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 .
Status of Claims
This is a Final Action on the merits in response to the claims filed on 11/05/2025.
Claims 1 – 4, 7 – 11, 14 – 18, and 21 have been amended;
Claims 1 – 21 are pending in this application.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/12/2025 and 01/26/2026 have been acknowledged. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner. The initialed and dated copy of Applicant’s IDS form, 1449, are attached to the instant Office Action.
Response to Remarks
Examiner’s Response to Remarks
Response to Claim Rejections Under 35 U.S.C. § 101;
Response to Claim Rejections Under 35 U.S.C. § 103.
Examiner’s Response to Claim Rejections Under 35 U.S.C. § 101 and § 103.
Applicant submits that the amendments have overcome rejections under 35 U.S.C. § 101 and 35 U.S.C. § 103 rejections. However, this is not persuasive. See rejections below.
Claim Rejections – 35 U.S.C. § 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 – 21 are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 8, and 15 recite:
obtaining member data and project data, wherein the member data includes information corresponding to a member and one or more family members, wherein the project data corresponds to a project to be performed on behalf of the one or more family members, and wherein the project data is obtained through an ongoing communications session between the member and a representative;
generating a project-specific interface corresponding to the project, wherein the project- specific interface facilitates a project-specific communications session between the member and the representative, and wherein the ongoing communications session and the project- specific communications session are distinct;
processing the set of proposals, the project data, and the member data through a proposal ranking machine learning algorithm to generate a ranking of the set of proposals;
selecting one or more proposals and one or more data fields based on the ranking;
updating the project-specific interface to provide the one or more proposals and the one or more data fields through the project-specific communications session.
Claim 1 recites the abstract idea of certain methods of organizing human activity, and the claim particularly recites commercial interactions. For example, claim 1 recites obtaining member data and project data, wherein the member data includes information corresponding to a member and one or more family members, wherein the project data corresponds to a project to be performed on behalf of the one or more family members; generating a project-specific interface corresponding to the project, wherein the project- specific interface facilitates a project-specific communications session between the member and the representative; processing the set of proposals, the project data, and the member data through a proposal ranking machine learning algorithm to generate a ranking of the set of proposals; selecting one or more proposals and one or more data fields based on the ranking; and updating the project-specific interface to provide the one or more proposals and the one or more data fields through the project-specific communications session; and these all recite commercial interactions and business relations between humans such as the members collaborating on the project. Claims 8 and 15 are substantially similar and recite the same subject matter as claim 1. Accordingly, claims 1, 8, and 15 recite certain methods of organizing human activity.
The dependent claims encompass the same abstract ideas as well. For instance, claims 2, 9, and 16 are directed towards evaluating the project data to incorporate the additional input and updating the project data; claims 3, 10, and 17 are directed towards observing one or more proposals are selected based on a set of preferences associated with the member and the one or more family members; claims 4, 11, and 18 are directed towards observing the one or more proposals are further selected according to a set number of proposals presentable, and wherein the set number of proposals is evaluated according to the member data; claims 5, 12, and 19 are directed towards observing wherein the one or more proposals are communicated through the project-specific interface according to a cognitive load associated with the member and the one or more family members; claims 6, 13, and 20 are directed towards observing performing the one or more tasks further includes: interacting with a third-party service to evaluate performance of the one or more tasks associated with the project; and claims 7, 14, and 21 are directed towards observing updating the member data further comprises: monitoring communications exchanged over the project-specific communications session to detect completion of the one or more tasks and the message feedback. Thus, the dependent claims further limit the abstract ideas.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the
