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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 6/24/2025, Applicant, on 9/23/2025, amended claims 1, 6, 8, 11, 16-18 and 20. Claims 1-20 are pending in this application and have been rejected below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this
application is eligible for continued examination under 37 CFR 1.114, and the fee set
forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action
has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on
9/23/2025 has been entered.
Priority
The Examiner has noted this Application is a Continuation of Application 18/172,069 filed February 21, 2023 which is a Continuation of Application 16/739,801 filed January 10, 2020.
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by Applicant on 28 June 2022. 1449' s are attached. Examiner notes that due to the excessively voluminous Information Disclosure Statement submitted by Applicant, the Examiner has given only a cursory review of the listed references. In accordance with MPEP 609.04(a), Applicant is encouraged to provide a concise explanation of why the information is being submitted and how it is understood to be relevant. Concise explanations (especially those which point out the relevant pages and lines) are helpful to the Office, particularly where documents are lengthy and complex and Applicant is aware of a section that is highly relevant to patentability or where a large number of documents are submitted and applicant is aware that one or more are highly relevant to patentability. See MPEP.2004 Aids to Compliance With Duty of Disclosure
"13. It is desirable to avoid the submission of long lists of documents if it can be avoided. Eliminate clearly irrelevant and marginally pertinent cumulative information. If a long list is submitted, highlight those documents which have been specifically brought to applicant’s attention and/or are known to be of most significance. See Penn Yan Boats, Inc. v. Sea Lark Boats, Inc., 359 F. Supp. 948, 175 USPQ 260 (S.D. Fla. 1972), aff ’d, 479 F.2d 1338, 178 USPQ 577 (5th Cir. 1973), cert. denied, 414 U.S. 874 (1974). But cf. Molins PLC v. Textron Inc., 48 F.3d 1172, 33 USPQ2d 1823 (Fed. Cir. 1995)."
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amendment has been considered and is insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed September 23, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed September 23, 2025.
On pg. 9-11 regarding the 35 U.S.C. § 101 rejection, Applicant states in Step 2A of each of the amended claims 1, 11 and 20 shows that the claim as a whole successfully integrates the judicial exception. Specifically, the Applicant respectfully contends that the instant claims provide a specific improvement to a computer "to generate new or transformed GUIs based on a data generated, recorded, and/or aggregated from various different platforms. in response, Examiner respectfully disagrees. The present claims amount to no more than utilizing computer components specifically drag and drop feature as a tool for task assignment. Examiner finds the present claims improve an existing business process of task management and there are currently no functional advancements to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Applicant has not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves. Utilizing computer components and drag and drop features are all, both individually and in combination, generic computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (See MPEP 2106.05(d)(II). For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
On pg. 10-11 regarding the 35 U.S.C. § 101 rejection, Applicant states Example 37 "recite a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices." which "as a whole integrates the mental process into a practical application." Similar to Example 37, amended claim 1 recites "recite a specific manner of "receiv[ing] ... an indication of a suggested task for assignment by the first user to at least one user among the plurality of users, the suggested task being determined via one or more machine learning models based upon the activity data,""display[ing], via a graphical user interface (GUI) of the dashboard application, the suggested task as a first drag-and-drop item and the plurality of users as respective items of a second plurality of drag-and-drop items,"obtain[ing], via the GUI, user input indicating one or more drag-and-drop operations from the first user to (i) select the suggested task from the first drag-and-drop item and (ii) select a second user on the second plurality of drag-and-drop items," and "caus[ing] the suggested task to be assigned to the selected second user." In response, Examiner respectfully disagrees. Example 37 demonstrates improvements to a graphical user interface (i.e., automatic display of icon), thus improving icon display without requiring a user's constant intermediation with significant support in the specification. In contrast, the present claims contain improvements to the task assignment of an existing business process and not one of a technology or technological field. This is evident in the specification where it states on at least pg. 14, par. 53, “…A team leader 210 may assign or reassign a task to a team member by dragging and dropping the team member card onto the team task card. …”.
On page 12-14, regarding the 35 U.S.C. § 103 rejection, Applicant states that does not disclose or suggest "display[ing], via a graphical user interface (GUI) of the dashboard application, the suggested task as a first drag-and-drop item and the plurality of users as respective items of a second plurality of drag-and-drop items; obtain[ing], via the GUI, user input indicating one or more drag-and-drop operations from the first user to (i) select the suggested task from the first drag-and-drop item and (ii) select a second user on the second plurality of drag-and-drop items; and caus[ing] the suggested task to be assigned to the selected second user," as recited by amended claim 1. In response, new ground(s) of rejection is made necessitated by amendment see MPEP 706.07a where Bodnick is now applied for Claims 1, 11 and 20. Regarding the 35 U.S.C. § 103 rejection, Applicant’s arguments with respect to claims has been considered but are moot in view of the new grounds of rejection.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-10 are directed to an article of manufacture for predicting team management decisions, Claims 11-19 are directed to a method for predicting team management decisions and Claim 20 is directed to a system for predicting team management decisions.
