DETAILED ACTION
Notice to Applicant
1. The following is a NON-FINAL Office action upon examination of application number 18/980,564 filed on 12/13/2024. Claims 1-21 are pending in the application and have been examined on the merits discussed below.
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3. Application 18/980,564 filed 12/13/2024 is a Continuation of application 17/814,146, filed 07/21/2022. Application 17/814,146 claims Priority from Provisional Application 63/224,435, filed 07/22/2021.
Information Disclosure Statement
4. The information disclosure statements (IDS) filed on 12/13/2024, 03/25/2025, 07/24/2025, 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.
Duplicate Claim Warning
5. Claim 21 is a substantial duplicate of claim 14. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
6. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
7. Claims 1-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
8. Claim 1 recites “detecting that the delegation control is activated and a corresponding delegation indication is transmitted.” The term “a corresponding delegation indication” lacks antecedent basis, as no delegation indication is previously introduced in the claim. Further, the use of the term “corresponding” implies a relationship to a previously introduced element, however no such element is identified, therefore rendering the claim indefinite. Independent claims 8 and 15 recite similar limitations as those discussed above and are therefore found to be indefinite for the same reasons as claim 1.
All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
9. 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.
10. Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
11. Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-7), system (claims 8-14, 21), and non-transitory computer-readable medium (claims 15-20) are directed to at least one potentially eligible category of subject matter (i.e., process, machine, and article of manufacture, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-21 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing user tasks, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions). With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: processing a user model using a machine-learning algorithm to generate a recommended task, wherein the user model includes family data associated with a user, and wherein processing the user model includes applying the machine-learning algorithm to the family data to generate the recommended task; receiving an indication to enable a delegation control for the recommended task; enabling the delegation control on a user interface in response to receiving the indication; detecting that the delegation control is activated and a corresponding delegation indication is transmitted; and facilitating a task facilitation service to delegate the recommended task. These steps describe managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and are organizing human activity because the limitations are directly tied to managing user tasks.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Independent claims 8 and 15 recite similar limitations as those discussed above and are therefore found to recite the same or substantially the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: a machine-learning algorithm and a user interface (claim 1), one or more processors, a non-transitory computer-readable medium storing instructions, a machine-learning algorithm, and a user interface (claim 8), a non-transitory computer-readable medium storing instructions, one or more processors, a machine-learning algorithm, and a user interface (claim 15). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the step for transmitting is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution activity, which is not sufficient to amount to a practical application. See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: a machine-learning algorithm and a user interface (claim 1), one or more processors, a non-transitory computer-readable medium storing instructions, a machine-learning algorithm, and a user interface (claim 8), a non-transitory computer-readable medium storing instructions, one or more processors, a machine-learning algorithm, and a user interface (claim 15). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that virtually any type of computing device under the sun can be used to implement the claimed invention (Specification at paragraph [0287]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
Even if the step for transmitting is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-7, 10-14, and 16-21 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-7 recite “wherein the family data includes composition of one or more family members associated with the user,” “wherein the family data includes one or more events associated with one or more family members,” “wherein enabling the delegation control includes displaying a visual element corresponding to the delegation control, and wherein the visual element reflects a state of the delegation control,” “wherein enabling the delegation control includes displaying a visual element corresponding to the delegation control, the method further comprising displaying a task object corresponding to the recommended task, wherein the visual element is visually associated with the task object, and wherein the task object is displayed in a list of task objects,” “wherein enabling the delegation control includes displaying a visual element corresponding to the delegation control, the method further comprising displaying a task object corresponding to the recommended task, wherein the visual element is visually associated with the task object, and wherein each of the task object and the visual element are displayed within a chat session,” “wherein the user model is generated by: generating one or more vectors based on the family data; applying a clustering algorithm to the one or more vectors to generate an output, wherein the output is generated based on a comparison between the one or more vectors and clusters that correspond to other members that share one or more characteristics with the user; and generating the user model based on the output of the clustering algorithm,” however these limitations cover organizing human activity since they flow directly from the schedule management involving human interaction, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. Dependent claims 10-14 and 16-21 have been fully considered as well however, similar to claims 2-7, the elements set forth in these claims describe steps/details falling within the same “certain methods of organizing human activity” abstract idea groupings as described by independent claims 1/8/15. The additional elements recited in the dependent claims include: a graphical user interface (claim 4-6), chat session (claim 6). However, these elements are recited at a high level of generality and fail to yield any discernible improvement to the computer or to any technology, nor set forth any additional function or result that provided meaningful limitation beyond linking the abstract idea to a particular technological environment (i.e., automated/computing environment), and thus fail to integrate the abstract idea into a practical application. When evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
12. 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.
13. 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 of this title, 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.
14. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
15. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
16. Claims 1-5, 8-12, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al., Pub. No.: US 2019/0213528 A1, [hereinafter Gupta], in view of Rowe et al., Pub. No.: US 2011/0145822 A1, [hereinafter Rowe] .