additional elements of wherein the project data is obtained through an ongoing communications session between the member and a representative; wherein the ongoing communications session and the project- specific communications session are distinct; processing the member data and the project data through a machine learning algorithm to generate a set of proposals and a set of data fields presentable with the set of proposals, wherein the machine learning algorithm is trained and implemented to reduce unnecessary interactions with the set of proposals and the set of data fields, and wherein the machine learning algorithm is trained using sample interactions with sample proposals and sample data fields; and monitoring ongoing messages exchanged between the member and the representative through the project-specific communications session to obtain message feedback corresponding to the one or more proposals; in addition to reciting the additional elements of claim 1, claim 8 recites the additional elements of a system, one or more processors, and memory; and in addition to reciting the additional elements of claim 1, claim 15 recites the additional elements of a non-transitory computer-readable storage medium, one or more processors, and a computer system. The additional elements of a memory, system, one or more processors, a computer system, non-transitory computer-readable storage medium, wherein the project data is obtained through an ongoing communications session between the member and a representative; wherein the ongoing communications session and the project- specific communications session are distinct; processing the member data and the project data through a machine learning algorithm to generate a set of proposals and a set of data fields presentable with the set of proposals, wherein the machine learning algorithm is trained and implemented to reduce unnecessary interactions with the set of proposals and the set of data fields, and wherein the machine learning algorithm is trained using sample interactions with sample proposals and sample data fields; and monitoring ongoing messages exchanged between the member and the representative through the project-specific communications session to obtain message feedback corresponding to the one or more proposals are considered generic computer components performing generic computer functions as per Applicant’s Specifications shown below:
“[0039] FIG. 1 shows an illustrative example of an environment 100 in which a task facilitation service 102 assigns a representative 106 to a member 118 through which various tasks performable for the benefit of the member 118 can be recommended for performance by the representative 106 and/or one or more third-party services 116 in accordance with various embodiments. The task facilitation service 102 may be implemented to reduce the cognitive load on members and their families in performing various tasks in and around their homes by identifying and delegating tasks to representatives 106 that may coordinate performance of these tasks for the benefit of these members. In an embodiment, a member 118, via a computing device 120 (e.g., a laptop computer, smartphone, etc.), may submit a request to the task facilitation service 102 to initiate an onboarding process for assignment of a representative 106 to the member 118 and to initiate identification of tasks that are performable for the benefit of the member 118. For instance, the member 118 may access the task facilitation service 102 via an application provided by the task facilitation service 102 and installed onto a computing device 120. Additionally, or alternatively, the task facilitation service 102 may maintain a web server (not shown) that hosts one or more websites configured to present or otherwise make available an interface through which the member 118 may access the task facilitation service 102 and initiate the onboarding process.
and thus, are not practically integrated nor significantly more.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). the additional elements do not impose meaningful limits on practicing the idea. Thus, the claims are directed to an abstract idea. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 7, 9 – 14, and 16 – 21, when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 21 are not patent eligible.
Claim Rejections – 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
6. Claims 1 – 21, are rejected under 35 U.S.C. 103 as being unpatentable over Culver, Andrew et al. (U.S. Patent No. 8,788,590) hereinafter “Culver” in view of Relangi, Aditya (U.S. Patent No. 10,891,571) hereinafter “Relangi” in view of Slaughenhoupt, Bryan (U.S. Publication No. 2013/0246119) hereinafter “Slaughenhoupt”.
