DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed December 1, 2025
Claims 1-18 are pending
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 .
Response to Amendment(s)
Claims 1-18 are pending and are still interpreted under 112(f).Examiner notes 112(f) assumes structure and is an interpretation of the claims. No 112 rejection was given to the claims.
Claim Rejection - 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 1-18 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter the grounds set out in detail below:
Independent Claims 1, 7, and 13:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of method.
Independent claim 7 falls within the statutory category of machine.
Independent claim 13 falls within the statutory category of article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 7, and 13 claimed invention are directed to a judicial exception.
The claim elements in the independent claims 1, 7, and 13 (claim 1 as representative) which set forth the abstract idea are:
A computer-implemented method comprising: collecting data in real-time from one or more data sources for one or more subjects;
aggregating the data of each of the one or more subjects with associated one or more …[…]…to build a persona for each of the one or more subjects;
deploying at least one service within each sublayer of a subject-centric matrix, wherein each sublayer of a subject centric matrix is customized to include the at least one service …[…]…,
wherein a service is customized according to the persona associated with each of the one or more subjects,
and wherein the service includes …[…]…configured to collect and store data,
…[…]…exchange data, and …[…]…perform data analytics on collected data, and wherein the subject-centric matrix includes:
a first sublayer including one or more first services to know a subject by collecting the data …[…]…for creating the persona associated with the subject, or providing a real-time communication between a plurality of subjects;
a second sublayer including one or more second services for collecting the data from one or more subject-based applications or one …[…]…for predictive and prescriptive analysis of the one or more subjects within and outside…[…]…;
and a third sublayer including one or more third services that are based on data-driven …[…]…for enhancing experience of the one or more subjects, the one or more third services include quick registration, …[…]…voice scribe, …[…]…, or real-time tracking of the one or more subjects by tracking geolocation of the one or more subjects inside and assisting the one or more subiects to reach a location, and that automate routine tasks, enable remote video consultations, food or medications delivery to the one or more subjects and/or that assist the one or more subjects for a safe mobility;
and deploying …[…]…configured to host one or more services, wherein …[…]…includes:
configured to synchronize a plurality of disparate datasets for establishing data exchange…[…]…;
…[…]…configured to provide a consistent data format and exchange of the data between sublayers in the subject-centric matrix;
…[…]…configured to customize the one or more services …[…]…and for a particular subject according to the persona associated with the subject.
which falls within “certain methods of organizing human activity” as following rules and instructions to aggregate data, analyze data, and personalize services for a particular subject according to the persona associated with a subject See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent Claims 1, 7, and 13 this judicial exception is not integrated into a practical application.
In Claim 1 the additional elements are:
A computer
a data processing module
electronic medical records
the particular and plurality of healthcare facilities
a communication interface module
artificial intelligence-based engine
subject-mounted devices
augmented/virtual reality (AR/VR)
building with internet of things (IoT)
a cloud platform
an interoperability module
an integration module
and a customization module
robotic applications
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a), is recited as executing the abstract idea as “apply-it”
The additional elements, (b), (e), (g), (h), (i), (j), (k), (l), and (m) are using computer elements as a tool to apply the abstract idea as “apply-“it for gathering and analyzing data
The additional elements, (c ) and (d), is generally linking the abstract idea to the environment of healthcare
The additional elements, (f), is generally linking the abstract idea to the environment of artificial intelligence.
The additional elements, (n), is generally linking the abstract idea to the environment of robotics
In Claim 7 the additional elements not already recited in the independent claim 1 are:
one or more data processors; and a non-transitory computer readable storage medium containing instructions
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a), is recited as executing the abstract idea as “apply-it”
In Claim 13 the additional elements not already recited in the independent claim 1 are:
A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions and one or more data processors
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a), is recited as executing the abstract idea as “apply-it”
Accordingly, claims 1, 7, and 13 do not integrate the abstract idea into a practical application.
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims 1, 7, and 13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than generally linking the abstract idea and or applying the abstract idea thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Dependent Claims 2-6, 8-12, and 14-18:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2-6 fall within the statutory category of method.
The dependent claims 8-12 fall within the statutory category of machine.
The dependent claims 14-18 fall within the statutory category of article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-6, 8-12, and 14-18 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1, 7, and 13 abstract idea by (1) further limiting the data source, (2) further limiting evaluation of a performance of a service, and (3) further limiting security for communication. Therefore, the dependent claims inherit the same abstract idea which falls within “certain methods of organizing human activity” as following rules and instructions to aggregate data, analyze data, and personalize services for a particular subject according to the persona associated with a subject See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-6, 8-12, and 14-18 this judicial exception is not integrated into a practical application.
The dependent claims recite the additional claim elements below not previously cited in the independent claims:
subject-mounted device, a network device, an administration application, a clinical application, or a subject application
natural language processing (NLP) techniques
one or more machine learning models
one or more devices of a subject and the one or more devices of a healthcare provider
virtual reality
augmented reality
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The noted above additional claim elements, (a), (d), (e), and (f) , are applying the abstract idea as “apply-it” to gather and analyze data
The noted above additional claim elements, (b) and (c) , are generally linking the abstract idea to the technological field of machine learning
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are merely generally linking and/or applying the abstract idea and therefore insufficient to amount to significantly more. The claims are patent ineligible.
