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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 21, 2026 has been entered.
Status of the Claims
The office action is in response to the claims filed on January 21, 2026, for the application filed on April 7, 2022, which is a continuation-in-part of Non-Provisional Application No. 17/379,661 filed on July 19, 2021, which is a continuation of Non-Provisional Application No. 17/147,232 filed on January 12, 2021, which is a continuation-in-part of Non-Provisional Application No. 17/021,895 filed on September 15, 2020, which claims priority to Provisional Application No. 62/910,232 filed on October 3, 2019. Claims 1 – 45 are currently pending and have been examined as discussed below.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 – 7, 15 – 22 , 30 – 37 30 – 37 , and 45 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over an associated one of independent claims 1, 14, and 27 of Mason ‘417 (copending Application No. 18/324,417) in view of Brown (U.S. Pub. No. 2020/0411170 A1) and Nolan (U.S. Pub. No. 2016/0275259 A1).
Regarding independent claims 1, 16, and 31 of the instant application, representative claim 27 of Mason ‘417 reads the limitations of representative claim 31 identified in bold (i.e., bold italicized font and bold regular font) as:
a. A system (Amended claim 27 of Mason ‘417 as filed on December 6, 2024, “A system”) comprising: (This limitation in italics is not recited in claims 1 and 16.)
b. a processor (Amended claim 27 of Mason ‘417 as filed on December 6, 2024, “a processor”); and (This limitation in italics is not recited in claim 1.)
c. a memory including instructions that, when executed by the processor, cause the processor to (Amended claim 27 of Mason ‘417 as filed on December 6, 2024, “a memory including instructions that, when executed by the processor, cause the processor to”): (This limitation in italics is not recited in claim 1.)
d. receive a set of treatment plans, wherein each treatment plan comprising the set of treatment plans is associated with a user capable of using a treatment device to perform the associated treatment plan (Amended claim 27 of Mason ‘417 as filed on December 6, 2024, “wherein the at least one resource deployment prediction indicates at least one treatment device to be used by the user to perform a treatment plan”; see also amended claim 28 of Mason ‘417 as filed on December 6, 2024, “wherein, the user performs the treatment plan using at least one treatment device contained in the set of treatment devices.);
e. receive healthcare professional profile information associated with respective healthcare professionals comprising a set of healthcare professionals (Amended claim 27 of Mason ‘417 as filed on December 6, 2024, “receive healthcare professional profile information associated with respective healthcare professionals which comprises a set of healthcare professionals capable of interacting with the user”) capable of utilizing at least one aspect of the one or more treatment plans comprising the set of treatment plans;
f. receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users;
g. generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans (Amended claim 27 of Mason ‘417 as filed on December 6, 2024, “identify treatment device information for each treatment device which comprises a set of treatment devices capable of being used by users having user profiles at least partially associated with the at least one user profile of the user”); and
h. use an artificial intelligence engine that is configured to use at least one machine learning model that is configured to generate resource deployment predictions, generating at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information (Amended claim 27 of Mason ‘417 as filed on December 6, 2024, “generate, using an artificial intelligence engine that uses at least one machine learning model configured to generate resource deployment predictions, at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least part of the at least one user profile, user profiles associated with other users, at least some of the healthcare professional profile information, and at least some of the treatment device information, and wherein the at least one resource deployment prediction indicates at least one treatment device to be used by the user to perform a treatment plan.”).
Claim 27 of Mason ‘417 discloses generating the treatment plan, based on the at least one resource deployment prediction, for the user.
Claim 27 of Mason ‘417 does not appear to explicitly recite, but Brown teaches the limitation “cause the processor to: receive a set of treatment plans” (Paragraph [0073] of Brown, The patient information 252 can include patient information regarding current patients of one or more healthcare systems (e.g., patients that have entered the healthcare system via at least one entry point) and their medical needs. For example, the patient information 252 can include care plan information 240 that describes or defines care plans for the patients (if available). For example, the care plan can include information a list or timeline of the various prescribed clinical treatment to for the patient in association with a course of patient care. In some implementations, the care plan information can also be associated with information identifying patient rest and recovery periods/times, such as amounts of time and/or periods of time during which the patient is required or preferred to rest (e.g., between procedures or appointments and the like)… The patient information can also include medical history information for current and past patients, such as that provided in the EHR data 244. For example, the patient medical history information can include … information aggregated for patients across various disparate healthcare organizations/vendors (e.g., internal and third-party organizations/vendors) via a healthcare information exchange system (HIE).)
Claim 27 of Mason ‘417 does not appear to explicitly recite, but Nolan teaches the limitation “receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users” (Paragraphs [0040], [0059] – [0061], and [0071] – [0073], and [0080] of Nolan. The broadest reasonable interpretation of “receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices” reads on the patient feedback system in Nolan (Paragraphs [0059] – [0061]) receiving, from the monitoring device, collected monitoring data as personal profile data for the patient being monitored, while other patients use respective therapeutic devices with associated therapeutic device settings stored in the database as reference device settings, such that the database is filled automatically and may grow dynamically. The broadest reasonable interpretation of “wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users” reads on the patient feedback system in Nolan (Paragraphs [0040] and [0071] – [0073]) determining the reference patients having identical profile data as the patient (e.g. having the same age, the same physics and the same type and extent of disease, information on those other people choose their therapeutic device settings) and a recommendation adapted to that patient’s particular situation, with the other patients; therapeutic device settings serving as a basis for the patient to change the settings of the therapeutic device if the settings of the therapeutic device are outside the normal use.).
Claim 27 of Mason ‘417 does not appear to explicitly recite, but Nolan teaches the limitation “generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans” (Paragraph [0073] of Nolan, The broadest reasonable interpretation of “generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information” reads on the type identifier in Nolan (Paragraph [0073]) identifying the therapeutic device of the patient or reference patient could be included in the profile data (in the personal profile data of the patient or the reference profile data of each of the reference patients), with the type identifier being a device type identifier indicating which type of device the patient or reference patient uses, or a serial number for uniquely identifying the exact device the patient or reference patient uses.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Mason ‘417 (claims 1, 14, and 27) to cause the processor to: receive the set of treatment plans, as taught by Brown (Paragraph [0073]) in order to coordinate operating entities and patients in a meaningful fashion to deliver optimal operating efficiency utilizing all available resources in a network to drive an optimized, and patient specific experience according to patient preference (Paragraph [0004] of Brown); receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users, and generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans, as taught by Nolan (Paragraphs [0040], [0059] – [0061], and [0071] – [0073], and [0080]) in order to obtain dedicated feedback on how other patients with comparable profiles (comparison of personal profile data of the patient to reference profile data, i.e. personal profile data of the reference patients) use their therapeutic devices (Paragraph [0033] of Nolan).
Regarding claims 2, 17, and 32 of the instant application, claims 2, 15, and 28 of Mason ‘417 recite that, according to the at least one resource deployment prediction, at least one user associated with at least one treatment plan of the set of treatment plans is enabled to perform the at least one treatment plan using at least one treatment device comprising the set of treatment devices (Amended claims 2, 15, and 28 of Mason ‘417 as filed on December 6, 2024, “wherein, the user performs the treatment plan using at least one treatment device contained in the set of treatment devices.”).
Regarding claims 4, 19, and 34 of the instant application, claims 4, 17, and 30 of Mason ‘417 as filed on December 6, 2024 recite “wherein, for a respective healthcare professional contained in the set of healthcare professionals, the healthcare professional profile information includes at least one of credential information associated with the respective healthcare professional, professional experience information associated with the respective healthcare professional, and availability information associated with the respective healthcare professional.”
The claims of Mason ‘417 as modified by Brown and Nolan and applied to an associated one of claims 1, 16, and 31 teaches that, for a respective healthcare professional comprising the set of healthcare professionals, the healthcare professional profile information includes at least one of information associated with … credential or degree information associated with the respective healthcare professional (Paragraph [0034] of Brown, The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like. Paragraph [0048] of Brown, The task assessment module 110 can also determine and associate resource attributes with the healthcare tasks that identify or indicate requirements for resources to be used for the tasks, including requirements for healthcare workers authorized to perform the tasks (e.g., based on credentials, job description, performance levels, fatigue levels, etc.).).
Regarding claims 5, 20, and 35 of the instant application, the claims of Mason’417 as modified by Brown and Nolan and applied to an associated one of claims 1, 16, and 31 teaches that, for a respective healthcare professional comprising the set of healthcare professionals, the healthcare professional profile information includes at least one of identities of healthcare professionals potentially available to treat the user if the respective healthcare professional is unavailable, and information associated with the healthcare professionals potentially available (Paragraph [0034] of Brown, The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like.).
Regarding claims 6 and 21 of the instant application, claims 5 and 18 of Mason ‘417 and applied to an associated one of claims 1 and 16 recite that an associated one of the method and computer readable medium that, for a respective treatment device comprising the set of treatment devices, the treatment device information includes at least one of identification information associated with the respective treatment device; location information associated with the respective treatment device; and availability information associated with the respective treatment device (Amended claims 5 and 18 of Mason ‘417 as filed on December 6, 2024, “wherein, for a respective treatment device of the set of treatment devices, the treatment device information includes at least one of identification information associated with the respective treatment device; location information associated with the respective treatment device; and availability information associated with the respective treatment device.”).