Claims 1, 8, and 15:
Culver teaches the following:
A computer-implemented method, comprising: obtaining member data and project data, wherein the member data includes information corresponding to a member and one or more family members, wherein the project data corresponds to a project to be performed on behalf of the one or more family members; Culver teaches in col. 1, lines 45 – 48, a method for enabling collaboration between individuals to design, construct and maintain a building and providing a network based computer system including at least one server; claim 1 teaches a computer readable storage medium encoded with non-transitory instructions for execution by a CPU; Culver teaches in col. 1, lines 12 – 15, a network based collaboration tool used to enable communication and collaboration between people working on industrial, commercial or residential construction projects; Culver teaches in col. 6, lines 13 – 20, a projects module supports a community management module that allows certain project-related information about projects that involved a particular user community member to be viewed by other members of the user community that may be likened to member data and project data, wherein the member data includes information corresponding to a member and one or more family members, and wherein the project data corresponds to a project to be performed on behalf of the one or more family members; Culver further teaches the community management module communicates with the user profile database that stores user profiles for the user community; and Culver teaches in col. 6, lines 27 – 30, each profile in the set of user profiles may be unique and encodes information related to a corresponding user of the user community;
generating a project-specific interface corresponding to the project, wherein the project- specific interface facilitates a project-specific communications session between the member and the representative; Culver teaches in col. 5, lines 32 – 36, a community management module that may be likened to a project-specific interface, may assist users to communicate with other users and obtain information relating to a construction or building project; Culver teaches in col. 71, lines 55 – 67, As presented above, the S/T/S control 1495A is used to find content associated with a particular tag, such as the tag 1595 in Fig. 15F. Alternatively, the control 1495, and more specifically elements in the interface 1594 could also be used to apply a tag to content in the UI 1430. For example, if a new message in the messaging area 1580 relates to the Board of Advisors tag 1595, the tag (or a representative icon thereof) could be dragged onto the relevant message in this area in order to create an association between the tag and the message. Thus, when the Board of Advisors tag 1595 is subsequently opened, it may show four (4) items, including a project, a person, a company and the new message that was tagged in the media/messaging UI element 1470.
selecting one or more proposals; Culver teaches col. 23, lines 46 – 47, selecting one or more contractors for the project based on the proposal;
updating the project-specific interface to provide the one or more proposals and the one or more data fields through the project-specific communications session; Culver teaches in col. 15, lines 15 – 27, the identifier 720 may also include an alphanumeric value that can be defined by the user who creates the project. For example, a user may assign a common name, such as: a building's name (e.g., "High Holborne Mall") a building's address (e.g., "785 Bay Street"); and/or a code word or phrase that represents a proxy for its name (e.g., "Project Phoenix Ascendant. In such cases, the project identifier 720 may be separate from the commonly-known name of the project, or the commonly-known name may be included in the identifier 720. In the former case, the building projects database 312 may provide a field or other placeholder that associates the project identifier 720 with its commonly-known name;
monitoring ongoing messages exchanged between the member and the representative through the project-specific communications session to obtain message feedback corresponding to the one or more proposals; Culver teaches in col. 20, lines 1 – 12, another example of how user notification related to events may occur can be seen when a document is `pushed` by one user to another. In this non-limiting embodiment, a first user designates a second user as the target for a document associated with an event, which may be currently checked-in or checked-out. If the document associated with the event is currently checked-in, the document event is assigned to the designated second user and the system 10 notifies this user of the new event. Otherwise, the document event is assigned to the designated second user once its associated document is checked-in and notification related to this action is provided to the second user at this point. Culver teaches in col. 20, lines 13 – 17, it will be appreciated that an event can be pushed to more than one person, either simultaneously or in a particular sequence or chain. The system 10 can be configured to notify users when the document moves between each person in the chain in order that its progress can be monitored.
an ongoing communications sessions; Culver teaches in col. 55, lines 15 – 24, it will be appreciated that the above messages relating to the invitation of a member of the user community 14 to assume responsibility for a role, as well as the member's subsequent response indicating his or her acceptance or refusal, are examples of task-related communications. As used here, the term "task-related communications" refers to communications (which may comprise both synchronous and asynchronous forms of communications such as internal messages and/or email) that are associated with a particular activity.