As per claim 1, Gupta teaches a computer-implemented method (paragraph 0004) comprising:
processing a user model to generate a recommended task, wherein the user model includes family data associated with a user (paragraph 0004, discussing a method for managing impersonated tasks including creating a user generate impersonated task (UGIT) based on an input received through an interaction model of a shared device; paragraph 0022, discussing that the shared device recording a user's device may record a first user voice, recognizes the first user voice as belonging to the first user, and then perform the instructions based on the permissions associated with the first user...The speaker of the shared device may be able to project and emit the audio announcing the tasks of an identified user to an authorized user. One example of a closed group could be a family with two parents and two children. In this example, the parents may have permissions associated with their user profiles in the shared device that allow them to assign or delegate tasks they create; paragraph 0048, discussing that the data store that stores user tasks for the user also stores the inferences learned from multiple user signals from user's devices, wherein the signals can include browsing, search engine, apps usage, cloud storage usage, location stream; paragraph 0121, discussing that a method for managing impersonated tasks, including creating a user generate impersonated task (UGIT) based on an input received through an interaction model of a shared device. The method also includes graphically presenting, via a display, an identified task-user and the UGIT; paragraph 0120), and
receiving an indication to enable a delegation control for the recommended task (paragraph 0022, discussing that the shared device recording a user's device may record a first user voice, recognizes the first user voice as belonging to the first user, and then perform the instructions based on the permissions associated with the first user...The speaker of the shared device may be able to project and emit the audio announcing the tasks of an identified user to an authorized user. One example of a closed group could be a family with two parents and two children. In this example, the parents may have permissions associated with their user profiles in the shared device that allow them to assign or delegate tasks they create; paragraph 0045, discussing that the term permission refers whether or not the target user has given permissions for tasks to be assigned by an input-user or any user creating tasks from the shared device. The consent service storage indicates which users have consented to notification, delegation [i.e., indication to enable a delegation control for the recommended task], status, notification, between users; paragraph 0053, discussing that the system may include instructions that when executed on a processor may send the UGIT (user generated impersonated task) to a data store for a task-user device in response to a verified communication permission status…the consent service storage may host information related to input-user specific permissions to send, assign, and delegate tasks to other users who access the shared device; paragraph 0064, discussing that the computing system, may also include a user interface such as the I/O device interface, with inputs for task creation, task sharing, and task delegation by a user; paragraph 0120, discussing that the method comprises presenting, with the display, an interactive area that, in response to a selection action by the user, displays an option to toggle between a disabled and an enabled capability for task creation for the UGIT; paragraphs 0046, 0047);
enabling the delegation control on a user interface in response to receiving the indication (paragraph 0025, discussing that the disclosed impersonated tasks technique handles creation, coordination and tracking progress on delegated tasks with other users in a group with which the user is collaborating; paragraph 0028, discussing that the disclosed shared tasks can refer to tasks created by a user for multiple users and where any one of those users can complete the task. For example, a parent user may be in proximity to a shared digital assistant and vocalize a request for the digital assistant to create a reminder for watering plants. This reminder may be assigned to all users in the family, for example, child users, spouse users, or other family members or housemates. The created reminder may then trigger on the personal devices of each of the members in the group to which this task is assigned...Tasks that are delegated rather than shared can be tasks created by a user for a second user where in response to the second user completing the task, both the first user who did the assigning and the second user who completed the task are notified of the tasks completion; paragraph 0029, discussing that the notification may be a message, a push notification, an email,…, a displayed icon, displayed text…; paragraph 0047, discussing that delegated tasks may be delegated to one person or to a group of people; paragraph 0050, discussing that the tasks may be triggered for one or more users at the right time depending on whether it was a delegated task or shared task. The task triggering is taken care of by the task triggering technique that talks to consent service storage for knowing the permissions on the task resource. These tasks can be handed over to the third-party providers for completion of task; paragraph 0080, discussing that the impersonated task input includes a first task-user selector and a second task-user selector. For the first task-user selector a list of potential users may be presented on a display, where the first-task user may have a task assigned or delegated to them. The first-task user may be chosen from a list provided to the display from the people graph; paragraph 0087, discussing that the assignment of a task may include a delegation of a task...As above, the display or announcing of these tasks for the user from others may be separated from other groups of tasks or individual tasks of the task review…; paragraphs 0033, 0034, 0120);