Claim Interpretation - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are: “and deploying a cloud platform configured to host the services, wherein the cloud platform includes: an interoperability module configured to synchronize a plurality of disparate datasets for establishing data exchange between a plurality of healthcare facilities; an integration module configured to provide a consistent data format and exchange of the data between sublayers in the subject-centric matrix; and a customization module configured to customize the one or more services for the particular healthcare facility and for a particular subject according to the persona associated with the subject.” in claims 7 and 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1, 2, 4, 7, 8, 10, 13, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Vesto et. al (hereinafter Vesto) (US11087878B2) in view of Angle et. al (hereinafter Angle) (US10661433B2)
As per Claim 1, Vesto teaches:
A computer-implemented method comprising: collecting data in real-time from one or more data sources for one or more subjects; (Col. 7 lines 2-9 discloses, “Certain examples provide real-time (or at least substantially real time assuming some system delay) patient data from one or more information technology (IT) systems and facilitate comparison(s) against evidence-based best practices. Certain examples provide one or more dashboards for specific sets of patients. Dashboard(s) can be based on condition, role, and/or other criteria to indicate variation(s) from a desired practice, for example.” And see Col. 37 lines 6-10 discloses, “The remote ICU monitoring technology that tele-ICU enables, allows an intensivist at the command center to monitor real-time parameters of critically ill patients from remote ICUs/Hospitals on a 24/7 basis.”)
aggregating the data of each of the one or more subjects with associated one or more electronic medical records to build a persona for each of the one or more subjects; (Col. 6 lines 21-27 discloses, “Interconnection of multiple data sources helps enable an engagement of all relevant members of a patient's care team and helps improve an administrative and management burden on the patient for managing his or her care. Particularly, interconnecting the patient's electronic medical record and/ or other medical data can help improve patient care and management of patient information.” And see Col. 8 lines 3-6 discloses, “As another example, the processor 130 can process updated patient information obtained via the input 110 to provide an updated patient record to an EMR via the communication interface 150.” And see Col. 11 lines 13-31 discloses, “The example data center 212 of FIG. 2 is an archive to store information such as images, data, medical reports, and/or, more generally, patient medical records. In addition, the data center 212 can also serve as a central conduit to information located at other sources such as, for example, local archives, hospital information systems/radiology information systems (e.g., the HIS 204 and/or the RIS 206), or medical imaging/storage systems ( e.g., the PACS 208 and/or connected imaging modalities). That is, the data center 212 can store links or indicators (e.g., identification numbers, patient names, or record numbers) to information. In the illustrated example, the data center 212 is managed by an application server provider (ASP) and is located in a centralized location that can be accessed by a plurality of systems and facilities (e.g., hospitals, clinics, doctor's offices, other medical offices, and/or terminals). In some examples, the data center 212 can be spatially distant from the HIS 204, the RIS 206, and/or the PACS 208 (e.g., at GENERAL ELECTRIC® headquarters). The example data center 212 of FIG. 2 includes a server 228, a database 230, and a record organizer 232. The server 228 receives, processes, and conveys information to and from the components of the healthcare system 200. The database 230 stores the medical information described herein and provides access thereto. The example record organizer 232 of FIG. 2 manages patient medical histories, for example. The record organizer 232 can also assist in procedure scheduling, for example.” )
deploying at least one service within each sublayer of a subject-centric matrix, wherein each sublayer of a subject centric matrix is customized to include the at least one service for a particular healthcare facility, (Col. 15 lines 66-67 discloses, “FIGS. SA-SC illustrate IntelliLink Core Services 500 provided as an Interface as a Service (IaaS) 501 “ and see Col. 22 lines 3-9 discloses, “FIG. 9 depicts example software agent and management services 900 used to fulfill certain core interface functions and adjacencies. For example, a software agent module 901 (deployed on the cloud along with IntelliLink) can be used in conjunction with IntelliLink to fulfill certain core functions and adjacencies such as security, data ingestion, automatic interface provisioning, service portal, alerts and audit.” And see Col. 24 lines 27-32 discloses, “Certain examples provide a service portal 909 to manage a deployed messaging infrastructure and configured routes. The example service portal 909 includes a self-service component 910, an auto-provision service component 911, an alert component 912, an audit component 913, and a serviceability component 914…[…]… In certain examples, a graph database of care connections (e.g., OrientDB, Neo4J, AllegroGraph, etc.) is used to store relationships within the healthcare ecosystem.” And see Col. 26 lines 15-31 discloses, “Certain examples include a machine learning engine to leverage analytics and other information gathered. A machine learning engine ( e.g., an R engine) can be trained to discover patterns in message exchanges and taught to detect relationships hidden in large data sets. A predictive capability of the machine learning engine can be used to provide recommendations to the users (providers and patients). In certain examples, machine