Claim 1 recites an article of manufacture for predicting team management decisions, Claim 11 recites a method for predicting team management decisions and Claim 10 recites a system for predicting team management decisions, which include providing activity data defining a set of tasks of a plurality of users other than the first user, the plurality of users being associated with the first user; receiving an indication of a suggested task for assignment by the first user to at least one user among the plurality of users; displaying the suggested task; obtain user input indicating one or more drag-and-drop operations from the first user to (i) select the suggested task from the first drag-and-drop item and (ii) select a second user on the second plurality of drag-and-drop items; and cause the suggested task to be assigned to the selected second user. As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity”- managing interactions. The recitation of “computer-readable medium”, “processor”, “computer”, “server”, “application”, “graphical user interface”, “computing system”, and “memories”, does not take claims out of the certain methods of organizing human activity grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “computer-readable medium”, “processor”, “computer”, “server”, “application”, “graphical user interface”, “computing system”, and “memories” are recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, claim 1, claim 11 and claim 20 recite using one or more machine learning techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses 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. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in task assignment analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computer-readable medium”, “processor”, “computer”, “server”, “application”, “graphical user interface”, “computing system”, and “memories” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving activity data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding Step 2B and the additional element of “machine learning” it is MPEP 2106.05(f) – Mere Instructions to Apply an Exception - the machine learning is solely used a tool to perform the instructions of the abstract idea.
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-10 and 12-19 recite the additional elements one or more machine learning models are trained to determine the suggested task based upon geographic location characteristics associated with the first user or the plurality of users; one or more machine learning models are trained to determine the suggested task based upon team size characteristics associated with the first user or the plurality of users; one or more machine learning models are trained to determine the suggested task based upon team member characteristics associated with the first user or the plurality of users; one or more machine learning models are trained to determine the suggested task based upon time efficiency or resource efficiency characteristics associated with the first user or the plurality of users; one or more machine learning models are trained to determine the suggested task based upon comparison of the activity data associated with the plurality users to activity data of a further plurality of users; displaying an indication of a suggested user for assignment, from among the plurality of users; the suggested user is identified based upon a licensing or compliance status of the suggested user, a skill profile of the suggested user, or a workload capacity of the suggested user; instructions to display the suggested task based upon an authorization received from the first user; the displayed second plurality of drag-and-drop items indicate whether corresponding ones of the plurality of users are available for assignment of the suggested task; and further narrowing the abstract idea. These recited limitations in the dependent claims are mere instructions for applying the abstract idea on a computerized system which are operating such that they do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 11 and 20. Regarding Claim 7 recites the additional elements of “processor”, “computer”, and it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding Claims 2-6 and 12-15 and the additional element of “machine learning” - the machine learning is solely used a tool to perform the instructions of the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2-7, 11-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaeian et al., US Publication No. 20200125586 A1 [hereinafter Rezaeian], in view of Bodnick et al. , US Publication No. 20190286462A1, [hereinafter Bodnick], and in further view of Grosz , US Publication No. 20170093967A1, [hereinafter Grosz].
Regarding Claim 1,
Rezaeian teaches
One or more non-transitory computer-readable media storing instructions that, when executed via one or more processors of one or more computers, cause the one or more computers to: provide, to one or more servers via a dashboard application associated with a first user, activity data defining a set of tasks of a plurality of users other than the first user, the plurality of users being associated with the first user (Rezaeian Par. 5; Par. 11-“ the application facilitating performance of one or more tasks, and the interaction being associated with a task previously selected by a user using the application; generating contextual user data for each user of a plurality of users, the contextual user data for each user of the plurality of users being generated by aggregating the one or more attributes associated with the user across the plurality of log records. The computer-implemented method also includes performing a clustering operation on the contextual user data of the plurality of users, the performance of the clustering operation causing a formation of one or more clusters of users, and each cluster of the one or more clusters representing one or more users that share a common attribute. The computer-implemented method also includes determining a set of tasks performable using one or more applications, each task of the set of tasks including one or more actions performable using an application of the one or more applications;”; Par. 47);
receive, from the one or more servers, an indication of a suggested task for assignment by the first user to at least one user among the plurality of users, the suggested task being determined via one or more machine leaning models based upon the activity data; (Rezaeian Abstract-“The present disclosure relates to an intelligent user interface that predicts tasks for users to complete using a trained machine-learning model. In some implementations, when a user accesses the intelligent user interface, the available tasks and a user profile can be inputted into the trained machine-learning model to output a prediction of one or more tasks for the user to complete. Advantageously, the trained machine-learning model outputs a prediction of tasks that the user will likely need to complete, based at least in part on the user's profile and previous interactions with applications.”; Par. 3-4-“For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete. In some implementations, to enhance the accuracy of task prediction, the contextual user profiles for a plurality of users associated with an entity can be clustered using one or more clustering techniques (e.g., k-means clustering). Clustering the user profiles may result in the formation of one or more clusters of users.”)
