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 first action on the merits in response to the application filed 25 June 2025. Claims 1-20 are pending and have been examined.
Priority
This application repeats a substantial portion of prior Application No. 17/886343 filed 11 August 2022 as a continuation of Application No. 17/492003, filed 01 October 2021, and adds disclosure not presented in the prior applications. Because this application names the inventor or at least one joint inventor named in the prior application, it constitutes a continuation-in-part of the prior application. Examiner notes that the limitations of independent claims 1 and 11 are supported by the disclosures of the parent applications. Dependent claims 2-9, 12-20, and Figures 3A, 3C, 3F-3G add new matter that is not supported by the disclosures of the parent applications and therefore are examined based upon the filing date herein and not the priority filing date of the parent applications.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 25 June 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 11 recite the limitation "the at least a user schedule" in the step for generating, using a scheduling machine learning model … the at least a user schedule. There is insufficient antecedent basis for this limitation in the claim. Claims 2-10 depend from claim 1 and inherit this deficiency. Claims 11-20 depend from claim 11 and inherit this deficiency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 -20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 recites a process of exploiting value within a certain domain, and independent claim 11 recites a system for exploiting values within a certain domain. Independent claims 1 and 11 recite substantially similar limitations.
Taking claim 1 as representative, claim 1 recites the following limitations:
receiving, by a computing device: scheduling data; at least a domain, wherein a quantity of domains in the at least a domain is between one and a predetermined maximum number of domains selected by a user; and domain-specific data, wherein the domain-specific data is a function of the at least a domain;
generating, using a target-setting machine learning model that has been trained on target-setting training data comprising an exemplary plurality of domain- specific data correlated to an exemplary domain target, at least a domain target for the at least a domain as a function of the domain-specific data;
generating, using a scheduling machine learning model that has been trained on scheduling training data comprising exemplary domain targets with exemplary user schedules, the at least a user schedule, wherein generating the at least a user schedule comprises:
receiving a status of at least a domain;
assigning one or more state variables to the at least a domain, wherein the one or more state variables represent the status of the at least a domain; and
generating the at least a user schedule as a function of the one or more state variables;
and displaying, by the computing device, the at least a user schedule and the at least a domain target to the user.
Under Step 1, independent claims 1 and 11 recite at least one step or act, including receiving scheduling data. Thus, the claims fall within one of the statutory categories of invention.
Under Step 2A Prong One, the claim limitations recite an abstract idea of collecting schedule related data, analyzing it, and generating an optimal schedule for a user, without significantly more. The limitations recited in claim 1 for receiving data, generating at least a domain target, generating a user schedule, receiving a status of at least a domain, assigning one or more state variables to the at least a domain, generating the at least a user a schedule as a function of the state variables, and displaying the at least a user schedule and the at least a domain target to the user , as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (evaluations, observations, and determinations) because none of the additional elements preclude the steps from practically being performed in the human mind, or by a human using a pen and paper. A person could use a pen and paper to detail a weekly schedule for a specific purpose (work, fitness, childcare, cleaning, social events, business meetings) and allocate specific time slots for each activity based on set durations, past occurrences, and future plans. Therefore, the limitations fall into the mental processes grouping and accordingly the claims recite an abstract idea. Because managing a schedule for a user is a form of managing personal behavior or relationships or interactions between people the claims fall within the abstract concept grouping of certain methods of organizing human activity.
Under Step 2A Prong 2, the judicial exception of claim 1 is not integrated into a practical application. In particular, the claims only recite a computing device for performing the recited steps. This element is recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amounts to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0011] states: “Computing device 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure.” The Specification does not provide additional details about the computing device that would distinguish it from any generic processing devices that analyze data and communicate with one another in a network environment. Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(d).
While the claim includes a target-setting machine learning model trained on target setting data, and a scheduling machine learning model trained on scheduling training data, per paragraph [0091] of the Specification “A ‘machine-learning model,’ as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory. … For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum.” Because the machine learning models are broadly and generically claimed to include mathematical models for processing input and generating an output, the claimed machine learning models cannot integrate the recited abstract idea into a practical application because the models perform data analysis steps and do not provide technical or technological improvements. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and do not improve the functioning of a computer, or any other technology or technical field.