learning combined with graph analytics provide recommendations and just-intime contextual information to healthcare providers and users of a healthcare system. For example, a referring healthcare provider can be provided with just-in-time recommendations for specialists and services available within the patient's network. Similarly, recommendations can be provided to the healthcare providers to improve the patient satisfaction by suggesting follow-up communication, for example.” And see Col. 27 lines 21-29 discloses, “A corresponding graph database is updated. An IaaS, such as the IaaS described in relation to FIGS. 4, 5, 9, etc., can be provided to facilitate interfacing, intelligent translation and routing, analytics (e.g., referral optimizer, usage analytics, B2B analytics, etc.), and a plurality of services ( e.g., a notification service, a bid solicitation service, a scheduling service, an image and document sharing service, and/or other complementary services, etc.) via a service bus.” And see Col. 33 lines 20-33 discloses, “As shown in the example of FIG. 16B, the eICU command center 1610 communicates with the remote site(s) 1620, 1625, 1630 via an Interface as a service (IaaS) 1640 (e.g., offered via "cloud" B). The IaaS 1640 includes an Intellink service bus 1650 to acquire data, compose message(s), store data, and route messages via one or more Intellink interface(s) 1660. The interface(s) 1660 include one or more device data interfaces, patient data interfaces, and routing infrastructure/information, for example, to enable the command center 1610 to interface with one or more of the remote sites 1620, 1625, 1630 in the example of FIGS. 16A-16B.” / examiner notes that one of ordinary skill would understand that an IaaS is composed of sublayers )
a first sublayer including the one or more first services to know the subject by collecting the data from one or more electronic medical records for creating the persona associated with the subject, or providing a real-time communication between a plurality of subjects; (Col. 32 lines 10-67 and Col. 33 lines 1-31 discloses communication real time between a plurality of subjects via a sublayer if the IaaS via a service)
a second sublayer including the one or more second services for collecting the data from one or more subject-based applications or one or more subject-mounted devices for predictive and prescriptive analysis of the one or more subjects within and outside the healthcare facility; (Col. 11 lines 57-67 discloses, “In certain examples, users (e.g., a patient and/or care provider) can access functionality provided by the system 200 via a software-as-a-service (SaaS) implementation over cloud or other computer network, for example. In certain examples, all or part of the system 200 can also be provided via platform as a service (PaaS), infrastructure as a service (IaaS), etc. For example, the system 200 can be implemented as a cloud-delivered Mobile Computing Integration Platform as a Service. A set of consumer-facing Web-based, mobile, and/or other applications enable users to interact with the PaaS, for example. Col. 12 lines 30-57 discloses, “As shown in the example of FIG. 3, a plurality of devices (e.g., information systems, imaging modalities, etc.) 310- 312 can access a cloud 320, which connects the devices 310-312 with a server 330 and associated data store 340. Information systems, for example, include communication interfaces to exchange information with server 330 and data store 340 via the cloud 320. Other devices, such as medical imaging scarmers, patient monitors, etc., can be outfitted with sensors and communication interfaces to enable them to communicate with each other and with the server 330 via the cloud 320. Thus, machines 310-312 in the system 300 become "intelligent" as a network with advanced sensors, controls, and software applications. Using such an infrastructure, advanced analytics can be provided to associated data. The analytics combines physics-based analytics, predictive algorithms, automation, and deep domain expertise. Via the cloud 320, devices 310-312 and associated people can be connected to support more intelligent design, operations, maintenance, and higher server quality and safety, for example. Using the industrial internet infrastructure, for example, a proprietary machine data stream can be extracted from a device 310. Machine-based algorithms and data analysis are applied to the extracted data. Data visualization can be remote, centralized, etc. Data is then shared with authorized users, and any gathered and/or gleaned intelligence is fed back into the machines 310-312.” And see Col. 14 lines 52-56 discloses, “Certain examples provide a reference realization of a remote ICU monitoring solution (tele-ICU) which allows an intensivist at the command center to monitor real-time device parameters of critically ill patients from remote ICUs/Hospitals on a 24 hour /7 days a week basis. “ and see Col. 32 lines 41-44 discloses, “Device and Patient Data IntelliLink interfaces are used to capture, parse, aggregate and cache the content specific to device or patient data streaming from each remote site.” )
and a third sublayer including the one or more third services that are based on data-driven and artificial intelligence for enhancing experience of the one or more subjects, the one or more third services include quick registration, augmented/virtual reality (AR/VR), voice scribe, building with internet of things (IoT), or real-time tracking of the one or more subjects by tracking geolocation of the one or more subjects inside the particular healthcare facility and assisting the one or more subjects to reach a location, (Col. 12 lines 1-67 and see Col. 26 lines 15-67 and see Col. 27 lines 1-6 discloses, utilizing internet of things service based on machine learning to enhance experience by making intelligent referrals for example. /the claim recites “or” between the series of elements. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).)
