DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is response to application filed 02/28/2028.
Status of the claims
Claims 1-20 are currently pending for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/10/2025 is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12287849. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of instant application recited similar limitations. Therefore, they are rejected on the ground of nonstatutory double patenting.
Instant application
1. A system configured to train a model to generate classifications of records managed by a collaboration environment, the system comprising: one or more physical processors configured by machine-readable instructions to: obtain model training information, the model training information including information from work unit records characterizing units of work associated with a collaboration environment, the work unit records including work unit information describing the units of work, wherein one of the work unit records corresponds to one of the units of work, wherein collections of the work unit records are organized into hierarchies, wherein individual hierarchies are defined by hierarchical information, and wherein the work unit information describes individual units of work by one or more of title, assignee, description of individual actions expected to be accomplished to complete the individual units of work, due date, completion status, or position within the individual hierarchies, the work unit records including a first work unit record for a first unit of work and a second work unit record for a second unit of work, the first work unit record and the second work unit record being part of a first individual hierarchy; and train a model using the model training information to generate a trained model, the trained model being configured to output a classification of a third work unit record within one or more of the hierarchies based on input of a subset of the work unit information included in the third work unit record, such that the classification of the third work unit record within the one or more of the hierarchies generates the hierarchical information for the third work unit record.
2. The system of claim 1, wherein hierarchical organization among the first work unit record and the second work unit record causes the first work unit record to be designated as subordinate to the second work unit record.
3. The system of claim 2, wherein the second work unit record is restricted from access until the first work unit record is marked complete.
4. The system of claim 1, wherein the subset of the work unit information includes a title and/or a description of the third work unit record.
5. The system of claim 4, wherein the model uses natural language processing to determine meanings of words and/or phrases.
6. The system of claim 5, wherein the model is a machine-learning model.
7. The system of claim 1, wherein individual ones of the collections of the work unit records are further organized into individual clusters of the work unit records, wherein an individual cluster conveys a common organizational designation among the work unit records in an individual collection of the work unit records.
8. The system of claim 7, wherein the individual clusters are defined by clustering information included in the work unit records.
9. The system of claim 8, wherein the trained model is configured to output classifications within the individual clusters.
10. The system of claim 9, wherein the classifications within the individual clusters are independent from hierarchical organization into the individual hierarchies.
11. A system to classify records managed by a collaboration environment, the system comprising: one or more physical processors configured by machine-readable instructions to: manage environment state information maintaining a collaboration environment, the collaboration environment being configured to facilitate interaction by users with the collaboration environment, the environment state information including work unit records, the work unit records including work unit information describing units of work, wherein one of the work unit records corresponds to one of the units of work, wherein the work unit information describes individual units of work by one or more of title, assignee, description of individual actions expected to be accomplished to complete the individual units of work, due date, completion status, or position within individual hierarchies, and wherein the work unit records include a first work unit record for a first unit of work and a second work unit record for a second unit of work, the first work unit record and the second work unit record being part of a first individual hierarchy; provide a subset of the work unit information included in a collection of the work unit records as input for a trained model, the trained model being configured to generate output of classifications of the work unit records in the collection within one or more hierarchies; and generate, from the output, hierarchical information for the work unit records in the collection, the hierarchical information defining hierarchical organization among the work unit records in the collection.
12. The system of claim 11, wherein the hierarchical organization among the work unit records in the collection causes a work unit record to be designated as subordinate to another work unit record.
13. The system of claim 12, wherein the work unit record being designated as subordinate to the other work unit record restricts the work unit record from access until the other work unit record is marked complete.
14. The system of claim 11, wherein the subset of the work unit information includes titles and/or descriptions.
15. The system of claim 14, wherein the trained model uses natural language processing to determine meanings of words and/or phrases used in the titles and/or the descriptions.
16. The system of claim 11, wherein individual collections of the work unit records are further organized into individual clusters of the work unit records.
17. The system of claim 16, wherein the individual clusters of the work unit records are defined by clustering information included in the work unit records.