Regarding claim 36 of the instant application, the claims of Mason ‘417 as modified by Brown and Nolan and applied to claim 31 teaches the system that, for a respective treatment device comprising the set of treatment devices, the treatment device information includes at least one of identification information associated with the respective treatment device; location information associated with the respective treatment device; and availability information associated with the respective treatment device (Paragraph [0054] of Brown, Although the embodiments, described above are directed to evaluating and determining availability of healthcare workers (e.g., humans), the resource assessment module 114 can also evaluate relevant dynamic operating data 104 to determine information regarding the availability of other system resources. For example, the other system resources can include supplies, instruments, equipment, machines, technology, and the like that are needed to perform and/or facilitate performance of the healthcare tasks. Thus, in some embodiments, the resources availability data 116 can also include information regarding availability of other resources, such as current availability status of the resources (e.g., whether they are in-use, clean, dirty, in repair, offline, overloaded, power levels, etc.), expected availability status of the resources (e.g., when they will be available for use), locations of the resources, and the like.).
Regarding claims 7 and 22 of the instant application, claims 6 and 19 of Mason ‘417 recite that an associated one of the method and the computer readable medium includes the at least one resource deployment prediction defining a mapping of at least some of the healthcare professionals contained in the set of healthcare professionals and at least some treatment devices contained in the set of treatment devices (Amended claims 6 and 19 of Mason ‘417 as filed on December 6, 2024, “wherein the at least one resource deployment prediction defines a mapping of at least some of the healthcare professionals contained in the set of healthcare professionals, and at least some treatment devices contained in the set of treatment devices.”).
Claims 6 and 19 of Mason ‘417 as modified by Brown and Nolan and applied to an associated one of claims 1 and 16 teaches that the at least one resource deployment prediction defines a mapping between or among at least some of the healthcare professionals comprising the set of healthcare professionals, the users associated with respective treatment plans comprising the set of treatment plans, and at least some treatment devices comprising the set of treatment devices (Paragraph [0034] of Brown, The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like.).
Regarding claim 37 of the instant application, claim 31 of Mason ‘417 as modified by Brown and Nolan teaches that the at least one resource deployment prediction defines a mapping between or among at least some of the healthcare professionals comprising the set of healthcare professionals, the users associated with respective treatment plans comprising the set of treatment plans, and at least some treatment devices comprising the set of treatment devices (Paragraph [0034] of Brown, In one or more embodiments, the system collects and combines real-time and historical data from various integrated healthcare provider systems and sources regarding patient needs and all aspects of operations of the different healthcare providers that are available to provide healthcare services to patients. In this regard, the system can access and retrieve or receive operating information from different operating entities in real-time over a course of operating of the one or more operating entities regarding what task needs to be done (e.g., clinical tasks and non-clinical task for performance by a wide range of clinicians, healthcare workers and the like, when and where within the healthcare system at a current point in time and/or over a defined, upcoming period of time. The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. The system can further evaluate the information using various machine learning models and/or optimization models/algorithms to determine how to schedule performance of the tasks with respect to time and location and how to assign resources (e.g., workers and optionally non-human resources) to the tasks in a manner that results in performing the tasks in the most efficient and effective manner, using the right resources at the right time for the right patient in the right place. For example, the system can determine how to optimize the operations of individual operating entities with respect to scheduling and managing performance of all healthcare tasks at the individual operating entities in a manner that results in the most efficient and effective utilization of available system resources while accounting for the collective and personal needs and preferences of all patients. Using similar machine learning and optimization techniques, the system can further determine how to optimize the operations of the different operating entities as a whole to achieve even greater efficiency in terms of resource utilization while providing patients more personalized and timely access to appropriate clinical care by coordinating and synchronizing patient and provider needs leveraging shared resources. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like. In some implementations, the healthcare tasks can include non-clinical tasks, such as tasks involving transporting patients and/or supplies, EVS tasks, task involving equipment/technology repair and maintenance, administrative tasks (e.g., verifying insurance qualifications, obtaining authorization for procedures, etc.), and the like. Although various embodiments are described with the assumption that the one or more individuals include humans, in some implementations, the one or more individuals can include machines that are configured to perform certain healthcare tasks autonomously and/or at the control of a human (e.g., intelligent machines, robots, self-driving vehicles, etc.). The term healthcare worker is used herein to refer to any individual or machine associated with an operating entity that can perform a healthcare task.).
Regarding claims 15, 30, and 45 of the instant application, claims 1, 14, and 27 of Mason ‘417 as modified by Brown and Nolan teaches that the treatment device information includes at least information indicating an availability of the treatment device (Paragraph [0054] of Brown, Although the embodiments, described above are directed to evaluating and determining availability of healthcare workers (e.g., humans), the resource assessment module 114 can also evaluate relevant dynamic operating data 104 to determine information regarding the availability of other system resources. For example, the other system resources can include supplies, instruments, equipment, machines, technology, and the like that are needed to perform and/or facilitate performance of the healthcare tasks. Thus, in some embodiments, the resources availability data 116 can also include information regarding availability of other resources, such as current availability status of the resources (e.g., whether they are in-use, clean, dirty, in repair, offline, overloaded, power levels, etc.), expected availability status of the resources (e.g., when they will be available for use), locations of the resources, and the like.).
Claims 8 – 12, 23 – 27, and 38 – 42 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over an associated one of independent claims 1, 14, and 27 of Mason ‘417 as modified by Brown and Nolan and applied to claims 1, 16, and 31, and further in view of Gnanasambandam (U.S. Pub. No. 2024/0177846 A1).
Regarding claims 8, 23, and 38 of the instant application, claims 1, 14, and 27 of Mason ‘417 as modified by Brown and Nolan do not appear to explicitly disclose, but Gnanasambandam teaches that the method, computer readable medium, and system implement the at least one resource deployment prediction is associated with an optimized outcome for the cohort of users associated with the respective treatment plans (Paragraph [0217] of Gnanasambandam, In some embodiments, the artificial intelligence engine 100 may generate the resource utilization plan to minimize costs to the healthcare facility. For example, the machine learning models may be trained to minimize a cost objective function that performs numerous iterations adjusting costs associated with resources to find a combination of resource utilization that provides a lowest cost relative to other combinations. The iterations may be performed in various simulations using various utilization types (e.g., admittance of a patient, emergency, specialist, specialist follow-up, primary care, and laboratory) and resource requirements for an integrated delivery network to determine a maximum resource utilization at a minimum cost. A simulation may include scheduling a first number of healthcare professionals at a first cost and ordering a first number of laboratory testing supplies at a first cost and determining a first resource utilization level and a first total cost; then, another simulation may include scheduling a second number of healthcare professionals at a second cost and ordering a second number of laboratory testing supplies at a second cost and determining a second resource utilization level and second total cost. The artificial intelligence engine 100 may compare the first resource utilization level and total cost to the second resource utilization level and total cost to determine which resource utilization plan and/or total cost are more desirable.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Mason ‘417 (claims 1, 14, and 27) such that the at least one resource deployment prediction is associated with an optimized outcome for the cohort of users associated with the respective treatment plans, as taught by Gnanasambandam (Paragraph [0217]) in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs (Paragraph [0002] of Gnanasambandam).
Regarding claims 9, 24, and 39 of the instant application, claims 1, 14, and 27 of Mason ‘417 as modified by Brown and Nolan do not appear to explicitly disclose, but Gnanasambandam teaches that the method, computer readable medium, and system include instructions further causing the processor to identify super-cohorts of users comprising the cohort of users associated with respective treatment plans comprising the set of treatment plans, wherein, further, based on the identified super-cohorts of users, the at least one machine learning model generates the at least one resource deployment prediction (Paragraph [0202] of Gnanasambandam, As depicted in FIG. 17 , the data has been assigned to different cohorts. Cohort A includes data for patients having similar first attributes, first disease progression levels, first treatment plans, first results, first utilization types, first resources, and first costs. Cohort B includes data for patients having similar second attributes, second disease progression levels, second treatment plans, second results, second utilization types, second resources, and second costs. For example, cohort A may include first attributes of patients in their twenties without any additional medical conditions, and such cohort A patients' disease progression levels may indicate a low risk of reaching the next stage of a disease continuum. Based on the disease progression level of the patients, cohort A may include first resources for these users that staff a low amount of healthcare professionals over a certain period of time because it is unlikely they will be needed at a healthcare facility for these patients. In such a way, resources may not be wasted by over staffing a healthcare facility and the resources not staffed may be staffed somewhere else. Paragraph [0203] of Gnanasambandam, Further, cohort B may include second attributes of patients in their sixties with one or more additional medical conditions, and cohort B patients' disease progression levels may indicate a high risk of reaching the next stage of the disease continuum. Based on the disease progression level of the patients, cohort B may include second resources for these users that staff a high amount of healthcare professionals over a certain period of time because it is likely they will be needed at a healthcare facility for these patients. In such a way, resources may be staffed where needed as appropriate.).
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Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Mason ‘417 (claims 1, 14, and 27) such that the instructions further cause the processor to identify super-cohorts of users comprising the cohort of users associated with respective treatment plans comprising the set of treatment plans, wherein, further, based on the identified super-cohorts of users, the at least one machine learning model generates the at least one resource deployment prediction, as taught by Gnanasambandam (Paragraphs [0202] and [0203]) in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs (Paragraph [0002] of Gnanasambandam).
