DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The office action is in response to the claim amendments and remarks filed on October 23, 2025 for the application filed September 22, 2021 which claims priority to a provisional application filed on September 25, 2020. Claims 1-2, 12-13, 26, 28 and 29 have been amended, claims 3-7, 9-11, 14-18, 20 and 27 have been cancelled and claims 30-33 have been newly added. Claims 1-2, 8, 12-13, 19 and 21-26 and 28-33 are currently pending and have been examined.
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 recites “the received the one or more second measurements” which should recite “the received one or more second measurements”. Claim 1 recites “the machine learning algorithm” in the first updating step, which should recite “the first machine learning algorithm”. Appropriate correction is required.
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 1-2, 8, 12-13, 19, 21-26 and 28-33 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, claims 1-2, 8 and 21-26, 28 and 30-33 are directed towards a method (i.e. a process), which is a statutory category. Claims 12-13 and 19 are directed towards a system (i.e. a machine), which is a statutory category. Claim 29 is directed to a non-transitory computer-readable medium (i.e. a manufacture) Since the claims are directed toward statutory categories, it must be determined if the claims are directed towards a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea). In the instant application, the claims are directed towards an abstract idea.
Eligibility Step 2A, Prong One:
Under step 2A, prong one of the 2019 Revised Patent Subject Matter Eligibility Guidance, independent claims 1, 12 and 29 are determined to be directed to an judicial exception because an abstract idea is recited in the claims which fall within the subject matter groupings of abstract ideas. The abstract idea (identified in bold) recited in the representative claim 12 is identified as:
memory; and
one or more processors coupled to the memory, the one or more processors being configured to:
determine an agenda of a consultation with a patient using a machine learning algorithm, the agenda including a first item of a first clinical specialty and a second item of a second clinical specialty, the agenda indicating a sequence in which the first item and the second item are to be addressed during the consultation;
automatically load an identifier associated with the patient responsive to reaching a time for the consultation with the patient;
display simultaneously to the patient, by a display device at a medical care site;
dynamic contextual data relevant to a current context of the consultation
items of the agenda listed in an order to be addressed during the consultation, the items including the first item of the first clinical specialty and the second item of the second clinical specialty subsequent to the first item in the order, and
dynamic suggestions indicating one or more actions, tests or treatments to be taken in the current context of the consultation;
responsive to reaching the first item for addressing during the consultation:
automatically load a first selected portion of patient data relevant to the first item using the loaded identifier;
display simultaneously to the patient, by the display device, the first selected portion of the patient data and a first transcription of a speech recognized during the consultation of the first item;
automatically receive one or more first measurements associated with the first item from one or more first devices;
automatically update the first selected portion of the patient data according to the received one or more first measurements;
determine a first series of actions to be taken with respect to the first item by a second machine learning algorithm based on the first selected portion of patient data;
display, to the patient, the updated selected portion of the data;
updating the second item of the agenda in real time using the machine learning algorithm, during the consultation according to the received one or more first measurements associated with the first item;
responsive to reaching the updated second item for addressing subsequent to concluding of the first item during the consultation:
automatically load a second selected portion of the patient data relevant to the second item and different from the first selected portion;
determining a second series of actions to be taken with respect to the second item by the second machine learning algorithm based on the selected second portion of the data (claim 1);
automatically display simultaneously to the patient, by the display device, the second selected portion of the patient data and a second transcription of speech recognized during the consultation of the second item;
automatically receive one or more second measurements associated with the second item from one or more second devices;
update the second selected portion of the patient data according to the received one or more measurements; and
display, to the patient, the updated selected portion of the data.
The identified limitations of the abstract idea of claims 1 and 12 fall within the subject matter grouping of certain methods of organizing human activity related and the sub grouping of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The claims recite a method of determining an agenda of a sequence of clinical specialty items to be addressed during a consultation for a patient, determining dynamic contextual information, determining dynamic suggestions, loading patient data related to the items, receiving measurements from devices related to the items, updating the patient data based on the received data and determining a series of actions to be taken for items based on patient data. This is a method of managing the personal behavior of a physician during a patient consultation, which includes the interaction between a physician and a computer, such as a EMR system computer, for viewing and updating patient data relevant to items to be addressed during the consultation using measurements obtained during the patient consultation. For example, physicians determining a list of ordered items to be addressed during a patient consultation and interacting with an EMR system to load patient data relevant to the items, receiving measurements, updating the patient data based on received measurements to address the items is an human activity and determining actions to be taken. See MPEP §2106.04(a)(2)II. Simply reciting that the method is performed automatically using one or more processors and memory and does not take the claim out of the certain methods of organizing human activity grouping.
The limitation of “determining an agenda of a consultation”, “updating the second item…” and determining a series of actions to be taken” are determined to fall within the subject matter grouping of mental processes as this may be performed in the human mind using observations, judgements, evaluations and opinions. Additionally, while the claims recite displaying dynamic contextual information, items of the agenda,, dynamic suggestions and transcriptions of speech, it stands to reason that in order to display certain information, it must first be identified/determined, which may be performed in the human mind using observations, judgements, evaluations and opinions and therefore, these limitations also fall within the subject matter grouping of mental processes.
Accordingly, claims 1, 12 and 29 recite an abstract idea under step 2A, prong one.
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 identified abstract ideas are integrated into a practical application. After evaluation, there is no indication that any additional elements or combination of elements integrate the abstract idea 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.
