DETAILED ACTION
Status of Application
This action is in response to the request for continued examination filed on October 10, 2025.
Claim 1 has been amended.
Claims 2-9, 14, 15, and 18-20 have been canceled.
Claims 23-31 have been added.
Claims 1, 10-13, 16, 17, and 21-31 are pending and rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Claim Interpretation
Applicant has significantly amended the claims, including using the term “tasks” instead of “chores.” Paragraph 0018 of Applicant’s Specification states “[a] family set within the chore management system can typically include activities and tasks (commonly referred to herein as ‘chores’) for various individual members (e.g., children within the family unit) that are expected to be completed by them due time following a schedule.” Per this disclosure, the term “tasks” is interpreted as “chores.”
Shifting Inventions
The claims filed on February 11, 2025, are significantly different from the claims that were originally examined. These claims would normally be non-responsive and not examined. To avoid a notice of non-responsive amendment, future amendments should be drawn to an invention that has already been examined.
Response to Arguments
Regarding the rejection under 35 U.S.C. 101, Applicant argues that the rejection “oversimplifies the claimed invention by evaluating the existing claim language at an impermissibly high level of abstraction, ignoring the specific technological implementation recited in the claims and described in the Specification, divorcing the claims from their specific technological context and the particular problems they solve. This approach contravenes both Ex parte Desjardins and binding Federal Circuit Precedent.” Remarks at 8. With respect to claim 1, there is a claimed memory that stores three datasets. How this data is generated is outside of the scope of the claim. Claim 1 also recites a processor which is configured to execute a machine learning engine to access data, process data, and generate and output a task schedule. The claimed processor is being used to implement an abstract idea, as addressed in the rejection. The technological implementation recited in the claims has not been ignored.
Regarding step 2A, prong one, Applicant refers to the August 4, 2025 memo. Remarks at 8. However, the memo states, at page 1, that “[t]his memorandum is not intended to announce any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance. Examiners should consult the specific MPEP sections referenced below for more thorough information on each topic.” The instant rejection is based on MPEP 2106. Regarding the groupings, per the rejection, the claims recite both certain methods of organizing human activity and mental processes. Applicant argues that the claimed “operations cannot practically be performed in the human mind.” Id. at 9. Applicant’s reasoning is that the “Specification makes clear that the system operates at massive scale” and high speeds. Id. However, claim 1 does not require a minimum amount of data and speed. Additionally, the claims recite certain methods of organizing human activity because the processor is performing a process for generating a refined task schedule.
Applicant further argues that “[a]s in Example 39, the present claims do not set out a mental step or mathematical formula by name. Instead claim 1 recites a processor executing a machine learning engine…culminating in automatic generation of a refined task schedule provided to a user interface. These operations produce improved task recommendations through specific technical implementation rather than generic data manipulation.” Remarks at 10-11. Example 39 only recites steps for training a neural network. On the other hand, instant claim 1 recites steps for generating a refined task schedule.
Regarding Step 2A, prong two, Applicant argues that “[t]he practical application here is providing for easy setup and maintenance of a chore management system using artificial intelligence, machine learning, and natural language processing that combines crowd-sourced information from a plurality of users with minimal information needed from a new user.” Remarks at 11. However, Applicant has not shown that there is an improvement to the technology or technological field. Rather, Applicant is showing that existing technology is being used to improve a chore management system.
Applicant further argues that “there is a novel data architecture.” Remarks at 12. However, Applicant is pointing to three data sets. The data itself, i.e., initial task data, user attribute scores, customization attributes, performance metrics, prior task outcomes, is part of the abstract idea.
Applicant further argues that “there is crowd intelligence processing.” Remarks at 13. However, processing data from multiple users does not provide an improvement to the system
Applicant further argues that “the system achieves predictive analytics through its technical implementation.” Remarks at 13. However, although technology may be used for predictive analysis, the technology isn’t being improved.
Applicant further argues that “the claims improve the technological process of task management systems by implementing a specific machine learning architecture.” Remarks at 14. However, instead, the claims are allegedly improving the process of task management while using a programmed general purpose computing device.
Regarding Step 2B, Applicant argues that “the claims provide an inventive concept under Step 2B because they recite significantly more than the abstract idea itself. Remarks at 14. Specifically, Applicant asserts that there is “a feedback loop that continuously improves system performance based on crowd-sourced data,” (Id.) “a specific algorithmic approach that goes beyond generic computer implementation,” (Id. at 15) and “predictive pattern recognition achieved through the claimed calibration step.” Id. However, Applicant has not shown how the claims require more than a programmed general purpose computing device.
Applicant further points to policy considerations. See Remarks at 15-16. The claims were examined and rejected according to MPEP 2106. The guidance has not changed.
As such, the rejection under 35 U.S.C. 101 is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 10-13, 16, 17, and 21-31 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 1, 10-13, 16, 17, and 21-31, which recite a server that comprises a processor and, therefore, are directed to the statutory class of machine or manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims identify the limitations that recite additional elements in bold and abstract idea in regular text:
1. A server for adaptive task management, the server comprising:
a memory including:
an alpha dataset comprising initial task data and associated user attribute scores;
a beta dataset comprising customization attributions derived from prior task adjustments;
an omega dataset comprising performance metrics obtained from prior task outcomes; and
a processor configured to execute a machine learning engine configured to:
access the initial task data from the alpha dataset to determine a task configuration;
apply the customization attributes from the beta dataset to modify the task configuration;
calibrate the modified task configuration using the performance metrics from the omega dataset to refine task parameters; and
generate a refined task schedule based on the refined task parameters and provide the refined task schedule to a user interface.
10. The server of claim 1, wherein the first user characteristic includes one or more of: a number of family members associated with the first user, an age of a family member associated with the first user, a number of children associated with the first user that are under 18 years of age, and a location associated with the first user.