Culver teaches obtaining information, synchronous communication, selecting contractors, sending notifications to users, create task events, generate the event group, and proposal task event and Culver relates to Relangi through managing tasks and managing projects and Relangi further teaches the following:
and wherein the project data is obtained through an ongoing communications session between the member and a representative; Relangi teaches in col. 3, lines 32 – 42, some implementations described herein include a task management platform to use machine learning to intelligently generate recommendations that optimize completion and/or performance of a set of tasks, whereby completion of the set of tasks may be influenced by real-time events. For example, the task management platform may receive, from a user device, a request for a recommendation to identify a task to perform for a job within an organization. The job may include performing a set of tasks relating to managing applications of individuals that are applying for a loan, a job, a type of insurance, and/or the like. Relangi teaches in col. 3, lines 43 – 60, the job may be a relationship manager of a loan provider. The relationship manager may be assigned a set of tasks that include managing applications of a group of individuals that are applying for loans, contacting employees within the organization to inquire about a status of the applications, contacting vendor sites of one or more vendor organizations who are authorized to submit applications for loans on behalf of prospective clients, and/or the like. A vendor organization may offer a product (e.g., a vehicle, a home, and/or the like) that may be purchased with a loan offered by the organization. As such, the relationship manager may authorize dealers at vendor sites to offer prospective clients loans at particular annual percentage rates (APRs). Additionally, or alternatively, the relationship manager may be tasked with contacting employees at these vendor sites to perform customer service calls, sales calls, calls to further relationships with the dealers at the vendor sites, and/or the like.
and wherein the ongoing communications session and the project- specific communications session are distinct; Relangi teaches in col. 11, lines 28 – 40, a user (e.g., a relationship manager of a loan provider) may interact with an interface of the user device to submit a request for a task. For example, the user interface may display a task generator, which may include one or more selectable task settings indicating that the task generator may be used to select a next task (e.g., to be completed immediately), a list of daily tasks, a list of weekly tasks, and/or the like. In some cases, the user may adjust other task-related settings, such as whether tasks are permitted to be transferred to other users within the organization, whether tasks are permitted to be transferred to the user from the other users within the organization, and/or the like. Relangi teaches in col. 15, lines 37 – 47, the task management platform may generate a recommendation to have a task performed automatically. For example, if the set of tasks include a task that is capable of being performed automatically, the task management platform may generate a recommendation to have the task performed automatically. In some cases, a task may be capable of being performed automatically, but may be preferred to be performed by a human user. In this case, the task management platform may generate a recommendation to have the task performed automatically if a priority value score for the task satisfies a threshold confidence level.
processing the member data and the project data through a machine learning algorithm to generate a set of proposals and a set of data fields presentable with the set of proposals, wherein the machine learning algorithm is trained and implemented to reduce unnecessary interactions with the set of proposals and the set of data fields, and wherein the machine learning algorithm is trained using sample interactions with sample proposals and sample data fields; Under the broadest reasonable interpretation, Examiner understands a proposal is a recommendation. Relangi teaches in col. 1, lines 47 – 67, and col. 2, lines 1 – 6, a request for a recommendation identifying a task to be performed. The task may be part of a set of tasks that are to be performed as part of a job to manage a set of applications for a product or a service. The method may include obtaining, by the device and after receiving the request, data identifying a set of application status metrics for a group of sites where prospective borrowers are able to submit particular applications. The method may include obtaining, by the device, events data identifying real-time events associated with the group of sites. The events data may include at least one of: internal events data identifying events within the group of sites, or external events data identifying events that are external to the group of sites and that are likely to influence the events within the group of sites. The method may include generating, by the device, a set of priority values for the set of tasks by processing the data identifying the set of application status metrics and the events data using a data model that has been trained using one or more machine learning techniques. The