The additional elements recited in representative claim 12 are identified in italics as:
memory; and
one or more processors coupled to the memory, the one or more processors being configured to:
determine an agenda of a consultation with a patient using a machine learning algorithm, the agenda including a first item of a first clinical specialty and a second item of a second clinical specialty, the agenda indicating a sequence in which the first item and the second item are to be addressed during the consultation;
automatically load an identifier associated with the patient responsive to reaching a time for the consultation with the patient;
display simultaneously to the patient, by a display device at a medical care site;
dynamic contextual data relevant to a current context of the consultation
items of the agenda listed in an order to be addressed during the consultation, the items including the first item of the first clinical specialty and the second item of the second clinical specialty subsequent to the first item in the order, and
dynamic suggestions indicating one or more actions, tests or treatments to be taken in the current context of the consultation;
responsive to reaching the first item for addressing during the consultation:
automatically load a first selected portion of patient data relevant to the first item using the loaded identifier;
display simultaneously to the patient, by the display device, the first selected portion of the patient data and a first transcription of a speech recognized during the consultation of the first item;
automatically receive one or more first measurements associated with the first item from one or more first devices;
automatically update the first selected portion of the patient data according to the received one or more first measurements;
determine a first series of actions to be taken with respect to the first item by a second machine learning algorithm based on the first selected portion of patient data;
display, to the patient, the updated selected portion of the data;
updating the second item of the agenda in real time using the machine learning algorithm, during the consultation according to the received one or more first measurements associated with the first item;
responsive to reaching the updated second item for addressing subsequent to concluding of the first item during the consultation:
automatically load a second selected portion of the patient data relevant to the second item and different from the first selected portion;
determining a second series of actions to be taken with respect to the second item by the second machine learning algorithm based on the selected second portion of the data (claim 1);
automatically display simultaneously to the patient, by the display device, the second selected portion of the patient data and a second transcription of speech recognized during the consultation of the second item;
automatically receive one or more second measurements associated with the second item from one or more second devices;
update the second selected portion of the patient data according to the received one or more measurements; and
display, to the patient, the updated selected portion of the data.
The additional limitations of “memory”, “one or more processors coupled to the memory, the one or more processors being configured to”, “using a first machine learning algorithm”, “by a/the second machine learning algorithm”, “by a display device” and “automatically” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f). The memory, processor and display device are recited at a high level of generality and used in their ordinary capacity to present data and update data. The limitations reciting “using a machine learning algorithm” and “by a/the second machine learning algorithm” provides nothing more than mere instructions to implement an abstract idea on a generic computer. The machine learning algorithms are used to generally apply the abstract idea without placing any limits on how the machine learning algorithms function. Rather, these limitations only recite the outcome of “determining an agenda” and “determining a series of actions to be taken” and do not include any details about how the “determining” are accomplished. Therefore, these additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or no more than mere instructions to implement an abstract idea or other exception on a computer or no more than merely using a computer as a tool to perform an abstract idea.
The additional limitations of “displaying simultaneously …” and “displaying, to a patient…” are determined to be no more than insignificant extra-solution activity to the judicial exception under MPEP §2106.05(g). As an initial matter, it is noted that certain claim limitations require more than just the identification of information (e.g., dynamic contextual data, items of an agenda, dynamic suggestions, patient data, transcriptions of speech), but also the display of such information. It stands to reason that in order to display certain information, it first must be identified. The additional aspect of displaying that which has been identified is merely extra-solution activity that does not confer patent eligibility. See, e.g., Elec. Power, 830 F.3d at 1355 (explaining that “selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes”); Elec. Power, 830 F.3d at 1354 (recognizing “that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis’). Therefore the additional limitations of “displaying simultaneously...” and “displaying simultaneously, to a patient…” is considered to be insignificant extra-solution activity. That the claims recite displaying specific information does not otherwise confer patent eligibility. See MPEP §2106.05(g).
Accordingly, claims 1, 12 and 29 do not recite additional elements which integrate the abstract idea into a practical application.
Eligibility Step 2B:
Under step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “memory”, “one or more processors coupled to the memory, the one or more processors being configured to”, “using a machine learning algorithm”, “automatically” and “by a display device” are determined to be mere instructions to apply an abstract idea under MPEP §2106.05(f), and the “displaying…” limitations are determined to be no more than the insignificant extra-solution activity of necessary data outputting to the judicial exception under MPEP §2106.05(g), which is do not amount to significantly more than the abstract idea and is well-understood, routine and conventional as evidenced by MPEP §2106.05(g). Furthermore, displaying to a patient, transcriptions of speech recognized during a consultations simultaneously with other information is also determined to be well-understood, routine and conventional as evidenced by Leonard (U.S. Pub. No. 2018/0018/966) as it is well known to display this information and the claims provide no details as to improvements to the technology for speech recognition itself.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Dependent Claims:
The dependent claims merely present additional abstract information in tandem with further details regarding the elements from the independent claims and are, therefore, directed to an abstract idea for similar reasons as given above. Dependent claims 2 and 13 merely further define the consultation actions, which is encompassed by the abstract idea identified in the claims. Dependent claims 8 and 19 merely define the devices from which data is received, which does not change the analysis that the receiving of data is the insignificant extra-solution activity of receiving data and well-understood, routine and conventional as evidenced by MPEP §2106.05(g). Claims 21-23 merely recite the human activity of sharing real-time data and receiving data applied using generic devices which may be wearable which is mere instructions to apply an abstract idea under MPEP §2106.05(f). Dependent claims 24-26 and 28 merely describe making determinations and generating data which are mental processes and human activities and the additional limitations of using generic machine learning, display devices which are mere instructions to apply an abstract idea under MPEP §2106.05(f) and the displaying of data which is the insignificant extra-solution activity mere necessary data outputting under MPEP §2106.05(g). Dependent claims 30-33 merely describe the human activity and mental process of generating plans and series of steps associated with the plans and displaying the plans and series of steps along with other information during the relevant consultations which is the insignificant extra-solution activity mere necessary data outputting under MPEP §2106.05(g).
Therefore, whether taken individually or as an ordered combination, Claims 1-2, 8, 12-13, 19 and 21-26 and 28-33 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 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 1-2, 8, 12-13, 19, 21-23, 26 and 29-33 are rejected under 35 U.S.C. 103 as being unpatentable over Rush et al. (U.S. Pub. No. 2021/0134446) in view of Morgan et al. (U.S. Patent No. 10,943,407) and Leonard (U.S. Pub. No. 2018/0018/966).
