DETAILED ACTION
Status of Claims
This is a non-final office action on the merits in response to the arguments and/or amendments filed on 5 January 2026 and the request for continued examination filed on 2 February 2026.
Claim(s) 1, 16, and 18 is/are amended.
Claim(s) 1-20 is/are currently pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5 January 2026 has been entered.
Claim Objections
Claim(s) 1, 15, 16, 18, and 20 is/are objected to because of the following informalities:
Claim 1 recites “each to be serviced by one the mobile care units”, which contains a typographic error of omission and should recite: “each to be serviced by one of the mobile care units.” Claims 15, 16, 18, and 20 recite similar errors with claim 18 including two instances.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims not listed below are rejected for dependency.
Claim 1 recites the non-original limitation “wherein the constraint solver includes a trained model that predicts scheduling feasibility.” This limitation was added to the claims in the amendments filed 5 September 2025. The associated remarks do not identify support for the identified limitation. [0063] appears to be the most relevant portion of the originally filed disclosure.
[0063] Soft Constraints 350 are functions that output less heavily weighted values (i.e., less heavily than the medium constraints 348) that lend toward the Solver 344 maximizing the value of the Soft Constraints 350 at a priority tier below the Medium Constraints 348. Example soft constraints include early mobile care unit arrival times, mobile care unit capability that exceeds the medical needs of the new patient 302, additional services offered by the mobile care unit to the new patient 302 in excess of what is expected (e.g., vaccinations), and so on. In some implementations, the Solver 344 adopts a machine-learning based model for predicting and refining predictions of on-scene time. The predicted on-scene time may be used as an input into one of the Soft Constraints 350.
This disclosure describes the solver as using machine learning to predict on-scene time. Because on-scene time is not equivalent to scheduling feasibility, this does not reasonably support a trained model that predict scheduling feasibility. The remainder of the originally filed disclosure similarly fails to support the identified limitation.
Because the claimed invention includes a non-original limitation which is not supported by the originally filed disclosure, one of ordinary skill in the art would not recognize applicant as possessing the claimed invention at the time of filing. Therefore the claim is rejected based on the written description requirement. Claims 16 and 18 are similarly rejected.
Claim 1 recites the non-original limitation “wherein a scheduling output is stored in the memory and sent to the assigned mobile care unit via a network interface for execution.” This limitation was added to the claims in the amendments filed 5 September 2025. The associated remarks do not identify support for the identified limitation. [0123] appears to be the most relevant portion of the originally filed disclosure.
The Patient Schedules 653 are sent to four types of recipients: 1) to the mobile care units in the form of a sequential listing of appointments with patients for each of the mobile care units; 2) to the Remote Caregivers 652 in the form of a sequential listing of appointments with patients for each of the Remote Caregivers 652; 3) to the patients in the form of a time window within which each patient is to expect arrival of an assigned mobile care unit, and perhaps an identifier of the assigned mobile care unit; and 4) to operations teams, who receive a full schedule of all care teams, virtual and in-person, and all visits across markets. As the Optimization Engine 644 is constantly running and dynamically or adaptively re-optimizing the Patient Schedules 653 as patients are added and removed from a queue for treatment and mobile care units/remote caregivers move through their respective sequential listing of appointments, the Patient Schedules 653 may change and updates may be sent out to the patients awaiting treatment and the mobile care units/remote caregivers in the form of updated sequential listings of appointments.
This disclosure describes sending a “patient schedule”, described as “sequential listings of appointment” to the mobile car units in contrast to the claim which recites sending a “scheduling output.” These terms are not synonymous, and scheduling output is meaningfully broader than “schedule.” For example, an alert regarding an upcoming appointment could be considered a “scheduling output” but would not be considered a “sequential listing of appointments.” As such, the claim is broader than the original disclosure. The original disclosure does not appear to contemplate or suggest sending anything other than “patient schedules” to mobile care units. As such, this disclosure does not reasonably support the identified limitation. The remainder of the originally filed disclosure similarly fails to support the identified limitation.
Because the claimed invention includes a non-original limitation which is not supported by the originally filed disclosure, one of ordinary skill in the art would not recognize applicant as possessing the claimed invention at the time of filing. Therefore the claim is rejected based on the written description requirement. Claims 16 and 18 are similarly rejected.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims not listed below are rejected for dependency.