Rezaeian teaches intelligent user interface and the feature is expounded upon by the teaching of Bodnick:
display, via a graphical user interface (GUI) of the dashboard application, the suggested task as a first drag and drop item and the plurality of users as respective ones of a second plurality of drag and drop items;(Bodnick Par. 126- Upon selecting “My Activities” tab 1302, the hardware processor can present a list, as shown, for example, in interface 1300 of FIG. 13. In another example, a user can also access this list on a dashboard. The hardware processor can allow the user to organize the interactive checklists into categories of their choosing, such as “Priority 1, Priority 2, Priority” and/or “This Week, Next Week, In Two Weeks.” In accordance with some embodiments, nested “child-level” checklists of an Activity that can be viewed at “My Activities” will not be displayed there. In accordance with some embodiments, the hardware processor can allow a user can reorder or move interactive checklists from one category to another using a drag and drop mechanism. Once a particular interactive checklist is selected from the list, the hardware processor can take the user to an interface within the interactive checklist itself. In some embodiments, using an interface 1400 that may be presented by a hardware processor of a computing device, the hardware processor can allow a user to work on the interactive checklist, for example as shown in FIG. 14. Additionally or alternatively, the hardware processor can present the interactive checklist from this interface to a new, second interface. The hardware processor can receive a number of user inputs indicating actions such as including, splitting, unsplitting, delegating and/or undelegating steps.” ‘ Par. 174)
and cause the suggested task to be assigned to the selected second user (Bodnick Par. 131- In some embodiments, the hardware processor can receive a user input indicating one or more parameters and that the user is assigning multiple activities for each parameters, as shown in interface 1812 of FIG. 18E. For example, assigning multiple activities for each parameter can include assigning the same report to multiple employees, following the same sales process for multiple different prospects, preparing monthly invoices for each of a company's clients, assigning a project update report for multiple projects to a single individual, and/or any other suitable scenario. In some embodiments, the prefilled answers can be retrieved from external sources (e.g., a Web service or a database) or through data stored internally.; Par 263; Par. 273-274 ) (Rezaeian Par. 3-4-“For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete. In some implementations, to enhance the accuracy of task prediction, the contextual user profiles for a plurality of users associated with an entity can be clustered using one or more clustering techniques (e.g., k-means clustering). Clustering the user profiles may result in the formation of one or more clusters of users.”
Rezaeian and Bodnick are directed to task management. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the intelligent interface of Rezaeian, as taught by Bodnick, by utilizing match processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian with the motivation of displaying assigned interactive checklists, due dates and/or time, current status, and/or any other available information. (Bodnick Par. 127).
Rezaeian in view of Bodnick teach task analysis and the feature is expounded upon by Grosz”
obtain, via the GUI, user input indicating one or more drag-and-drop operations from the first user to (i) select the suggested task from the first drag-and-drop item and (ii) select a second user on the second plurality of drag-and-drop items; (Grosz Par. 119-120- “Option 311 allows the operating user to select from pre-generated activity profiles. The operating user may see a list of activity profiles by selecting option 311 and may select an activity profile to add to field 310 by drag and drop operation.”; Par. 132; Par. 136; Par. 155-156); Par. 207-“ Template 1100 includes a text input field 1105 for adding specific users to a group profile and a text input field 1106 for adding additional constraints to a group profile. In one embodiment specific users might be added by typing into text input field 1105 an identification of one or more users to add. In one embodiment a list of users may be dragged and dropped into text interface 1105.”)
Rezaeian , Bodnick and Grosz are directed to task management. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the intelligent interface of Rezaeian in view of Bodnick, as taught by Grosz, by utilizing drag and drop processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian in view of Bodnick with the motivation of intelligently and automatically grouping individuals into groups for the purpose of participating in activities. (Grosz Par. 10).