method may include generating, by the device, the recommendation to perform the task based on the set of priority values for the set of tasks. The method may include providing, by the device, the recommendation to the user device to allow the task to be performed. The method may include performing, by the device and after generating the recommendation, one or more actions associated with assisting in performance of the task. Relangi teaches in col. 13, lines 41 – 67, and col. 14, lines 1 – 4, in some implementations, one or more sensor devices described herein may be configured with a set of thresholds that allow the one or more sensor devices to convert events data to pre-scored values that may be used by the data model. For example, if a sensor device captures weather data indicating that a storm is coming, the sensor device may convert the weather data value to a value associated with an increased likelihood of a task to call an individual at the vendor site being performed without delay (e.g., because the storm may reduce foot traffic within the vendor site and make the employee of the vendor site more likely to be available for a phone call). In this case, the sensor device may provide the pre-scored value to the task management platform. In this way, the task management platform receives pre-scored values that may be used to score a particular task, thereby reducing a utilization of processing resources of the task management platform when generating the recommendation and improving a speed at which the user device is able to receive the recommendation. The task management platform may obtain user workload data from the third data storage element. For example, the task management platform may obtain user workload data identifying, for a particular time period, a workload capacity for a group of users that perform the job for the organization. The user workload data may include work schedule data identifying a number of tasks allocated to a worker's schedule, worker deadline data identifying a deadline indicating when tasks assigned to a worker are to be completed, worker status data identifying whether a worker is currently engaging in performance of a task, and/or the like. Relangi teaches in col. 15, lines 48 – 57, the task management platform may provide the recommendation to the user device. As shown by reference number 160, the user device may display the recommendation on a user interface, such that the user may view and select the recommendation. The task management platform uses a data model trained using machine learning to generate and provide the user device with a recommendation that that considers real-time events that are capable of influencing whether particular tasks are able to be performed. Relangi teaches in col. 18, lines 21 – 42, task management platform 230 may obtain historical data from the first data storage device 220. In some implementations, task management platform 230 may obtain application status data from the second data storage device 220. Additionally, or alternatively, task management platform 230 may obtain user workload data from the third data storage device 220. Additionally, or alternatively, task management platform 230 may obtain events data for a group of sites from the group of data storage devices. Task management platform 230 may provide a recommendation to user device 210. In some implementations, task management platform 230 may provide user device 210 and/or client device 250 (e.g., a vendor device) with one or more custom tools to assist in performance of a task. In some implementations, task management platform 230 may interact with a user account to schedule performance of the task. In some implementations, task management platform 230 may provide instructions to automatically initiate performance of the task to a mobile device (e.g., which may be the same user device 210 that provided the request for a recommendation or a different user device. Relangi teaches in col. 21, lines 57 – 67, and col. 22, lines 1 – 2, a flow chart of an example process 400 for using machine learning to generate recommendations that optimize completion and/or performance of a set of tasks that are to be performed as part of a job within an organization. In some implementations, one or more process blocks of Fig. 4 may be performed by a task management platform (e.g., task management platform 230). In some implementations, one or more process blocks of Fig. 4 may be performed by another device or a group of devices separate from or including the task management platform, such as a user device (e.g., user device 210), one or more data storage devices (e.g., data storage devices 220), and/or a client device.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method for enabling collaboration between individuals to design, construct and maintain a building of Culver with a device may receive, from a user device, a request for a recommendation identifying one or more tasks to be performed, of a set of tasks that are part of a job for an organization and associated with managing a set of applications for a product or a service of Relangi to assist businesses with implementing machine learning techniques to provide recommendations for management of tasks and projects using task management platforms (Relangi, Spec. col. 5 lines 60 – 67, and col. 6, lines 1 – 5).