18. The system of claim 17, wherein the trained model is configured to output classifications of the work unit records within the individual clusters.
19. The system of claim 18, wherein the work unit records are classified into the individual clusters independently from being classified into the individual hierarchies.
20. The system of claim 19, wherein the one or more physical processors are further configured by the machine-readable instructions to: receive, from a client computing platform, validation of the hierarchical information and/or the clustering information generated from the output of the trained model; and refine the trained model based on the validation.
U.S. Patent No. 12287849
1. A system configured to train a machine-learning model to predict classifications of records managed by a collaboration environment, the system comprising: non-transitory electronic storage storing model training information, the model training information including information from work unit records characterizing units of work managed, created, and/or assigned within a collaboration environment to users who are expected to accomplish one or more actions to complete the units of work, the collaboration environment being configured to facilitate interaction by the users with the collaboration environment, the work unit records including work unit information describing the units of work, wherein one of the work unit records corresponds to one of the units of work, wherein collections of the work unit records are organized into individual hierarchies of the work unit records, wherein an individual hierarchy conveys hierarchical organization among the work unit records in an individual collection of the work unit records, wherein the individual hierarchies of the work unit records are defined by hierarchical information included in the work unit records, and wherein the work unit information describes individual units of work by title, assignee, description of individual actions expected to be accomplished to complete the individual units of work, due date, completion status, and position within the individual hierarchies, the work unit records including a first work unit record for a first unit of work and a second work unit record for a second unit of work, the first work unit record and the second work unit record being part of a first individual hierarchy, the model training information including: training input information, the training input information including a subset of the work unit information included in the work unit records; and training output information, the training output information including the hierarchical information of the work unit records; and one or more physical processors configured by machine-readable instructions to: obtain the model training information; train a model using the model training information to generate a trained model, the trained model being configured to output classifications of other ones of the work unit records within the individual hierarchies based on input of the subset of the work unit information included in the other ones of the work unit records, such that the classifications of the other ones of the work unit records within the individual hierarchies generates the hierarchical information for the other ones of the work unit records; and store the trained model in the non-transitory electronic storage.
2. The system of claim 1, wherein the hierarchical organization among the work unit records in the individual collection of the work unit records causes a work unit record to be designated as subordinate to another work unit record.
3. The system of claim 2, wherein the work unit record being designated as subordinate to the other work unit record restricts the work unit record from access until the other work unit record is marked complete.
4. The system of claim 1, wherein the subset of the work unit information includes the title and/or the description of the individual work unit records.
5. The system of claim 4, wherein the model uses natural language processing to determine meanings of words and/or phrases used in the title and/or the description, and wherein the training input information includes the meanings of the words and/or the phrases used in the title and/or the description.
6. The system of claim 5, wherein the model is a machine-learning model.
7. The system of claim 1, wherein individual ones of the collections of the work unit records are further organized into individual clusters of the work unit records, wherein an individual cluster conveys a common organizational designation among the work unit records in an individual collection of the work unit records classified within the individual cluster, and wherein the organizational designation includes a stated purpose or goal of the work unit records classified within the individual cluster.
8. The system of claim 7, wherein the individual clusters of the work unit records are defined by clustering information included in the work unit records.
9. The system of claim 8, wherein the trained model is configured to output classifications of the other ones of the work unit records within the individual clusters based on input of the subset of the work unit information included in the other ones of the work unit records, such that the classifications of the other ones of the work unit records within the individual clusters generates the clustering information for the other ones of the work unit records.
10. The system of claim 9, wherein the other ones of the work unit records are classified into the individual clusters independently from being classified into the individual hierarchies.
