Office Action 1: Non-Final Rejection (2023-12-01)
Rejection types: 102 - Lack of Novelty, 103 - Obviousness

CLAIMS:
What is claimed is:

1\. An apparatus for delegating a role associated with an NFT using a self-executable data structure, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least a processor to:
receive a data collection, wherein the data collection comprises a plurality of data objects associated with at least one NFT;
verify at least a usage of the at least one NFT as a function of the data collection;
determine at least a role associated with the at least one NFT; and
generate a self-executable data structure, wherein the self-executable data structure is configured to delegate the at least a role associated with the at least one NFT.

2\. The apparatus of claim 1, wherein the data collection comprises information associated with an owner of the at least one NFT.

3\. The apparatus of claim 1, wherein verifying the at least a usage of the at least one NFT comprises generating a verification datum as a function of the data collection.

4\. The apparatus of claim 1, wherein determining the at least a role associated with the at least one NFT comprises utilizing a role designation machine-learning model configured to correlate the at least a usage to a role datum.

5\. The apparatus of claim 1, wherein delegating the at least a role associated with the at least one NFT comprises delegating at least a usage right of the at least one NFT.

6\. The apparatus of claim 5, wherein the at least a usage right comprises a temporary usage right.

7\. The apparatus of claim 1, wherein the at least one NFT comprises an NFT selected from the group consisting of a utility NFT, a rental NFT, a fractional NFT, and a governance NFT.

8\. The apparatus of claim 1, wherein the self-executable data structure is configured to be executed on a decentralized platform.
9\. The apparatus of claim 1, wherein the at least a processor is further configured to analyze the data collection using a decision tree, wherein the decision tree comprises a plurality of nodes.

10\. The apparatus of claim 1, further comprising an interface communicatively connected to the at least a processor, wherein the interface is configured to display the at least one NFT.

11\. A method for delegating a role associated with an NFT using a self-executable data structure, the method comprising:
receiving, by at least a processor, a data collection, wherein the data collection comprises a plurality of data objects associated with at least one NFT;
verifying, by the at least a processor, at least a usage of the at least one NFT as a function of the data collection;
determining, by the at least a processor, at least a role associated with the at least one NFT; and
generating, by the at least a processor, a self-executable data structure, wherein the self-executable data structure is configured to delegate the at least a role associated with the at least one NFT.

12\. The method of claim 11, wherein the data collection comprises information associated with an owner of the at least one NFT.

13\. The method of claim 11, wherein verifying the at least a usage of the at least one NFT comprises generating a verification datum as a function of the data collection.

14\. The method of claim 11, wherein determining the at least a role associated with the at least one NFT comprises utilizing a role designation machine-learning model configured to correlate the at least a usage to a role datum.

15\. The method of claim 11, wherein delegating the at least a role associated with the at least one NFT comprises delegating at least a usage right of the at least one NFT.

16\. The method of claim 15, wherein the at least a usage right comprises a temporary usage right.

17\. The method of claim 11, wherein the at least one NFT comprises an NFT selected from the group consisting of a utility NFT, a rental NFT, a fractional NFT, and a governance NFT.
18\. The method of claim 11, wherein the self-executable data structure is configured to be executed on a decentralized platform.

19\. The method of claim 11, further comprising analyzing, by the at least a processor, the data collection using a decision tree, wherein the decision tree comprises a plurality of nodes.

20\. The method of claim 11 further comprising displaying the at least one NFT using an interface communicatively connected to the at least a processor. 60Attorney Docket No. 1397-012USU1 61Attorney Docket No. 1397-012USU1 62Attorney Docket No. 1397-012USUI