and deploying a cloud platform configured to host one or more services, wherein the cloud platform includes: an interoperability module configured to synchronize a plurality of disparate datasets for establishing data exchange between a plurality of healthcare facilities; (Col. 36 lines 1-67 and Col. 37 lines 1-20 discloses a cloud platform for hosting services with an interoperability module to synchronize disparate data between healthcare facilities. and see Fig. 9)
an integration module configured to provide a consistent data format and exchange of the data between sublayers in the subject-centric matrix; (Col. 19 lines 21-34 discloses, “The IntelliLink IaaS platform can then derive intelligence from configuration information contained within each "plugged-in" interface which includes a contractual input/ output format types, rules for message enrichment (e.g., a listing of callback services to a calling system's service layer (and/or other third party systems via a Business Process Management (BPM) or Business Process Execution Language (BPEL) based process manager, etc.) along with query/search criteria for the callback action to be executed, additional business-level rules that govern message routing, and message filtering criteria to be applied, for example. The configuration can be implemented using XML syntax and/or in some examples, using custom expressions based syntax, and may be unique to each interface.” And see Col. 28 lines 17-35 discloses, “Certain examples leverage document data exchanges. A clinical document format for data exchange can use the Consolidated Clinical Document Architecture (CCDA). CCDA represents the result of harmonization efforts from Health Level 7 (HL 7), Integrating the Health Enterprise (IHE), Health Information Technology Standards Panel (HITSP), components from the IHE Patient Care Coordination (PCC) and Continuity of Care (CCD). The Consolidated CDA format can be used to cover the following structured clinical documents: Continuity of Care Document (CCD), Discharge Summary, Consultation Notes, Digital Imaging and Communications in Medicine (DICOM) Diagnostic Imaging reports, History and Physical, Operative Note, Progress Note and Procedure Note. The CCDA mandates specific content for each type of the fore-mentioned documents, embodied within sectional clinical data elements. The sectional data is required to be codified with support required for unique Vocabularies including SNOMED- CT and LOINC.” And see Col. 22 lines 3-12 discloses, “FIG. 9 depicts example software agent and management services 900 used to fulfill certain core interface functions and adjacencies. For example, a software agent module 901 (deployed on the cloud along with IntelliLink) can be used in conjunction with IntelliLink to fulfill certain core functions and adjacencies such as security, data ingestion, automatic interface provisioning, service portal, alerts and audit. The software agent 901 includes an interfaces manager 908 to facilitate interaction with one or more provisioned interfaces)”
and a customization module configured to customize the one or more services for the particular healthcare facility and for a particular subject according to the persona associated with the subject. (Col. 20 lines 64-67 and Col. 21 lines 1-4 discloses, “At block 817, using an editor, backend code is constructed using, for example, a Camel Integration software development kit (SDK) for a model-driven assembly. For example, based on the classes, code is constructed to form the interface. At block 818, interface generated classes are available for edit and customization. For example, additional review, revision, and/or other customization can be provided at this stage.” And see Col. 34 lines 50-65 discloses, the CRM can be models of instructions)
However, Vesto does not teach:
and robotic applications that automate routine tasks, enable remote video consultations, food or medications delivery to the one or more subjects in the particular healthcare facility and/or that assist the one or more subjects for a safe mobility;
However, Angle does teach:
and robotic applications that automate routine tasks, enable remote video consultations, food or medications delivery to the one or more subjects in the particular healthcare facility and/or that assist the one or more subjects for a safe mobility; (Col. 11 lines 56-67 and Col. 12 lines 1-3 discloses, e.g. robot delivering medication)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vesto’s teachings of reviewing clinical workflow data and utilizing machine learning for contextual awareness and to improve a healthcare ecosystem (see Col. 13 lines 4-20 and see Col. 14 lines 24-28) with Angle’s teachings of utilizing robotic applications, the motivation being Vesto already teaches the use of remote tele-ICU and consultation system for improved data communication (see Col. 5 lines 5-35) therefore it would be predictable to combine the teachings of Angle as it would improve the immersive feedback and service to allow improved contextual awareness as well as improving interactive communication with the ICU environment and compliance to improve the patient outcomes and increase individualized care.
As per Claim 2, Vesto teaches:
a. The computer-implemented method of claim 1, wherein a data source is a subject-mounted device, a network device, an administration application, a clinical application, or a subject application. (Col. 12 lines 30-57 discloses, “As shown in the example of FIG. 3, a plurality of devices (e.g., information systems, imaging modalities, etc.) 310- 312 can access a cloud 320, which connects the devices 310-312 with a server 330 and associated data store 340. Information systems, for example, include communication interfaces to exchange information with server 330 and data store 340 via the cloud 320. Other devices, such as medical imaging scarmers, patient monitors, etc., can be outfitted with sensors and communication interfaces to enable them to communicate with each other and with the server 330 via the cloud 320. Thus, machines 310-312 in the system 300 become "intelligent" as a network with advanced sensors, controls, and software applications. Using such an infrastructure, advanced analytics can be provided to associated data. The analytics combines physics-based analytics, predictive algorithms, automation, and deep domain expertise. Via the cloud 320, devices 310-312 and associated people can be connected to support more intelligent design, operations, maintenance, and higher server quality and safety, for example. Using the industrial internet infrastructure, for example, a proprietary machine data stream can be extracted from a device 310.” And see Col. 14 lines 52-56 discloses, “Certain examples provide a reference realization of a remote ICU monitoring solution (tele-ICU) which allows an intensivist at the command center to monitor real-time device parameters of critically ill patients from remote ICUs/Hospitals on a 24 hour /7 days a week basis.” )
6. As per claim 4, Vesto teaches:
a.The computer-implemented method of claim 1, further comprising: providing security to a real-time communication between one or more devices of a subject and the one or more devices of a healthcare provider by using a secure channel of communication with one or more guardrails to comply with one or more security constraints. (Col. 14 lines 9-15 discloses, “Certain examples provide a software-based method to secure transport of a message payload using an intelligent discovery process for public key infrastructure (PKI) security and trust certificates between the system (e.g., referred to as "IntelliLink") and multiple partner systems within the inter-connected ecosystem.” And see Col. 22 lines 22-34 discloses, “In an example, the security component 902 is deployed as a software bundle that is plugged in when IntelliLink and the core messaging routes are initialized. The security unit 902 intercepts and processes outgoing messages to be signed and encrypted according to the security and trust specification defined between IntelliLink and the partner system. Inversion of Control (IoC) and Dependency Injection (DI) patterns can be used for various components to switch bindings, providers and modules, for example. Some core components within the Security module 902 that would be loaded via IoC and DI include: a security interceptor 903, a cryptographer 905, and a certificate resolver 904, as well as a certificate and key store 906.”)