Regarding claim 1, Rush discloses a method comprising
determining an agenda of a consultation with a patient Paragraph [0052], , the routine 900 can stock specific physical health testing devices into the modular system in accordance with the user's account and/or subscription tier. Paragraph [0064], the software application can provide the user step-by-step instructions for performing a physical exam using the physical health testing devices and related objects included in the modular system. Paragraph [0065], The physical exam overview UI can provide the user an overview of a sequence of physical health tests that are included in the physical exam. Paragraph [0031], the system 200 can include additional physical health testing devices and/or related objects in addition to or in lieu of the physical health testing devices and related objects illustrated in FIG. 2B. The systems 200 illustrated in FIGS. 2B and/or 2C may include vision testing systems, dermatological screening systems, ophthalmic physical health testing devices. Fig. 10J-10L show tests associated with cardiac conditions, such as tachycardia. Paragraph [0094], the routine 900 can recommend that a user consult a healthcare professional in the event that generated health data falls outside of a corresponding healthy and/or normal range of data. the routine 900 can recommend specific healthcare professionals or hospitals (e.g., healthcare professionals or hospitals in the user's geographic area, healthcare professionals specializing in a corresponding medical field, etc.). A sequence of tests to be performed during an physical exam which includes tests related to cardiology (i.e. tachycardia), dermatology, vision, ophthalmology, etc., is construed as an agenda indicating a sequence of first test items and second test items having different clinical specialties to be addressed during a consultation.);
automatically loading an identifier associated with the patient responsive to reaching a time for the consultation with the patient (Paragraph [0054], the routine 900 registers the modular physical health testing system (e.g., the hub of the modular system) to a specific user's account (e.g., such that the modular system is operable only by the user and only when the user (i) logs into his/her account on a related software application (block 905) and (ii) connects the software application to the communications hub of the modular system (block 906)). Paragraph [0055], the routine 900 continues by supplying the modular physical health testing system to the user. …the routine 900 can supply the modular system to the user via other means (e.g., by making the system available for checkout to the user, such as at a pharmacy or hospital). Paragraph [0050], the service is an agreement to supply… a modular physical health testing system 200 to a user (e.g., once, twice, or more per year). …the user performs a physical exam each time he/she is supplied the system 200 to generate data corresponding to his/her physical health. Creating/logging into an account when the modular system is supplied to a user according to an agreement is construed as loading an identifier at a time for consultation with the patient.);
displaying simultaneously to the patient, by a display device at a medical care site (Paragraph [0050], users are able to run a variety of physical health tests from any location. Paragraph [0055], … such as at a pharmacy or hospital. Paragraph [0065], the user can view, access, and/or interface with several of these services and/or functions via one or more UIs of the software application that can be presented on a screen of the user's device when the software application is open and/or connected to the hub.):
dynamic contextual data relevant to a current context of the consultation (Paragraph [0065], The physical exam overview UI can provide the user an overview of a sequence of physical health tests that are included in the physical exam. In some embodiments, the physical exam overview UI can be presented to the user before starting each of the physical health tests. In these and other embodiments, the physical exam overview UI can include visual indicators that inform the user (a) which physical health tests have been completed; (b) which physical health tests have yet to be started or completed; and/or (c) which physical health test is next in the sequence of physical health tests of the physical exam.);
items of the agenda in an order to be addressed during the consultation, the items including the first item of the first clinical specialty and the second item of the second clinical specialty subsequent to the first item in the order (Paragraph [0065], The UI can include one or more buttons or menu options that correspond to one or more of the physical health tests, services, and/or functions of the system. The physical exam overview UI can provide the user an overview of a sequence of physical health tests that are included in the physical exam. Paragraph [0066], one or more other UIs of the software application can be presented to the user that include options to start, stop, or skip tests. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. Fig. 9 shows that the physical health tests include at least a first test and a subsequent second test. Also see fig. 10F.), and
responsive to reaching the first item for addressing during the consultation:
automatically loading a first selected portion of patient data relevant to the first item using the loaded identifier (Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. Paragraph [0051], The account can be unique to the user such that any health data generated by the modular system can be associated with the user via the account and stored for future reference, analysis, and/or review. Paragraph [0054], the routine 900 registers the modular physical health testing system (e.g., the hub of the modular system) to a specific user's account (e.g., such that the modular system is operable only by the user and only when the user (i) logs into his/her account on a related software application (block 905) and (ii) connects the software application to the communications hub of the modular system (block 906)). Paragraph [0070], The hub can then communicate the user BP/HR data to the software application running on the user's device. Under broadest reasonable interpretation, associating a software application with a user account so that a plurality of physical test reports can be displayed/loaded is construed as automatically loading the patient data using the loaded identifier.);
automatically displaying Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. …one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. Paragraph [0070], The hub can then communicate the user BP/HR data to the software application running on the user's device.);
automatically receiving one or more first measurements associated with the first item from one or more first devices (Paragraph [0068], At block 907, the routine 900 continues when a user initiates a blood pressure and/or heart rate (BP/HR) physical health test of the physical exam to generate the user's blood pressure and/or heart rate data. Paragraph [0070], the BP/HR cuff and/or monitor communicates all requested user BP/HR data to the hub.);
automatically updating the first selected portion of the patient data according to the received one or more first measurements (Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. A repeated physical test that generates a physical test report, such as BP/HR data, is construed as updating the physical test report/BP/HR data.);
determining a first series of actions to be taken with respect to the first itemParagraph [0093], In some embodiments, the routine 900 can (e.g., via one or more UIs of the software application) provide the user tips and suggestions for improving their health, encourage the user to set goals to better his/her health, and/or allow the user to set reminders (e.g., to drink more water). The tips, suggestions, goals, and/or reminders can be based at least in part on results of individual physical health tests and/or on results of a collection of two or more physical health tests. Also see paragraph [0090].);
displaying, to the patient, the updated selected portion of the data (Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. Paragraph [0070], The hub can then communicate the user BP/HR data to the software application running on the user's device.);
responsive to reaching the
automatically loading a second selected portion of the patient data relevant to the second item and different from the first selected portion (Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. Paragraph [0051], The account can be unique to the user such that any health data generated by the modular system can be associated with the user via the account and stored for future reference, analysis, and/or review. Paragraph [0054], the routine 900 registers the modular physical health testing system (e.g., the hub of the modular system) to a specific user's account (e.g., such that the modular system is operable only by the user and only when the user (i) logs into his/her account on a related software application (block 905) and (ii) connects the software application to the communications hub of the modular system (block 906)). Paragraph [0073], The hub can then communicate the user's ECG data to the software application running on the user's device. Under broadest reasonable interpretation, associating a software application with a user account so that a plurality of physical test reports can be displayed/loaded is construed as automatically loading the patient data.);
determining a second series of actions to be taken with respect to the second item Paragraph [0093], In some embodiments, the routine 900 can (e.g., via one or more UIs of the software application) provide the user tips and suggestions for improving their health, encourage the user to set goals to better his/her health, and/or allow the user to set reminders (e.g., to drink more water). The tips, suggestions, goals, and/or reminders can be based at least in part on results of individual physical health tests and/or on results of a collection of two or more physical health tests. Also see paragraph [0090].);
automatically displaying simultaneously to the patient, by the display device, the second selected portion of the patient data and a second transcription of speech recognized during the consultation of the second item (Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. Paragraph [0073], The hub can then communicate the user's ECG data to the software application running on the user's device.);
automatically receiving one or more second measurements associated with the second item from one or more second devices (Paragraph [0071], At block 908, the routine 900 continues when a user initiates an ECG or EKG physical health test to generate the user's ECG or EKG data. Paragraph [0073], the ECG device communicates the data stream(s) to the hub.);
updating the second selected portion of the patient data according to the received the one or more second measurements (Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. A repeated physical test that generates a physical test report, such as ECG data, is construed as updating the physical test report/ECG data.); and
displaying, to the patient, the updated selected portion of the data (Paragraph [0096], the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test or at the conclusion of the entire physical exam) and/or that can be individually stored for future reference, analysis, and/or review. Paragraph [0099], the steps of routine 900 are discussed and illustrated in a particular order. one or more steps of the routine 900 illustrated in FIG. 9 can be omitted and/or repeated in some embodiments. Paragraph [0073], The hub can then communicate the user's ECG data to the software application running on the user's device.).