OFFICE 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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-8, 11-13, 15-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Capability-based non-fungible tokens approach for a decentralized AAA framework in IoT by Omar et al. hereafter Omar.
1. Omar discloses an apparatus for delegating a role associated with an NFT using a self-executable data structure, the apparatus comprising:
at least a processor (section 2, it is inherent that devices have a processor); and
a memory communicatively connected to the at least processor (section 2, it is inherent that devices have a memory), the memory containing instructions configuring the at least a processor to:
receive a data collection, wherein the data collection comprises a plurality of data objects associated with at least one NFT (section 4, Access Control Methods; section 6.2 and 6.3, non-fungible capability token structure);
verify at least a usage of the at least one NFT as a function of the data collection (sections 6.2-6.4);
determine at least a role associated with the at least one NFT (sections 6.2-6.4); and
generate a self-executable data structure, wherein the self-executable data structure is configured to delegate the at least a role associated with the at least one NFT (section 6.5).
2. Omar discloses the apparatus of claim 1, wherein the data collection comprises information associated with an owner of the at least one NFT (section 6.2-6.3).
3. Omar discloses the apparatus of claim 1, wherein verifying the at least a usage of the at least one NFT comprises generating a verification datum as a function of the data collection (section 6.2-6.3).
5. Omar discloses the apparatus of claim 1, wherein delegating the at least a role associated with the at least one NFT comprises delegating at least a usage right of the at least one NFT (section 6.5).
6. Omar discloses the apparatus of claim 5, wherein the at least a usage right comprises a temporary usage right (section 6.5).
7. Omar discloses the apparatus of claim 1, wherein the at least one NFT comprises an NFT selected from the group consisting of a utility NFT, a rental NFT, a fractional NFT, and a governance NFT (section 2, privileges other devices/users have on the device and what actions they can perform. Thus, it defines the process of policies enforcement by determining the types of activities, resources, or services a device/user is permitted to perform or access (utility); see also section 6.2-6.3).
8. Omar discloses the apparatus of claim 1, wherein the self-executable data structure is configured to be executed on a decentralized platform (section 6).
Claims 11-13 and 15-17 are similar in scope to claims 1-3 and 5-7 and are rejected under similar rationale.
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) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omar as applied to claim 1, 11 above, and further in view of Dotan-Cohen et al. (US 2019/0340554) hereafter Dotan-Cohen.
4. Omar discloses the apparatus of claim 1, but does not explicitly disclose wherein determining the at least a role associated with the at least one NFT comprises utilizing a role designation machine-learning model configured to correlate the at least a usage to a role datum.  However, in an analogous art, Dotan-Cohen discloses engagement levels and role assignments including determining the at least a role associated with the at least one NFT comprises utilizing a role designation machine-learning model configured to correlate the at least a usage to a role datum (Abstract, para 137, 195).  It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Omar with the implementation of Dotan-Cohen in order to reduce resources for content delivery and increase security (para 7).
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omar as applied to claims 1 and 11 above, and further in view of Claybrook et al. (US 11,109,214) hereafter Claybrook.
9. Omar discloses the apparatus of claim 1, but does not explicitly disclose wherein the at least a processor is further configured to analyze the data collection using a decision tree, wherein the decision tree comprises a plurality of nodes.  Claybrook discloses client access devices based on assigned roles including analyze data using a decision tree (col 13, 5-27, a deep-learning based algorithm; a neural network; a generalized linear regression algorithm; a random forest algorithm; a support vector machine algorithm; a gradient boosting regression algorithm; a decision tree algorithm; a generalized additive model; evolutionary programming algorithms; Bayesian inference algorithms, reinforcement learning algorithms, and the like). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Omar with the implementation of Claybrook in order to identify trends and patterns to evaluate possible outcomes (a natural advantage of machine learning).
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Omar as applied to claims 1 and 11 above, and further in view of Examiner’s Official Notice.
10. Omar discloses the apparatus of claim 1, but does not explicitly disclose further comprising an interface communicatively connected to the at least a processor, wherein the interface is configured to display the at least one NFT.  The examiner takes official notice that a display interface was well known at the time of invention.  It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Omar with the implementation of examiner’s official notice in order to allow a user to view content.
Conclusion