Claim 1 recites “loading hard, medium, and soft constraints into a mobile care unit dispatching tool.” The terms “medium constraint” and “soft constraint” in the claim are relative terms which renders the claim indefinite. The terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The disclosure describes medium constraints at [0062] stating “Medium Constraints 348 are functions that output heavily weighted values that lend toward the Solver 344 meeting most, if not all, the medium constraints 348.” The disclosure describes soft constraints at [0063] stating “Soft Constraints 350 are functions that output less heavily weighted values (i.e., less heavily than the medium constraints 348) that lend toward the Solver 344 maximizing the value of the Soft Constraints 350 at a priority tier below the Medium Constraints 348.” Despite these disclosures the specification does not actually provide a standard for determining whether a constraint is a medium constraint or a soft constraint. The distinction between hard constraints and either medium or soft constraints is clear; per [0061], hard constraints “must be satisfied”, which does not apply to either medium or soft constraints. But there is no clear boundary between medium and soft, only an indication that soft constraints are “less” heavily weighted, which is just more relative terminology. One of ordinary skill in the art would not be able to determine a boundary between medium constraints and soft constraints, and as such would not be able to determine the bounds of the claimed invention, rendering the claim indefinite. Claims 16 and 18 are similarly rejected.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 18, which is representative of claims 1 and 16, recites a
loading hard, medium, and soft constraints
identifying one or more mobile care units, and capabilities specific to each of the mobile care units
setting goals for optimizing routing of the mobile care units, at least one of which is maximizing the value points,
receiving a new patient into
applying the hard, medium, and soft constraints to the new patient;
iteratively solving scheduling options for adding the new patient to queues of patients, each to be serviced by one [of] the mobile care units,
scheduling the new patient in a time slot within one of the queues of patients serviced by an assigned one of the mobile care units that maximizes the goals, while satisfying the constraints, wherein a scheduling output is stored
servicing the patients according to the queues of patients, the queues of patients created
identifying a regularity within the queues of patients; and
applying the regularity [of] the mobile care units.
The preceding recitation of the claim has had strikethroughs applied to the additional elements beyond the abstract idea to more clearly demonstrate the limitations setting forth the abstract idea. The remaining limitations describe a concept of optimizing, scheduling, and managing mobile care units. Optimization, scheduling, and management of a finite resource is a fundamental economic practice. As such, the identified concept falls within the fundamental economic practices or principles sub-grouping of the methods of organizing human activity. Alternatively, the identified concept describes a mental process that a mobile care unit scheduler should follow to create and update schedules similar to the “mental process that a neurologist should follow when testing a patient for nervous system malfunctions” given in MPEP 2106.04(a)(2)(II)(C) as an example of managing personal behavior in the managing personal behavior or relationships or interactions between people sub-grouping of methods of organizing human activity. Therefore the claims are determined to recite an abstract idea.
MPEP 2106, reflecting the 2019 PEG, directs examiners at Step 2A Prong Two to consider whether the additional elements of the claims integrate a recited abstract idea into a practical application.
Claim 1 and 18 recites a mobile care unit dispatching tool, wherein the tool includes a memory … and a processor configured to execute. Claim 16 recites the additional element of a mobile health care unit routing tool comprising: a datastore… and a processor. These additional elements are recited at a high level of generality, and may be interpreted as generic computing devices used to implement the abstract idea. Per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not integrate an abstract idea into a practical application in Step 2A Prong Two, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, these additional elements do not integrate the abstract idea into a practical application.
The claims further recite the additional element of an artificial intelligence enabled constraint solver. This additional element does not constitute an improvement in the functioning of a computer or technical field, does not require any particular machine, does not effect a transformation or reduction of a particular article, and does not apply or use the judicial exception in a meaningful way. Instead, this additional element only generally links the abstract idea to a technological environment of computer implemented optimization. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional element of retrieving data from the memory and an output stored in the memory. This additional element does not constitute an improvement in the functioning of a computer or technical field, does not require any particular machine, does not effect a transformation or reduction of a particular article, and does not apply or use the judicial exception in a meaningful way. Instead, this additional element only generally links the abstract idea to a technological environment of a computer. As such, this additional element does not integrate the abstract idea into a practical application.
The claims further recite the additional elements of an output sent via a network interface. This additional element does not constitute an improvement in the functioning of a computer or technical field, does not require any particular machine, does not effect a transformation or reduction of a particular article, and does not apply or use the judicial exception in a meaningful way. Instead, this additional element only generally links the abstract idea to a technological environment of a computer. As such, this additional element does not integrate the abstract idea into a practical application.
There are no further additional elements. When considered as a combination, the combination of additional elements only generally links the abstract idea to a technological environment of computer implemented optimization. As such, the combination of additional elements does not integrate the abstract idea into a practical application. Because the additional elements, individually and as a combination, do not integrate the abstract idea into a practical application the claim is determined to be directed to an abstract idea.
At Step 2B of the Mayo/Alice analysis, examiners are to consider whether the additional elements amount to significantly more than the abstract idea.