detecting that the delegation control is activated and a corresponding delegation indication is transmitted (paragraph 0025, discussing that the disclosed impersonated tasks technique handles creation, coordination and tracking progress on delegated tasks with other users in a group with which the user is collaborating; paragraph 0028, discussing that the disclosed shared tasks can refer to tasks created by a user for multiple users and where any one of those users can complete the task. For example, a parent user may be in proximity to a shared digital assistant and vocalize a request for the digital assistant to create a reminder for watering plants. This reminder may be assigned to all users in the family, for example, child users, spouse users, or other family members or housemates. The created reminder may then trigger on the personal devices of each of the members in the group to which this task is assigned...Tasks that are delegated rather than shared can be tasks created by a user for a second user where in response to the second user completing the task, both the first user who did the assigning and the second user who completed the task are notified of the tasks completion; paragraph 0029, discussing that the notification may be a message, a push notification, an email,…, a displayed icon, displayed text [i.e., a corresponding delegation indication is transmitted]…; paragraph 0040, discussing that the UPLDTST 104 may also include a task triggering technique 208 to maintain a status for a use of a task that is due to be completed. In an example, if a task were scheduled for a certain time, the task triggering technique 208 manages the activation of a display of the task or communicating with third party applications through third party delegation. The status of a task being triggered may be maintained and stored for a user to access in the UPLDTST 104. After a task has been triggered by the task triggering technique 208, the task completion service provider technique 210, may process and store a notification that a task has been completed. The task completion service provider technique 210 may initiate for a user device an action to indicate to a user that the task is completed; paragraph 0042, discussing that the impersonated tasks runtime technique (ITRT) 108 can include a number of services and modules for managing and creating and distributing tasks among a number of users and user storage techniques. The impersonation service 300 may create an impersonated task based on the manipulations of one user and provide that impersonated task to a number of users with the task service 302. In an example, the task service may send out tasks such as reminders and to dos. The ITRT may also include a consent service storage 304 which stores and consolidates the permissions associated with each task. The consent service storage 304 may process and store permissions gathered from users and may provide the permissions to a task triggering technique. The ITRT 108 ma include a social accessor service 306 to allow access to social media platforms to potentially either pull or push task notifications to ensure that tasks and events between platforms are synchronized. The ITRT 108 may include the task completion provider and selection service 308 to provide and receive updates regarding the completion status of a task to a UPLDTST 104. The ITRT 108 may include a provider selection and handover module that may communicate with for third party delegation and facilitate the handover of user data to a third party where useful; paragraph 0045, discussing that the term permission refers whether or not the target user has given permissions for tasks to be assigned by an input-user or any user creating tasks from the shared device. The consent service storage indicates which users have consented to notification, delegation, status, notification, between users; paragraph 0047, discussing that delegated tasks may be delegated to one person or to a group of people; paragraph 0050, discussing that the task triggering is taken care of by the task triggering technique that talks to consent service storage for knowing the permissions on the task resource…; paragraph 0053, discussing that the system may include instructions that when executed on a processor may send the UGIT (user generated impersonated task) to a data store for a task-user device in response to a verified communication permission status…the consent service storage may host information related to input-user specific permissions to send, assign, and delegate tasks to other users who access the shared device; paragraph 0080, discussing that the impersonated task input includes a first task-user selector and a second task-user selector. For the first task-user selector a list of potential users may be presented on a display, where the first-task user may have a task assigned or delegated to them. The first-task user may be chosen from a list provided to the display from the people graph; paragraph 0087, discussing that the assignment of a task may include a delegation of a task...As above, the display or announcing of these tasks for the user from others may be separated from other groups of tasks or individual tasks of the task review…; paragraphs 0021, 0022); and
facilitating a task facilitation service to delegate the recommended task (paragraph 0025, discussing that the disclosed impersonated tasks technique handles creation, coordination and tracking progress on shared and delegated tasks with other users in a group with which the user is collaborating; paragraph 0028, discussing that for example, a parent user may be in proximity to a shared digital assistant and vocalize a request for the digital assistant to create a reminder for watering plants. This reminder may be assigned to all users in the family, for example, child users, spouse users, or other family members or housemates. The created reminder may then trigger on the personal devices of each of the members in the group to which this task is assigned...Tasks that are delegated rather than shared can be tasks created by a user for a second user where in response to the second user completing the task, both the first user who did the assigning and the second user who completed the task are notified of the tasks completion; paragraph 0029, discussing that the notification may be a message, a push notification, an email,…, a displayed icon, displayed text…; paragraph 0033, discussing that if a user has an online shopping task, the third-party delegation may be to an online shopping application or service. In another example, the user may request a ride from a taxi or ride-sharing application at a certain time, where the third-party delegation would include passing relevant information to a third-party ride-sharing application at the assigned time; paragraph 0047, discussing that delegated tasks may be delegated to one person or to a group of people; paragraph 0050, discussing that the tasks may be triggered for one or more users at the right time depending on whether it was a delegated task or shared task. The task triggering is taken care of by the task triggering technique that talks to consent service storage for knowing the permissions on the task resource. These tasks can be handed over to the third-party providers for completion of task; paragraph 0080, discussing that the impersonated task input includes a first task-user selector and a second task-user selector. For the first task-user selector a list of potential users may be presented on a display, where the first-task user may have a task assigned or delegated to them. The first-task user may be chosen from a list provided to the display from the people graph; paragraph 0087, discussing that the assignment of a task may include a delegation of a task...As above, the display or announcing of these tasks for the user from others may be separated from other groups of tasks or individual tasks of the task review, although these tasks may all be intermingled based on other criteria such as task creation time or task due date; paragraph 0034).