NEXT OFFICE 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 .
Examiner’s Amendment
An examiner’s amendment to the record appears below.  Should the changes and/or additions be unacceptable to Applicant, an amendment may be filed as provided by 37 CFR 1.312. To ensure consideration of such an amendment, it MUST be submitted no later than the payment of the issue fee.
Claims
1.(Currently Amended) An apparatus for delegating a role associated with [[an NFT]]a non-fungible token (NFT) using a self-executable data structure, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a data collection, wherein the data collection comprises a plurality of data objects associated with at least one NFT;
verify at least a usage of the at least one NFT as a function of the data collection;
determine at least a role associated with the at least one NFT, wherein determining the at least a role comprises:
retrieving training data from a blockchain storage of a data store, wherein the training data comprises a plurality of usage data correlated to a plurality ofr ole data;
training, iteratively, a role designation machine-learning model as a function of the training data; and
determining the at least a role as a function of the at least a usage of the at least one NFT using the trained role designation machine-learning model; and
generate a self-executable data structure, wherein the self-executable datastructure is configured to delegate the at least a role associated with the at least one NFET.
11. (Currently Amended) A method for delegating a role associated with [[an NFT]]a non-fungible token (NFT) using a self-executable data structure, the method comprising:
receiving, by at least a processor, a data collection, wherein the data collection comprises a plurality of data objects associated with at least one NFT;
verifying, by the at least a processor, at least a usage of the at least one NFT as a function of the data collection;
determining, by the at least a processor, at least a role associated with the at least oneNFT, wherein determining the at least a role comprises:
retrieving training data from a blockchain storage of a data store, wherein the training data comprises a plurality of usage data correlated to a plurality of role data;
training, iteratively, a role designation machine-learning model as a function of the training data; and
determining the at least a role as a function of the at least a usage of the at least one NFT using the trained role designation machine-learning model;
generating, by the at least a processor, a self-executable data structure, wherein the self-executable data structure is configured to delegate the at least a role associated with the at least one NFT.
Allowable Subject Matter
Examiner’s Statement of Reasons for Allowance
The following is an examiner’s statement for reasons for allowance:
Independent claims 1 and 11 are allowable based on the remark presented on 5/31/2024 and in view of the claim language.
None of the previously cited closest prior art(s) by Omar and Dotan-Cohen, either singularly or in combination would result in a proper rejection under 35 USC 102 or 103.
All of the cited prior art of record above, whether singularly or in combination, fails to disclose the feature limitation verify at least a usage of the at least one NFT as a function of the data collection; determine at least a role associated with the at least one NFT, wherein determining the at least a role comprises: retrieving training data from a blockchain storage of a data store, wherein the training data comprises a plurality of usage data correlated to a plurality of role data; training, iteratively, a role designation machine-learning model as a function of the training data; and determining the at least a role as a function of the at least a usage of the at least one NFT using the trained role designation machine-learning model; and generate a self-executable data structure, wherein the self-executable data structure is configured to delegate the at least a role associated with the at least one NF.
Additional search does not yield suitable references that reasonably, either singularly or in combination with previous cited references, would result in a proper rejection under 35 USC 102 or 103.
Claims dependent on independent claims are also allowed.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled Comments on Statement of Reasons for Allowance.
Conclusion