11. A system to classify records managed by a collaboration environment, the system comprising: one or more physical processors configured by machine-readable instructions to: manage environment state information maintaining a collaboration environment, the collaboration environment being configured to facilitate interaction by users with the collaboration environment, the environment state information including work unit records, the work unit records including work unit information describing units of work created, managed, and/or assigned within the collaboration environment to the users who are expected to accomplish one or more actions to complete the units of work, wherein one of the work unit records corresponds to one of the units of work, and wherein the work unit information describes individual units of work by title, assignee, description of individual actions expected to be accomplished to complete the individual units of work, due date, completion status, and position within individual hierarchies, and wherein the work unit records include a first work unit record for a first unit of work and a second work unit record for a second unit of work, the first work unit record and the second work unit record being part of a first individual hierarchy; provide a subset of the work unit information included in a collection of the work unit records as input for a trained model, the trained model being configured to output classifications of the work unit records in the collection within one or more hierarchies, wherein an individual hierarchy conveys hierarchical organization among the work unit records classified into the individual hierarchy; obtain the output from the trained model; and generate, from the output, hierarchical information for the work unit records in the collection, the hierarchical information defining the hierarchical organization among the work unit records in the collection derived from the classifications into the one or more hierarchies.
12. The system of claim 11, wherein the hierarchical organization among the work unit records in the collection of the work unit records causes a work unit record to be designated as subordinate to another work unit record.
13. The system of claim 12, wherein the work unit record being designated as subordinate to the other work unit record restricts the work unit record from access until the other work unit record is marked complete.
14. The system of claim 11, wherein the subset of the work unit information includes the title and/or the description of the individual work unit records.
15. The system of claim 14, wherein the trained model uses natural language processing to determine meanings of words and/or phrases used in the title and/or the description.
16. The system of claim 11, wherein individual collections of the work unit records are further organized into individual clusters of the work unit records, wherein an individual cluster conveys a common organizational designation among the work unit records in an individual collection of the work unit records classified within the individual cluster, and wherein the organizational designation includes a stated purpose or goal of the work unit records classified within the individual cluster.
17. The system of claim 16, wherein the individual clusters of the work unit records are defined by clustering information included in the work unit records.
18. The system of claim 17, wherein the trained model is configured to output classifications of the work unit records within the individual clusters based on input of the subset of the work unit information included in the work unit records, such that the classifications of the work unit records within the individual clusters generates the clustering information for the work unit records.
19. The system of claim 18, wherein the work unit records are classified into the individual clusters independently from being classified into the individual hierarchies.
20. The system of claim 19, wherein the one or more physical processors are further configured by the machine-readable instructions to: receive, from a client computing platform, validation of the hierarchical information and/or the clustering information generated from the output of the trained model; and refine the trained model based on the validation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Step 1
The claims recite a system to generate classifications of records managed by a collaboration environment (claims 1 and 11). These claims fall within at least one of the four categories of patentable subject matter.
Step 2A Prong One
The claim recites obtain model training information, the model training information including information from work unit records characterizing units of work associated with a collaboration environment, the work unit records including work unit information describing the units of work, wherein one of the work unit records corresponds to one of the units of work, wherein collections of the work unit records are organized into hierarchies, wherein individual hierarchies are defined by hierarchical information, and wherein the work unit information describes individual units of work by one or more of title, assignee, description of individual actions expected to be accomplished to complete the individual units of work, due date, completion status, or position within the individual hierarchies, the work unit records including a first work unit record for a first unit of work and a second work unit record for a second unit of work, the first work unit record and the second work unit record being part of a first individual hierarchy; and train a model using the model training information to generate a trained model, the trained model being configured to output a classification of a third work unit record within one or more of the hierarchies based on input of a subset of the work unit information included in the third work unit record, such that the classification of the third work unit record within the one or more of the hierarchies generates the hierarchical information for the third work unit record.
The limitation of obtain model training information, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “obtain” in the context of this claim encompasses the user receiving information from work unit. Similarly, the limitation of train a model using the model training information to generate a trained model, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “by a processor” language, “generate a trained model” in the context of this claim encompasses the user consider as a mental process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Claims 2-10, 12-20 recite limitations that are further extensions of generate a trained model to output a classification of a third work unit.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform both the obtaining and generating the output steps. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of obtaining model training information and generating the output) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea on a computer.