As per claims 7, 8, and 10 they are system claims which repeat the same limitations of claims 1, 2, and 4 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Vesto and Angle as well as motivations to combine disclose the underlying process steps that constitute the methods of claims 1, 2, and 4 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 7, 8, and 10 are rejected for the same reasons given above for claims 1, 2, and 4.
As per claims 13, 14, and 16 it is an article of manufacture claim which repeats the same limitations of claim 1, 2, and 4, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Vesto and Angle as well as motivation to combine disclose the underlying process steps that constitute the method of claims 1, 2, and 4 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 13, 14, and 16 are rejected for the same reasons given above for claims 1, 2, and 4
Claims 3, 5, 6, 9, 11, 12, 15, 17, and 18 are rejected to under 35 U.S.C. 103 as being unpatentable over Vesto et. al (hereinafter Vesto) (US11087878B2) in view of Angle et. al (hereinafter Angle) (US10661433B2) and in further view of Wang (US20230245651A1)
As per claim 3, Vesto and Angle do not teach:
a.The computer-implemented method of claim 1, further comprising: receiving, via one or more applications and channels, feedback data from the one or more subjects, wherein a feedback is a response to a type of an experience of the one or more subjects for the one or more services; employing one or more natural language processing (NLP) techniques to preprocess the feedback data;
b.extracting a subset of the feedback data where the subset represents textual and contextual information; utilizing topic modeling to categorize the subset of the feedback data into one or more areas wherein each of the one or more areas indicate the type of the experience of the one or more subjects for the one or more services;
c. utilizing one or more machine learning models to gauge whether the feedback is positive, negative or neutral;
d. identifying the one or more areas to flag for improvement;
e. and conducting statistical analysis and data visualization for the one or more subjects to evaluate performance of the one or more services.
However, Wang does explicitly teach:
a.The computer-implemented method of claim 1, further comprising: receiving, via one or more applications and channels, feedback data from the one or more subjects, wherein a feedback is a response to a type of an experience of the one or more subjects for the one or more services; employing one or more natural language processing (NLP) techniques to preprocess the feedback data; ([0226] discloses, “For instance, if the AI system collects patient data from various sources such as medical devices, electronic health records, and patient feedback, the AI system can use this data to generate personalized health recommendations and notifications for the patient. However, before sending any notifications, the AI system should ensure that the recipient is an authorized patient or healthcare provider.” And see [0353] discloses, “The AI system can actively ask questions or solicit feedback from the user to gather more information about their preferences and needs, allowing it to provide more relevant contextual information as the interaction progresses. “ and see [0477] discloses, “Once the Al system has received user input, it retrieves user data such as preferences and behavior history 2202. This data is then filtered and analyzed by the Al system to identify relevant options that can be recommended to the user 2203. Based on this analysis, the Al system generates personalized recommendations or suggestions that are tailored to the user's preferences and behavior 2204. These recommendations can take the form of product suggestions, content recommendations, or any other relevant options based on the user's input and data.” And see [0478] discloses, “The Al system then presents these personalized recommendations or suggestions to the user 2205, who can provide feedback on the options presented 2206. This feedback is valuable in improving the recommendations or suggestions for future interactions with the user. The Al system uses feedback from the user to update the recommendations or suggestions 2207. This iterative process of feedback and data analysis allows the Al system to continually learn and adapt to the user's preferences and behavior. The Al system uses the updated information to update the OKB to improve future recommendations or suggestions 2208.” And see [0479] discloses, “The process ends when the user has received and responded to the personalized recommendations or suggestions with satisfactory 2209. By leveraging the user's data and feedback, the Al system can provide more relevant and personalized recommendations over time, improving the user experience and driving engagement.” [0480] FIG. 23 illustrates the process by which the Al agent can learn and adapt based on user feedback using advanced reasoning algorithms 2300. The chart highlights the steps involved in receiving user feedback, updating the OKB, analyzing the feedback, and adjusting the Al agent's responses accordingly.” And see [0481] discloses, “The process begins when the user provides feedback to the Al agent, such as correcting a response or indicating dissatisfaction with a recommendation 2301. The Al agent then analyzes the feedback to generate an understanding of user feedback 2302. Next, the Al system searches for responses in the OKB 2303. The Al system determines whether a predefined response is available in the OKB 2304. If there are no predefined responses in the OKB, the Al agent interacts with the user and generates understanding and responses using NLU and NLG 2305. Using advanced reasoning algorithms, the Al agent can determine the root cause of the feedback and adjust its responses accordingly 2306.”) b.extracting a subset of the feedback data where the subset represents textual and contextual information; utilizing topic modeling to categorize the subset of the feedback data into one or more areas wherein each of the one or more areas indicate the type of the experience of the one or more subjects for the one or more services; ([0037] discloses, “In other embodiments, the system may be employed in healthcare to assist patients with medication reminders and symptom tracking.” And see [0391] discloses, “The extracted information and analyzed contextual information are then classified and categorized using ML algorithms 1803, to generate a set of most likely intents and objectives 1804. The AI system then evaluates each generated intent and objective based on a set of predetermined criteria 1805, such as how well it aligns with the available contextual information and how likely it is to achieve the user's goal in the environment.”)