CITED REFERENCE SUMMARIES: Ref US20220351165A1: Claims:
A method for creating, maintaining and tracking a cryptographic digital asset tied to physical asset, the method comprising: creating, via a first computing node within a distributed computing network, a digital asset file representative of a respective physical asset in possession of a first party; initializing a digital asset generator to create a blockchain-enabled non-fungible token (NFT) of the digital asset file, wherein the NFT is assigned a unique digital asset identification (ID) code; linking the unique digital asset ID code with a unique user ID code, wherein the unique user ID code is associated with the first party; creating a first transaction block including the unique digital asset code and the unique user ID code, via the first computing node in a distributed blockchain ledger, to record possession of the cryptographic digital asset and the respective physical asset with the first party; receiving an asset transfer request to transfer the cryptographic digital asset and respective physical asset to a second party; verifying a second unique user ID code which is associated with the second party; linking the cryptographic digital asset with the second unique user ID code; and creating a second transaction block including the unique digital asset ID code and the second unique user ID code in the distributed blockchain ledger for recordation of the transfer of possession of the cryptographic digital asset and the respective physical asset from the first party to the second party.
creating, via a first computing node within a distributed computing network, a digital asset file representative of a respective physical asset in possession of a first party;
initializing a digital asset generator to create a blockchain-enabled non-fungible token (NFT) of the digital asset file, wherein the NFT is assigned a unique digital asset identification (ID) code;
linking the unique digital asset ID code with a unique user ID code, wherein the unique user ID code is associated with the first party;
creating a first transaction block including the unique digital asset code and the unique user ID code, via the first computing node in a distributed blockchain ledger, to record possession of the cryptographic digital asset and the respective physical asset with the first party;
receiving an asset transfer request to transfer the cryptographic digital asset and respective physical asset to a second party;
verifying a second unique user ID code which is associated with the second party;
linking the cryptographic digital asset with the second unique user ID code; and
creating a second transaction block including the unique digital asset ID code and the second unique user ID code in the distributed blockchain ledger for recordation of the transfer of possession of the cryptographic digital asset and the respective physical asset from the first party to the second party.
The method of claim 1 , wherein the physical asset comprises lease records and wherein the digital asset file is stored within a digital vault accessible to only the first and second parties.
The method of claim 1 , wherein the physical asset comprises physical evidence and wherein the digital asset file is stored within a digital vault accessible to only the first and second parties.
The method of claim 1 , wherein the physical asset comprises feature film and documentary materials.
The method of claim 1 , wherein the physical asset comprises organs, body parts and pieces for transplant.
The method of claim 1 , wherein the physical asset comprises intellectual property and trade secret materials.
The method of claim 1 , wherein the physical asset comprises military contingency plans, operations plans or time phased force deployment data.
The method of claim 1 , wherein the physical asset comprises livestock and animal provenance and bloodline authenticity materials.
The method of claim 1 , wherein the physical asset comprises mineral and water rights materials.
The method of claim 1 , wherein the physical asset comprises data and materials for an electricity generating windmill.
The method of claim 10 , wherein electricity generated by the windmill is tokenized and sold on an associated tokenized commodities exchange.
The method of claim 1 , wherein the physical asset comprises data and materials associated with an oil, water or gas well.
The method of claim 12 , wherein output of the well is tokenized and sold on an associated tokenized commodities exchange
A decentralized computing system for creating, maintaining and tracking a cryptographic digital asset tied to physical asset, the decentralized computing system comprising: a cryptographic digital asset marketplace configured to store a plurality of digital assets and a plurality of unique digital asset identification (ID) codes; and a first computing node operatively connected to the cryptographic digital asset marketplace, the first computing node being programmed to: create a digital asset file representative of a respective physical asset in possession of a first party; initialize a digital asset generator to create a blockchain-enabled non fungible token (NFT) of the digital asset file, wherein the NFT is assigned a respective unique digital asset ID code; link the respective unique digital asset ID code with a unique user ID code, wherein the unique user ID code is associated with the first party; create a first transaction block including the respective unique digital asset ID code and the unique user ID code, via the first computing node in a distributed blockchain ledger, to record possession of the cryptographic digital asset and the respective physical asset with the first party; receive an asset transfer request to transfer the cryptographic digital asset and respective physical asset to a second party; verify a second unique user ID code which is associated with the second party; link the cryptographic digital asset with the second unique user ID code; create a second transaction block including the respective unique digital asset ID code and the second unique user ID code in the distributed blockchain ledger; and store the digital asset file within a digital vault wherein the digital asset fill is accessible only to a respective party identified within the first transaction block or second transaction block.
a cryptographic digital asset marketplace configured to store a plurality of digital assets and a plurality of unique digital asset identification (ID) codes; and