The dependent claims do not recite limitations which would integrate the judicial exception into a practical application from the independent claim. Therefore, the claims do not integrate the judicial exception into a practical application.
Step 2B
The claims 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 element of using a processor to perform both the obtaining and generate a trained model processes amounts to no more than mere instructions to apply the exception using a generic computer component.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Nouri et al. (US 20230206152, hereafter Nouri).
Regarding claim 1, Nouri discloses: A system configured to train a model to generate classifications of records managed by a collaboration environment, the system comprising:
one or more physical processors configured by machine-readable instructions to ([0086]):
obtain model training information, the model training information including information from work unit records characterizing units of work associated with a collaboration environment, the work unit records including work unit information describing the units of work, wherein one of the work unit records corresponds to one of the units of work (Nouri [0080] discloses: receiving, at a machine learning model trained on task significance information, at least a portion of the task graph; and generating, based on the at least a portion of the task graph, the task significance information for the least one task of the plurality tasks (task as the units of work) using the machine learning model; [0070] discloses: collaborative data),
wherein collections of the work unit records are organized into hierarchies, wherein individual hierarchies are defined by hierarchical information, and wherein the work unit information describes individual units of work by one or more of title, assignee, description of individual actions expected to be accomplished to complete the individual units of work, due date, completion status, or position within the individual hierarchies (Nouri [fig. 3] discloses: the list of tasks, such task related information may include high level task feature information, each task may be associated with a group identifier 306, such as Group_ID_1, Group_ID_2, task name, due date, assigned user(s) 310, checklist of items (e.g., subtask information), and completion status of task and checklist items; [0048] discloses: task feature including but not limited to textual features, titles, descriptions, textual features, telemetry features. Numerical features—ex. how many changes for each field, changes for description, assignor and assignee may also be incorporated as task feature information information),
the work unit records including a first work unit record for a first unit of work and a second work unit record for a second unit of work, the first work unit record and the second work unit record being part of a first individual hierarchy (Nouri [fig 3; 0036] discloses: task related information may include high level task feature information arranged as a board of tasks, as a chart of tasks, or as a schedule of tasks. As depicted in FIG. 3, task feature information is presented as a board of tasks. Each task may be associated with a group identifier 306, such as Group_ID_1, Group_ID_2, etc. In accordance with examples of the present disclosure, the high level task feature information may be presented in a board representation 308 form and may include but is not limited to task name, due date if known, assigned user(s) 310, checklist of items (e.g., subtask information), and completion status of task and checklist items); and
train a model using the model training information to generate a trained model, the trained model being configured to output a classification of a third work unit record within one or more of the hierarchies based on input of a subset of the work unit information included in the third work unit record, such that the classification of the third work unit record within the one or more of the hierarchies generates the hierarchical information for the third work unit record (Nouri [0002; 0033] discloses: A node classification model may then determine a significance, or priority, associated with each node in the task graph. Such significance information may then be used to rank or adjust an order of tasks being presented to a user and the task information generation module 210 may learn the representations of the nodes and be trained as a model to classify the nodes).
Regarding claim 2, Nouri discloses: The system of claim 1, wherein hierarchical organization among the first work unit record and the second work unit record causes the first work unit record to be designated as subordinate to the second work unit record (Nouri [0034; 0035] discloses: the task information generation module 210 may further rank one or more tasks based on the task priority or task significance. Accordingly, the task adjustment module 212 may assign a rank, for example, to each of the tasks. The task presentation module 216 may then cause the task and associated task information to be presented to a user interface at a computing device. Alternatively, or in addition, a task storage module 218 may cause the task and associated task information to be stored at the task management server 202 or otherwise in a task repository, where task feature information for a processed task may include the task priority, task significance, and/or task ranking information).