Regarding Claim 11,
Rezaeian teaches
A computer-implemented method implemented via one or more processors, the method comprising: providing, to one or more servers via a dashboard application associated with a first user, activity data defining a set of tasks of a plurality of users other than the first user, the plurality of users being associated with the first user (Rezaeian Par. 5; Par. 11-“ the application facilitating performance of one or more tasks, and the interaction being associated with a task previously selected by a user using the application; generating contextual user data for each user of a plurality of users, the contextual user data for each user of the plurality of users being generated by aggregating the one or more attributes associated with the user across the plurality of log records. The computer-implemented method also includes performing a clustering operation on the contextual user data of the plurality of users, the performance of the clustering operation causing a formation of one or more clusters of users, and each cluster of the one or more clusters representing one or more users that share a common attribute. The computer-implemented method also includes determining a set of tasks performable using one or more applications, each task of the set of tasks including one or more actions performable using an application of the one or more applications;”; Par. 47);
receiving, from the one or more servers, an indication of a suggested task for assignment by the first user to at least one user among the plurality of users, the suggested task being determined via one or more machine leaning models based upon the activity data; (Rezaeian Abstract-“The present disclosure relates to an intelligent user interface that predicts tasks for users to complete using a trained machine-learning model. In some implementations, when a user accesses the intelligent user interface, the available tasks and a user profile can be inputted into the trained machine-learning model to output a prediction of one or more tasks for the user to complete. Advantageously, the trained machine-learning model outputs a prediction of tasks that the user will likely need to complete, based at least in part on the user's profile and previous interactions with applications.”; Par. 3-4-“For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete. In some implementations, to enhance the accuracy of task prediction, the contextual user profiles for a plurality of users associated with an entity can be clustered using one or more clustering techniques (e.g., k-means clustering). Clustering the user profiles may result in the formation of one or more clusters of users.”)
Rezaeian teaches intelligent user interface and the feature is expounded upon by the teaching of Bodnick:
displaying, via a graphical user interface (GUI) of the dashboard application, the suggested task as a first drag and drop item and the plurality of users as respective ones of a second plurality of drag and drop items;(Bodnick Par. 126- Upon selecting “My Activities” tab 1302, the hardware processor can present a list, as shown, for example, in interface 1300 of FIG. 13. In another example, a user can also access this list on a dashboard. The hardware processor can allow the user to organize the interactive checklists into categories of their choosing, such as “Priority 1, Priority 2, Priority” and/or “This Week, Next Week, In Two Weeks.” In accordance with some embodiments, nested “child-level” checklists of an Activity that can be viewed at “My Activities” will not be displayed there. In accordance with some embodiments, the hardware processor can allow a user can reorder or move interactive checklists from one category to another using a drag and drop mechanism. Once a particular interactive checklist is selected from the list, the hardware processor can take the user to an interface within the interactive checklist itself. In some embodiments, using an interface 1400 that may be presented by a hardware processor of a computing device, the hardware processor can allow a user to work on the interactive checklist, for example as shown in FIG. 14. Additionally or alternatively, the hardware processor can present the interactive checklist from this interface to a new, second interface. The hardware processor can receive a number of user inputs indicating actions such as including, splitting, unsplitting, delegating and/or undelegating steps.” ‘ Par. 174)
and causing the suggested task to be assigned to the selected second user (Bodnick Par. 131- In some embodiments, the hardware processor can receive a user input indicating one or more parameters and that the user is assigning multiple activities for each parameters, as shown in interface 1812 of FIG. 18E. For example, assigning multiple activities for each parameter can include assigning the same report to multiple employees, following the same sales process for multiple different prospects, preparing monthly invoices for each of a company's clients, assigning a project update report for multiple projects to a single individual, and/or any other suitable scenario. In some embodiments, the prefilled answers can be retrieved from external sources (e.g., a Web service or a database) or through data stored internally.; Par 263; Par. 273-274 ) (Rezaeian Par. 3-4-“For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete. In some implementations, to enhance the accuracy of task prediction, the contextual user profiles for a plurality of users associated with an entity can be clustered using one or more clustering techniques (e.g., k-means clustering). Clustering the user profiles may result in the formation of one or more clusters of users.”
Rezaeian and Bodnick are directed to task management. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the intelligent interface of Rezaeian, as taught by Bodnick, by utilizing match processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian with the motivation of displaying assigned interactive checklists, due dates and/or time, current status, and/or any other available information. (Bodnick Par. 127).