11. The server of claim 1, wherein the customization attribute comprises an adjustment made by the user to one or more of following: a reminder frequency, a repetition frequency, a reward magnitude, and a characteristic adjustment.
12. The server of claim 1, wherein the behavior attribute comprises a one or more of: a time provided by the user for a task associated with the user attribute score in the alpha dataset determined to be nearest to the first attribute score, a number of reminders generated for the task, and a time interval taken beyond a deadline generated by the server for completion of the task.
13. The server of claim 1, wherein the behavior attribute is a rejection by the first user of at least one of the determined user attribute scores in the alpha dataset, the beta dataset, and the omega dataset.
16. The server of claim 1, wherein the server is further configured to generate a ranked list of user attribute scores for each request recommendable chores, such that each particular chore within the ranked predicted list of chores has an associated rank.
17. The server of claim 16, wherein the server is further configured to assign a plurality of user attribute scores for the first user, wherein the plurality of user attribute scores is ranked based on the first user characteristic.
21. The server of claim 1, wherein the plurality of user attribute scores in the alpha dataset range from 0 to 100.
22. The server of claim 1, further comprising generating a non-fungible token (NFT) and storing the NFT on a digital ledger that certifies the NFT to be unique and not interchangeable, wherein the NFT represents a digital asset including one of a photo, a video, and an audio file.
23. The server of claim 1, wherein the machine learning engine is further configured to retrieve a structured attribute score for a first user characteristic from the alpha dataset based on predefined numerical thresholds or computational rules for comparing user attribute scores and task attributes.
24. The server of claim 23, wherein the machine learning engine is further configured to apply adjustment parameters from the beta dataset to generate updated scheduling parameters, wherein the updated scheduling parameters are output to a task scheduling module separate from the machine learning engine.
25. The server of claim 24, wherein the machine learning engine is further configured to update interaction metrics in the omega dataset using a structured set of predefined computational processes, such that the updated interaction metrics are used in subsequent operations to improve task alignment with user interaction patterns.
26. The server of claim 25, wherein the machine learning engine is further configured to generate the task schedule by executing a computational process that reduces conflicts and improves resource allocation based on user interaction metrics and historical behavior patterns.
27. The server of claim 26, wherein the refined task schedule is transmitted over a network connection to a device associated with a task recipient.
28. The server of claim 1, wherein the alpha dataset further comprises preliminary data representative of a domain environment, and the beta dataset further comprises modification parameters derived from prior model adjustments.
29. The server of claim 28, wherein the machine learning engine is configured to generate an initial model configuration based on the preliminary data in the alpha dataset and then update one or more hyperparameters of the initial model configuration using the modification parameters stored in the beta dataset.
30. The server of claim 29, wherein the machine learning engine is further configured to calibrate the updated model configuration by referencing the performance metrics in the omega dataset, wherein the calibration process refines the model for improved inference accuracy by operating the machine learning engine in at least one of an unsupervised or supervised learning mode.
31. The server of claim 30, wherein the machine learning engine stores or outputs the refined model configuration in memory for subsequent inference operations and/or further iterative refinements.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite certain methods of organizing human activity. The claims are directed to method for generating a task schedule. This type of method of organizing human activity is similar to managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. The claims also recite mental processes. For example, the “access” limitation involves observation. The “apply” and “calibrate” limitations involve evaluation. The “generate” limitation involves judgment or opinion. Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea. For example, the claimed processor and machine learning engine are claimed at a high level and are being used as a tool to facilitate the process of generating a refined task schedule. Simply implementing the abstract idea using generic computers or devices is not a practical application of the abstract idea. Additionally, here there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
Relevant Prior Art
Birt et al., U.S. Patent Application Publication Number 2019/0172366 A1. This reference teaches a system for facilitating savings of money based on allowance and chores. Chore data is received from a parent device and transmitted to a child device. A reward is determined based on the status of the chore.
Scott et al., U.S. 2020/0065781 A1. This reference teaches a system for earning monetary rewards for in-game purchases. At least one task, including a deadline and monetary reward, to be performed by a child is entered into the system. When the task or chore is completed and verified, the portfolio of the child is credited.
Adams et al., U.S. Patent Application Publication Number 2017/0061406 A1. This reference teaches a method and system for periodic saving using account controls. A parent may set a reward for the finishing of chores by a child.
Logan, U.S. Patent Application Publication Number 2020/0242606 A1. This reference teaches a system for improving access to financial services for children.
Singh, U.S. Patent Number 11,436,936 B2. This reference teaches a method of providing a reward to a child who completes a chore.
Gautier et al., U.S. Patent Application Publication Number 2004/0215534 A1. This reference teaches a method and system for network-based allowance control.
Kurian et al., U.S. Patent Application Publication Number 2020/0092373 A1. This reference teaches an invention for assigning actions to dependent users through the use of devices located at the location of the dependent users. See paragraph 0058 for a discussion of using machine learning to determine actions for dependent users.
Zhu, U.S. Patent Application Publication Number 2018/0068276 A1. This reference teaches task scheduling.
Glocker, U.S. Patent Application Publication Number 2022/0270021 A1. This reference teaches using artificial intelligence for the scheduling and management of tasks and resources.
Vukich, U.S. Patent Number 11,049,077 B1. This reference teaches a task estimation machine learning model and a meeting scheduling machine learning model.
Liu et al., U.S. Patent Number 11,010,697 B1. This reference teaches a system that uses machine learning for scheduling resources to perform discrete tasks.
Email Communications
Per MPEP 502.03, Applicant may authorize email communications by filing Form PTO/SB/439, available at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf, via the USPTO patent electronic filing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Anderson, can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ELIZABETH H ROSEN/Primary Examiner, 3693