Regarding claim 3, Nouri discloses: The system of claim 2, wherein the second work unit record is restricted from access until the first work unit record is marked complete (Nouri [0027; 0038] discloses: the updated task information may indicate that one or more tasks are a blocking task because many resources or people may dependent on the completion of the task. Thus, a blocking task may have a higher importance or significance because of the impact on the progress of the project or other tasks).
Regarding claim 4, Nouri discloses: The system of claim 1, wherein the subset of the work unit information includes a title and/or a description of the third work unit record (Nouri [0048] discloses: task nodes may include information including but not limited to textual features, titles, descriptions, textual features, telemetry features).
Regarding claim 5, Nouri discloses: The system of claim 4, wherein the model uses natural language processing to determine meanings of words and/or phrases (Nouri [0023] discloses: a plurality of tasks and/or subtasks may be automatically generated based on a natural language processing process, image analysis process, and/or a task generative model).
Regarding claim 6, Nouri discloses: The system of claim 5, wherein the model is a machine-learning model (Nouri [0035]).
Regarding claim 7, Nouri discloses: The system of claim 1, wherein individual ones of the collections of the work unit records are further organized into individual clusters of the work unit records, wherein an individual cluster conveys a common organizational designation among the work unit records in an individual collection of the work unit records (Nouri [0036] discloses: include a selection pane 304 that allows a user to select one or more plans that include one or more tasks. In examples, a plan may be representative of the information depicted as 302, where the information depicted as 302 may present, render, or otherwise display task related information in an organized manner. Such task related information may include high level task feature information arranged as a board of tasks, as a chart of tasks, or as a schedule of tasks. As depicted in FIG. 3, task feature information is presented as a board of tasks. Each task may be associated with a group identifier 306, such as Group_ID_1, Group_ID_2, etc. In accordance with examples of the present disclosure, the high level task feature information may be presented in a board representation 308 form and may include but is not limited to task name, due date if known, assigned user(s) 310, checklist of items (e.g., subtask information), and completion status of task and checklist items).
Regarding claim 8, Nouri discloses: The system of claim 7, wherein the individual clusters are defined by clustering information included in the work unit records (Nouri [0021] discloses: A node classification process may be utilized to learn the priority and importance of the tasks and predict or otherwise assign a label to the tasks, where the label may be utilized in a ranking algorithm to rank the nodes based on the importance. The determined importance of the task may then be presented in a user interface. In some examples, where a task importance may change, a notification may be presented to the user interface thereby informing the user of the change in task importance).
Regarding claim 9, Nouri discloses: The system of claim 8, wherein the trained model is configured to output classifications within the individual clusters (Nouri [0021] discloses: A node classification process may be utilized to learn the priority and importance of the tasks and predict or otherwise assign a label to the tasks, where the label may be utilized in a ranking algorithm to rank the nodes based on the importance. The determined importance of the task may then be presented in a user interface. In some examples, where a task importance may change, a notification may be presented to the user interface thereby informing the user of the change in task importance).
Regarding claim 10, Nouri discloses: The system of claim 9, wherein the classifications within the individual clusters are independent from hierarchical organization into the individual hierarchies (Nouri [0002] discloses: determining an importance of a task based on the impact of the task to the progress of a project that may be implemented by a group or person. In examples, a representation of one or more tasks is created as nodes in a task graph; [0025] discloses: the graph may be localized to a specific project, to a specific individual, and/or to a specific hierarchical task).