Regarding claims 10, 25, and 40 of the instant application, claims 1, 14, and 27 of Mason ‘417 as modified by Brown and Nolan do not appear to explicitly disclose, but Gnanasambandam teaches that the method, computer readable medium, and system include instructions further causing the processor to receive, subsequent to the at least one machine learning model generating the at least one resource deployment prediction, at least one of a subsequent treatment plan, subsequent healthcare professional profile information, and subsequent treatment device information (Paragraph [0217] of Gnanasambandam, In some embodiments, the artificial intelligence engine 100 may generate the resource utilization plan to minimize costs to the healthcare facility. For example, the machine learning models may be trained to minimize a cost objective function that performs numerous iterations adjusting costs associated with resources to find a combination of resource utilization that provides a lowest cost relative to other combinations. The iterations may be performed in various simulations using various utilization types (e.g., admittance of a patient, emergency, specialist, specialist follow-up, primary care, and laboratory) and resource requirements for an integrated delivery network to determine a maximum resource utilization at a minimum cost. A simulation may include scheduling a first number of healthcare professionals at a first cost and ordering a first number of laboratory testing supplies at a first cost and determining a first resource utilization level and a first total cost; then, another simulation may include scheduling a second number of healthcare professionals at a second cost and ordering a second number of laboratory testing supplies at a second cost and determining a second resource utilization level and second total cost. The artificial intelligence engine 100 may compare the first resource utilization level and total cost to the second resource utilization level and total cost to determine which resource utilization plan and/or total cost are more desirable.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Mason ‘417 (claims 1, 14, and 27) including instructions that further cause the processor to receive, subsequent to the at least one machine learning model generating the at least one resource deployment prediction, at least one of a subsequent treatment plan, subsequent healthcare professional profile information, and subsequent treatment device information, as taught by Gnanasambandam (Paragraph [0217]) in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs (Paragraph [0002] of Gnanasambandam).
Regarding claims 11, 26, and 41 of the instant application, claims 1, 14, and 27 of Mason ‘417 as modified by Brown, Nolan, and Gnanasambandam and applied to claims 10, 25, and 40 teach that the method, computer readable medium, and system include:
a. the subsequent treatment plan corresponds to one of at least one treatment plan comprising the set of treatment plans or to a treatment plan not in the set of treatment plans (Paragraph [0041] of Gnanasambandam, Medical data may be stored in the data store 108 in the form of electronic health records (EHRs) that are associated with one or more patients. In some implementations, EHRs from different, disparate medical providers of a patient are stored in the data store 108. The health information exchanged between computing devices in the system 100 (e.g., between client computing device 104 and another computing device) may include health records associated with a patient such as medical and treatment histories of the patient but can go beyond standard clinical data collected by a healthcare provider. For example, health records may include a patient's medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory and test results.);
b. the subsequent healthcare professional profile information corresponds to one of at least one healthcare professional comprising at least the set of healthcare professionals and at least a healthcare professional not in the set of healthcare professionals (Paragraph [0034] of Gnanasambandam, “Results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. A “medical action(s)” may refer to any suitable action(s) performed by a healthcare professional, and such action or actions may include diagnoses, prescriptions for treatment plans); and
c. the subsequent treatment device information corresponds to one of at least one treatment device comprising the set of treatment devices and at least one treatment device not in the set of treatment devices (Paragraph [0038] of Gnanasambandam, The server 102 is configured to store and to provide data related to managing treatment plans. The server 102 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 102 may be configured to store data regarding treatment plans. For example, the server 102 may be configured to hold system data, such as data pertaining to treatment plans for treating one or more patients. The server 102 may also be configured to store data regarding performance by a patient in following a treatment plan. For example, the server 102 may be configured to hold medical data, such as data pertaining to one or more patients, including data representing each patient's performance within the treatment plan. In addition, the server 102 may store attributes (e.g., personal, performance, measurement, etc.) of patients, disease progression levels of medical conditions of patients, treatment plans followed by patients, results of the treatment plans, utilization types (e.g., admittance to healthcare facility, emergency, specialty healthcare professional, specialty follow-up, lab work, etc.) resources of healthcare facilities (e.g., available healthcare professionals, available rooms, available medical imaging devices, available laboratory testing supplies, etc.).).
Regarding claims 12, 27, and 42 of the instant application, claims 1, 14, and 27 of Mason ‘417 as modified by Brown, Nolan, and Gnanasambandam and applied to claims 10, 25, and 40 teaches that the method, computer readable medium, and system include instructions further causing the processor to generate, using the at least one machine learning model via the artificial intelligence engine , at least one subsequent resource deployment prediction, wherein the at least one machine learning model generates the at least one subsequent resource deployment prediction based, at least in part, on the subsequent treatment plan, the subsequent healthcare professional profile information, and the subsequent treatment device information (Paragraph [0216] of Gnanasambandam, In some embodiments, the actionable item may include ordering one or more laboratory diagnostic test supplies treatment plan may indicate the patient should start taking one or more new medications, adjust dosage levels of one or more medications, stop taking one or more medications, or a combination thereof. Alternatively, or in addition, actionable items may include one or more lab test to perform on the patient. For example, the treatment plan may indicate the patient should start having one or more new lab tests. Paragraph [0217] of Gnanasambandam, In some embodiments, the artificial intelligence engine 100 may generate the resource utilization plan to minimize costs to the healthcare facility. For example, the machine learning models may be trained to minimize a cost objective function that performs numerous iterations adjusting costs associated with resources to find a combination of resource utilization that provides a lowest cost relative to other combinations. The iterations may be performed in various simulations using various utilization types (e.g., admittance of a patient, emergency, specialist, specialist follow-up, primary care, and laboratory) and resource requirements for an integrated delivery network to determine a maximum resource utilization at a minimum cost. A simulation may include scheduling a first number of healthcare professionals at a first cost and ordering a first number of laboratory testing supplies at a first cost and determining a first resource utilization level and a first total cost; then, another simulation may include scheduling a second number of healthcare professionals at a second cost and ordering a second number of laboratory testing supplies at a second cost and determining a second resource utilization level and second total cost. The artificial intelligence engine 100 may compare the first resource utilization level and total cost to the second resource utilization level and total cost to determine which resource utilization plan and/or total cost are more desirable.).
Claims 13, 14, 28, 29, 43, and 44 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over an associated one of independent claims 1, 14, and 27 of Mason ‘417 in view of Brown and Nolan and applied to claims 1, 16, and 31, and further in view of Einav (U.S. Pub. No. 2006/0277074 A1).
Regarding claims 13, 28, and 43, claims 1, 14, and 27 of Mason ‘417 as modified by Brown and Nolan and applied to claims 1, 16, and 31 do not appear to explicitly disclose, but Einav teaches that the method, computer readable medium, and system with at least one treatment device of the set of treatment devices includes at least one pedal (Paragraph [0120] of Einav, Mr. Smith's therapy plan may include some exercises designed to develop coordinated movement of upper and lower body parts. Optionally, this may be accomplished by use of multiple modules 350 as detailed hereinabove. Alternately, or additionally, an exercise device 600 in accordance with an exemplary embodiment of the invention (FIG. 6) may be employed, for example a full body treatment device located in daily activity room 280 or private room 235. According to this embodiment of the invention, optional lower limb training section includes a base 602 having a pedal 604 mounted thereon. Optionally, pedal 604 is capable of rotary motion relative to a horizontal axis (e.g., as in a bicycle) alternatively or additionally to rotation around its axis.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Mason ‘417 (claims 1, 14, and 27) such that at least one treatment device of the set of treatment devices includes at least one pedal, as taught by Einav (Paragraph [0120]) in order to organize rehabilitation to reduce wastage of time of the patient, reduce waiting time, and make efficient use of the patient's time and/or rest periods (Paragraph [0006] of Einav).
Regarding claims 14, 29, and 44 of the instant application, claims 1, 14, and 27 of Mason ‘417 as modified by Brown and Nolan and applied to claims 1, 16, and 31 do not appear to explicitly disclose, but Einav teaches that the method, computer readable medium, and system have at least one treatment device of the set of treatment devices including at least one hand grip or hand pedal (Paragraph [0120] of Einav, Mr. Smith's therapy plan may include some exercises designed to develop coordinated movement of upper and lower body parts. Optionally, this may be accomplished by use of multiple modules 350 as detailed hereinabove. Alternately, or additionally, an exercise device 600 in accordance with an exemplary embodiment of the invention (FIG. 6) may be employed, for example a full body treatment device located in daily activity room 280 or private room 235. According to this embodiment of the invention, optional lower limb training section includes a base 602 having a pedal 604 mounted thereon. Optionally, pedal 604 is capable of rotary motion relative to a horizontal axis (e.g., as in a bicycle) alternatively or additionally to rotation around its axis.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Mason ‘417 (claims 1, 14, and 27) such that at least one treatment device of the set of treatment devices includes at least one hand grip or hand pedal, as taught by Einav (Paragraph [0120]) in order to organize rehabilitation to reduce wastage of time of the patient, reduce waiting time, and make efficient use of the patient's time and/or rest periods (Paragraph [0006] of Einav).
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 – 45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Eligibility Step 1:
Under step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim as a whole falls within any statutory category (i.e., a process, machine, manufacture, or composition of matter). In the instant application, claims 1 – 15 are directed to a method (i.e., a process), claims 16 – 35 are directed to a non-transitory computer-readable medium (i.e., a machine), and claims 36 – 45 are directed to a system (i.e., a machine).
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim is directed to a judicial exception (i.e., whether each claim recites an abstract idea, law of nature, or natural phenomenon). Independent claims 1, 16, and 31 are determined to be directed to a judicial exception because abstract ideas and mental processes are recited in the claims.