c.utilizing one or more machine learning models to gauge whether the feedback is positive, negative or neutral; ([0443] discloses, “Furthermore, language models can recognize and respond to users' emotions and sentiments. Sentiment analysis is a natural language processing technique that involves identifying the emotional tone of text, typically as positive, negative, or neutral.” And see [0470] discloses, “For example, if a user is expressing frustration or anger, a conversational Al agent may respond in a more empathetic tone, acknowledging the user's feelings and offering solutions to their problem. On the other hand, if the user is expressing joy or satisfaction, the conversational Al agent may respond with a more positive and congratulatory tone. Throughout this process, the conversational Al agent continually evaluates the user's emotional states and sentiments, adjusting its responses as needed to provide the most appropriate and effective communication.”)
d.identifying the one or more areas to flag for improvement; ([0097] discloses, “Moreover, integrating human-in-the-loop strategies can be valuable in providing insights and improving the model iteratively. This could involve using expert feedback to label or annotate data, validate AI model outputs, or prioritize areas of improvement. This collaborative approach between human experts and AI models can result in more accurate, reliable, and interpretable systems.
e.and conducting statistical analysis and data visualization for the one or more subjects to evaluate performance of the one or more services. ([0186] discloses, “In another embodiment, the analysis modules are used for statistical data analysis, inductive learning, casebased reasoning, and visualization. An alternative embodiment employs one or more analysis modules, in combination with a rules engine, to conduct predictive modeling, data analysis, reasoning, and inductive learning for AI agents to perform rationally. Additionally, in yet another embodiment, the AI system employs a computer vision system to analyze image data for object recognition and identification. Finally, analysis modules can also be used for image data analysis, such as object recognition and identification, using a computer vision system. Overall, analysis modules are an important part of an AI system as they help to improve the system's performance and accuracy in understanding user intent, context, and generating appropriate responses.” And see [0226] discloses, “For instance, if the AI system collects patient data from various sources such as medical devices, electronic health records, and patient feedback, the AI system can use this data to generate personalized health recommendations and notifications for the patient. However, before sending any notifications, the AI system should ensure that the recipient is an authorized patient or healthcare provider.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vesto’s teachings of reviewing clinical workflow data and utilizing machine learning for contextual awareness and to improve a healthcare ecosystem (see Col. 13 lines 4-20 and see Col. 14 lines 24-28) and Angles teachings of robotic applications as previously cited with Wang’s teachings of gathering feedback and utilizing machine learning to such as NLP for contextual awareness, the motivation being Vesto already teaches the use of machine learning analysis and reviewing data as well as gathering improvements to make the system better for patient outcomes (see Col. 25 lines 24-36) therefore it would be predictable to combine the teachings of Wang as it is simple substitution for one type of machine learning for another to gather contextual awareness as well as improving patient outcomes through explicit feedback and review to allow for decreased poor outcomes and increased individualized care.
As per claim 5, Vesta and Angle do not teach:
a.The computer-implemented method of claim 1, wherein the one or more third services include virtual reality.
However, Wang does teach:
a.The computer-implemented method of claim 1, wherein the one or more third services include virtual reality. ([0046] discloses, “The AI system 101 can also integrate with various sensors 113, smart devices 114, virtual reality (VR) / augmented reality (AR) headsets 115, and Internet of Things (IoT) 116. This allows the AI system 101 to identify physical objects 112 in the environment and gather data such as object location, relative positions, and temperature readings. To obtain contextual information about physical objects, the system can use a range of sensors, including temperature sensor 117, light sensor 118, noise sensor 119, motion sensor 120, and presence sensor 121. This information is then preprocessed and categorized before being displayed to users.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vesto’s teachings of reviewing clinical workflow data and utilizing machine learning for contextual awareness and to improve a healthcare ecosystem (see Col. 13 lines 4-20 and see Col. 14 lines 24-28) and Angle’s teachings of robotic applications with Wang’s teachings of utilizing virtual reality, the motivation being Vesto already teaches the use of remote tele-ICU and consultation system for improved data communication (see Col. 5 lines 5-35) therefore it would be predictable to combine the teachings of Wang as it would improve the immersive feedback and service to allow improved contextual awareness as well as improving interactive communication with the ICU environment to improve the patient outcomes and increase individualized care.
As per claim 6, Vesta and Angle do not teach:
a. The computer-implemented method of claim 1, wherein the one or more third services include augmented reality.
However, Wang does teach:
a.The computer-implemented method of claim 1, wherein the one or more third services include augmented reality. ([0046] discloses, “The AI system 101 can also integrate with various sensors 113, smart devices 114, virtual reality (VR) / augmented reality (AR) headsets 115, and Internet of Things (IoT) 116. This allows the AI system 101 to identify physical objects 112 in the environment and gather data such as object location, relative positions, and temperature readings. To obtain contextual information about physical objects, the system can use a range of sensors, including temperature sensor 117, light sensor 118, noise sensor 119, motion sensor 120, and presence sensor 121. This information is then preprocessed and categorized before being displayed to users.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Vesto’s teachings and Angle’s teachings with Wang’s teachings for the same reasons given above for claim 5.