Regarding claim 11, Nouri discloses: A system to classify records managed by a collaboration environment, the system comprising: one or more physical processors configured by machine-readable instructions to:
manage environment state information maintaining a collaboration environment, the collaboration environment being configured to facilitate interaction by users with the collaboration environment, the environment state information including work unit records, the work unit records including work unit information describing units of work (Nouri [0080] discloses: receiving, at a machine learning model trained on task significance information, at least a portion of the task graph; and generating, based on the at least a portion of the task graph, the task significance information for the least one task of the plurality tasks (task as the units of work) using the machine learning model; [0070] discloses: collaborative data),
wherein one of the work unit records corresponds to one of the units of work, wherein the work unit information describes individual units of work by one or more of title, assignee, description of individual actions expected to be accomplished to complete the individual units of work, due date, completion status, or position within individual hierarchies (Nouri [fig. 3] discloses: the list of tasks, such task related information may include high level task feature information, each task may be associated with a group identifier 306, such as Group_ID_1, Group_ID_2, task name, due date, assigned user(s) 310, checklist of items (e.g., subtask information), and completion status of task and checklist items; [0048] discloses: task feature including but not limited to textual features, titles, descriptions, textual features, telemetry features. Numerical features—ex. how many changes for each field, changes for description, assignor and assignee may also be incorporated as task feature information information), and
wherein the work unit records include a first work unit record for a first unit of work and a second work unit record for a second unit of work, the first work unit record and the second work unit record being part of a first individual hierarchy (Nouri [fig 3; 0036] discloses: task related information may include high level task feature information arranged as a board of tasks, as a chart of tasks, or as a schedule of tasks. As depicted in FIG. 3, task feature information is presented as a board of tasks. Each task may be associated with a group identifier 306, such as Group_ID_1, Group_ID_2, etc. In accordance with examples of the present disclosure, the high level task feature information may be presented in a board representation 308 form and may include but is not limited to task name, due date if known, assigned user(s) 310, checklist of items (e.g., subtask information), and completion status of task and checklist items); and
provide a subset of the work unit information included in a collection of the work unit records as input for a trained model, the trained model being configured to generate output of classifications of the work unit records in the collection within one or more hierarchies (Nouri [0002; 0033] discloses: A node classification model may then determine a significance, or priority, associated with each node in the task graph. Such significance information may then be used to rank or adjust an order of tasks being presented to a user and the task information generation module 210 may learn the representations of the nodes and be trained as a model to classify the nodes); and
generate, from the output, hierarchical information for the work unit records in the collection, the hierarchical information defining hierarchical organization among the work unit records in the collection (Nouri [0025] discloses: the graph may be localized to a specific project, to a specific individual, and/or to a specific hierarchical task. That is, one or more graphs may be generated, built, updated, etc. based on a specified criteria. For example, multiple graphs may be arranged or otherwise generated based on the same plurality of nodes; thus, one or more graphs may be generated or otherwise structured in a manner that is specific to people, specific to a task, specific to a task assigner, specific to a utilized resource, or otherwise specific to another relationship that may be common between tasks).
Regarding claim 12, Nouri discloses: The system of claim 11, wherein the hierarchical organization among the work unit records in the collection causes a work unit record to be designated as subordinate to another work unit record (Nouri [0034; 0035] discloses: the task information generation module 210 may further rank one or more tasks based on the task priority or task significance. Accordingly, the task adjustment module 212 may assign a rank, for example, to each of the tasks. The task presentation module 216 may then cause the task and associated task information to be presented to a user interface at a computing device. Alternatively, or in addition, a task storage module 218 may cause the task and associated task information to be stored at the task management server 202 or otherwise in a task repository, where task feature information for a processed task may include the task priority, task significance, and/or task ranking information).
.
Regarding claim 13, Nouri discloses: The system of claim 12, wherein the work unit record being designated as subordinate to the other work unit record restricts the work unit record from access until the other work unit record is marked complete (Nouri [0027; 0038] discloses: the updated task information may indicate that one or more tasks are a blocking task because many resources or people may dependent on the completion of the task. Thus, a blocking task may have a higher importance or significance because of the impact on the progress of the project or other tasks).
Regarding claim 14, Nouri discloses: The system of claim 11, wherein the subset of the work unit information includes titles and/or descriptions (Nouri [0048] discloses: task nodes may include information including but not limited to textual features, titles, descriptions, textual features, telemetry features).
Regarding claim 15, Nouri discloses: The system of claim 14, wherein the trained model uses natural language processing to determine meanings of words and/or phrases used in the titles and/or the descriptions (Nouri [0023] discloses: a plurality of tasks and/or subtasks may be automatically generated based on a natural language processing process, image analysis process, and/or a task generative model).