Regarding representative claim 31, the abstract ideas are identified in bold as:
a. A system comprising: (This limitation in italics is not recited in claims 1 and 16.)
b. a processor; and (This limitation in italics is not recited in claim 1.)
c. a memory including instructions that, when executed by the processor, cause the processor to: (This limitation in italics is not recited in claim 1.)
d. receive a set of treatment plans, wherein each treatment plan comprising the set of treatment plans is associated with a user capable of using a treatment device to perform the associated treatment plan;
e. receive healthcare professional profile information associated with respective healthcare professionals comprising a set of healthcare professionals capable of utilizing at least one aspect of the one or more treatment plans comprising the set of treatment plans;
f. receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices , wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users;
g. generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans; and
h. use an artificial intelligence engine that is configured to use at least one machine learning model that is configured to generate resource deployment predictions, generating at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information.
The enumerated groupings of abstract ideas are defined as certain methods of organizing human activities (“CMOHA”) and mental processes. See MPEP 2106.04(a). The limitations in bold fall within the subject matter grouping of CMOHA because those limitations are rules or instructions followed by a healthcare professional to perform human activities (i.e., the well-known activity of managing and administering healthcare resources). It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings: managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. See MPEP §2106.04(a)(2)(II). Accordingly, claims 1, 16, and 31 recite abstract ideas (i.e., CMOHA) under step 2A, prong one.
The limitations in bold can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. See MPEP 2106.04(a)(2)(III). The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (“[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.”). In the instant application, the limitations of “receive, while one or more users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors” and “generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device,” and “generating at least one resource deployment prediction… generates the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information” fall within the subject matter grouping of mental process (thinking) because, with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper. Accordingly, for this additional reason, claims 1, 16, and 31 recite abstract ideas (i.e., mental processes) under step 2A, prong one.
An example of a claim that recites mental processes includes a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. See MPEP 2106.04(a)(2)(III) citing Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). In claims 1, 16, and 31 of the instant application, the claimed functions “receive a set of treatment plans,” “receive healthcare professional profile information,” and “receive one or more measurements from one or more sensors” are directed to collecting information. The claimed function “generating at least one resource deployment prediction, … generates the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information” is directed to analyzing the information. These data analysis steps are recited at a substantially high level of generality because claims 1, 16, and 31 do not recite how to generate treatment device information and generate the at least one resource deployment prediction. Accordingly, for this additional reason, claims 1, 16, and 31 recite abstract ideas (i.e., mental processes) under step 2A, prong one. Accordingly, for this additional reason, claims 1, 16, and 31 recite abstract ideas under step 2A, prong one.
Dependent claims 2 – 20, 22 – 30, and 32 – 45 recite abstract ideas that further define abstract ideas recited in associated independent claims 1, 16, and 31 as follows:
according to the at least one resource deployment prediction, at least one user associated with at least one treatment plan of the set of treatment plans being enabled to perform the at least one treatment plan using at least one treatment device (claims 2, 17, and 32) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31;
during a telemedicine session, the at least one user associated with the at least one treatment plan is enabled to perform the at least one treatment plan using the at least one treatment device (claims 3, 18, and 33) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31;
for a respective healthcare professional, the healthcare professional profile information includes at least one of information associated with the respective healthcare professional, credential or degree information associated with the respective healthcare professional, professional experience information associated with the respective healthcare professional, and availability information associated with the respective healthcare professional (claims 4, 19, and 34) is simply defining the healthcare professional profile information of claims 1, 16, and 31;
for a respective healthcare professional, the healthcare professional profile information includes at least one of identities of healthcare professionals potentially available to treat the user if the respective healthcare professional is unavailable, and information associated with the healthcare professionals potentially available (claims 5, 20, and 35) is simply defining the healthcare professional profile information of claims 1, 16, and 31;
for a respective treatment device comprising the set of treatment devices, the treatment device information includes at least one of identification information associated with the respective treatment device; location information associated with the respective treatment device; and availability information associated with the respective treatment device (claims 6, 21, and 36) is simply defining the treatment device information of claims 1, 16, and 31;
the at least one resource deployment prediction defines a mapping between or among at least some of the healthcare professionals comprising the set of healthcare professionals, the users associated with respective treatment plans comprising the set of treatment plans, and at least some treatment devices comprising the set of treatment devices (claims 7, 22, and 37) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31;
the at least one resource deployment prediction is associated with an optimized outcome for the cohort of users associated with the respective treatment plans (claims 8, 23, and 38) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31;
the instructions further cause the processor to identify super-cohorts of users comprising the cohort of users associated with respective treatment plans comprising the set of treatment plans, wherein, further, based on the identified super-cohorts of users, … generates the at least one resource deployment prediction (claims 9, 24, and 39) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31;
the instructions further cause the processor to receive, subsequent to … generating the at least one resource deployment prediction, at least one of a subsequent treatment plan, subsequent healthcare professional profile information, and subsequent treatment device information (claims 10, 25, and 40) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31;
the subsequent treatment plan corresponds to one of at least one treatment plan comprising the set of treatment plans or to a treatment plan not in the set of treatment plans; the subsequent healthcare professional profile information corresponds to one of at least one healthcare professional comprising at least the set of healthcare professionals and at least a healthcare professional not in the set of healthcare professionals; and the subsequent treatment device information corresponds to one of at least one treatment device comprising the set of treatment devices and at least one treatment device not in the set of treatment devices (claims 11, 26, and 41) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31;
the instructions further cause the processor to generate… at least one subsequent resource deployment prediction, … generates the at least one subsequent resource deployment prediction based, at least in part, on the subsequent treatment plan, the subsequent healthcare professional profile information, and the subsequent treatment device information (claims 12, 27, and 42) is simply defining the at least one resource deployment prediction of claims 1, 16, and 31 and applying the abstract idea to the machine learning model;
at least one treatment device of the set of treatment devices includes at least one pedal (claims 13, 28, and 43) is simply defining the at least one treatment device of claims 1, 16, and 31;
at least one treatment device of the set of treatment devices includes at least one hand grip or hand pedal (claims 14, 29, and 44) is simply defining the at least one treatment device of claims 1, 16, and 31; and
the treatment device information includes at least information indicating an availability of the treatment device (claims 15, 30, and 45) is simply defining the treatment device information of claims 1, 16, and 31.
Eligibility Step 2A, Prong Two:
Under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the claims recite any additional elements or combination of elements that integrate the judicial exceptions (i.e., the identified abstract ideas) into a practical application. After evaluation, it has been determined that claims 1-47 do not recite any additional elements or combination of elements that integrate one or more abstract ideas into a practical application, such as through: an additional element that reflects an improvement to the functioning of a computer, or an improvements to any other technology or technical field; an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; an additional element that implements the judicial exception with, or uses the judicial exception in connection with, a particular machine or manufacture that is integral to the claim; an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. As shown below, the additional elements, other than the abstract idea per se, when considered both individually and as an ordered combination, amount to no more than a recitation of: generally linking the abstract idea to a particular technological environment or field of use; insignificant extra-solution activity to the judicial exception; and/or adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as evidenced below.
Regarding representative independent claim 31, the additional elements are identified in bold (i.e., bold italicized font) as:
a. A system comprising: (This limitation in bold italicized font is not recited in claims 1 and 16.)
b. a processor; and (This limitation in bold italicized font is not recited in claim 1.)
c. a memory including instructions that, when executed by the processor, cause the processor to: (This limitation in bold italicized font is not recited in claim 1.)
d. receive a set of treatment plans, wherein each treatment plan comprising the set of treatment plans is associated with a user capable of using a treatment device to perform the associated treatment plan;
e. receive healthcare professional profile information associated with respective healthcare professionals comprising a set of healthcare professionals capable of utilizing at least one aspect of the one or more treatment plans comprising the set of treatment plans;
f. receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users;
g. generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans; and
h. use an artificial intelligence engine that is configured to use at least one machine learning model that is configured to generate resource deployment predictions, generating at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information.
The additional limitations including “the system,” “the processor,” “the memory,” “the artificial intelligence engine that is configured to use at least one machine learning model that is configured to generate resource deployment predictions,” and “the at least one machine learning model” in claims 16 and 31 are determined to implement the judicial exception (i.e., CMOHA, mental processes) recited at a high level of generality and be mere instructions to apply an abstract idea to generic computer components. See MPEP 2106.05(f). Accordingly, for these additional reasons, claims 1, 16, and 31 do not recite additional elements which integrate the abstract idea into a practical application.
The additional limitations including “receive a set of treatment plans,” “receive healthcare professional profile information,” and “receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users” in claims 1, 16, and 31 are determined to be the insignificant pre-solution activities of well-known necessary input data gathering that are nominal or tangential related to the invention. See MPEP 2106.05(g). Accordingly, for these additional reasons, claims 1, 16, and 31 as a whole and the additional limitations, individually or in combination, do not integrate the abstract idea into a practical application under Step 2A, Prong Two.
Regarding dependent claims 2 – 15, 17 – 30, and 32 – 45, it has been determined that claims 2 – 15, 17 – 30, and 32 – 45 merely further define the judicial exceptions (i.e., CMOHA, mental processes) in associated independent claims 1, 16, and 31, and therefore, claims 2 – 15, 17 – 30, and 32 – 45 do not recite any additional elements or combinations of additional elements whatsoever, let alone any additional elements that integrate the judicial exception into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether the claims provide an inventive concept by determining if the claims include an element or a combination of elements that are sufficient to amount to significantly more than the judicial exception (i.e., whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry).