Rezaeian in view of Bodnick teach task analysis and the feature is expounded upon by Grosz”
obtaining, via the GUI, user input indicating one or more drag-and-drop operations from the first user to (i) select the suggested task from the first drag-and-drop item and (ii) select a second user on the second plurality of drag-and-drop items; (Grosz Par. 119-120- “Option 311 allows the operating user to select from pre-generated activity profiles. The operating user may see a list of activity profiles by selecting option 311 and may select an activity profile to add to field 310 by drag and drop operation.”; Par. 132; Par. 136; Par. 155-156); Par. 207-“ Template 1100 includes a text input field 1105 for adding specific users to a group profile and a text input field 1106 for adding additional constraints to a group profile. In one embodiment specific users might be added by typing into text input field 1105 an identification of one or more users to add. In one embodiment a list of users may be dragged and dropped into text interface 1105.”)
Rezaeian , Bodnick and Grosz are directed to task management. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the intelligent interface of Rezaeian in view of Bodnick, as taught by Grosz, by utilizing drag and drop processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian in view of Bodnick with the motivation of intelligently and automatically grouping individuals into groups for the purpose of participating in activities. (Grosz Par. 10).
Regarding Claim 20,
Rezaeian teaches
A computing system comprising: one or more processors; and one or more non-transitory memories storing instructions that, when executed via the one or more processors, cause the computing system to: provide, to one or more servers via a dashboard application associated with a first user, activity data defining a set of tasks of a plurality of users other than the first user, the plurality of users being associated with the first user (Rezaeian Par. 5; Par. 11-“ the application facilitating performance of one or more tasks, and the interaction being associated with a task previously selected by a user using the application; generating contextual user data for each user of a plurality of users, the contextual user data for each user of the plurality of users being generated by aggregating the one or more attributes associated with the user across the plurality of log records. The computer-implemented method also includes performing a clustering operation on the contextual user data of the plurality of users, the performance of the clustering operation causing a formation of one or more clusters of users, and each cluster of the one or more clusters representing one or more users that share a common attribute. The computer-implemented method also includes determining a set of tasks performable using one or more applications, each task of the set of tasks including one or more actions performable using an application of the one or more applications;”; Par. 47);
receive, from the one or more servers, an indication of a suggested task for assignment by the first user to at least one user among the plurality of users, the suggested task being determined via one or more machine leaning models based upon the activity data; (Rezaeian Abstract-“The present disclosure relates to an intelligent user interface that predicts tasks for users to complete using a trained machine-learning model. In some implementations, when a user accesses the intelligent user interface, the available tasks and a user profile can be inputted into the trained machine-learning model to output a prediction of one or more tasks for the user to complete. Advantageously, the trained machine-learning model outputs a prediction of tasks that the user will likely need to complete, based at least in part on the user's profile and previous interactions with applications.”; Par. 3-4-“For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete. In some implementations, to enhance the accuracy of task prediction, the contextual user profiles for a plurality of users associated with an entity can be clustered using one or more clustering techniques (e.g., k-means clustering). Clustering the user profiles may result in the formation of one or more clusters of users.”)
Rezaeian teaches intelligent user interface and the feature is expounded upon by the teaching of Bodnick:
display, via a graphical user interface (GUI) of the dashboard application, the suggested task as a first drag and drop item and the plurality of users as respective ones of a second plurality of drag and drop items;(Bodnick Par. 126- Upon selecting “My Activities” tab 1302, the hardware processor can present a list, as shown, for example, in interface 1300 of FIG. 13. In another example, a user can also access this list on a dashboard. The hardware processor can allow the user to organize the interactive checklists into categories of their choosing, such as “Priority 1, Priority 2, Priority” and/or “This Week, Next Week, In Two Weeks.” In accordance with some embodiments, nested “child-level” checklists of an Activity that can be viewed at “My Activities” will not be displayed there. In accordance with some embodiments, the hardware processor can allow a user can reorder or move interactive checklists from one category to another using a drag and drop mechanism. Once a particular interactive checklist is selected from the list, the hardware processor can take the user to an interface within the interactive checklist itself. In some embodiments, using an interface 1400 that may be presented by a hardware processor of a computing device, the hardware processor can allow a user to work on the interactive checklist, for example as shown in FIG. 14. Additionally or alternatively, the hardware processor can present the interactive checklist from this interface to a new, second interface. The hardware processor can receive a number of user inputs indicating actions such as including, splitting, unsplitting, delegating and/or undelegating steps.” ‘ Par. 174)
and cause the suggested task to be assigned to the selected second user (Bodnick Par. 131- In some embodiments, the hardware processor can receive a user input indicating one or more parameters and that the user is assigning multiple activities for each parameters, as shown in interface 1812 of FIG. 18E. For example, assigning multiple activities for each parameter can include assigning the same report to multiple employees, following the same sales process for multiple different prospects, preparing monthly invoices for each of a company's clients, assigning a project update report for multiple projects to a single individual, and/or any other suitable scenario. In some embodiments, the prefilled answers can be retrieved from external sources (e.g., a Web service or a database) or through data stored internally.; Par 263; Par. 273-274 ) (Rezaeian Par. 3-4-“For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete. In some implementations, to enhance the accuracy of task prediction, the contextual user profiles for a plurality of users associated with an entity can be clustered using one or more clustering techniques (e.g., k-means clustering). Clustering the user profiles may result in the formation of one or more clusters of users.”