Culver teaches obtaining information, selecting contractors, sending notifications to users, create task events, generate the event group, and proposal task event and Relangi teaches a task management platform using machine learning techniques and Culver and Relangi are similar to Slaughenhoupt where Slaughenhoupt managing projects and tasks; and Slaughenhoupt further teaches the following:
processing the set of proposals, the project data, and the member data through a proposal ranking machine learning algorithm to generate a ranking of the set of proposals; Slaughenhoupt teaches in ¶ 0155, a project ranking application with a processor in the application server enable the system to learn from previous targeted projects using machine learning techniques, where targeted projects may include project data and member data and machine learning techniques may be likened to machine learning algorithm; Slaughenhoupt further teaches in ¶ 0028, project ranking operations for the project that may be likened to proposal ranking;
and one or more data fields based on the ranking; Slaughenhoupt teaches in 0083, In the middle of the project details form 400 are two tabs: a project management tab 430 and a results tab 432, the project management tab 430 being selected for display in Fig. 4. A variety of project management information can be displayed and captured under the project management tab 430. For example, the project management tab 430 can include commands, fields, and notes that facilitate the workflow and the user while processing the project. These fields and commands can include project priority, deactivation, status, to-dos, processing status, territory, responsible, market research analyst, salesperson, data source, related projects, and analyst comments. The data contained within the project management tab 430 can be viewed, entered, edited, deleted, and appended by the user or the project management and ranking system at which time the project ranking application 126 analyzes the data and adjusts the project ranking accordingly.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method for enabling collaboration between individuals to design, construct and maintain a building of Culver and a device may receive, from a user device, a request for a recommendation identifying one or more tasks to be performed, of a set of tasks that are part of a job for an organization and associated with managing a set of applications for a product or a service of Relangi with a system and method for targeting, processing, and managing project leads and reports for target marketing products and services through the use of analytics, data mining, and the application of numerical weights to data criteria of Slaughenhoupt to assist businesses with implementing a ranking system with project management (Slaughenhoupt, Spec. 0052).
Claims 2, 9, and 16:
Culver, Relangi, and Slaughenhoupt teach claims 1, 8, and 15. Culver further teaches the following:
determining that additional input is required for the project; Culver teaches in col. 96, lines 45 – 46, identifying tasks or events that require additional attention;
updating the project-specific interface to display one or more prompts for the additional input; Culver teaches in col. 9, lines 23 – 25, prompting a member of the user community to enter additional information for authentication to confirm any changes to the their profile; Culver teaches in col. 9, lines 4 – 6, the authentication may be part of the community management module; Culver teaches in col. 9, lines 14 – 16, a user interface where requests may be made to provide information on the user involved with the project. Culver teaches in col. 36, lines 64 – 67, and col. 37, lines 1 – 31, the result of step 1340 is the assemblage of the project team. In the case where a project team member has a user profile within the user profile database 314, the acceptance of the invitation may cause several actions to occur, including: updating the member's user profile 340.sub.i (and more specifically, the list of active building projects 520) to indicate that he or she is now participating in the project; updating the project team parameters 740i to indicate that the user has accepted responsibility for the indicated role; and updating the events in the event group 730i related to the accepted role to indicate the user now responsible for those tasks. In the case where a project team member is not a member of the user community 14, their acceptance of a role within the project typically results in the member joining the user community 14 and creating a user profile 340.sub.i within the user profile database 312. This process was described previously with respect to Fig. 12 and so need not be explained here. Once the team member has obtained a user profile 340.sub.i, however, his or her profile is updated in a similar manner to that described above. At step 1350, the permissions for certain project team members may be adjusted where necessary. This step is optional and may occur when a role in the project is changed and/or the original first user fulfilling that role is replaced by a new second user.
and updating the project data to incorporate the additional input; Culver teaches in col. 52, lines 43 – 65, At step 1340H, replies to invitations from the previous step are monitored by the system 10 to determine acceptance or refusal of project roles by their prospective team members. In particular, the projects module 302 and/or the community management module 304 receive: indication of acceptance or refusal from a user of interest for a targeted invitation; or indication of acceptance or refusal from one or more prospective candidate for an open invitation. In the case where the user accepts responsibility for the project role in a targeted invitation, the projects module 302 can update the projects database 312 to indicate that the user accepted the project role and so has become a project team member. This may cause the team member viewer 1130 to be updated to indicate that the role was accepted and list the user's name associated with the role. In a similar manner, the community management module 304 may update the user's profile 340.sub.i within the user profile database 314 to indicate that the user has accepted responsibility for the invited role and has thus become a member of that particular project. This may cause an interface shown to the user to include the new project on which the user is now going to be working.