a first computing node operatively connected to the cryptographic digital asset marketplace, the first computing node being programmed to: create a digital asset file representative of a respective physical asset in possession of a first party; initialize a digital asset generator to create a blockchain-enabled non fungible token (NFT) of the digital asset file, wherein the NFT is assigned a respective unique digital asset ID code; link the respective unique digital asset ID code with a unique user ID code, wherein the unique user ID code is associated with the first party; create a first transaction block including the respective unique digital asset ID code and the unique user ID code, via the first computing node in a distributed blockchain ledger, to record possession of the cryptographic digital asset and the respective physical asset with the first party; receive an asset transfer request to transfer the cryptographic digital asset and respective physical asset to a second party; verify a second unique user ID code which is associated with the second party; link the cryptographic digital asset with the second unique user ID code; create a second transaction block including the respective unique digital asset ID code and the second unique user ID code in the distributed blockchain ledger; and store the digital asset file within a digital vault wherein the digital asset fill is accessible only to a respective party identified within the first transaction block or second transaction block.
create a digital asset file representative of a respective physical asset in possession of a first party;
initialize a digital asset generator to create a blockchain-enabled non fungible token (NFT) of the digital asset file, wherein the NFT is assigned a respective unique digital asset ID code;
link the respective unique digital asset ID code with a unique user ID code, wherein the unique user ID code is associated with the first party;
create a first transaction block including the respective unique digital asset ID code and the unique user ID code, via the first computing node in a distributed blockchain ledger, to record possession of the cryptographic digital asset and the respective physical asset with the first party;
receive an asset transfer request to transfer the cryptographic digital asset and respective physical asset to a second party;
verify a second unique user ID code which is associated with the second party;
link the cryptographic digital asset with the second unique user ID code;
create a second transaction block including the respective unique digital asset ID code and the second unique user ID code in the distributed blockchain ledger; and
store the digital asset file within a digital vault wherein the digital asset fill is accessible only to a respective party identified within the first transaction block or second transaction block.
Ref US20190340554A1: [content unavailable]
Ref US11109214B1: [content unavailable]
Ref US20190340554: Claims:
1. A computer-implemented system, comprising:
one or more processors; and one or more computer-readable storage media containing instructions which when executed on the one or more processors, cause the one or more processors to perform a method comprising: determining, from user activity data from sensor data from at least one user device, entity features of project entities; grouping the project entities into projects based at least in part on patterns formed by the entity features between the project entities; determining, from content of the project entities of a project of the projects, occurrences of events with respect to users, each event corresponding to one or more predefined user activities; analyzing the occurrences of the events to determine, for each user of a plurality of the users, an engagement level of the user with the project; assigning a user a role for the project from predefined roles based on applying a role feature corresponding to the engagement level of the user to a machine learning model that represents the role, each of the predefined roles represented by a corresponding machine learning model that uses a different set of role features; based on the assigning of the user to the role, incorporating an assignment of the user to the role into a project repository of the project, the project repository comprising assignments between users and the predefined roles for the project; and determining, based on one or more of the assignments in the project repository, a notification in association with the project.
2. The computer-implemented system of claim 1, wherein the project entities comprise emails, meetings, and files.
3. The computer-implemented system of claim 1, wherein the engagement level is based on a frequency of the occurrences of the events for the user.
4. The computer-implemented system of claim 1, wherein a plurality of the predefined roles correspond to different magnitudes of engagement levels with the project.
5. The computer-implemented system of claim 1, wherein the project repository includes engagement levels of users for the project, and the determining the notification is further based on one or more of the engagement levels in the project repository.
6. The computer-implemented system of claim 1, wherein the machine learning model for the role uses an additional role feature corresponding to the occurrences of an event of the events for the user, the event comprising the user setting one or more priorities for the project.
7. The computer-implemented system of claim 1, wherein the machine learning model for the role uses an additional role feature corresponding to the occurrences of an event of the events for the user, the event comprising the user producing content of an output of the project.
8. The computer-implemented system of claim 1, wherein the machine learning model for the role uses an additional role feature corresponding to the occurrences of an event of the events for the user, the event comprising the user assigning a task of the project to one or more other users.
9. The computer-implemented system of claim 1, wherein the machine learning model for the role uses an additional role feature corresponding to the occurrences of an event of the events for the user, the event comprising the user providing feedback on one or more aspects of the project.
10. The computer-implemented system of claim 1, determining importance levels for the occurrences of the events based on project state categories assigned to the occurrences of the events, wherein the machine learning model for the role uses an additional role feature that is based on the importance levels for the occurrences of the events that are associated with the user.