Rezaeian and Bodnick are directed to task management. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the intelligent interface of Rezaeian, as taught by Bodnick, by utilizing match processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian with the motivation of displaying assigned interactive checklists, due dates and/or time, current status, and/or any other available information. (Bodnick Par. 127).
Rezaeian in view of Bodnick teach task analysis and the feature is expounded upon by Grosz”
obtain, via the GUI, user input indicating one or more drag-and-drop operations from the first user to (i) select the suggested task from the first drag-and-drop item and (ii) select a second user on the second plurality of drag-and-drop items; (Grosz Par. 119-120- “Option 311 allows the operating user to select from pre-generated activity profiles. The operating user may see a list of activity profiles by selecting option 311 and may select an activity profile to add to field 310 by drag and drop operation.”; Par. 132; Par. 136; Par. 155-156); Par. 207-“ Template 1100 includes a text input field 1105 for adding specific users to a group profile and a text input field 1106 for adding additional constraints to a group profile. In one embodiment specific users might be added by typing into text input field 1105 an identification of one or more users to add. In one embodiment a list of users may be dragged and dropped into text interface 1105.”)
Rezaeian , Bodnick and Grosz are directed to task management. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the intelligent interface of Rezaeian in view of Bodnick, as taught by Grosz, by utilizing drag and drop processing with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian in view of Bodnick with the motivation of intelligently and automatically grouping individuals into groups for the purpose of participating in activities. (Grosz Par. 10).
Regarding Claim 2 and Claim 12,
wherein the one or more machine learning models are trained to determine the suggested task based upon geographic location characteristics associated with the first user or the plurality of users (Rezaeian Par.3-“ Certain aspects and features of the present disclosure relate to systems and methods for using a trained machine-learning model to predict tasks for users to complete, and to efficiently display the predicted tasks as selectable links on an intelligent user interface (UI). The trained machine-learning model may learn the tasks that users typically perform over time and continuously update itself. A set of all available tasks can be determined by harvesting and evaluating log records across various applications. As a non-limiting example, the log records, which may record users' interactions (e.g., clicks) within applications, may be used to identify all tasks previously completed by users associated with an entity. The log records may capture the metadata of interactions between an application and a user. For example, the metadata may include a user identifier, a task identifier for a task selected to be performed, the application that facilitates performance of the task, and other suitable metadata. Further, a contextual user profile can be created to store various information about a user, such as the user's role, access level, current location, device characteristics, previous interactions with applications, and other suitable information. For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete.”)
Regarding Claim 3 and Claim 13,
wherein the one or more machine learning models are trained to determine the suggested task based upon team size characteristics associated with the first user or the plurality of users ( Rezaeian Par. 6-“ The user vector may be fed into the machine-learning model to predict which tasks the user will need to complete (e.g., on a given day). The machine-learning model can output a prediction of one or more tasks that the user will likely need to complete. The machine-learning model prediction is based, at least in part, on the user vector, the set of suggestable tasks, and/or the tasks completed by users with similar attributes (e.g., users in the same location). “)
Regarding Claim 4 and Claim 14,
wherein the one or more machine learning models are trained to determine the suggested task based upon team member characteristics associated with the first user or the plurality of users ( Rezaeian Par. 6-“ The user vector may be fed into the machine-learning model to predict which tasks the user will need to complete (e.g., on a given day). The machine-learning model can output a prediction of one or more tasks that the user will likely need to complete. The machine-learning model prediction is based, at least in part, on the user vector, the set of suggestable tasks, and/or the tasks completed by users with similar attributes (e.g., users in the same location).; Par. 11- “generating contextual user data for each user of a plurality of users, the contextual user data for each user of the plurality of users being generated by aggregating the one or more attributes associated with the user across the plurality of log records. The computer-implemented method also includes performing a clustering operation on the contextual user data of the plurality of users, the performance of the clustering operation causing a formation of one or more clusters of users, and each cluster of the one or more clusters representing one or more users that share a common attribute.”)