Gupta does not explicitly teach processing a user model using a machine-learning algorithm to generate a recommended task, and wherein processing the user model includes applying the machine-learning algorithm to the family data to generate the recommended task. However, Rowe in the analogous art of task recommendation systems teaches these concepts. Rowe teaches:
processing a user model using a machine-learning algorithm to generate a recommended task (paragraph 0005, discussing that a Task Management Engine or TME, comprising data from a data source and one or more software modules executed on a server or client within a data center may import one or more action items from one or more client machines into a task list within the electronic organizer. The TME may then extract key action words, and use these key action words to search an existing user profile, various information related to third party partnerships and the Internet to generate recommended solutions for the action item(s) and set reminders for due dates. This list of recommendations may then be displayed to the user, and the system may learn from the decisions or preferences selected by the user; paragraph 0011, discussing receiving a proposed solution, marking the task as complete and updating the profile; paragraph 0019, discussing that the TME may learn from the response to these suggested solutions, as well as preferences and ratings received from the user and update the profile accordingly; paragraph 0052, discussing that the TME may import the email into John's task list and may extract the keywords "Grandma's birthday," "this week," buying," "flowers," and "Thursday," and focus on the keywords "buy" and "flowers" to search for recommended solutions and present them to the user; paragraph 0053, discussing that the TME has determined that since John and his spouse are in charge of buying flowers for Grandma's birthday party, the TME may extract the keywords "buy" and "flowers" to search for recommended solutions. This may be accomplished, in this non-limiting example, by either highlighting the keywords that the TME considers the most relevant or allowing the user to select and/or highlight the keywords that the user considers most important. The TME's selection and/or highlighting of the most important keywords may be based on the logic above, analysis of the user's past experience, selections, actions, preferences and/or ratings, analysis of the user's profile or any combination thereof; paragraph 0054, discussing that based on analysis of the user's past experience, selections, actions, preferences, ratings, profile, etc., the TME may parse out the words from the text of the email in FIG. 4 and determine that the keywords in this email are "Grandma's birthday," "buy," "flowers" and "Thursday."…; paragraph 0060, discussing that when the email is flagged to import into the task list, the software module logic selecting keywords by the user or TME as disclosed may be used to determine which words to import into the task list. These words may be arranged in such a way that they are a task list item that can be read by the user. For example, as highlighted, the phrase would read "Grandma's birthday this week buying flowers Thursday," which has all keywords but is difficult to read. The system may rearrange these keywords to read "Buy flowers for Grandma's birthday," as illustrated, thereby making the task list more readable; paragraph 0080, discussing that the TME marks the task complete with feedback to help the system learn preferences for future suggestions; paragraph 0087, discussing that if the TME software modules were to learn that John is scheduling certain events, such as anniversaries or birthdays on certain days each year, the logic for the TME and the related software modules may learn from John's schedule to know which days to schedule related shopping, meals, and which partners John uses the most to recommend the solutions for the action items to best meet his needs; paragraphs 0031, 0062, 0081), and
wherein processing the user model includes applying the machine-learning algorithm to the family data to generate the recommended task (paragraph 0023, discussing accessing partner information to recommend a proposed solution based on the partner information, and/or searches of profile and/or Internet solutions, to set due dates for the task, to learn from and update the user's profile; paragraph 0043, discussing an example interface using the disclosed structure that may be used for receiving an action item communication with an action item the user must complete by the end of a current time period. The events in the illustrated task list are for fictitious events, including "Buy flowers for Grandma's birthday," "Get oil change" and "Return Amy's phone call." These events (possibly displayed on a web based email, calendar organizer or within a related task list) may be displayed as shown on a client browser or other software using the structure disclosed herein, and may be used in disclosed embodiments to demonstrate an event extracted from a generated action item communication and displayed with recommendations, due dates, user profile information and updates to the user; paragraph 0044, discussing that for example, John's spouse Amy may send him an email to remind him to buy flowers for grandma's birthday...In the email shown in FIG. 4, the TME may use the logic, data, software and structure described elsewhere in this application to determine that the action item to be completed includes buying flowers for Grandma's birthday. The TME may further use keywords to determine that the deadline for buying the flowers is Thursday of the current week, according to the parsing and extracting of the action and task item described below; paragraph 0045, discussing that likewise, John may have received an SMS, text or other communication reminding him that he needs to get his oil changed this week, as the mileage has exceeded safe use for his previous oil change. This information may have been stored within John's user profile, allowing him to know the approximate time that the oil will need to be changed, based on his regular mileage, date of last several oil changes, or any other factors that would cause John to need to change his oil. Thus, the current time period is not limited to day, week, month, etc. In this example, the current time period may be determined by monitoring an occurrence that has happened regularly enough that the system recognizes the time period in which the occurrence will likely happen again, and may propose solutions based on this time period and possible deadline; paragraph 0046, discussing that the TME system may further learn the time period in which the action item may occur in a time period such as an annual, monthly, weekly or daily event. Because of the repetitiveness of the event at regular intervals, the system may also learn to repeat suggestions for the event, and suggest solutions accordingly; paragraph 0049, discussing that FIG. 4 shows an example interface using the disclosed structure that may be used for flagging the action item communication by the user so that the TME may import it into the task list. For example, John may have flagged the email from his spouse Amy reminding him that his Grandmother's birthday is this Thursday, and that he and Amy are in charge of buying the flowers for the party. John may have flagged the email when received, in this non-limiting example, by clicking on a checkbox which begins the importing process into the task list; paragraph 0060, discussing that when the email is flagged to import into the task list, the software module logic selecting keywords by the user or TME as disclosed may be used to determine which words to import into the task list. These words may be arranged in such a way that they are a task list item that can be read by the user. For example, as highlighted, the phrase would read "Grandma's birthday this week buying flowers Thursday," which has all keywords but is difficult to read. The system may rearrange these keywords to read "Buy flowers for Grandma's birthday," as illustrated, thereby making the task list more readable; paragraph 0062, discussing that the TME may extract key words and search for suggested solutions to accomplish the task list items. The TME may suggest such solutions based on the users profile preferences and past actions; paragraph 0086, discussing that the TME may use the hardware, software and data from the data center, or any related data centers, to learn from the user's actions. As a non-limiting example, if the system notices that John is changing the oil every 6 months, the system may learn that John needs a reminder for that particular task every 6 months. The appropriate data may be updated in John's TME profile and stored using logic within related software modules to remind John every 6 months to change his oil and to search appropriate partners, Internet or profile information to recommend a best solution to John to make sure he gets the oil changed in an efficient manner; paragraph 0087).