As previously noted, the claims recite additional elements which may be interpreted as generic computing devices used to implement the abstract idea. However, per MPEP 2106.05(f), implementing an abstract idea on a generic computing device does not add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea on a generic computer. As such, this additional element does not amount to significantly more.
As previously noted, the claims recite the additional element of an artificial intelligence enabled constraint solver. The disclosure at [0026] states “The Dispatching Tool 120 includes an artificial intelligence enabled constraint solver (e.g., OptaPlanner, Gurobi, LocalSolver, and Google OR-Tools).” This identification of several commercially available products as being examples of the artificial intelligence enabled constraint solver is a disclosure in a manner that indicates that this additional element is sufficiently well-known that the specification need not describe the particulars of the additional element to satisfy 112(a). Further, De Smet (Re: Which programming languages/systems are the constraint programming community using these days?) demonstrates (“OptaPlanner (open source, apache license, pure Java) is used across the globe … it dominates the professional enterprise open source scene” Page 1) the popularity of OptaPlanner before the priority date of the claimed invention. And Guo et al. (Generation expansion planning with revenue adequacy constraints) demonstrates (“Gurobi is a widely-used MILP solver” page 3) the widespread use of Gurobi before the priority date of the claimed invention. As such, this additional element is considered well-known, routine, and conventional and as such does not amount to significantly more than the abstract idea.
As previously noted, the claims recite the additional elements of retrieving data from the memory and an output stored in the memory. Per MPEP 2106.05(d)(II), the courts have recognized storing and retrieving information in memory as a well-understood, routine, and conventional computer function. As such does not amount to significantly more than the abstract idea.
As previously noted, the claims recite the additional element of an output sent via a network interface. Per MPEP 2106.05(d)(II), the courts have recognized transmitting data over a network as a well-understood, routine, and conventional computer function. As such does not amount to significantly more than the abstract idea.
There are no further additional elements. When considered as a combination, the combination of additional elements only generally links the abstract idea to a technological environment of computer implemented optimization. As such, the combination of additional elements does not amount to significantly more than the abstract idea. Therefore, when considered individually and as a combination, the additional elements of the independent claims do not amount to significantly more than the abstract idea. Thus the independent claims are not patent eligible.
Dependent claims 2-15, 17, 19, and 20 further narrow the abstract idea, but the claims continue to recite an abstract idea. Dependent claims 2-15, 17, 19, and 20 recite no further additional elements. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for equivalent reasons as provided above. Therefore these claims continue to be directed to an abstract idea. The previously identified additional elements, individually and as a combination, do not amount to significantly more than the narrowed abstract idea for equivalent reasons as provided above. Thus as the dependent claims 2-15, 17, 19, and 20 remain directed to a judicial exception, and as the additional elements of these claims do not amount to significantly more, dependent claims 2-15, 17, 19, and 20 are not patent eligible.
Response to Arguments
Applicant’s Argument Regarding 101 Rejections of claims 1-20:
Amended Claim 1 explicitly recites: a memory storing an artificial intelligence-enabled constraint solver and a processor configured to execute the solver; a trained model …; and a network interface for transmitting scheduling outputs to mobile care units… . These limitations define a non-generic computing system configured for AI-based optimization.
The claimed steps involve: dynamic updates of stratified constraints (hard, medium, soft) based on real-time data inputs … iterative solving for scheduling options to optimize routing under multiple constraints… integration of predictive analytics through a trained model. … These operations require high-speed computation, multithreaded processing, and real-time data integration, none of which cannot be performed mentally or with pen and paper.
The invention provides a specific technological solution to a recognized problem in mobile healthcare logistics: real-time routing and scheduling under dynamic conditions. By leveraging an AI-enabled constraint solver and predictive modeling, the claimed methods: enhanced patient care through rapid, data-driven decisions … This is not an abstract idea implemented on a generic computer; it is a practical application of advance computing techniques to a technical problem.
Mobile healthcare delivery presents unique challenges, for example: dynamic routing and scheduling of vehicles equipped with differing medical equipment and personnel; real-time adaptation to patient acuity, traffic conditions, and resource availability; and efficient allocation of constrained resources under unpredictably conditions. These challenges are not business abstractions.
The presently disclosed technology provides: constraint stratification with embedded value functions… predictive analytics integrated into the constraint solver… iterative solving techniques for dynamic queues… These features represent specific improvements in computer functionality and healthcare logistics.
Unlike claims found ineligible in cases such as Recentive Analytics, the present claims do not merely forecast or optimize data. They: operate within a specialized computing environment; produce tangible outputs used to control physical mobile care units; and solve real-world technical problems in distributed healthcare delivery.