Regarding Claim 5 and Claim 15,
wherein the one or more machine learning models are trained to determine the suggested task based upon time efficiency or resource efficiency characteristics associated with the first user or the plurality of users ( Rezaeian Par. 6-“ The machine-learning model may represent a model of some or all of the users of an entity and their interactions with various applications (e.g., which tasks those users have previously completed). When a particular user accesses the intelligent UI, a learner system may generate a user vector for that particular user. In some implementations, the user vector may be a vector representation of various information about the user. For example, the user vector may include the user's access level, current location, whether the user is working remotely, previous interactions with applications, previous tasks completed using the applications, frequency of completing certain tasks, and other suitable information. The user vector may be fed into the machine-learning model to predict which tasks the user will need to complete (e.g., on a given day). The machine-learning model can output a prediction of one or more tasks that the user will likely need to complete. The machine-learning model prediction is based, at least in part, on the user vector, the set of suggestable tasks, and/or the tasks completed by users with similar attributes (e.g., users in the same location).”)
Regarding Claim 6 and Claim 16,
wherein the one or more machine learning models are trained to determine the suggested task based upon comparison of the activity data associated with the plurality users to activity data of a further plurality of users ( Rezaeian Par. 58-59-“ At a “cluster” stage 313, log data is further analyzed to assign individual log messages to a cluster. Specifically, multiple initial clusters to which log messages were assigned during an intake process (e.g., at 304) can be assessed to determine whether some of the initial clusters are to be merged together. The assessment can include identifying one or more representative samples for each cluster and performing pair-wise quantitative comparative assessments. Cluster pairs assessed via a pair-wise comparative assessment can include clusters with log messages having same or similar number of components (or words). In some instances, each pair of clusters includes clusters associated with a number of components that are the same or different from each other by less than a threshold number (e.g., that is predefined, a default number, or identified by a user) is evaluated using the assessment. The comparative assessment may be performed iteratively and/or in a structured manner (e.g., such that pairs with a same number of components are evaluated prior to evaluating pairs with a different number of components).”)
Regarding Claim 7 and Claim 17,
wherein the instructions, when executed via the one or more processors, further cause the one or more computers to display an indication of a suggested user for assignment, from among the plurality of users; (Rezaeian Par. 3-4-“For example, each time the user accesses the intelligent UI, the full set of tasks and the user profile can be inputted into a trained machine-model to output a prediction of one or more tasks for that user to complete. In some implementations, to enhance the accuracy of task prediction, the contextual user profiles for a plurality of users associated with an entity can be clustered using one or more clustering techniques (e.g., k-means clustering). Clustering the user profiles may result in the formation of one or more clusters of users.”; Par. 79-“FIG. 4 shows an example network environment 400 that enables an interface associated a cloud network to display suggested tasks for users. In some implementations, network environment 400 may include user device 480, contextual user data generator 440, learner system 450, performance tasks 460, and intelligent UI (user interface) generator 470. While user device 480 is shown as a mobile device (e.g., a smartphone), it will be appreciated that user device 480 can be any computing device that is operated by a user. User device 480 can access an interface. …”);
Regarding Claim 9 and Claim 18,
wherein the instructions to display the suggested task include instructions to display the suggested task based upon an authorization received from the first user; (Rezaeian Par. 143-“ In certain embodiments, cloud infrastructure system 1000 may include an identity management module 1028. Identity management module 1028 may be configured to provide identity services, such as access management and authorization services in cloud infrastructure system 1000. In some embodiments, identity management module 1028 may control information about customers who wish to utilize the services provided by cloud infrastructure system 1002. Such information can include information that authenticates the identities of such customers and information that describes which actions those customers are authorized to perform relative to various system resources (e.g., files, directories, applications, communication ports, memory segments, etc.) Identity management module 1028 may also include the management of descriptive information about each customer and about how and by whom that descriptive information can be accessed and modified.);
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Rezaeian et al., US Publication No. 20200125586 A1 [hereinafter Rezaeian], in view of Bodnick et al. , US Publication No. 20190286462A1, [hereinafter Bodnick], in further view of Grosz , US Publication No. 20170093967A1, [hereinafter Grosz], and in further view of Bouillet , US Publication No. US20180090022A1, [hereinafter Bouillet].