Gupta is directed to a method for managing tasks. Rowe relates to systems and methods for generating and recommending task solutions. Therefore, they are deemed to be analogous as they both are directed towards solutions for task recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gupta with Rowe because the references are analogous art because they are both directed to solutions for task management, which falls within applicant’s field of endeavor (task delegation systems), and because modifying Gupta to include Rowe’s features for including processing a user model using a machine-learning algorithm to generate a recommended task, and wherein processing the user model includes applying the machine-learning algorithm to the family data to generate the recommended task, in the manner claimed, would serve the motivation of allowing the system to learn from the user's preference and recommending a best solution to the user (Rowe at paragraph 0085, 0086); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the Gupta-Rowe combination teaches the computer-implemented method of claim 1. Gupta further teaches wherein the family data includes composition of one or more family members associated with the user (paragraph 0022, discussing that a closed group may refer to a typically smaller group like a home or family, where each of the members is in a similar physical location or is related genealogically… One example of a closed group could be a family with two parents and two children. In this example, the parents may have permissions associated with their user profiles in the shared device that allow them to assign or delegate tasks they create to the children for completion; paragraph 0092, discussing showing how contacts may be stored in a client 1002…these contacts 1006 may be saved names and phone numbers associated with a friend, business associate, or family member stored local to the client 1002; paragraph 0095, discussing that in a family situation, a parent may have a different user specific impersonated task storage than a child so that when a parent asks for tasks assigned to the parent, the tasks assigned to the parent may be returned and output. Similarly, if a child were to request their tasks, the tasks stored in the user specific impersonated task storage of the child would return the tasks of the child; paragraph 0120).
As per claim 3, the Gupta-Rowe combination teaches the computer-implemented method of claim 1. Gupta further teaches wherein the family data includes one or more events associated with one or more family members (paragraph 0022, discussing that a closed group may refer to a typically smaller group like a home or family, where each of the members is in a similar physical location or is related genealogically…One example of a closed group could be a family with two parents and two children. In this example, the parents may have permissions associated with their user profiles in the shared device that allow them to assign or delegate tasks they create to the children for completion; paragraph 0028, discussing that the disclosed shared tasks can refer to tasks created by a user for multiple users and where any one of those users can complete the task. For example, a parent user may be in proximity to a shared digital assistant and vocalize a request for the digital assistant to create a reminder for watering plants. This reminder may be assigned to all users in the family, for example, child users, spouse users, or other family members or housemates; paragraph 0120, discussing that the system where the verified communication permission status is based on a familial relationship between the task-user and the input user where the higher status of an older generation in a family structure allows an input-user of an older generation to delegate tasks to a task-user in response to a determination that the task-user is in a younger generation).