Regarding claim 16, Nouri discloses: The system of claim 11, wherein individual collections of the work unit records are further organized into individual clusters of the work unit records (Nouri [0036] discloses: include a selection pane 304 that allows a user to select one or more plans that include one or more tasks. In examples, a plan may be representative of the information depicted as 302, where the information depicted as 302 may present, render, or otherwise display task related information in an organized manner. Such task related information may include high level task feature information arranged as a board of tasks, as a chart of tasks, or as a schedule of tasks. As depicted in FIG. 3, task feature information is presented as a board of tasks. Each task may be associated with a group identifier 306, such as Group_ID_1, Group_ID_2, etc. In accordance with examples of the present disclosure, the high level task feature information may be presented in a board representation 308 form and may include but is not limited to task name, due date if known, assigned user(s) 310, checklist of items (e.g., subtask information), and completion status of task and checklist items).
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Regarding claim 17, Nouri discloses: The system of claim 16, wherein the individual clusters of the work unit records are defined by clustering information included in the work unit records (Nouri [0021] discloses: A node classification process may be utilized to learn the priority and importance of the tasks and predict or otherwise assign a label to the tasks, where the label may be utilized in a ranking algorithm to rank the nodes based on the importance. The determined importance of the task may then be presented in a user interface. In some examples, where a task importance may change, a notification may be presented to the user interface thereby informing the user of the change in task importance).
Regarding claim 18, Nouri discloses: The system of claim 17, wherein the trained model is configured to output classifications of the work unit records within the individual clusters (Nouri [0021] discloses: A node classification process may be utilized to learn the priority and importance of the tasks and predict or otherwise assign a label to the tasks, where the label may be utilized in a ranking algorithm to rank the nodes based on the importance. The determined importance of the task may then be presented in a user interface. In some examples, where a task importance may change, a notification may be presented to the user interface thereby informing the user of the change in task importance).
Regarding claim 19, Nouri discloses: The system of claim 18, wherein the work unit records are classified into the individual clusters independently from being classified into the individual hierarchies (Nouri [0025] discloses: the graph may be localized to a specific project, to a specific individual, and/or to a specific hierarchical task. That is, one or more graphs may be generated, built, updated, etc. based on a specified criteria. For example, multiple graphs may be arranged or otherwise generated based on the same plurality of nodes; thus, one or more graphs may be generated or otherwise structured in a manner that is specific to people, specific to a task, specific to a task assigner, specific to a utilized resource, or otherwise specific to another relationship that may be common between tasks).
Regarding claim 20, Nouri discloses: The system of claim 19, wherein the one or more physical processors are further configured by the machine-readable instructions to: receive, from a client computing platform, validation of the hierarchical information and/or the clustering information generated from the output of the trained model (Nouri [0048] discloses: receive a list of tasks and generate a task node or otherwise verify that a task node exists for each task in the list of tasks. At 812, task information including, but not limited to, task feature information may be associated with respective task nodes in the task graph. That is, task nodes may include information including but not limited to textual features, titles, descriptions, textual features, telemetry features. Numerical features—ex. how many changes for each field, changes for description, assignor and assignee may also be incorporated as task feature information); and
refine the trained model based on the validation (Nouri [0046] discloses: the generation of the task significance information may be performed by a machine learning model trained on training data, where the training data may be representative of a task and task significance information and further task feature information, the method 700 may proceed to 720, where a task adjustment module, such as the task adjustment module 212, may assign a rank, for example, to each of the tasks as previously described. In examples, the method 700 may proceed to 720 where the task adjustment module may update the tasks with the generated task information (e.g., priority and ranking information).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CINDY NGUYEN whose telephone number is (571)272-4025. The examiner can normally be reached M-F 8:00-4:30.
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/CINDY NGUYEN/Examiner, Art Unit 2156
/AJAY M BHATIA/Supervisory Patent Examiner, Art Unit 2156