Regarding Claim 8 Rezaeian in view of Bodnick in further view of Grosz teach The one or more non-transitory computer-readable media of claim 7,…
Rezaeian in view of Bodnick in further view of Grosz teach suggested task assignment and the feature is expounded upon by Bouillet :
wherein the suggested user is identified based upon a licensing or compliance status of the suggested user, a skill profile of the suggested user, or a workload capacity of the suggested user (Bouillet Par. 14-“ Accordingly, the present invention provides for a targeted recommendation and recruitment system for customized education, training, and task assignment. The mechanisms of the illustrated embodiments adaptively build and maintain open, portable and sharable user profiles containing learned knowledge, learning paths, and cognitive qualities that can be inferred from learning paths (e.g., a most efficient delivery means and method, a best time of day according to a user preference or interest, rest times, and the like). In one aspect, a user from a pool of candidates may be identified according to attributes of the sharable user profile that more closely matches a particular job, course (e.g., training or educational), and/or task as compared to other user profiles. For example, in one aspect, the most closely matching or most qualified user profile(s) may indicate a matching score that ranks higher than other user profiles in the pool of candidates. A customized learning experience and one or more course suggestions may be provided to a selected user or a group of users (including students, groups, employees, teams, etc.) based on each of the selected users or a group of users.”)
Rezaeian, Bodnick and Grosz are directed to task analysis. Bouillet improves upon the task assignment. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the suggested task assignment of Rezaeian in view of Bodnick in further view of Grosz, as taught by Bouillet, by additional task analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian in view of Bodnick in further view of Grosz with the motivation of improved targeted learning and recruitment by using attributes obtained from a user profile to identify a user as a potential candidate for performing an activity (Bouillet Par. 48).
Claim 10 and Claim 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rezaeian et al., US Publication No. 20200125586 A1 [hereinafter Rezaeian], in view of Bodnick et al. , US Publication No. 20190286462A1, [hereinafter Bodnick], in further view of Grosz , US Publication No. 20170093967A1, [hereinafter Grosz], and in further view of Bills , US Patent No. US9727376B1, [hereinafter Bills].
Regarding Claim 10 and Claim 19 Rezaeian in view of Grosz teach The one or more non-transitory computer-readable media of claim 7,…
Rezaeian in view of Bodnick in further view of Grosz teach suggested task assignment and the feature is expounded upon by Bills :
wherein the displayed second plurality of drag-and-drop items indicate whether corresponding ones of the plurality of users are available for assignment of the suggested task. (Bills Col 17-“ In an embodiment, block 340 comprises receiving input that identifies a geographic location to associate with the task object. In this embodiment, block 340 further comprises determining to associate the particular data object with the task object based on comparing the geographic location to one or more location fields in the particular data object. For instance, upon creating a location-sensitive task object per the techniques described in other sections, a task object may automatically be attached to the closest data object of a certain type, such as a user object or event object. In another embodiment, an association between the task object and the closest data object may be suggested via the graphical user interface. The user is given an option to confirm the association.”; Col 8-“ Other data objects 123 are not necessarily associated with location data. Both geospatial objects 122 and other data objects 123 may include a variety of types of objects. Example object types include event objects comprising data describing events that have occurred, report objects comprising data describing reports generated by users such as operators or field analysts, user objects comprising data describing individual persons, resource objects or asset objects comprising data describing physical assets or resources that are available, and so forth.”; In yet another embodiment, the graphical user interface may display an object icon that represents a particular data object, such as a report object, event object, or user object, on the map. The particular data object stores information describing a geographic location, and is thus displayed at that geographic location. Col 14- A user may drag and drop a task icon or task creation icon on the object icon. Or, the user may select a graphical interface control associated with the second icon, such as by right-clicking on the object icon, or tapping on a button control while the object icon is in focus. Responsive this input, the graphical user interface may create, or show a menu for creating, the task object at the same geographic location as the particular data object. In an embodiment, the particular data object may furthermore be attached to the task object, and/or data from the particular data object may be linked to or imported into the task object, as described in other sections.)
Rezaeian, Bodnick and Grosz are directed to task analysis. Bills improves upon the task assignment. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improved upon the suggested task assignment of Rezaeian in view of Bodnick in further view of Grosz, as taught by Bills, by additional task analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Rezaeian in view of Bodnick in further view of Grosz with the motivation of managing activities related to tasks (Bills Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. US 20130174070 A1 to Briand et al. - A method and apparatus are provided for indicating drag and drop targets for a first object among a plurality of second objects in a graphical user interface (GUI). The method includes rendering with a first rendering mode a first group of second objects within the GUI onto which a selected first object is likely to be dropped, rendering with a second rendering mode distinct from the first rendering mode a second group of second objects, the second group consisting of the remaining second objects, receiving a drag input on the first object, and updating the groups based at least upon an updated position of the first object derived from the received drag input.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624