As per claim 4, the Gupta-Rowe combination teaches the computer-implemented method of claim 1. Gupta further teaches wherein enabling the delegation control includes displaying a visual element corresponding to the delegation control in a graphical user interface (paragraph 0064, discussing that the computing system may also include a user interface such as the I/O device interface, with inputs for task creation, task sharing, and task delegation by a user. In an example, the user interface is a microphone recording human speech, a digital touch display, or an augmented reality display with motion tracking for input through user movement in relation to objects displayed by the augmented reality display; paragraph 0050, discussing that the tasks may be triggered for one or more users at the right time depending on whether it was a delegated task or shared task. The task triggering is taken care of by the task triggering technique that talks to consent service storage for knowing the permissions on the task resource. These tasks can be handed over to the third-party providers for completion of task; paragraph 0070, discussing that the shared device may also include a user interface with inputs for task creation, task sharing, and task delegation by a user; paragraph 0080, discussing that the impersonated task input includes a first task-user selector and a second task-user selector. For the first task-user selector a list of potential users may be presented on a display, where the first-task user may have a task assigned or delegated to them. The first-task user may be chosen from a list provided to the display from the people graph; paragraph 0087, discussing that the assignment of a task may include a delegation of a task...As above, the display or announcing of these tasks for the user from others may be separated from other groups of tasks or individual tasks of the task review…), and wherein the visual element reflects a state of the delegation control (paragraph 0004, discussing that the method may also graphically present, via the display, an impersonated task status indicated by the UGIT (user generated impersonated task); paragraph 0055, discussing that the system executing the method and its variations may include a verification sequence, screen, or process for the input-user to confirm that the task-user has granted the ability for the input-user to assign tasks to the task-user. The permissions check may be conducted through a search of a consent service storage stored locally within the physical shared device hardware. In an example, if the permissions check reveals that the input-user does not currently have permission to assign a task to a specific task-user, then the shared device may send a notification of a task assignment request to the task-user device. This task assignment request may include an access request notification to the data store of the task-user in response to an unverified communication permission status; paragraph 0081, discussing that many other options and display regions may be presented on the shared device interaction interface including status updates of previously entered tasks; paragraph 0050).
As per claim 5, the Gupta-Rowe combination teaches the computer-implemented method of claim 1. Gupta further teaches wherein enabling the delegation control includes displaying a visual element corresponding to the delegation control in a graphical user interface (paragraph 0064, discussing that the computing system may also include a user interface such as the I/O device interface, with inputs for task creation, task sharing, and task delegation by a user. In an example, the user interface is a microphone recording human speech, a digital touch display, or an augmented reality display with motion tracking for input through user movement in relation to objects displayed by the augmented reality display; paragraph 0050, discussing that the tasks may be triggered for one or more users at the right time depending on whether it was a delegated task or shared task. The task triggering is taken care of by the task triggering technique that talks to consent service storage for knowing the permissions on the task resource. These tasks can be handed over to the third-party providers for completion of task; paragraph 0070, discussing that the shared device may also include a user interface with inputs for task creation, task sharing, and task delegation by a user; paragraph 0080, discussing that the impersonated task input includes a first task-user selector and a second task-user selector. For the first task-user selector a list of potential users may be presented on a display, where the first-task user may have a task assigned or delegated to them. The first-task user may be chosen from a list provided to the display from the people graph; paragraph 0087, discussing that the assignment of a task may include a delegation of a task...As above, the display or announcing of these tasks for the user from others may be separated from other groups of tasks or individual tasks of the task review…), the method further comprising displaying a task object corresponding to the recommended task in the graphical user interface, wherein the visual element is visually associated with the task object, and wherein the task object is displayed in a list of task objects (paragraph 0046, discussing that the tasks service may include reminders of tasks or a compiled to do list of tasks to provide to a user; paragraph 0054, discussing that the system may execute a method where the system can modify a data object corresponding to the task based on a received indicator from the task-user device to reflect a completion status; paragraph 0091, discussing that the creation of the task object in software can be done through instructions located on the client prior to storage or transmission; paragraph 0119, discussing that the system can include identify, in response to input from an input-user, a user generated impersonated task (UGIT) and a task-user from a people graph. The system can include send the UGIT to a data store for a task-user device in response to a verified communication permission status. The system can include monitor for an indicator from the task-user device, where the indicator corresponds to completion of the UGIT. The system can include modify a data object corresponding to the UGIT based on the indicator from the task-user device to reflect a completion status and a set of users related to the UGIT to be notified of the completion status. The system can include transmit a digital notification to the set of users based on the modified UGIT.).
Claims 8 and 15 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. As per claim 8, the Gupta-Rowe combination teaches a system comprising: one or more processors; and a non-transitory computer-readable medium storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations (paragraph 0003, discussing a system for managing impersonated tasks through a shared device can include a processor and a storage with instructions for execution by the processor. In an example, the system includes a processor and storage; paragraph 0072, discussing that the various software components discussed herein may be stored on the tangible, computer-readable storage media 700, as indicated in FIG. 7. The tangible, computer-readable storage media 700 may be accessed by a processor 702 over a computer bus 704. Furthermore, the tangible, computer-readable storage media 700 may include code to direct the processor 702 to perform the steps of the current method 600; paragraph 0073). As per claim 15, the Gupta-Rowe combination teaches a non-transitory computer-readable medium storing instructions that when executed by one or more processors, cause the one or more processors to perform operations (paragraph 0072, discussing that the various software components discussed herein may be stored on the tangible, computer-readable storage media 700, as indicated in FIG. 7. The tangible, computer-readable storage media 700 may be accessed by a processor 702 over a computer bus 704. Furthermore, the tangible, computer-readable storage media 700 may include code to direct the processor 702 to perform the steps of the current method 600; paragraph 0073, discussing that the various software components discussed herein may be stored on the tangible, computer-readable storage media).
Claims 9 and 16 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 2, as discussed above.