Rush does not appear to disclose that the agenda of the consultation with the patient is determined using a first machine learning algorithm; displaying simultaneously dynamic suggestions indicating actions, test or treatment to be taken in the current context of the consultation; automatically displaying simultaneously to the patient, the first selected portion of the patient data and a first transcription of speech recognized during the consultation of the first item; that the first series of actions to be taken are determined by a second machine learning algorithm; updating the second item of the agenda in real time using the machine learning algorithm, during the consultation according to the received one or more first measurements associated with the first item; that the second series of actions are determined by the second machine learning algorithm; or automatically displaying simultaneously to the patient the second selected portion of patient data and a second transcription of speech recognized during consultation of the second item.
Morgan teaches that it was old and well known in the art of health platforms at the time of the filing to determine an agenda of a consultation with a patient using a first machine learning algorithm (Morgan, column 30, lines 54-63 after the initial information inputs are set, clinicians, ML/AI models, and/or other platform features then use the initial information inputs to populate a curated problem list, a set of customized and/or personalized goals, and/or other selected points of platform data. The curated problem list, set of customized and/or personalized goals, and/or other selected points of platform data are then subsequently utilized to inform the creation and/or initial configuration of the personalized scenes, sessions, and/or regimens by clinicians and/or by ML/AI models. Column 2, lines 37-69, " Session ” means one continuous usage period of XR , one continuous usage of a web portal , and/or one continuous usage of a companion application. Also see column 13, lines 13-18, Each of the exemplary modules may comprise features applicable for creating, configuring, and/or deploying tailored, personalized, adaptive and/or problem-focused scenes, sessions, and/or regimens to deliver, perform, and/or deploy diagnostic tests, screening tests, therapeutic features, and/or care delivery features and column 14, lines 1-26.);
dynamic suggestions indicating actions, test or treatment to be taken in the current context of the consultation (Fig. 21 shows that each scene includes features comprising actions or tests to be performed during a session which are dynamically suggested as discussed in column 19, lines 44-55, column 23, lines 34-41, column 44, lines 50-54 and column 113, lines 35-39.); and
updating the second item of the agenda in real time using the machine learning algorithm, during the consultation according to the received one or more first measurements associated with the first item (Morgan, column 36, lines 59-63, utilizing a session comprised of personalized and interactive scenes, delivering personalized feedback and/or educational information, and then iteratively adapting subsequent sessions and/or scenes based on previous results can be appreciated. Column 37, lines 19-23,. Using the results obtained for each scene or session, and/or using points of other platform data available, a set of problem-focused and/or goal-focused recommendations to carry out and/or address in subsequent scenes and/or sessions are developed by clinicians and/or by ML/AI models. Column 36, lines 2-6, real-time and/or near-real-time creation of one or more tailored, personalized, adaptive and/or problem-focused scenes, sessions, and/or regimens containing diagnostic, screening, therapeutic, and/or care delivery features.) to allow for personalized, tailored, iterative, actionable, and/or distributed care solutions (Column 12, lines 5-8).
Therefore, it would have been obvious to one of ordinary skill in the art of health platforms at the time of the filing to modify the method of Rush such that: the agenda is determined using a machine learning algorithm; such that dynamic suggestions indicating actions, test or treatment to be taken in the current context of the consultation (i.e. features of scenes) are simultaneously displayed with the items of the agenda (i.e. scenes of a session) and dynamic contextual data; and such that the second agenda item is updated in real time using the machine learning algorithm, during the consultation according to the received one or more first measurements associated with the first items, as taught by Morgan, in order to allow for personalized, tailored, iterative, actionable, and/or distributed care solutions.