The well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). See MPEP 2106.05(d). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity. After evaluation, there is no indication that an element or combination of elements are sufficient to amount to significantly more than the judicial exception.
In determining patent eligibility, examiners should consider whether the claim “purport(s) to improve the functioning of the computer itself” or “any other technology or technical field.” See MPEP 2106.05(a). An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. See MPEP 2106.05(a) citing McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP 2106.05(f)). It is important to note that a general purpose computer or generic computer components that apply a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. See MPEP 2106.05(b). In the instant application, the additional elements “system,” “processor,” and “memory” are merely generic computer components. Therefore, for this additional reason, it is determined that claims 1, 16, and 31 do not improve technology because the claims cover the abstract ideas (CMOHA and mental processes) applied to generic computer components.
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. See MPEP 2106.05(b). In the instant application, paragraph [0064] states:
[0064] Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the healthcare professional may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the healthcare professional's experience using the computing device and may encourage the healthcare professional to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the healthcare professional does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine may be configured to provide, dynamically on the fly, the treatment plans and excluded treatment plans.
It is determined that the claimed invention does not improve upon conventional functioning of a computer or upon conventional technology or technological processes because the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize that the claimed invention is providing any such improvement. To the contrary, paragraph [0064] discloses how a computer or conventional technology or technological processes used to improve upon the abstract ideas (i.e., CMOHA and mental processes of managing and administering healthcare resources, i.e., generating resource deployment predictions based on at least some treatment plans, at least some healthcare profile information, and at least some treatment device information). Put another way, the additional limitations “system,” “processor,” and “memory” in claims 1, 16, and 31 are used to implement the abstract ideas recited at a high level of generality and are determined to be no more than mere instructions to implement the abstract idea or other exception on a computer. See MPEP 2106.05(f). Accordingly, the additional elements are well-understood, routine, conventional activities previously known to the industry, and claims 1, 16, and 31 do not recite significantly more than a judicial exception.
The courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See MPEP 2106.05(d)(II). A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. See MPEP 2106.05(d). The required factual determination must be expressly supported in writing. Appropriate forms of support include one or more of the following: (a) A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s); (b) A citation to one or more of the court decisions discussed in Subsection II of MPEP 2106.05(d) as noting the well-understood, routine, conventional nature of the additional element(s); (c) A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and (d) A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s). In the instant application, the limitations of “receive a set of treatment plans,” “receive healthcare professional profile information,” and “receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users” in claims 1, 16, and 31 are determined to be receiving or transmitting data over a network (see MPEP 2106.05(d)(II)(i)) and storing and retrieving information in memory (see MPEP 2106.05(d)(II)(iv)). Paragraph [0137] of U.S. Pub. No. 2016/0273049 demonstrates the well-understood, routine, conventional nature of a network interface device receiving or transmitting software over a network and storing and retrieving information in memory. Furthermore, regarding extra-solution activity in claims 1, 16, and 31, the limitations of “receive a set of treatment plans,” “receive healthcare professional profile information,” and “receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users” in claims 1, 16, and 31 are determined to be insignificant pre-solution activity (e.g., well known and mere data gathering; see MPEP 2106.05(g)(1) and (3)) to the judicial exception. The limitation of the memory including instructions that, when executed by the processor, cause the processor to use an artificial intelligence engine that is configured to use at least one machine learning model (i.e., wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information) is determined to be insignificant post-solution activity (e.g., well known and mere data outputting, i.e., an output device in the form of a computer monitor or a display screen on a tablet, smartphone, or a smart watch as discussed in Paragraph [0079]; see MPEP 2106.05(g)(1) and (3)). Accordingly, for these additional reasons, the additional elements are well-understood, routine, conventional activities previously known to the industry, and claims 1, 16, and 31 do not recite significantly more than a judicial exception.
When viewed individually, as a whole, and as an ordered combination, the claims do not include additional limitations that are sufficient to amount to significantly more than the judicial exception because the claims recite functions that are routine and well-understood in the art of managing and administering healthcare resources, and simply implementing these functions on a computer(s) is not enough to qualify as “significantly more”. Specifically, the applicant is taking the well-understood process of medical data mining for managing healthcare resources on a computer, which does not qualify as significantly more.
As previously discussed under step 2A, prong two of the 2019 Revised Patent Subject Matter Eligibility Guidance, it has been determined that dependent claims 2-15, 17-30, and 32-45 merely further define the judicial exceptions (i.e., CMOHA, mental processes) in associated independent claims 1, 16, and 31. Therefore, claims 2-15, 17-30, and 32-45 do not include an element or a combination of elements that are sufficient to amount to significantly more than the judicial exception (i.e., whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry.
These limitations (i.e., when viewed individually, as a whole, and as an ordered combination) are simply taking the well-understood process of medical data mining for managing healthcare resources on a computer, which does not qualify as significantly more. The limitations (i.e., when viewed individually, as a whole, and as an ordered combination) represent insignificant conventional activities well-understood in the industry of medical data mining and healthcare resource management, and narrowing the idea to using a computer to manage healthcare resources is merely an attempt to limit the use of the abstract idea to a particular technological environment. Furthermore, the additional elements or combination of elements in the dependent claims (claims 2-15, 17-30, and 32-45), other than the abstract idea per se, amount to no more than a recitation of:
A) Generic computer structure that serves to perform generic computer functions that serve to merely link the abstract idea to a particular technological environment (i.e. computer).
B) Generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. mapping, determining, analyzing, normalizing, and grouping).
Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 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.
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.
Claims 1 – 7, 15 – 22, 30 – 37, and 45 are rejected under 35 U.S.C. 103(a) as being unpatentable over Brown in view of Nolan.
Regarding independent claims 1, 16, and 31, Brown teaches the limitations of representative claim 31 identified in bold (i.e., bold italicized font and bold regular font) as:
a. A system (Paragraph [0036] of Brown, Turning now to the drawings, FIG. 1 illustrates a block diagram of an example, non-limiting system 100 for coordinating and optimizing healthcare resource utilization and delivery of healthcare services across an integrated healthcare system using a machine learning framework, in accordance with one or more embodiments of the disclosed subject matter. Embodiments of systems described herein can include one or more machine-executable components embodied within one or more machines (e.g., embodied in one or more computer-readable storage media associated with one or more machines). Such components, when executed by the one or more machines (e.g., processors, computers, computing devices, virtual machines, etc.) can cause the one or more machines to perform the operations described.) comprising: (This limitation in bold italicized font is not recited in claims 1 and 16.)
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b. a processor (Paragraph [0037] of Brown, For example, in the embodiment shown, system 100 includes a healthcare delivery optimization server device 108 and a plurality of healthcare information systems/sources 102. The healthcare delivery optimization server device 108 can include various computer executable components, including but not limited to, a task assessment module 110, a resource assessment module 112 and a task scheduling and resource assignment optimization module 118. The healthcare delivery optimization server device 108 can include or be operatively coupled to at least one memory 124 and at least one processor 122.); and (This limitation in bold italicized font is not recited in claim 1.)
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c. a memory including instructions that, when executed by the processor, cause the processor to (Paragraph [0037] of Brown, For example, in the embodiment shown, system 100 includes a healthcare delivery optimization server device 108 and a plurality of healthcare information systems/sources 102. The healthcare delivery optimization server device 108 can include various computer executable components, including but not limited to, a task assessment module 110, a resource assessment module 112 and a task scheduling and resource assignment optimization module 118. The healthcare delivery optimization server device 108 can include or be operatively coupled to at least one memory 124 and at least one processor 122.): (This limitation in bold italicized font is not recited in claim 1.)