As per claims 9, 11, and 12 they are system claims which repeat the same limitations of claims 3, 5, and 6 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings as well as motivations to combine of Vesto, Angle, and Wang disclose the underlying process steps that constitute the methods of claims 3, 5, and 6 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 9, 11, and 12 are rejected for the same reasons given above for claims 3, 5, and 6.
As per claims 15, 17, and 18 it is an article of manufacture claim which repeats the same limitations of claim 3, 5, and 6, the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings as well as the motivations to combine of Vesto, Angle, and Wang disclose the underlying process steps that constitute the method of claims 3, 5, and 6 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 15, 17, and 18are rejected for the same reasons given above for claims 3, 5, and 6
Response to Arguments Regarding 35 U.S.C § 101 Rejections
Applicant’s arguments on pages 1-4 of remarks have been considered and responded to below. Applicant arguments are that the claims are not directed to a judicially recognized exception of an abstract idea, at least for the reason(s) stated below. The claimed invention relates to a healthcare management system that dynamically builds and updates a persona for each subject in real-time based on continuously collected data. The claimed invention improves the functioning of healthcare by introducing a subject-centric matrix architecture that dynamically builds and updates a persona profile using real-time data. The features in the present claimed invention are related to collecting data in real-time from data sources for one or more subjects, aggregating the data of each of the one or more subjects with associated electronic medical records to build a persona for each of the one or more subjects, deploying at least one service within each sublayer of a subject-centric matrix, and deploying a cloud platform to host the services. Each sublayer of a subject-centric matrix is customized to include the at least one service for a particular healthcare facility. The sublayers include a first sublayer, a second sublayer, and a third sublayer. The first sublayer includes one or more first services to know the subject by collecting the data from one or more electronic medical records for creating the persona associated with the subject or providing a real-time communication between a plurality of subjects. The second sublayer includes the one or more second services for collecting the data from one or more subject-based applications or one or more subject- mounted devices for predictive and prescriptive analysis of the one or more subjects within and outside the particular healthcare facility, and the third sublayer focuses on the personalized experience of a subject by providing one or more third services such as quick registration through a number of smart identification technologies, real-time tracking services track geolocation of a subject inside a healthcare facility and/or assist the subjects to reach their desired location, and robotic applications that automate the routine tasks resulting in reduced infection rates among the medical staff. Robotic applications can enable remote video consultations, food or medications delivery to the subjects in a hospital and/or assisting for a safe mobility. (See paragraph [0015] of the published application and Figs. 3 and 9 of the drawings). Further, the term "certain" qualifies the "certain methods of organizing human activity" grouping as a reminder of several important points. First, not all methods of organizing human activity are abstract ideas (e.g., "a defined set of steps for combining particular ingredients to create a drug formulation" is not a certain "method of organizing human activity"), In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1160-61, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018). Second, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in MPEP § 2106.04(a)(3). For at least these or similar reasons, the independent claims and claims dependent thereon are not directed to the alleged abstract idea and are patent eligible.
Applicant submits that, even if the features of any of the independent claims are directed to the abstract idea as alleged by the Examiner, the claims, when considered as a whole, integrate the alleged abstract idea into a practical application. Applicant submits that the present application relates to a subject-centric smart hospital for providing customized healthcare facilities. Smart hospitals can introduce systems for tracking and predicting the health of a subj ect using subj ect-mounted devices and electronic healthcare data recorded in a subject health application by using machine learning models. A health record of a subject can be used to monitor data of the subjects in real-time, which can help identify and track health patterns to inform clinical decision making. The present application has implementations in the hospitals and healthcare industry. (See paragraphs [0005] and [0013] of the published application).
The present invention helps reduce bottlenecks that hamper information sharing, care coordination, and subject engagement in hospital settings by fusing a centralized cloud platform with customed services that are hosted in it. Unlike the conventional hospital setup, the smart hospital is equipped with Internet of Things (IoT) sensors and devices to create a centralized, cost-efficient system for hospital operations which seamlessly integrates augmented and virtual reality (AR/VR) services to support interactive training and education for medical professionals, thereby reducing the need for complex, expensive, and time-consuming traditional training procedures. The present disclosure may integrate a computer-implemented method, facilitating remote video consultation with subjects of an infectious disease, automating routine tasks using robots for supplying food or medication to subjects in a healthcare facility and avoiding high risk of mortality in subjects because of exposure to infectious organisms in hospital wards. This reduces infection rates among the medical staff, improves efficiency and optimizes human resource utilization. The automation of tasks may also increase utilization of nurses and auxiliary staff to avoid performing mundane and repetitive tasks. Further, the cloud platform of the claimed invention provides cutting-edge services to the hospitals as well as the subjects for operational efficiency and better outcomes in a value-based care ecosystem. The various smart hospital units access the data of subjects that are stored on the cloud platform through internet. Furthermore, smart hospitals can exchange the data of subjects with one another through the cloud platform. (See paragraphs [0015] and [0067] of the published application).
