus of claim 6, wherein the team improvement intervention component comprises a team improvement workflow interface component that is configured for rendering to a team work interface displayed by the team member client device ([0068-0071] – an automated process wherein an intervention can be determined based on monitoring individuals and other members and includes automatically presenting a recommendation or intervention to a user).
As per claim 9, Sorensen teaches the apparatus of claim 1, wherein the contextually triggered team improvement engine is configured to compare a team health metrics set to a team health metric threshold set to generate a team health dashboard metrics set, and to output the team health dashboard metrics set for rendering to a team health dashboard interface of a team assessment interface ([0028, 0066-0068] – collaboration metrics used to generate and present visualizations; collaboration scores compared to thresholds and recommendations for action or other suitable notification is presented).
As per claim 10, Sorensen teaches a computer-implemented method for configuring a contextually triggered team improvement engine for operation in a collaborative enterprise platform, the computer-implemented method comprising:
accessing at least one of external collaborative work data from a plurality of external collaboration platforms, or internal collaborative work data from a plurality of internal collaboration platforms ([0011, 0029-0031, 0066, 0083] – collection of collaboration metrics and data);
generating a team health training dataset based on the collaborative work data ([0094] - the collaboration data and business outcome data for last year can be used to train a machine learning algorithm);
training a machine learning model based on the team health training dataset to generate a trained team health improvement machine learning model ([0094] - Once trained, the machine learning algorithm can be used to model what effect a change in the collaboration graph may have in the future);
configuring the contextually triggered team improvement engine based on the trained team health improvement machine learning model ([0065-0068, 0093, 0094] - the system can use the collaboration metrics(s), model outputs, and/or business metrics, optionally with additional data (e.g., collaboration data, organizational data, business outcome data, and/or collaboration graph data) to generate and present a notification. The notification can be notice of an issue that is arising (e.g., a warning that a member or group has a collaboration score that is beyond a threshold, such as above a maximum threshold, below a minimum threshold, outside of a threshold range, or inside of a threshold range); a recommendation for an action),
wherein the contextually triggered team improvement engine is configured to monitor user engagement with the collaborative enterprise platform and to generate team improvement insight components for outputting to a team assessment interface for rendering on a team member client device ([0027-0028, 0068-0071] – a recommendation can be determined based on monitoring individuals and other members – an individual feeling siloed from others (low engagement) will trigger a recommendation to be provided; a recommendation to encourage collaboration between two members; or any other suitable notification. The notification can be presented to the user via any suitable technique, such as presentation on a display device (e.g., a monitor));
wherein the first team improvement insight component is configured to render as a graphical interface element on a display of the team member client device rendering the first team assessment interface ([0068, 0097-0099] – the insights are visualized using a GUI).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Sorensen and Ramaswamy et al discloses the training dataset based on external and internal collaborative work data but fails to explicitly disclose while Zhuk et al discloses wherein the team health training dataset is generated based on applying sentiment analysis operations to the external collaborative work data and the internal collaborative work data [0116, 0150]. Further, the combination fails to explicitly disclose while Zhuk et al discloses generate, by the contextually triggered team improvement engine and based on monitoring user engagement, a first team improvement insight component and output, by transmitting to a team member client device, the first team improvement insight component to a first team assessment interface [0156, 0194, 0207]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen and Ramaswamy et al the ability to apply sentiment analysis and generate and output insights as taught by Zhuk et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, Sorensen teaches the method of claim 10, the computer-implemented method further comprising: generating the team health training dataset based on additionally applying collaboration graph identification operations to the collaborative work data ([0029] – collaboration graphs created from collaboration data, etc. data can include content of collaborations (body of an email) and other metadata).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 12, Sorensen teaches the computer-implemented method of claim 11, wherein the collaboration graph identification operations are configured to generate a collaboration work graph based on the collaborative work data, and the machine learning model is trained to generate the trained team health improvement machine learning model based at least in part on the collaboration work graph ([0029] – collaboration graphs created from collaboration data, etc. data can include content of collaborations (body of an email) and other metadata; [0063, 0065] – machine learning modeling to make predictions and facilitate assessing or improving metrics).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 14, Sorensen teaches the method of claim 10, wherein outputting the team improvement insight components to the team assessment interface includes rendering one or more team improvement insight components that include at least one of a comparison of a first team health metric over a time period with a historical team health metric over the time period ([0093-0094] – time series analysis is performed to evaluation correlations between metrics and outcome metrics as well as analysis of historical metrics used to model what effect a change will have in the future such as effects on collaboration metrics).
As per claim 15, Sorensen teaches the computer-implemented method of claim 10, the computer-implemented method further comprising: configuring the contextually triggered team improvement engine to monitor user engagement with the collaborative enterprise platform and to generate a team improvement intervention component for outputting to a team member client device of the collaborative enterprise platform ([0068-0071] – an intervention can be determined based on monitoring individuals and other members which will trigger a recommendation or intervention to be provided).
As per claim 17, Sorensen teaches the computer-implemented method of claim 15, wherein the team improvement intervention component comprises a team improvement workflow interface component that is configured for rendering to a team work interface displayed by the team member client device ([0068-0071] – an automated process wherein an intervention can be determined based on monitoring individuals and other members and includes automatically presenting a recommendation or intervention to a user).