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d. receive a set of treatment plans, wherein each treatment plan comprising the set of treatment plans is associated with a user capable of using a treatment device to perform the associated treatment plan (Paragraph [0063] of Brown, In the embodiment shown, the healthcare information systems/sources 102 include one or more databases that provide static/semi-static system data 106 for an operating entity or group of operating entities, including … patient information 252. Paragraph [0073] of Brown, For example, the patient information 252 can include care plan information 240 that describes or defines care plans for the patients (if available). For example, the care plan can include information a list or timeline of the various prescribed clinical treatment to for the patient in association with a course of patient care. In some implementations, the care plan information can also be associated with information identifying patient rest and recovery periods/times, such as amounts of time and/or periods of time during which the patient is required or preferred to rest (e.g., between procedures or appointments and the like)… The patient information can also include medical history information for current and past patients, such as that provided in the EHR data 244. For example, the patient medical history information can include … information aggregated for patients across various disparate healthcare organizations/vendors (e.g., internal and third-party organizations/vendors) via a healthcare information exchange system (HIE).);
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e. receive healthcare professional profile information associated with respective healthcare professionals comprising a set of healthcare professionals capable of utilizing at least one aspect of the one or more treatment plans comprising the set of treatment plans (Paragraph [0034] of Brown, In one or more embodiments, the system collects and combines real-time and historical data from various integrated healthcare provider systems and sources regarding … all aspects of operations of the different healthcare providers that are available to provide healthcare services to patients. In this regard, the system can access and retrieve or receive operating information from different operating entities in real-time over a course of operating of the one or more operating entities regarding what task needs to be done (e.g., clinical tasks and non-clinical task for performance by a wide range of clinicians, healthcare workers and the like, when and where within the healthcare system at a current point in time and/or over a defined, upcoming period of time. The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care… Although various embodiments are described with the assumption that the one or more individuals include humans, in some implementations, the one or more individuals can include machines that are configured to perform certain healthcare tasks autonomously and/or at the control of a human (e.g., intelligent machines, robots, self-driving vehicles, etc.). The term healthcare worker is used herein to refer to any individual or machine associated with an operating entity that can perform a healthcare task.); 4
f. receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users;
g generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans (Paragraph [0048] of Brown, The task assessment module 110 can further determine … what additional (non-human) resources are used (e.g., supplies, instruments, equipment, technology, etc.). For example, the task assessment module 110 can determine and associate attribute information with each task that identifies a defined type or classification of the task (e.g., determined based on predefined task classification/type coding system), a time of origination of the task, a time or time frame for completing the task, an expected duration of the task, a location associated with the task, a priority level associated with the task, an and the like... The task assessment module 110 can also determine and associate resource attributes with the healthcare tasks that identify or indicate requirements for resources to be used for the tasks, including… requirements for non-human resources, such as required medications, medical supplies, devices, equipment, technology and the like for use in association with performing the respective tasks); and
h. use an artificial intelligence engine that is configured to use at least one machine learning model that is configured to generate resource deployment predictions, generating at least one resource deployment prediction, wherein the at least one machine learning model generates the at least one resource deployment prediction based on at least some treatment plans comprising the set of treatment plans, at least some of the healthcare professional profile information, and at least some of the treatment device information (Paragraph [0056] of Brown, Turning now to the third component, using parameters provided by the indexed task data 112, the resource availability data 116, the dynamic operating data 104 and the static/semi-static system data 106 as input, the task scheduling and resource assignment optimization module 118 can employ one or more machine learning and/or optimization algorithms/models to determine how to schedule performance of the healthcare tasks to optimize utilization of the available resources in view of patient preference and need. In this regard, the task scheduling and resources assignment optimization module 118 can intelligently determine if a task should performed (e.g., based on authorization restrictions, necessity etc.), when the task should performed, where the task should performed, how the task should be performed (e.g., in person, using telemedicine), who or whom should perform the task (e.g., a person or person), and/or what additional (non-human) resources should be used (e.g., supplies, instruments, equipment, technology, etc.), based one or more optimization criteria. For example, the optimization criteria can include (but is not limited to), facilitating optimal patient flow, minimizing delay between performance of tasks, meeting fixed constraints (e.g., regarding timing and order, location, quality/standard of care, etc.), meeting patient preferences/needs, maximizing utilization of resources, minimizing costs, and/or maximizing revenue. Paragraph [0105] of Brown, Various features and functionalities of the task assessment module 108 involve evaluating dynamic operating data 104 in real-time in view of coinciding static/semi-static system data to identify pending and/or forecasted healthcare tasks (e.g., by the task identification component 304), to determine grouping constraints for two or more of the healthcare tasks (e.g., by the task grouping component 308), to determine ordering constraints for two or more healthcare tasks (e.g., by the task ordering component 310), to determine relevant attributes associated with the healthcare tasks (e.g., by the attribute defining component 312), and/or to determine a status (e.g., currently pending or in-progress) of the healthcare tasks (e.g., by the task status monitoring component 314). In some embodiments, the task assessment module 106 can include task assessment machine learning component 318 to facilitate determining one or more of these task parameters in real-time using various suitable machine learning and/or artificial intelligence (AI)-based schemes.).
Brown does not appear to explicitly disclose, but Nolan teaches the limitation “receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users” (Paragraphs [0040], [0059] – [0061], and [0071] – [0073], and [0080] of Nolan. The broadest reasonable interpretation of “receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices” reads on the patient feedback system in Nolan (Paragraphs [0059] – [0061]) receiving, from the monitoring device, collected monitoring data as personal profile data for the patient being monitored, while other patients use respective therapeutic devices with associated therapeutic device settings stored in the database as reference device settings, such that the database is filled automatically and may grow dynamically. The broadest reasonable interpretation of “wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users” reads on the patient feedback system in Nolan (Paragraphs [0040] and [0071] – [0073]) determining the reference patients having identical profile data as the patient (e.g. having the same age, the same physics and the same type and extent of disease, information on those other people choose their therapeutic device settings) and a recommendation adapted to that patient’s particular situation, with the other patients; therapeutic device settings serving as a basis for the patient to change the settings of the therapeutic device if the settings of the therapeutic device are outside the normal use.).
Brown does not appear to explicitly disclose, but Nolan teaches the limitation “generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans” (Paragraph [0073] of Nolan, The broadest reasonable interpretation of “generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information” reads on the type identifier in Nolan (Paragraph [0073]) identifying the therapeutic device of the patient or reference patient could be included in the profile data (in the personal profile data of the patient or the reference profile data of each of the reference patients), with the type identifier being a device type identifier indicating which type of device the patient or reference patient uses, or a serial number for uniquely identifying the exact device the patient or reference patient uses.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the system of Brown to cause the processor to: receive, while a plurality of users of a cohort of users uses each respective treatment device of the set of treatment devices, one or more measurements from one or more sensors associated with respective treatment devices of a set of treatment devices, wherein each user of the plurality of users of the cohort of users has at least one healthcare characteristic that is the same as at least one healthcare characteristic of each other user of the plurality of users of the cohort of users, and wherein each treatment plan of the set of treatment plans is configured based on the at least one healthcare characteristic of each user of the plurality of users of the cohort of users, and generate, based on the one or more measurements from the one or more sensors associated with the respective treatment devices of the set of treatment devices, treatment device information for each treatment device of the set of treatment devices, wherein each treatment device is capable of being used by the cohort of users associated with respective treatment plans comprising the set of treatment plans, as taught by Nolan (Paragraphs [0040], [0059] – [0061], and [0071] – [0073], and [0080]) in order to obtain dedicated feedback on how other patients with comparable profiles (comparison of personal profile data of the patient to reference profile data, i.e. personal profile data of the reference patients) use their therapeutic devices (Paragraph [0033] of Nolan).
Regarding claims 2, 17, and 32, Brown as modified by Nolan and applied to claims 1, 16, and 31 teaches that, according to the at least one resource deployment prediction, at least one user associated with at least one treatment plan of the set of treatment plans is enabled to perform the at least one treatment plan using at least one treatment device comprising the set of treatment devices (Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like. In some implementations, the healthcare tasks can include non-clinical tasks, such as tasks involving transporting patients and/or supplies, EVS tasks, task involving equipment/technology repair and maintenance, administrative tasks (e.g., verifying insurance qualifications, obtaining authorization for procedures, etc.), and the like. Although various embodiments are described with the assumption that the one or more individuals include humans, in some implementations, the one or more individuals can include machines that are configured to perform certain healthcare tasks autonomously and/or at the control of a human (e.g., intelligent machines, robots, self-driving vehicles, etc.). The term healthcare worker is used herein to refer to any individual or machine associated with an operating entity that can perform a healthcare task.).
Regarding claims 3, 18, and 33, Brown as modified by Nolan and applied to claims 2, 17, and 32 teaches that, during a telemedicine session, the at least one user associated with the at least one treatment plan comprising the set of treatment plans is enabled to perform the at least one treatment plan using the at least one treatment device comprising the set of treatment devices (Paragraph [0031] of Brown, Various embodiments of the disclosed subject matter provide systems, methods and computer readable media that enable an integrated delivery system using a team-based approach to deliver healthcare services to patients across various patient care settings (e.g., inpatient, outpatient, home, doctor's office, telemedicine/virtual, etc.), balancing and coordinating all patient's needs, available operating entity capabilities and resources in real-time and space. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like. In some implementations, the healthcare tasks can include non-clinical tasks, such as tasks involving transporting patients and/or supplies, EVS tasks, task involving equipment/technology repair and maintenance, administrative tasks (e.g., verifying insurance qualifications, obtaining authorization for procedures, etc.), and the like. Although various embodiments are described with the assumption that the one or more individuals include humans, in some implementations, the one or more individuals can include machines that are configured to perform certain healthcare tasks autonomously and/or at the control of a human (e.g., intelligent machines, robots, self-driving vehicles, etc.). The term healthcare worker is used herein to refer to any individual or machine associated with an operating entity that can perform a healthcare task.).
Regarding claims 4, 19, and 34, Brown as modified by Nolan and applied to claims 1, 16, and 31 teaches that, for a respective healthcare professional comprising the set of healthcare professionals, the healthcare professional profile information includes at least one of information associated with the respective healthcare professional, credential or degree information associated with the respective healthcare professional, professional experience information associated with the respective healthcare professional, and availability information associated with the respective healthcare professional (Paragraph [0034] of Brown, The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like. Paragraph [0048] of Brown, The task assessment module 110 can also determine and associate resource attributes with the healthcare tasks that identify or indicate requirements for resources to be used for the tasks, including requirements for healthcare workers authorized to perform the tasks (e.g., based on credentials, job description, performance levels, fatigue levels, etc.).).
Regarding claims 5, 20, and 35, Brown as modified by Nolan and applied to claims 1, 16, and 31 teaches that, for a respective healthcare professional comprising the set of healthcare professionals, the healthcare professional profile information includes at least one of identities of healthcare professionals potentially available to treat the user if the respective healthcare professional is unavailable, and information associated with the healthcare professionals potentially available (Paragraph [0034] of Brown, The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like.).