Non-Generic Computing Architecture … These features define a specialized computing environment tailored for real-time optimization of mobile healthcare logistics, not a generic processor performing routine calculations.
Applicant’s Claims, as amended, generally introduce: … These features … represent a specific improvement in computer functionality and system responsiveness.
Applicant’s Claims are not confined to abstract data manipulation.
Examiner’s Response: Applicant's arguments filed 5 January 2026 have been fully considered but they are not persuasive.
Examiner notes that the referenced memory, processor, and network interface appear to be entirely generic for the purposes of subject matter eligibility. Applicant’s argument that these elements define a “specialized computing architecture” appears to be based on the programming of these elements. Applicant is advised that the “special computer test” of In re Alappat is considered superseded by the 2014 Alice decision (See MPEP 2106(I)) and that a generic computer processed programmed to do something remains a generic computer processor for the subject matter eligibility analysis.
Examiner notes that the presence of features that cannot be performed in the human mind does not prevent a claim from reciting a mental process. See MPEP 2106.04(a)(2)(III)(C): “Claims can recite a mental process even if they are claimed as being performed on a computer.” It is inherent in the two-step analysis that some limitations may be beyond the abstract idea. Thus the mere identification of limitations beyond the abstract idea fails to demonstrate eligibility. Further, Examiner notes that the claims here are identified as setting forth a method of organizing human activity, which MPEP 2106.04(a)(2)(III)(A) does not apply to. Further, Examiner notes that the claims do not require “high-speed computation, multithreaded processing, and real-time data integration” and that some of the referenced limitations are not recited at a scale that would actually exclude those operations from being practically performable in the human mind.
First, per MPEP 2106.05(a), “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” The present disclosure does not appear to provide a technical explanation of how to implement the asserted technical solution. As such, the claims do not appear to constitute a technical improvement. Second, Applicant’s apparent basis that the claimed invention constitutes a practical application is contrary to other court decision. Note OIP Techs., Inc. v. Amazon.com, Inc, where claims to offer-based price optimization were found to describe fundamental economic principles of practices. That case and the present claims both described the application of computer implemented optimization to a resource problem. As such, Applicant’s argument that the present optimization constitutes a practical application is unpersuasive.
Examiner disagrees. Resource allocation and scheduling is a quintessential business challenge and the claims are unambiguously directed to a problem of scheduling and allocating mobile care units.
Per MPEP 2106.05(a), “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.” The present disclosure does not appear to provide a technical explanation of how to implement the above identified features.
Examiner notes that under Applicant’s apparent standard for “specialized computing environment”, the claims in Recentive would also operate within a “specialized computing environment.” Regarding Applicant’s remaining two points, MPEP 2106 contradicts applicant’s argument with its guidance that “eligibility should not be evaluated based on whether the claim recites a ‘useful, concrete, and tangible result.’”
See response (1).
See response (5).
See response (6).
Applicant’s Argument Regarding 103 Rejections of claims 1-20: Amended claims 1, 16, and 18 each include: … a trained model that predict scheduling feasibility. … Neither Day nor Watson teaches or suggests this combination of claim elements.
Examiner’s Response: Applicant's arguments filed 5 January 2026 has been fully considered. The argument identified above is persuasive. Therefore the rejections under 103 are withdrawn.
Additional Considerations
The prior art made of record and not relied upon that is considered pertinent to applicant’s disclosure can be found in the PTO-892 Notice of References Cited.
Tiffert (Vehicle Routing with OptaPlanner in Practice) demonstrates the application of an artificial intelligence enabled constraint solver to optimize a vehicle scheduling problem. Further, Examiner notes that this reference links to and relies on a “OptaPlanner User Guide” which appears to disclose many elements of the current claim language including multi-level constraints, value constraints, constraints based on dynamic parameters, and goal maximization.
Lesaint et al. (US 6578005 B1) describes a system which adjusts resource allocations based on changing data, and specifically calls out the applicability of these techniques to ambulances (Column 1, Lines 9-19).
Gueta (WO 2022/211720 A1) describes a system which manages customer transport according to hard, medium, and soft constraints.
Scharaswak et al. (US 2016/0300186 A1) expressly notes that the constraints for their vehicle fleet control system may be updated in real time ([0057]).
Abdulkarim (US 2022/0359068 A1) describes a system that “automates and optimizes the workflows and transportation routes of one or more mobile medical units” (abstract) closely matching the present claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bion A Shelden whose telephone number is (571)270-0515. The examiner can normally be reached M-F, 12pm-10pm EST.
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, Kambiz Abdi can be reached at (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Bion A Shelden/ Primary Examiner, Art Unit 3685 2026-07-10
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