Claims 10 and 17 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 3, as discussed above.
Claims 11 and 18 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 4, as discussed above.
Claims 12 and 19 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 5, as discussed above.
17. Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Rowe, in further view of Jakobson et al., Pub. No.: US 2008/0091782 A1, [hereinafter Jakobson].
As per claim 6, the Gupta-Rowe combination teaches the computer-implemented method of claim 1. Gupta further teaches wherein enabling the delegation control includes displaying a visual element corresponding to the delegation control in a graphical user interface (paragraph 0064, discussing that the computing system may also include a user interface such as the I/O device interface, with inputs for task creation, task sharing, and task delegation by a user. In an example, the user interface is a microphone recording human speech, a digital touch display, or an augmented reality display with motion tracking for input through user movement in relation to objects displayed by the augmented reality display; paragraph 0070, discussing that The shared device may also include a user interface with inputs for task creation, task sharing, and task delegation by a user), the method further comprising displaying a task object corresponding to the recommended task in the graphical user interface, wherein the visual element is visually associated with the task object (paragraph 0046, discussing that the tasks service may include reminders of tasks or a compiled to do list of tasks to provide to a user; paragraph 0054, discussing that the system may execute a method where the system can modify a data object corresponding to the task based on a received indicator from the task-user device to reflect a completion status; paragraph 0091, discussing that the creation of the task object in software can be done through instructions located on the client prior to storage or transmission; paragraph 0119).
While Gupta describes a chat session (paragraphs 0025, 0108), the Gupta-Rowe combination does not explicitly teach wherein each of the task object and the visual element are displayed within a chat session. However, Jakobson in the analogous art of task management systems teaches this concept. Jakobson teaches:
wherein each of the task object and the visual element are displayed within a chat session (paragraph 0018, discussing a method and system for creating, delegating, exchanging and managing tasks over an instant messenger infrastructure; paragraph 0059, discussing that FIG. 8 illustrates a communication flow between two tasks modules in one embodiment of the invention. System 800 represents a model where two tasks-modules 802 and 804 are able to communicate with each other in a live session. while not required, the presently preferred embodiment utilizes the IM infrastructure to relay messages and information between two task modules. For example, System 800 may represent two IM applications, enabled with tasks modules, communicating over an IP network as part of a live chat session between the user of IM application A and the user of IM application B. Tasks module 802 may send a "READY" message 806 to tasks module 804, signaling to tasks module 804 that it is online and is ready to communicate. In response to ready message 806 received by tasks module 804, tasks module 804 may send "MY" tasks 808 to tasks module 802. "MY" tasks 808 may consist of one or more tasks originally delegated by the user of tasks module 802 to the user of tasks module 804. The "MY" tasks may be sent as a single message, or in a series of messages. The tasks 808 may have been modified by the user of tasks module 804 while offline and the modifications may have been saved by tasks module 804, prior to being sent 808 to tasks module 802. Tasks module 804 may send a "READY" message 810 to tasks module 802, signaling to tasks module 802 that it is online and is ready to communicate. In response to ready message 810 received by tasks module 802, tasks module 802 may send "MY" tasks 812 to tasks module 804. Tasks module 804 may then display tasks 812, which the user of tasks module 804 may have originally delegated to the user of tasks module 802; paragraph 0075).
The Gupta-Rowe combination describes features related to managing tasks. Jakobson relates to a method and system for delegating and managing tasks. Therefore, they are deemed to be analogous as they both are directed towards solutions for task assignment systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Gupta-Rowe combination with Jakobson because the references are analogous art because they are both directed to solutions for task management, which falls within applicant’s field of endeavor (task delegation systems), and because modifying the Gupta-Rowe combination to include Jakobson’s feature for wherein each of the task object and the visual element are displayed within a chat session, in the manner claimed, would serve the motivation of facilitating an exchange of tasks among information management applications (Jakobson at paragraph 0014); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 13 and 20 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 6, as discussed above.
18. Claims 7, 14, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Rowe, in further view of Ding et al., Pub. No.: US 2021/0118080 A1, [hereinafter Ding], in further view of Girshick, Pub. No.: US 2021/0134391 A1 [hereinafter Girshick].