Regarding claims 6, 21, and 36, Brown as modified by Nolan and applied to claims 1, 16, and 31 teaches that an associated one of the method and computer readable medium that, for a respective treatment device comprising the set of treatment devices, the treatment device information includes at least one of identification information associated with the respective treatment device; location information associated with the respective treatment device; and availability information associated with the respective treatment device (Paragraph [0054] of Brown, Although the embodiments, described above are directed to evaluating and determining availability of healthcare workers (e.g., humans), the resource assessment module 114 can also evaluate relevant dynamic operating data 104 to determine information regarding the availability of other system resources. For example, the other system resources can include supplies, instruments, equipment, machines, technology, and the like that are needed to perform and/or facilitate performance of the healthcare tasks. Thus, in some embodiments, the resources availability data 116 can also include information regarding availability of other resources, such as current availability status of the resources (e.g., whether they are in-use, clean, dirty, in repair, offline, overloaded, power levels, etc.), expected availability status of the resources (e.g., when they will be available for use), locations of the resources, and the like.).
Regarding independent claims 7, 22, and 37, Brown as modified by Nolan and applied to claims 1, 16, and 31 teaches that the at least one resource deployment prediction defines a mapping between or among at least some of the healthcare professionals comprising the set of healthcare professionals, the users associated with respective treatment plans comprising the set of treatment plans, and at least some treatment devices comprising the set of treatment devices (Paragraph [0034] of Brown, The system can further extract and receive up-to-date information from the different operating entities regarding who or whom is available to perform the tasks, and who is the best person/persons to perform the tasks. Paragraph [0043] of Brown, In this regard, the term healthcare task as used herein can include essentially any defined (predefined or defined by the task assessment module 110) task involved in a course of patient care that is provided by and/or controlled by one or more individuals that are associated with the operating entity or entities involved in the course of patient care. The one or more individuals can include employees/staff, volunteers, independent contractors, patients, and/or patient aids (e.g., a friend or family member of the patient, a personal assistant/aid of the patient, etc.). For example, the healthcare tasks can include clinical tasks, clerical tasks, administrative tasks, environmental services tasks and the like, performed by and/or facilitated by clinicians, physicians, nurses, pharmacists, social workers, therapists, emergency services clinicians/technicians, community health workers, transportation workers, care coordinators, law enforcement, complimentary/alternative medicine practitioners, behavioral health workers, environmental services workers, food service workers, and the like.).
Regarding independent claims 15, 30, and 45, Brown as modified by Nolan and applied to claims 1, 16, and 31 teaches that the method, computer readable medium, and system implementing the treatment device information includes at least information indicating an availability of the treatment device (Paragraph [0054] of Brown, Although the embodiments, described above are directed to evaluating and determining availability of healthcare workers (e.g., humans), the resource assessment module 114 can also evaluate relevant dynamic operating data 104 to determine information regarding the availability of other system resources. For example, the other system resources can include supplies, instruments, equipment, machines, technology, and the like that are needed to perform and/or facilitate performance of the healthcare tasks. Thus, in some embodiments, the resources availability data 116 can also include information regarding availability of other resources, such as current availability status of the resources (e.g., whether they are in-use, clean, dirty, in repair, offline, overloaded, power levels, etc.), expected availability status of the resources (e.g., when they will be available for use), locations of the resources, and the like.).
Claims 8 – 12, 23 – 27, and 38 – 42 are rejected under 35 U.S.C. 103(a) as being unpatentable over Brown as modified by Nolan and applied to claims 1, 16, and 31, and further in view of Gnanasambandam.
Regarding claims 8, 23, and 38, Brown as modified by Nolan and applied to claims 1, 16, and 31 does not appear to explicitly disclose, but Gnanasambandam teaches that the method, computer readable medium, and system implement the at least one resource deployment prediction is associated with an optimized outcome for the cohort of users associated with the respective treatment plans (Paragraph [0217] of Gnanasambandam, In some embodiments, the artificial intelligence engine 100 may generate the resource utilization plan to minimize costs to the healthcare facility. For example, the machine learning models may be trained to minimize a cost objective function that performs numerous iterations adjusting costs associated with resources to find a combination of resource utilization that provides a lowest cost relative to other combinations. The iterations may be performed in various simulations using various utilization types (e.g., admittance of a patient, emergency, specialist, specialist follow-up, primary care, and laboratory) and resource requirements for an integrated delivery network to determine a maximum resource utilization at a minimum cost. A simulation may include scheduling a first number of healthcare professionals at a first cost and ordering a first number of laboratory testing supplies at a first cost and determining a first resource utilization level and a first total cost; then, another simulation may include scheduling a second number of healthcare professionals at a second cost and ordering a second number of laboratory testing supplies at a second cost and determining a second resource utilization level and second total cost. The artificial intelligence engine 100 may compare the first resource utilization level and total cost to the second resource utilization level and total cost to determine which resource utilization plan and/or total cost are more desirable.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Brown as modified by Nolan such that the at least one resource deployment prediction is associated with an optimized outcome for the cohort of users associated with the respective treatment plans, as taught by Gnanasambandam (Paragraph [0217]) in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs (Paragraph [0002] of Gnanasambandam).
Regarding claims 9, 24, and 39, Brown as modified by Nolan and applied to claims 1, 16, and 31 does not appear to disclose, but Gnanasambandam teaches that the method, computer readable medium, and system include instructions further causing the processor to identify super-cohorts of users comprising the cohort of users associated with respective treatment plans comprising the set of treatment plans, wherein, further, based on the identified super-cohorts of users, the at least one machine learning model generates the at least one resource deployment prediction (Paragraph [0202] of Gnanasambandam, As depicted in FIG. 17 , the data has been assigned to different cohorts. Cohort A includes data for patients having similar first attributes, first disease progression levels, first treatment plans, first results, first utilization types, first resources, and first costs. Cohort B includes data for patients having similar second attributes, second disease progression levels, second treatment plans, second results, second utilization types, second resources, and second costs. For example, cohort A may include first attributes of patients in their twenties without any additional medical conditions, and such cohort A patients' disease progression levels may indicate a low risk of reaching the next stage of a disease continuum. Based on the disease progression level of the patients, cohort A may include first resources for these users that staff a low amount of healthcare professionals over a certain period of time because it is unlikely they will be needed at a healthcare facility for these patients. In such a way, resources may not be wasted by over staffing a healthcare facility and the resources not staffed may be staffed somewhere else. Paragraph [0203] of Gnanasambandam, Further, cohort B may include second attributes of patients in their sixties with one or more additional medical conditions, and cohort B patients' disease progression levels may indicate a high risk of reaching the next stage of the disease continuum. Based on the disease progression level of the patients, cohort B may include second resources for these users that staff a high amount of healthcare professionals over a certain period of time because it is likely they will be needed at a healthcare facility for these patients. In such a way, resources may be staffed where needed as appropriate.).
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Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Brown as modified by Nolan such that the instructions further cause the processor to identify super-cohorts of users comprising the cohort of users associated with respective treatment plans comprising the set of treatment plans, wherein, further, based on the identified super-cohorts of users, the at least one machine learning model generates the at least one resource deployment prediction, as taught by Gnanasambandam (Paragraphs [0202] and [0203]) in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs (Paragraph [0002] of Gnanasambandam).
Regarding claims 10, 25, and 40, Brown as modified by Nolan and applied to claims 1, 16, and 31 does not appear to explicitly disclose, but Gnanasambandam teaches that the method, computer readable medium, and system include instructions further causing the processor to receive, subsequent to the at least one machine learning model generating the at least one resource deployment prediction, at least one of a subsequent treatment plan, subsequent healthcare professional profile information, and subsequent treatment device information (Paragraph [0216] of Gnanasambandam, In some embodiments, the actionable item may include ordering one or more laboratory diagnostic test supplies treatment plan may indicate the patient should start taking one or more new medications, adjust dosage levels of one or more medications, stop taking one or more medications, or a combination thereof. Alternatively, or in addition, actionable items may include one or more lab test to perform on the patient. For example, the treatment plan may indicate the patient should start having one or more new lab tests. Paragraph [0217] of Gnanasambandam, In some embodiments, the artificial intelligence engine 100 may generate the resource utilization plan to minimize costs to the healthcare facility. For example, the machine learning models may be trained to minimize a cost objective function that performs numerous iterations adjusting costs associated with resources to find a combination of resource utilization that provides a lowest cost relative to other combinations. The iterations may be performed in various simulations using various utilization types (e.g., admittance of a patient, emergency, specialist, specialist follow-up, primary care, and laboratory) and resource requirements for an integrated delivery network to determine a maximum resource utilization at a minimum cost. A simulation may include scheduling a first number of healthcare professionals at a first cost and ordering a first number of laboratory testing supplies at a first cost and determining a first resource utilization level and a first total cost; then, another simulation may include scheduling a second number of healthcare professionals at a second cost and ordering a second number of laboratory testing supplies at a second cost and determining a second resource utilization level and second total cost. The artificial intelligence engine 100 may compare the first resource utilization level and total cost to the second resource utilization level and total cost to determine which resource utilization plan and/or total cost are more desirable.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Brown as modified by Nolan such that the instructions further cause the processor to receive, subsequent to the at least one machine learning model generating the at least one resource deployment prediction, at least one of a subsequent treatment plan, subsequent healthcare professional profile information, and subsequent treatment device information, as taught by Gnanasambandam (Paragraphs [0216] and [0217]) in order to improve the health outcomes of a group by improving clinical outcomes while lowering costs (Paragraph [0002] of Gnanasambandam).