Claims 3, 10, and 17:
Culver, Relangi, and Slaughenhoupt teach claims 1, 8, and 15. Culver further teaches the following:
wherein the one or more proposals are further selected based on a set of preferences associated with the member and the one or more family members; Culver teaches in col. 27, lines 63 – 67, and col. 28, lines 1 – 20, the selection interface 1010 may use techniques other than a list to allow the first user to define or select the project type. For example, the interface 1010 could identify certain project criteria by "interviewing" the first user by asking him or her project-related questions, including: what is the general type of building (e.g., residential, commercial or industrial) that the project will represent; what is the expected duration of the building's design and construction; whether achieving a particular certification (or level of certification) is a design consideration for the project; what type of design process is planned for the project (e.g., integrated design process, design-build process, design-tender-build process); and/or what is the expected lifespan of the building that the project represents. Based on the first user's responses to these questions (among others), the project type selection interface may be able to make suggestions or recommendations as to the best type(s) of project for what the first user seems to be indicating. For example, a project for a residential development with an expected 10- to 15-year lifespan would likely require different project parameters than a project for a commercial office tower with a 60- to 75-year lifespan.
Claims 4, 11, and 18:
Culver, Relangi, and Slaughenhoupt teach claims 1, 8, and 15. Relangi further teaches the following:
wherein the one or more proposals are further selected according to a set number of proposals presentable through the project-specific interface, and wherein the set number of proposals is determined according to the member data; Relangi teaches in col. 4, lines 6 – 25, the task management platform may generate the recommendation identifying the task to perform. For example, the task management platform may provide the obtained data as input to a data model that has been trained using one or more machine learning techniques, which may cause the data model to output a set of priority values for the set of tasks. The set of priority values may be based on a degree of importance in performing particular tasks (e.g., which may be based on application status metrics), a likelihood of real-time events delaying performance of particular tasks, availability of other users capable of performing particular tasks, and/or the like. Additionally, the task management platform may process the set of priority values to select a task with a highest-available priority value and may provide a recommendation to the user device to perform the task with the highest-available priority value. In some implementations, the task management platform may perform one or more actions associated with assisting in performance of the task. Relangi teaches in col. 4, lines 44 – 60, furthermore, several different stages of the process for generating the recommendation are automated, which may remove human subjectivity and waste from the process, and which may improve speed and efficiency of the process and conserve computing resources (e.g., processor resources, memory resources, and/or the like). Additionally, implementations described herein use a rigorous, computerized process to perform tasks or roles that were not previously performed or were previously performed using subjective human intuition or input. For example, the task management platform may intelligently identify and/or generate custom tools that may be used to assist users in performing tasks, such as by identifying contact information of an individual that needs to be contacted, identifying application status metrics that may be relevant when contacting the individual, generating templated scripts that are tailored to individual or vendor organization being contacted, and/or the like. Relangi teaches in col. 6, lines 20 – 38, the task management platform may obtain the historical data. For example, the task management platform may obtain the historical data by providing a request for the historical data to the first data storage element, which may cause the first data storage element to provide the historical data to the task management platform. The historical data may include data identifying applications that were managed as part of the job within the organization, data identifying historical application status metrics for the applications, historical events data identifying time stamped events that occurred at a group of sites (e.g., vendor sites that requested loans on behalf of prospective clients of the organization, organization sites supporting other employees that interact with loan applications, and/or the like), historical user workload data identifying a workload capacity for a group of users that are performing the job for the organization, historical task completion data (e.g., of users of the organization who completed various tasks), and/or the like. Relangi teaches in col. 26, lines 49 – 56, when providing the recommendation to the user device, the task management platform may provide, for display on an interface of the user device, an object identifying the recommendation and a task completion object that allows the user to mark the task as completed. The selection of the task completion object may cause the device to use the data model to generate a new recommendation identifying another task to