Under Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and machine learning models amounts to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept.
Dependent claims 2 - 10 and 12 - 20 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process/method of organizing human activity by describing how the data input is updated, manipulated, associated with motivation paths, analyzed using rules, and processed to make further determinations and recommendations to a user. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation, therefore the claims are directed to an abstract idea. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Therefore claims 1 -20 are ineligible under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Sar Shalom et al. (US 2021/0149671) - A machine learning method. A source domain data structure and a target domain data structure are combined into a unified data structure. First data in the source domain data structure are latent with respect to second data in the target domain data structure. The unified data structure includes user vectors that combine the first data and the second data. The user vectors are transformed into a transformed data structure by applying a mapping function to the user vectors. The mapping function relates, using at least one parameter, first relationships in the source domain data structure to second relationships in the target domain data structure. The at least one parameter is based on a combination of affinity scores relating items with which the user interacted and did not interact. The transformed data structure is input into a machine learning model, from which is obtained a recommendation relating to the target domain.
Contant et al. (US 2011/0184247) - A health guidance system receives health information from input devices and other sources, tracks one or more health goals, and enforces those goals by acting upon other devices. The health guidance system collects health information to paint a comprehensive picture of the user's health. The system uses the information it collects to interact with a variety of devices to enforce the user's health goals and ensure accountability. Thus, the health guidance system provides a comprehensive software system for collecting health information and using that information to produce positive changes in the health of users.
Margolis et al. (US 2017/0293923) - A system to assist users with the selection of and participation in wellness programs. The system assists a user in selecting wellness programs by recommending a curated set of wellness programs to the user. In recommending a curated set of wellness programs that a user can select, the system analyzes several factors. Some of the factors used in recommending wellness programs include user data, a population segment associated with a user, sponsor criteria, the likelihood that the user will be successful in recommended wellness programs, and user preferences.
Gnanasambandam et al. (US 2022/0384052) - method may include receiving a set of codes pertaining to an event performed for a patient, mapping the plurality of codes to a taxonomy of data to determine a utilization unit, mapping the utilization unit to ontological data of a medical condition, mapping the ontological data to a knowledge fragment pertaining to the medical condition and the patient, and causing the knowledge fragment to be presented on a computing device of a medical personnel. The AI engine may include machine learning models that are trained to schedule appointments for users, recommend appointments to users, determine costs of services, manage documents for users, extract data from images, provide curated content tailored for users, estimate wait times, perform natural language searching of curated content, and so forth. The cognitive intelligence platform provides several core features including: 1) the ability to identify an appropriate action plan using narrative style interactions that generates data that includes intent and causation and using narrative style interactions; 2) monitoring: integration of offline to online clinical results across the functional medicine clinical standards; 3) the knowledge cloud that includes a comprehensive knowledge base of thousands of health related topics, an educational guide to better health aligned to western and eastern culture; 4) coaching using artificial intelligence; and 5) profile and health store that offers a holistic profile of each consumers health risks and interactions, combined with a repository of services, products, lab tests, devices, deals, supplements, pharmacy & telemedicine.
Prakash et al. (US 2016/0012194) - a BSA system that facilitates the collection of relevant health-related data on a continuous basis, integrates such data with pertinent personal and aggregate information, enables users to purchase (directly and indirectly) health-related goods and services, and provides credit, discounts and other economic benefits in connection with such purchases that are determined dynamically based upon the nature and extent of users' interaction with the system. The BSA system facilitates a dynamic feedback process by continually monitoring user interaction and medical and financial behavior, which results in dynamic adjustments to their credit levels and offers of discounts and other promotions, which in turn incentivizes users to continue participating in the process (thereby modifying their system interactions and behavior, and thus perpetuating this feedback loop). As a result, users are incentivized to actively participate in the process and thereby enhance their wellness while reducing healthcare costs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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, Rutao WU can be reached at 571-272-6045. 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.
/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
Prosecution Insights