Leonard teaches that it was old and well known in the art of healthcare communications at the time of the filing to perform:
automatically displaying simultaneously to the patient, the first selected portion of the patient data and a first transcription of speech recognized during the consultation of the first item (Paragraph [0082], The interaction between the patient, provider and, optionally, user is separated into separate sentences and phrases for each. For example, audio of the physician 127, and audio of the other speakers 129. The audio is then transcribed and separated. The separation can be performed by a conversion or other like module. Once an interaction is broken into separate sentences and phrases for each participant 131, 133, and 135, the sentences and phrases can be classified to a predetermined ‘summary’ type 137. Paragraph [0083], Once the type of summary has been determined, the sentences and phrases are classified to a section directed toward, for example, History of Present Illness, Review of Symptoms, Past Medical History, Past Surgical History, Immunization Record, Allergies, Current and Past Medications, Laboratory Findings, Imaging and Other Study Summaries, Diagnosis and Assessment, Active and Inactive Issues, Patient Problem List, and Treatment Plan. Each class can be further subdivided or sub-classified, so for example, the Treatment Plan can include Follow-up, Activity Level, Expected Duration of Condition, New Medication, Discontinued Medication, Labs or Studies Still to be Completed, Therapy Interventions, Surgical Interventions, or Generalized Patient Education 139. Paragraph [0084], It is also noted that the interaction that generates the summary can be located at inpatient evaluations, outpatient evaluations, phone conversations, and telehealth evaluations. Paragraph [0087], Once the summary has been complete, an output is generated of the summary and transmitted to the patient, provider and/or user where appropriate 141. As indicated in the flow chart of FIG. 3B, a simpler summary can be directly generated with the sentences and phrases broken into the various speakers, in the absence of classification and mapping. Here the summary would provide a more basic interpretation of the conversations (135, 141). Paragraph [0090], In addition, comparisons can provide the patient and provider trends in the data, for example, the patient's blood pressure over the previous year, weight over the previous year, changes in medication, over the previous year. Also see paragraph [0073].);
determining a first series of actions to be taken with respect to the first item by a second machine learning algorithm based on the first selected portion of the patient data (Paragraph [0059], the artificial intelligence module 44 uses Intents 46 in combination with a Confidence Score 52 to determine when a speech component or other data in the raw information is relevant for inclusion in the output information such as in a summary, detail or follow up action. Paragraph [0063], Further, audio input 40 is fed to a conversion module 42 which translates the audio input 40 into a format that can be fed to the specially-trained artificial intelligence module 44 containing specially designed Intents 46 and Entities 48. The artificial intelligence module returns a response which comprises “Summary and Actions” 50 along with a Confidence score 52 to determine if a phrase heard as part of the interaction should be matched to a particular Intent 46 and other response data 54. The system creates unique output information comprising personalized “Summaries and Actions” 50 depending on the Intents 46 and Entities 48, along with other response data 54. Also see paragraph [0036] and examples 1-7 in paragraphs [0094]-[0113].);
automatically displaying simultaneously to the patient, the second selected portion of the patient data and a second transcription of speech recognized during the consultation of the second item(Paragraph [0082], The interaction between the patient, provider and, optionally, user is separated into separate sentences and phrases for each. For example, audio of the physician 127, and audio of the other speakers 129. The audio is then transcribed and separated. The separation can be performed by a conversion or other like module. Once an interaction is broken into separate sentences and phrases for each participant 131, 133, and 135, the sentences and phrases can be classified to a predetermined ‘summary’ type 137. Paragraph [0083], Once the type of summary has been determined, the sentences and phrases are classified to a section directed toward, for example, History of Present Illness, Review of Symptoms, Past Medical History, Past Surgical History, Immunization Record, Allergies, Current and Past Medications, Laboratory Findings, Imaging and Other Study Summaries, Diagnosis and Assessment, Active and Inactive Issues, Patient Problem List, and Treatment Plan. Each class can be further subdivided or sub-classified, so for example, the Treatment Plan can include Follow-up, Activity Level, Expected Duration of Condition, New Medication, Discontinued Medication, Labs or Studies Still to be Completed, Therapy Interventions, Surgical Interventions, or Generalized Patient Education 139. Paragraph [0084], It is also noted that the interaction that generates the summary can be located at inpatient evaluations, outpatient evaluations, phone conversations, and telehealth evaluations. Once the summary has been complete, an output is generated of the summary and transmitted to the patient, provider and/or user where appropriate 141. As indicated in the flow chart of FIG. 3B, a simpler summary can be directly generated with the sentences and phrases broken into the various speakers, in the absence of classification and mapping. Here the summary would provide a more basic interpretation of the conversations (135, 141). Paragraph [0090], In addition, comparisons can provide the patient and provider trends in the data, for example, the patient's blood pressure over the previous year, weight over the previous year, changes in medication, over the previous year. Also see paragraph [0073].); and
determining a second series of actions to be taken with respect to the second item by the second machine learning algorithm based on the selected second portion of the data (Paragraph [0059], the artificial intelligence module 44 uses Intents 46 in combination with a Confidence Score 52 to determine when a speech component or other data in the raw information is relevant for inclusion in the output information such as in a summary, detail or follow up action. Paragraph [0063], Further, audio input 40 is fed to a conversion module 42 which translates the audio input 40 into a format that can be fed to the specially-trained artificial intelligence module 44 containing specially designed Intents 46 and Entities 48. The artificial intelligence module returns a response which comprises “Summary and Actions” 50 along with a Confidence score 52 to determine if a phrase heard as part of the interaction should be matched to a particular Intent 46 and other response data 54. The system creates unique output information comprising personalized “Summaries and Actions” 50 depending on the Intents 46 and Entities 48, along with other response data 54. Also see paragraph [0036] and examples 1-7 in paragraphs [0094]-[0113].) to improve patient understanding and remembering what healthcare providers tell them during visits and other communications (Paragraph [0003]).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications at the time of the filing to modify the method of Rush to include the limitations above, as taught by Leonard, in order to improve patient understanding and remembering what healthcare providers tell them during visits and other communications.
For example, when a selected portion of patient data falls outside of a corresponding healthy and/or normal range of data, the patient may consult with a healthcare provider and send the data to the healthcare provider as described by Rush in paragraph [0094] and [0097] and the speech recognized during interaction can be transcribed and provided to the patient simultaneously with the physical test report of Rush described in paragraph [0096] in the form of a summary and actions report. Artificial intelligent models may be used to determine and present the series of actions to be taken in the summary and action report with respect to selected portion of the data (e.g. patient data falling outside of a corresponding healthy and/or normal range of data).