11. The computer-implemented system of claim 1, determining importance levels for the occurrences of the events based on content subject matter categories assigned to the occurrences of the events, wherein the machine learning model for the role uses an additional role feature that is based on a variance in the content subject matter categories for the occurrences of the events that are associated with the user.
12. The computer-implemented system of claim 1, wherein the machine learning model for the role uses an additional role feature corresponding to an event of the user receiving an output of the project.
13. The computer-implemented system of claim 1, wherein the determining the notification in association with the project comprises:
associating a meeting with the project based on comparing project characteristics of the project to meeting context of the meeting; and based on the meeting context of the meeting and the role of the user, selecting the user as an attendee for the meeting, wherein the notification is based on the selecting of the user as the attendee.
14. One or more computer storage media storing computer-usable instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform a method comprising:
determining, from user activity data from sensor data from at least one user device, entity features of project entities, the entity features including for each project entity of a plurality of the project entities, a set of users associated with the project entity; grouping the project entities into projects based at least in part on patterns formed by the set of users between the project entities; determining, from content of the project entities of a project of the projects, occurrences of events with respect to users, each event corresponding to one or more predefined user activities; assigning a user a role for the project from predefined roles based on applying a role feature corresponding to the occurrences for at least one event of the events that are associated with the user to a machine learning model that represents the role, each of the predefined roles represented by a corresponding machine learning model that uses a different set of role features that corresponds to a different set of the events than others of the predefined roles; and determining content related to the project based on the assigning of the user to the role.
15. The one or more computer storage media of claim 14, wherein the determining the content related to the project comprises:
identifying an absence of the user in a confirmed attendee list of a meeting associated with the project; and based on the identifying of the absence of the user in the confirmed attendee list, and based on the meeting context of the meeting and the role of the user, automatically determining an alternate meeting time for the meeting using a calendar associated with the user.
16. The one or more computer storage media of claim 14, wherein the determining the content related to the project comprises ranking a plurality of emails by similarity to content subject matter categories based on the role of the user and by project characteristics of the project, wherein the method further causes displaying one or more of the plurality of the emails to the user in an inbox based on the ranking of the plurality of the emails.
17. A computer-implemented method, comprising:
grouping project entities into projects based at least in part on similarities between entity features between the project entities; determining, from content of the project entities of a project of the projects, occurrences of events with respect to users, each event corresponding to one or more predefined user activities; analyzing the occurrences of the events to determine, for each user of a plurality of the users, an engagement level of the user with the project; assigning a user a role for the project from predefined roles based on applying a role feature corresponding to the engagement level of the user to a machine learning model that represents the role; and determining content related to the project based on the assigning of the user to the role.
18. The method of claim 17, further comprising analyzing the occurrences of the events to determine, for each user of a plurality of the users, an engagement level of the user with the project, wherein the machine learning model for the role uses an additional role feature corresponding to the engagement level of the user.
19. The method of claim 17, wherein the machine learning model for the role uses an additional role feature corresponding to the occurrences of an event of the events for the user, the event comprising the user setting one or more priorities for the project.
20. The method of claim 17, wherein the machine learning model for the role uses an additional role feature corresponding to the occurrences of an event of the events for the user, the event comprising the user producing content of an output of the project.
Ref US11109214: Claims:
1. A method comprising:
detecting, at a public safety answering point (PSAP) server, an authentication of a PSAP user via a client access device;
retrieving, by the PSAP server, a PSAP load comprising one or more of:
a current PSAP call-taking load and a current PSAP first-responder-dispatching load; and
an historical PSAP call-taking load and an historical PSAP first-responder-dispatching load;

assigning, by the PSAP server, based on the PSAP load, a role to the PSAP user, the role comprising one of: a call-taking role; a first-responder-dispatching role; and a combined call-taking and first-responder-dispatching role; and
serving, by the PSAP server, to the client access device, based on the role assigned to the PSAP user, a corresponding interface to one of: a call-taking application; a first-responder-dispatching application; and a combined call-taking and first-responder-dispatching application.
2. The method of claim 1, wherein:
the client access device is located at an on-premises PSAP location; and
the retrieving the PSAP load comprises retrieving the current PSAP call-taking load and the current PSAP first-responder-dispatching load.
3. The method of claim 1, further comprising:
determining that a PSAP system has one or more of: gone off-line, and entered a disaster recovery mode, and
wherein the retrieving the PSAP load comprises retrieving the historical PSAP call-taking load and the historical PSAP first-responder-dispatching load for a given time period prior to the PSAP system one or more of: going off-line; and entering the disaster recovery mode.
4. The method of claim 3, wherei
[Truncated to input limit]