perform. Relangi teaches in col. 28, lines 44 – 63, In some implementations, the events data may include at least one of: traffic data identifying traffic within the group of sites, or external events data identifying events that are external to the group of sites and that are likely to influence the traffic within the group of sites. In some implementations, when performing the one or more actions associated with assisting in the performance of the task, the task management platform may identify one or more custom tools to assist in performing the task. The one or more custom tools may include at least one of: contact information for a group of employees working at a site that is involved in performance of the task, particular application status metrics for the site, or a templated script identifying recommended ways to perform the task. In some implementations, the task management platform may provide the one or more custom tools for display on the interface of the user device to make the one or more custom tools accessible while the task is being performed. Relangi teaches in col. 28, lines 64 – 67, and col. 29, lines 1 – 8, in some implementations, when providing the recommendation to the user device, the task management platform may provide the recommendation for display on the interface of the user device. The interface may display an object identifying the task that is to be performed and one or more feedback objects that allow the user to agree or disagree with the recommendation. In some implementations, the task management platform may receive, from the user device, feedback data indicating whether the user agreed or disagreed with the recommendation. In some implementations, the task management platform may retrain the data model using the feedback data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method for enabling collaboration between individuals to design, construct and maintain a building of Culver and a system and method for targeting, processing, and managing project leads and reports for target marketing products and services through the use of analytics, data mining, and the application of numerical weights to data criteria of Slaughenhoupt with a device may receive, from a user device, a request for a recommendation identifying one or more tasks to be performed, of a set of tasks that are part of a job for an organization and associated with managing a set of applications for a product or a service of Relangi to assist businesses with implementing machine learning techniques to provide recommendations for management of tasks and projects using task management platforms (Relangi, Spec. col. 5 lines 60 – 67, and col. 6, lines 1 – 5).
Claims 5, 12, and 19:
Culver, Relangi, and Slaughenhoupt teach claims 1, 8, and 15. Culver further teaches the following:
wherein the one or more proposals are communicated through the project-specific interface according to a cognitive load associated with the member and the one or more family members; Culver teaches in col. 8, lines 40 – 47, users who have the same educational background (e.g., went to a particular university or college); users who have similar skills and/or work experience (e.g., maintenance experience with a particular building system); and/or users who are living (or working) within certain geographic locales (e.g., those living and/or working within 100 km of Montreal, Canada) and all may be likened to cognitive load of associated with the member and one or more family members.
Claims 6, 13, and 20:
Culver, Relangi, and Slaughenhoupt teach claims 1, 8, and 15. Culver further teaches the following:
wherein performing the one or more tasks further includes: interacting with a third-party service to coordinate performance of the one or more tasks associated with the project; Culver teaches in col. 79, lines 9 – 11, a third party that certifies a task has been performed and provides an update on the work through the user interface.
Claims 7, 14, and 21:
Culver, Relangi, and Slaughenhoupt teach claims 1, 8, and 15. Culver further teaches the following:
wherein updating the member data further comprises: monitoring communications exchanged over the project-specific communications session to detect completion of the one or more tasks and the message feedback; Culver teaches in col. 95, lines 34 – 35, monitoring of events associated with the review of the project; Culver teaches in col. 95, lines 63 – 65, reviewer may perform reviewing of tasks and update the project’s related events with a user interface. Culver teaches in col. 70, lines 1 – 13, In particular, these controls provide functionality by which a user can perform certain activities including: viewing a set of tags and their associated content; performing a search of the content of the system 10 and more specifically, of the projects database 312 and/or user profile database 314; saving and re-applying performed searches of the system 10; viewing new messages received by the user; ensuring that unfinished messages, tasks or events that were started by a user in the UI 1430 are either completed or discarded; and ending the user's session with system 10 and the UI 1430.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Blake, Bernard (U.S. Publication No. 2016/0086290) discloses storing data associated with a project in a data store, associating at least one user with the project using at least one data type and providing a communication tool to enable the user to communicate to another person about the project.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
02/27/2026
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625