Regarding claim 2, Rush further discloses wherein the first item is associated with at least one of performing a medical test, performing a medical examination, and measuring a health metric via one or more medical devices, wherein the medical test comprises at least one of a blood test, a scan, collecting and analyzing a specimen from the patient, a medical assessment, a genetic test, and a breathing test, and wherein the health metric comprises at least one of blood pressure, blood glucose levels, a pulse, a body temperature, and a body weight. (Paragraphs [0020], the physical health testing devices include a thermometer, a blood pressure and/or heart rate cuff and/or monitor, an ECG or EKG device, a stethoscope, a glucose and/or cholesterol blood test system, a scale, a tape measure, and/or other physical health testing devices. Using the physical health testing devices, a user performs a variety of physical health tests to generate data related to his/her health that can then be assessed and used by medical professionals and form a part of the patient's medical history. Also see fig. 10l.).
Regarding claim 8, Rush further discloses wherein at least part of the patient data is received from at least one of a client device associated with the patient and one or more sensors at the medical care site, wherein the client device comprises at least one of a smart phone and a smart wearable device, and wherein the one or more sensors comprise at least one of a wireless blood pressure sensor, a wireless heart rate sensor, a wireless body temperature sensor, a wireless pulse oximeter, a stethoscope, and an imaging sensor (Paragraphs [0020], the physical health testing devices include a thermometer, a blood pressure and/or heart rate cuff and/or monitor, an ECG or EKG device, a stethoscope, a glucose and/or cholesterol blood test system, a scale, a tape measure, and/or other physical health testing devices. Using the physical health testing devices, a user performs a variety of physical health tests to generate data related to his/her health that can then be assessed and used by medical professionals and form a part of the patient's medical history. Paragraph [0021], The physical health testing devices are in wired or wireless communication with the communications hub. Paragraph [0074], the software application can request the user to manually enter one or more components of his/her height and/or weight data after performing the height and/or weight physical health test. Paragraph [0065], The one or more devices 105 can include personal computers, server computers, handheld or laptop devices, cellular telephones, wearable electronics, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, or the like.).
Regarding claims 12-13, 19 and 29: all limitations as recited have been analyzed and rejected with respect to claims 1-2 and 8. Claims 12-13 and 19 pertain to a system, corresponding to the method of claims 1-2 and 8. Claim 29 pertains to a non-transitory computer-readable medium, corresponding to the method of claim 1. Claims 12-13, 19 and 29 do not teach or define any new limitations beyond claims 1-2 and 8 aside from the memory and processor disclosed by Rush in paragraphs [0100]-[0101]; therefore claims 12-15, 19 and 29 are rejected under the same rationale.
Regarding claim 21, Rush further discloses sending information to a user's device accessed by the patient and remote from the display device to display, on the user's device, content that is at least partially synchronized with content displayed at the display device (Paragraph [0034], the hub 215 can communicate all or a subset of the information (e.g., health data) to one or more devices 105 (e.g., a user's mobile device 105 e that is currently running a related software application) paired with the hub 215. Paragraph [0025], The one or more devices 105 can include personal computers, server computers, handheld or laptop devices, cellular telephones, wearable electronics, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, or the like. In these and other embodiments, the one or more devices 105 can include other remote or local devices.).
Regarding claim 22, Rush further discloses receiving biometric information of the patient from the user's device (Paragraph [0074], The software application can request the user to manually enter one or more components of his/her height and/or weight data after performing the height and/or weight physical health test.).
Regarding claim 23, Rush further discloses wherein the user's device is a wearable device (Paragraph [0065], The one or more devices 105 can include… wearable electronics...).
Regarding claim 26, Rush further discloses determining whether the patient has taken a predetermined action by analyzing the patient data; and generating a flag by the display device responsive to the predetermined action not having been taken by the patient (Paragraph [0094], the routine 900 can recommend that a user consult a healthcare professional in the event that a user skips one or more steps of a physical exam and/or in the event of an error when conducting a physical health test of a physical exam.).
Regarding claim 30, Rush as modified by Morgan and Leonard further discloses wherein alerts to the patient are displayed simultaneously with the first transcript, the alerts generated dynamically during the consultation (Rush, paragraph [0090], When a user's results fall greatly outside of a corresponding predetermined “healthy” range, one or more of the detailed results summary UIs of the software application can include health alerts to highlight potential health risks and concerns for the user. When combined with Leonard, the alerts would be provided in a summary and actions report, which includes the first transcript.).
Regarding claim 31, Rush as modified by Morgan and Leonard further discloses dynamically generating a first plan associated with the first item during the consultation, wherein the generated plan is displayed simultaneously with the first selected portion of the patient data during the consultation (Rush, paragraph [0096], In these and other embodiments, the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test. Paragraph [0094], the routine 900 can recommend that a user consult a healthcare professional. As discussed above, the routine 900 can recommend that a user consult a healthcare professional in the event that generated health data falls outside of a corresponding healthy and/or normal range of data. the routine 900 can recommend specific healthcare professionals or hospitals (e.g., healthcare professionals or hospitals in the user's geographic area, healthcare professionals specializing in a corresponding medical field, etc.). Paragraph [0097], the routine 900 continues by transmitting all or a subset of a user's generated health data to a healthcare professional. Leonard, paragraph [0083], Once the type of summary has been determined, the sentences and phrases are classified to a section directed toward, for example, History of Present Illness, Review of Symptoms, Past Medical History, Past Surgical History, Immunization Record, Allergies, Current and Past Medications, Laboratory Findings, Imaging and Other Study Summaries, Diagnosis and Assessment, Active and Inactive Issues, Patient Problem List, and Treatment Plan. Each class can be further subdivided or sub-classified, so for example, the Treatment Plan can include Follow-up, Activity Level, Expected Duration of Condition, New Medication, Discontinued Medication, Labs or Studies Still to be Completed, Therapy Interventions, Surgical Interventions, or Generalized Patient Education 139. Paragraph [0036], The output speech components can then be separated and categorized into a class, for example, a sentence or phrase associated with the patient's history, a sentence or phrase associated with the current examination, a sentence or phrase associated with the a diagnosis from the current examination, and sentences and phrases associated with the current strategy or treatment plan. Paragraph [0087], Once the summary has been complete, an output is generated of the summary and transmitted to the patient, provider and/or user where appropriate 141. Also see paragraph [0073] and examples 1-7 in paragraphs [0094]-[0113].).