As per claim 18, Sorensen teaches the apparatus of claim 1, wherein the contextually triggered team improvement engine is configured to compare a team health metrics set to a team health metric threshold set to generate a team health dashboard metrics set, and to output the team health dashboard metrics set for rendering to a team health dashboard interface of a team assessment interface ([0028, 0066-0068] – collaboration metrics used to generate and present visualizations; collaboration scores compared to thresholds and recommendations for action or other suitable notification is presented).
As per claim 19, Sorensen teaches a non-transitory computer-readable storage medium for configuring a contextually triggered team improvement engine for operation in a collaborative enterprise platform, the non-transitory computer-readable storage medium including instructions that when executed by at least one processor [0154, 0156] cause the at least one processor to:
access collaborative work data ([0011, 0029-0031, 0066, 0083] – collection of collaboration metrics and data);
generate a team health training dataset based on the collaborative work data ([0094] - the collaboration data and business outcome data for last year can be used to train a machine learning algorithm);
train a machine learning model based on the team health training dataset to generate a trained team health improvement machine learning model ([0094] - Once trained, the machine learning algorithm can be used to model what effect a change in the collaboration graph may have in the future);
configure the contextually triggered team improvement engine based on the trained team health improvement machine learning model ([0065-0068, 0093, 0094] - the system can use the collaboration metrics(s), model outputs, and/or business metrics, optionally with additional data (e.g., collaboration data, organizational data, business outcome data, and/or collaboration graph data) to generate and present a notification. The notification can be notice of an issue that is arising (e.g., a warning that a member or group has a collaboration score that is beyond a threshold, such as above a maximum threshold, below a minimum threshold, outside of a threshold range, or inside of a threshold range); a recommendation for an action),
wherein the contextually triggered team improvement engine is configured to monitor user engagement with the collaborative enterprise platform and to generate team improvement insight components for outputting to a team assessment interface for rendering on a team member client device ([0027-0028, 0068-0071] – a recommendation can be determined based on monitoring individuals and other members – an individual feeling siloed from others (low engagement) will trigger a recommendation to be provided; a recommendation to encourage collaboration between two members; or any other suitable notification. The notification can be presented to the user via any suitable technique, such as presentation on a display device (e.g., a monitor));
wherein the first team improvement insight component is configured to render as a graphical interface element on a display of the team member client device rendering the first team assessment interface ([0068, 0097-0099] – the insights are visualized using a GUI).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The combination of Sorensen and Ramaswamy et al discloses the training dataset based on external and internal collaborative work data but fails to explicitly disclose while Zhuk et al discloses wherein the team health training dataset is generated based on applying sentiment analysis operations to the external collaborative work data and the internal collaborative work data [0116, 0150]. Further, the combination fails to explicitly disclose while Zhuk et al discloses generate, by the contextually triggered team improvement engine and based on monitoring user engagement, a first team improvement insight component and output, by transmitting to a team member client device, the first team improvement insight component to a first team assessment interface [0156, 0194, 0207]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen and Ramaswamy et al the ability to apply sentiment analysis and generate and output insights as taught by Zhuk et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 20, Sorensen teaches generate the team health training dataset based on additionally applying collaboration graph identification operations to the collaborative work data ([0029] – collaboration graphs created from collaboration data, etc. data can include content of collaborations (body of an email) and other metadata).
Sorensen does not explicitly teach access external/internal collaborative work data from a plurality of external/internal collaboration platforms. Ramaswamy et al teaches an analogous collaboration analysis system wherein collaboration metrics are gathered based on internal and external collaboration data and systems [0045]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to access internal and external collaboration data as taught by Ramaswamy et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sorensen, US 2024/0119417, Ramaswamy et al 2021/0209555 and Zhuk et al, 2020/0287736, US in view of Yan et al, US 2022/0147900.
As per claims 4 and 13 the combination of Sorensen and Ramaswamy et al teach external collaborative work data or the internal collaborative work data, as described above, but the combination fails to teach the collaborative work data comprises team survey data. Yan et al teaches an analogous collaboration assessment system wherein survey questions are used to gather collaborative work data [0018, 0022]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen and Ramaswamy et al the ability to use survey responses as taught by Yan et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sorensen, US 2024/0119417, Ramaswamy et al 2021/0209555, and Zhuk et al, 2020/0287736, in view of Bird et al, US 2006/0143022.
As per claims 7 and 16, Sorensen teaches the apparatus of claim 6, wherein the team improvement intervention component comprises an interface component that is configured for rendering to a team work interface displayed by the team member client device ([0079-0071] – an automated action process wherein recommendations are provided automatically via email, etc.) Sorensen fails to explicitly teach a survey, however, Bird et al teaches intervention completion with follow up surveys [0015]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Sorensen the ability to present a survey as taught by Bird et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNA LOFTIS whose telephone number is (571)272-6736. The examiner can normally be reached M-F 7:00am-3:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
JOHNNA LOFTIS
Primary Examiner
Art Unit 3625
/JOHNNA R LOFTIS/Primary Examiner, Art Unit 3625
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