Furthermore, the final sublayer L3 of the subject-centric matrix focuses on the personalized experience of a subject by providing services such as quick registration through a number of smart identification technologies, and real-time tracking services track geolocation of a subject inside a healthcare facility and/or assist the subjects to reach their desired location, and robotic applications automate the routine tasks resulting in reduced infection rates among the medical staff. The services may also be used to track medical personnel and the assets of a hospital for efficient utilization of resources. Further, the robotic applications can enable remote video consultations, food or medications delivery to the subjects in a hospital and/or assist subjects for a safe mobility. For example, during the stay of a subject, the subject may encounter robotic assistants in various roles. For example, a robotic nurse delivers medications and supplies to the rooms for the timely delivery of supplies to the subjects, thereby reducing the workload of human nurses. Robotic cleaning staff members also maintain hygiene standards in the common areas. (See paragraphs [0035] and [0045] of the published application).
Therefore, the services of the sublayers especially the real-time tracking services and robotic applications improve the operational efficiency and subject experience in a healthcare facility does not fall under the sub-groupings of "certain methods of organizing human activity."
For at least these additional reasons, any abstract idea in the independent claims is integrated into a practical application, thereby indicating that the claims are patent eligible. The following arguments are presented with reference to independent claim 1. The same argument applies to other rejected independent claims.
Examiner appreciates applicant’s argument but does not find them persuasive.
The MPEP states The Alice/Mayo two-part test is the only test that should be used to evaluate the eligibility of claims under examination. While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. Instead it should be considered as part of the "integration" determination or "significantly more" determination articulated in the Alice/Mayo test. Bilski v. Kappos, 561 U.S. 593, 605, 95 USPQ2d 1001, 1007 (2010). See MPEP § 2106.04(d) for more information about evaluating whether a claim reciting a judicial exception is integrated into a practical application and MPEP § 2106.05(b) and MPEP § 2106.05(c) for more information about how the machine-or-transformation test fits into the Alice/Mayo two-part framework.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I); (Mathematical Calculations - A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.)
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. Furthermore, the MPEP state in 2106.04(d), “Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical applications.”
Therefore, respectfully, examiner disagrees with the applicant as the claim limitations must be reviewed in light of the specification and the specification cannot be read into the claims. The positively recited in claim 1 (as representative) are directed to a judicial exception (i.e. certain methods of organizing human activity) as following rules and instructions to aggregate data, analyze data, and personalize services for a particular subject according to the persona associated with a subject. This is abstract in substance as human can analyze data to personalize services for a particular subject according to their persona and execute tasks associated with the services by following rules and instructions. Being implemented by a computer environment does not make the recited claim dispositive of being certain methods of organizing human activity.
Further responding to applicants arguments, the judicial exception (abstract idea) cannot integrate itself into a practical application but identification of any additional elements recited in the claim can be evaluated to determine if the additional elements integrate the exception into a practical application. The claims additional elements are not recited as being an improvement to a technology field or a technology confined to the computer environment in which the claims recite. A technical problem must first be identified in instant application specification and reflected in the claims. Problems recited in the arguments are abstract problems related to clinical decision making, infection rates, human resources, and basic efficiencies. The additional elements are apply it or generally linking and the claims do not recite technical improvements whether alone or in combination with the abstract idea. The abstract idea cannot bring forth the practical application. If applicant’s line of reasoning were correct Alice corp. would have been deemed eligible. Examiner maintains the claims are directed to an abstract idea and do not integrate into a practical application. Therefore, they also do not amount to significantly more.
Examiner maintains the 35 U.S.C § 101 rejection
Response to Arguments Regarding 35 U.S.C § 102/103 Rejections
Applicant’s arguments on pages 4-5 of remarks have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Examiner does note that the independent claims recites “or” between the series of elements. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Examiner maintains the 35 U.S.C § 103 rejection.
Prior Art Cited But Not Relied Upon
US11899824B1 – Hall et. al (hereinafter Hall)
Disclosed are methods and systems for secure data communication amongst computer systems. Encrypted data in a first format is accessed over a secure communication channel from a first source for a first subject. Encrypted data in a second format is accessed over a secure communication channel from a second source for the first subject. The encrypted data in the first format from the first source and in the second format from the second source is decrypted. The decrypted data in the first format from the first source and in the second format from the second source is converted to a third format. At least partly in response to the request for information from a first system, at least a portion of the data from the first source and the second source is accessed from a database The accessed data is transmitted in encrypted form to the first system.
US12068082B2 - Subramanian et. al (hereinafter Subramanian)
A system and method for medical communications is disclosed. The system and method may operate to receive, from a user communication node, an electronic signal comprising a communication request including a request description; select, from the list of network entities corresponding to third parties, one or more network entities based at least in part on (i) a parameter profile associated with a third party, and (ii) criteria extracted from the communication request including the request description; transmit an electronic signal, to one or more network entities, the communication request including the request description; receive an action from one of the selected network entities corresponding to third parties in response to the communication request; and provide an interactive communication panel to the user communication node and the one of the selected network entities to facilitate the real-time communication session.
US20240233927A1 – Frankel et. al (hereinafter Frankel)
Disclosed are systems and methods that provide a novel framework for the real-time management and control of electronic/digital and/or physical activities performed in, around and/or in relation to a healthcare facility. The disclosed framework provides an interactive user interface (UI) that displays, as immersive and interactive interface cards, real-time digital data and content that corresponds to the digital and/or physical activities. The interactive interface cards are selectable, and configured with portal capabilities for the discovery of additional information, the creation of new forms of data, and the interaction with other users. entities, departments and other interactive capabilities in/around the facility. The disclosed framework enables a fully interactive, personalized and dynamic management platform for controlling operations of a facility while maintaining management control of those operations.
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 Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687