Regarding claim 32, Rush as modified by Morgan and Leonard further discloses dynamically generating a second plan associated with the second item during the consultation, wherein the generated plan is displayed simultaneously with the second selected portion of the patient data during the consultation (Rush, paragraph [0096], In these and other embodiments, the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test. Paragraph [0094], the routine 900 can recommend that a user consult a healthcare professional. As discussed above, the routine 900 can recommend that a user consult a healthcare professional in the event that generated health data falls outside of a corresponding healthy and/or normal range of data. the routine 900 can recommend specific healthcare professionals or hospitals (e.g., healthcare professionals or hospitals in the user's geographic area, healthcare professionals specializing in a corresponding medical field, etc.). Paragraph [0097], the routine 900 continues by transmitting all or a subset of a user's generated health data to a healthcare professional. Leonard, paragraph [0083], Once the type of summary has been determined, the sentences and phrases are classified to a section directed toward, for example, History of Present Illness, Review of Symptoms, Past Medical History, Past Surgical History, Immunization Record, Allergies, Current and Past Medications, Laboratory Findings, Imaging and Other Study Summaries, Diagnosis and Assessment, Active and Inactive Issues, Patient Problem List, and Treatment Plan. Each class can be further subdivided or sub-classified, so for example, the Treatment Plan can include Follow-up, Activity Level, Expected Duration of Condition, New Medication, Discontinued Medication, Labs or Studies Still to be Completed, Therapy Interventions, Surgical Interventions, or Generalized Patient Education 139. Paragraph [0036], The output speech components can then be separated and categorized into a class, for example, a sentence or phrase associated with the patient's history, a sentence or phrase associated with the current examination, a sentence or phrase associated with the a diagnosis from the current examination, and sentences and phrases associated with the current strategy or treatment plan. Paragraph [0087], Once the summary has been complete, an output is generated of the summary and transmitted to the patient, provider and/or user where appropriate 141. Also see paragraph [0073] and examples 1-7 in paragraphs [0094]-[0113].).
Regarding claim 33, Rush as modified by Morgan and Leonard further discloses wherein a first series of steps associated with the first plan is displayed during the consultation of the first item, and a second series of steps associated with the second plan is displayed during the consultation of the second item (Rush, paragraph [0096], In these and other embodiments, the routine 900 can generate a plurality of physical test reports that can be displayed to a user (e.g., as the user conducts each physical health test. Paragraph [0094], the routine 900 can recommend that a user consult a healthcare professional. As discussed above, the routine 900 can recommend that a user consult a healthcare professional in the event that generated health data falls outside of a corresponding healthy and/or normal range of data. the routine 900 can recommend specific healthcare professionals or hospitals (e.g., healthcare professionals or hospitals in the user's geographic area, healthcare professionals specializing in a corresponding medical field, etc.). Paragraph [0097], the routine 900 continues by transmitting all or a subset of a user's generated health data to a healthcare professional. Leonard, paragraph [0083], Once the type of summary has been determined, the sentences and phrases are classified to a section directed toward, for example, History of Present Illness, Review of Symptoms, Past Medical History, Past Surgical History, Immunization Record, Allergies, Current and Past Medications, Laboratory Findings, Imaging and Other Study Summaries, Diagnosis and Assessment, Active and Inactive Issues, Patient Problem List, and Treatment Plan. Each class can be further subdivided or sub-classified, so for example, the Treatment Plan can include Follow-up, Activity Level, Expected Duration of Condition, New Medication, Discontinued Medication, Labs or Studies Still to be Completed, Therapy Interventions, Surgical Interventions, or Generalized Patient Education 139. Leonard, paragraph [0087], Once the summary has been complete, an output is generated of the summary and transmitted to the patient, provider and/or user where appropriate 141. Also see paragraphs [0036], [0073] and examples 1-7 in paragraphs [0094]-[0113]. For example, when a first selected portion of data for a first item is outside a health range, the user consults with a healthcare professional who receives that data and dynamically generates a first plan with a series of steps and returns a summary and actions report with all received data received from the patient and provider. When, a second selected portion of data for a first item is outside a health range, the user consults with a healthcare professional who receives that data and dynamically generates a second plan with a series of steps and returns a summary and actions report with all received data received from the patient and provider.).
Claims 24-25 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Rush et al. (U.S. Pub. No. 2021/0134446) in view of Morgan et al. (U.S. Patent No. 10,943,407), Leonard (U.S. Pub. No. 2018/0018/966) and McNair et al. (U.S. Patent No. 10,446,273).
Regarding claim 24, Rush as modified by Morgan further discloses determining one or more actions associated with the patient to be performed Paragraph [0094], the routine 900 can recommend that a user consult a healthcare professional in the event that generated health data falls outside of a corresponding healthy and/or normal range of data.).
Rush as modified by Morgan does not appear to explicitly disclose using another machine learning algorithm.
McNair teaches that it was old and well known in the art of clinical decision support at the time of the filing to determining one or more actions associated with the patient to be performed using a machine learning algorithm (McNair, column 12, lines 50-56, In some embodiments, including the practical example described above, system 2130 includes one or more agents 2135, which process patient information 2110 using parameters 2120 to determine goals, plans, patient actions, orders, patient conditions and recommended treatments. Column 18, lines 22-31, Examples of agents, which may be used by the multi-agent systems of embodiments of our technologies, include: learning agents, including supervised learning, unsupervised learning, reinforcement learning, for example.) to promote optimal timing and intensity and duration of intervention (McNair, column 54, lines 51-52).
Therefore, it would have been obvious to one of ordinary skill in the art of patient healthcare systems at the time of the filing to modify the determining of the one or more actions of Rush as modified by Morgan such that the one or more actions are determined using another machine learning algorithm, as taught by McNair, in order to promote optimal timing and intensity and duration of intervention.