Regarding claims 11, 26, and 41, Brown as modified by Nolan and applied to claims 10, 25, and 40 does not appear to explicitly disclose, but Gnanasambandam teaches that the method, computer readable medium, and system include:
a. the subsequent treatment plan that corresponds to one of at least one treatment plan comprising the set of treatment plans or to a treatment plan not in the set of treatment plans (Paragraph [0041] of Gnanasambandam, Medical data may be stored in the data store 108 in the form of electronic health records (EHRs) that are associated with one or more patients. In some implementations, EHRs from different, disparate medical providers of a patient are stored in the data store 108. The health information exchanged between computing devices in the system 100 (e.g., between client computing device 104 and another computing device) may include health records associated with a patient such as medical and treatment histories of the patient but can go beyond standard clinical data collected by a healthcare provider. For example, health records may include a patient's medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory and test results.);
b. the subsequent healthcare professional profile information that corresponds to one of at least one healthcare professional comprising at least the set of healthcare professionals and at least a healthcare professional not in the set of healthcare professionals; and (Paragraph [0034] of Gnanasambandam, “Results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. A “medical action(s)” may refer to any suitable action(s) performed by a healthcare professional, and such action or actions may include diagnoses, prescriptions for treatment plans.); and
c. the subsequent treatment device information that corresponds to one of at least one treatment device comprising the set of treatment devices and at least one treatment device not in the set of treatment devices (Paragraph [0038] of Gnanasambandam, The server 102 is configured to store and to provide data related to managing treatment plans. The server 102 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 102 may be configured to store data regarding treatment plans. For example, the server 102 may be configured to hold system data, such as data pertaining to treatment plans for treating one or more patients. The server 102 may also be configured to store data regarding performance by a patient in following a treatment plan. For example, the server 102 may be configured to hold medical data, such as data pertaining to one or more patients, including data representing each patient's performance within the treatment plan. In addition, the server 102 may store attributes (e.g., personal, performance, measurement, etc.) of patients, disease progression levels of medical conditions of patients, treatment plans followed by patients, results of the treatment plans, utilization types (e.g., admittance to healthcare facility, emergency, specialty healthcare professional, specialty follow-up, lab work, etc.) resources of healthcare facilities (e.g., available healthcare professionals, available rooms, available medical imaging devices, available laboratory testing supplies, etc.).).
Regarding claims 12, 27, and 42, Brown as modified by Nolan and applied to claims 10, 25, and 40 does not appear to explicitly disclose, but Gnanasambandam teaches that that the method, computer readable medium, and system include instructions further causing the processor to generate, using the at least one machine learning model via the artificial intelligence engine , at least one subsequent resource deployment prediction, wherein the at least one machine learning model generates the at least one subsequent resource deployment prediction based, at least in part, on the subsequent treatment plan, the subsequent healthcare professional profile information, and the subsequent treatment device information (Paragraph [0216] of Gnanasambandam, In some embodiments, the actionable item may include ordering one or more laboratory diagnostic test supplies treatment plan may indicate the patient should start taking one or more new medications, adjust dosage levels of one or more medications, stop taking one or more medications, or a combination thereof. Alternatively, or in addition, actionable items may include one or more lab test to perform on the patient. For example, the treatment plan may indicate the patient should start having one or more new lab tests. Paragraph [0217] of Gnanasambandam, In some embodiments, the artificial intelligence engine 100 may generate the resource utilization plan to minimize costs to the healthcare facility. For example, the machine learning models may be trained to minimize a cost objective function that performs numerous iterations adjusting costs associated with resources to find a combination of resource utilization that provides a lowest cost relative to other combinations. The iterations may be performed in various simulations using various utilization types (e.g., admittance of a patient, emergency, specialist, specialist follow-up, primary care, and laboratory) and resource requirements for an integrated delivery network to determine a maximum resource utilization at a minimum cost. A simulation may include scheduling a first number of healthcare professionals at a first cost and ordering a first number of laboratory testing supplies at a first cost and determining a first resource utilization level and a first total cost; then, another simulation may include scheduling a second number of healthcare professionals at a second cost and ordering a second number of laboratory testing supplies at a second cost and determining a second resource utilization level and second total cost. The artificial intelligence engine 100 may compare the first resource utilization level and total cost to the second resource utilization level and total cost to determine which resource utilization plan and/or total cost are more desirable.).
Claims 13, 14, 28, 29, 43, and 44 are rejected under 35 U.S.C. 103(a) as being unpatentable over Brown as modified by Nolan and applied to claims 1, 16, and 31, and further in view of Einav.
Regarding claims 13, 28, and 43, Brown as modified by Nolan and applied to claims 1, 16, and 31 does not appear to explicitly disclose, but Einav teaches that the method, computer readable medium, and system with at least one treatment device of the set of treatment devices includes at least one pedal (Paragraph [0120] of Einav, Mr. Smith's therapy plan may include some exercises designed to develop coordinated movement of upper and lower body parts. Optionally, this may be accomplished by use of multiple modules 350 as detailed hereinabove. Alternately, or additionally, an exercise device 600 in accordance with an exemplary embodiment of the invention (FIG. 6) may be employed, for example a full body treatment device located in daily activity room 280 or private room 235. According to this embodiment of the invention, optional lower limb training section includes a base 602 having a pedal 604 mounted thereon. Optionally, pedal 604 is capable of rotary motion relative to a horizontal axis (e.g., as in a bicycle) alternatively or additionally to rotation around its axis.).
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Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Brown as modified by Nolan such that at least one treatment device of the set of treatment devices includes at least one pedal, as taught by Einav (Paragraph [0120]) in order to organize rehabilitation to reduce wastage of time of the patient, reduce waiting time, and make efficient use of the patient's time and/or rest periods (Paragraph [0006] of Einav).
Regarding claims 14, 29, and 44, Brown as modified by Nolan and applied to claims 1, 16, and 31 does not appear to explicitly disclose, but Einav teaches that the method, computer readable medium, and system with at least one treatment device of the set of treatment devices includes at least one hand grip or hand pedal (Paragraph [0120] of Einav, Mr. Smith's therapy plan may include some exercises designed to develop coordinated movement of upper and lower body parts. Optionally, this may be accomplished by use of multiple modules 350 as detailed hereinabove. Alternately, or additionally, an exercise device 600 in accordance with an exemplary embodiment of the invention (FIG. 6) may be employed, for example a full body treatment device located in daily activity room 280 or private room 235. According to this embodiment of the invention, optional lower limb training section includes a base 602 having a pedal 604 mounted thereon. Optionally, pedal 604 is capable of rotary motion relative to a horizontal axis (e.g., as in a bicycle) alternatively or additionally to rotation around its axis. As shown, two base sections 602 and 602' (with a pedal 604') are shown. A chair, for example, Mr., Smith's robotic chair 150 is optionally placed between the base sections. An optional upper limb section comprises at least one arcuate element 606 on which at least one limb unit 612 is attached. Arcuate element 606 is optionally hinged so that it can rotate around an axis 624. A first hinge 608 attaches arcuate element 606 to base 602. A second hinge 610 may be used to couple arcuate element 606 to its mirror element 606'. Limb unit 612 optionally includes a base section 614 capable of manual, motorized and/or resistance to motion along arcuate element 606. A handle 616 optionally telescopes from base section 614 and includes an optional grip 618 at its end.).
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Therefore, it would have been obvious to one of ordinary skill in the art of medical data mining and healthcare resource management at the time of the filing to modify the method, computer readable medium, and system of Brown as modified by Nolan such that the treatment device information includes at least information indicating an availability of the treatment device, as taught by Einav (Paragraph [0120]) in order to organize rehabilitation to reduce wastage of time of the patient, reduce waiting time, and make efficient use of the patient's time and/or rest periods (Paragraph [0006] of Einav).
Response to Amendment
Applicant’s arguments (Third Paragraph on Page XXX to Last Paragraph on Page 17of the Amendment filed January 21, 2026) regarding the provisional double patenting rejection of claims 1 – 45 have been fully considered and are moot in view of the new grounds of rejection necessitated by the amendment.
MPEP 804(I)(B)(1) states “A complete response to a nonstatutory double patenting (NSDP) rejection is either a reply by applicant showing that the claims subject to the rejection are patentably distinct from the reference claims, or the filing of a terminal disclaimer in accordance with 37 CFR 1.321 in the pending application(s) with a reply to the Office action (see MPEP § 1490 for a discussion of terminal disclaimers). Such a response is required even when the nonstatutory double patenting rejection is provisional. As filing a terminal disclaimer, or filing a showing that the claims subject to the rejection are patentably distinct from the reference application’s claims, is necessary for further consideration of the rejection of the claims, such a filing should not be held in abeyance. Only compliance with objections or requirements as to form not necessary for further consideration of the claims may be held in abeyance until allowable subject matter is indicated.”
Applicant's arguments (First Paragraph on Page 18 to Fourth Paragraph on Page 19 of the Amendment filed January 21, 2026) regarding the rejection of claims 1 – 45 under 35 U.S.C. § 101 have been fully considered and are moot in view of the new grounds of rejection necessitated by the amendment.
Applicant's arguments (Fifth Paragraph on Page 19 to Last Paragraph on Page 25 of the Amendment filed January 21, 2026) regarding the rejections of claims 1 – 45 under 35 U.S.C. § 103 have been fully considered and are moot in view of the new grounds of rejection necessitated by the amendment.
Conclusion
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/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686