As per claim 7, the Gupta-Rowe combination teaches the computer-implemented method of claim 1. While Gupta describes vectors of similarity between the member's and representative's demographic information (paragraph 0035), the Gupta-Rowe combination does not explicitly teach wherein the user model is generated by: generating one or more vectors based on the family data; applying a clustering algorithm to the one or more vectors to generate an output, wherein the output is generated based on a comparison between the one or more vectors and clusters that correspond to other members that share one or more characteristics with the user; and generating the user model based on the output of the clustering algorithm. Ding in the analogous art of recommendation systems teaches:
wherein the user model is generated by: generating one or more vectors based on the data (paragraph 0086, discussing that the processing engine may determine a travel feature vector of the target user based on the normalized count of (historical) requests of each transportation product and the normalized count of (historical) trips of each transportation product. The travel feature factor may include ratios of the normalized count of (historical) requests of each transportation product to the normalized count of (historical) trips of each transportation product. The travel feature vector may reflect user's requests for different transportation products and which requests are accepted. The processing engine may determine a first clustering model based on the travel feature vector of the target user; paragraph 0088, discussing that the processing engine may determine a populace feature vector by digitizing the natural attribute information and the social attribute information);
applying a clustering algorithm to the one or more vectors to generate an output, wherein the output is generated based on a comparison between the one or more vectors and clusters that correspond to other members that share one or more characteristics with the user (paragraph 0086, discussing that the processing engine may determine a first clustering model based on the travel feature vector of the target user. For example, the processing engine may determine a first clustering model corresponding to a set of plurality of users [i.e., other members] whose travel feature vectors are similar to that of the target user as the first clustering model of the target user; paragraph 0088, discussing that the processing engine may determine the second clustering model based on the populace feature vector of the target user. For example, the processing engine may determine a second clustering model corresponding to a set of plurality of users whose populace feature vectors are similar to that of the target user as the second clustering model of the target user; paragraph 0089); and
generating the user model based on the output of the clustering algorithm (paragraph 0086, discussing that the processing engine may determine a first clustering model based on the travel feature vector of the target user. For example, the processing engine may determine a first clustering model corresponding to a set of plurality of users [i.e., other members] whose travel feature vectors are similar to that of the target user as the first clustering model of the target user; paragraph 0095, discussing that the satisfying the request of the target user may refer to that, search for users who have similar social information to the target user according to the social information of the target user, and recommend a transportation product he/she may like, to the target user, to improve the user experience based on the historical travel information of the users who have similar social information to the target user; paragraph 0096, discussing that the processing engine may directly obtain the strategy set corresponding to the target user based on the clustering model; paragraph 0107, discussing generating a plurality of user selection behaviors. The plurality of user selection behaviors may reflect the historical transportation service request situation of the user; paragraphs 0097, 0132).
The Gupta-Rowe combination describes features related to managing tasks. Ding relates to a method and system for making recommendations. Therefore, they are deemed to be analogous as they both are directed towards solutions for recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Gupta-Rowe combination with Ding because the references are analogous art because they are both directed to solutions for task management, which falls within applicant’s field of endeavor (task delegation systems), and because modifying the Gupta-Rowe combination to include Ding’s features for including wherein the user model is generated by: generating one or more vectors based on the data; applying a clustering algorithm to the one or more vectors to generate an output, wherein the output is generated based on a comparison between the one or more vectors and clusters that correspond to other members that share one or more characteristics with the user; and generating the user model based on the output of the clustering algorithm, in the manner claimed, would serve the motivation of improving the user experience (Ding at paragraph 0095); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Gupta-Rowe-Ding combination does not explicitly teach generating one or more vectors based on the family data. However, Girshick in the analogous art of information management systems teaches this concept. Girshick teaches:
generating one or more vectors based on the family data (paragraph 0074, discussing that to generate a feature vector, the features may also be coded. The non-DNA features may be coded as counts of the number of members of the target individual's family tree that have a feature. Counts may be family-tree wide, or be generational or branch specific; paragraph 0077, discussing that the prediction may account for the heritability of the trait, referred to as an inheritance probability. The inheritance probability may be based on the likelihood that the target individual inherited the trait from one or more members of the target individual's family tree who have the trait. The inheritance probability may also be based on the likelihood that the target individual passed on the trait to one or more members of the target individual's family tree. The inheritance probability may be used as one of the features in the input feature vector for the machine learning model. The inheritance probability may also be used as a post-processing factor after the machine learning model has generated a prediction. The prediction may also account for the prevalence of the trait among one or more genetic communities of the target individual. In some embodiments, this is accounted for as a feature in the feature vector).
The Gupta-Rowe-Ding combination describes features related to managing information. Girshick relates to systems for predicting a trait of a target individual. Therefore, they are deemed to be analogous as they both are directed towards solutions for information analysis systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Gupta-Rowe-Ding combination with Girshick because the references are analogous art because they are both directed to solutions for information management, which falls within applicant’s field of endeavor (task delegation systems), and because modifying the Gupta-Rowe-Ding combination to include Girshick’s feature for including generating one or more vectors based on the family data, in the manner claimed, would serve the motivation of generating a better performance (Girshick at paragraph 0075); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 14 and 21 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 7, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sankaran et al., Pub. No.: US 2019/0147366 A1 – describes systems and methods for providing intelligent recommendations to users by modeling user profiles through deep learning of multimodal user data.
Pal et al., Pub. No.: US 2019/0220438 A1 – describes that input signals can be interpreted by a system to identify a user's intent. Based on data defining a user's intent, the system can generate data defining a number of actions to complete the task.
Barka, Ezedin, and Ravi Sandhu. "Framework for role-based delegation models." proceedings 16th annual computer security applications conference (ACSAC'00). IEEE, 2000 – describes that the basic idea behind delegation is that some active entity in a system delegates authority to another active entity to carry out some functions on behalf of the former.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
Prosecution Insights