Regarding claim 25, Rush does not appear to explicitly disclose but McNair teaches that it was old and well known in the art of clinical decision support at the time of the filing wherein the second selected portion of the patient data for loading is determined based on one or more actions taken to address the first item (McNair, column 43, lines 44-57, At a step 4920, based on the first set of clinical information, determining a preliminary likelihood of a clinical decision support event being associated with the patient. At a step 4930, determining that additional clinical information is not currently available for one or more clinical information factors relevant to the clinical decision support event. At a step 4940, generating a patient assessment for determining at least a portion of the additional clinical information, wherein the patient assessment is generated based on a treatment-session context. Column 49, lines 23-29, In some embodiments the dynamic assessment takes the form of a questionnaire, but may also take the form of prompts to appropriate caregiver or the patient to provide needed information, or scheduling of or requests for tests, visits, orders, or other actions to facilitate obtaining the information.) to promote optimal timing and intensity and duration of intervention (McNair, column 54, lines 51-52).
Therefore, it would have been obvious to one of ordinary skill in the art of patient healthcare systems at the time of the filing to modify the method of Rush such that the second selected portion of the patient data for loading is determined based on one or more actions taken to address the first item, as taught by McNair, in order to promote optimal timing and intensity and duration of intervention.
Regarding claim 28, Rush does not appear to explicitly disclose but McNair teaches that it was old and well known in the art of clinical decision support at the time of the filing to perform determining whether to take one or more actions on the patient with respect to the first item according to a template list of diagnostic actions, the template list of actions indicating one or more conditions for taking the one or more diagnostic actions (McNair, column 19, lines 22-43, Content tables 2124 provide parameters that specify information regarding conditions, drugs, contra-indications, treatments, orders or other actions, and other parameters used in conjunction with patient information to determine conditions and recommended treatments. For example, a content table may specify parameters relating to diabetes including what factors in patient information indicate that the patient is in hypoglycemia, what factors in patient information indicate that the patient is in hyperglycemia, contra-indications, treatments such as drugs and drug dosages that should be administered, or additional testing that should be ordered. Column 20, lines 1-4, content tables 2124 and patient information 2110 provide the information necessary for a solver to determine patient conditions and recommended treatments.) to promote optimal timing and intensity and duration of intervention (McNair, column 54, lines 51-52).
Therefore, it would have been obvious to one of ordinary skill in the art of patient healthcare systems at the time of the filing to modify the method of Rush to include determining whether to take one or more actions on the patient with respect to the first item according to a template list of actions, the template list of actions indicating one or more conditions for taking the one or more actions, as taught by McNair, in order to promote optimal timing and intensity and duration of intervention.
Response to Arguments
Applicant's arguments filed October 23, 2025 regarding claim 1-2, 8, 12-13, 19, 21-26 and 28-33 being rejected under 35 U.S.C. §101 have been fully considered but they are not persuasive.
Applicant argues that the claims do not fall within the certain methods of organizing human activity grouping and the sub grouping of managing personal behavior because the claims recite processes for automating patient consultations which are not abstract concepts but are integrates into a practical system that enhances the overall efficiency of healthcare delivery by reducing manual intervention.
The is not persuasive as the processes described in the claims are processes performed by a human during consultations with a patient. Simply reciting that the method is performed automatically using one or more processors and memory and does not take the claim out of the certain methods of organizing human activity grouping. The additional elements pertaining to the performing the method automatically using memory and processors are analyzed under step 2B prong two.
Applicant argues that “displaying simultaneously…: dynamic contextual data…, items of the agenda…, and dynamic suggestions…” and “automatically displaying simultaneously to a patient… a first selection portion of patient data and a first transcription of speech…” are additional limitations which are not well-understood, routine or conventional in the field of healthcare and amount to significantly more because that are meaningful limitations that beneficially facilitate the healthcare professional and the patient to share the items and relevant data, accelerate the making of decisions by both parties, and increase the patient engagement during the consultation,
While it is noted that certain claim limitations require more than just the identification of information (e.g., dynamic contextual data, items of an agenda, dynamic suggestions, patient data, transcribed speech), but also the display of such information, it stands to reason that in order to display certain information, it first must be identified. The identification of this information is determined to be directed to the abstract idea grouping of mental process as this may performed in the human mind using observations, evaluations, judgments and opinions. The additional aspect of displaying simultaneously that which has been identified is merely extra-solution activity that does not confer patent eligibility. See, e.g., Elec. Power, 830 F.3d at 1355 (explaining that “selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes”); Elec. Power, 830 F.3d at 1354 (recognizing “that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis’). Therefore the additional limitations of “displaying simultaneously...” is considered to be insignificant extra-solution activity. That the claims recite displaying specific information does not otherwise confer patent eligibility. MPEP §2106.05(g) also provides evidence that displaying information is well-understood routine and conventional.
Applicant's arguments filed October 23, 2025 regarding claim 1-2, 8, 12-13, 19, 21-26 and 28-33 being rejected under 35 U.S.C. §103 have been fully considered but they are not persuasive.
Applicant argues that the Rush as modified by Morgan does not teach displaying simultaneously, (i) dynamic contextual data, (ii) items of an agenda and (iii) dynamic suggestions to a patient at the same time on the same screen.
However, paragraph [0065] of Rush clearly states that the user can view, access, and/or interface with several of these services and/or functions via one or more UIs of the software application that can be presented on a screen of the user's device when the software application is open and/or connected to the hub, which includes visual indicators that inform the user (a) which physical health tests have been completed; (b) which physical health tests have yet to be started or completed; and/or (c) which physical health test is next in the sequence of physical health tests of the physical exam, construed as dynamic contextual data, and an overview of a sequence of physical health tests that are included in the physical exam, construed as items of an agenda. Morgan is used to teach that the items of the agenda may include dynamic suggestions to a patient as Fig. 21 shows that each scene( i.e. item) includes features comprising actions or tests to be performed during a session which are dynamically suggested as discussed in column 19, lines 44-55, column 23, lines 34-41, column 44, lines 50-54 and column 113, lines 35-39. Therefore, when modified by Morgan, the one UI of Rush would display (i) dynamic contextual data, (ii) items of an agenda and (iii) dynamic suggestions to a patient.
Applicant arguments pertaining the displaying of the transcription of speech is moot in view of the new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devin C. Hein whose telephone number is (303)297-4305. The examiner can normally be reached 9:00 AM - 5:00 PM M-F MDT.
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/DEVIN C HEIN/
Examiner, Art Unit 3686