DETAILED ACTION
Notices to Applicant
This communication is a First Action Non-Final on the merits. Claims 1-20 as filed 05/19/2025, are currently pending and have been considered below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The application claims priority to U.S. Provisional Patent Application Serial No. 63/705,074 filed on 10/09/2024.
Claim Objections
Applicant is advised that should claim 8 be found allowable, claim 16 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1-8 and 16 are drawn to a method for providing healthcare information to a plurality of healthcare providers, which is within the four statutory categories (i.e. method).
Independent Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites
1. A method of providing healthcare information to a plurality of healthcare providers, the method comprising:
receiving first historical information corresponding to the plurality of healthcare providers;
receiving second historical information corresponding to external health care information sources;
training an artificial intelligence (Al) model using the first historical information and the second historical information;
receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and
in response to the current information, generating one or more healthcare suggestions by the Al model and delivering the suggestions to one or more of the plurality of healthcare providers.
The claim limitations, as drafted, is a method that, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components. That is, other than reciting the above bolded language, for example “an artificial intelligence (AI) model,” nothing in the claim precludes the limitations from being directed to managing personal behavior or interactions between people through rules or instructions. For example, but for the above bolded language, receiving first historical information corresponding to the plurality of healthcare providers; receiving second historical information corresponding to external health care information sources; receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and in response to the current information, generating one or more healthcare suggestions and delivering the suggestions to one or more of the plurality of healthcare providers in the context of this claim encompasses rules or instructions for managing personal behavior or interactions between people for providing healthcare information to a plurality of healthcare providers. The limitations If a claim limitation, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using, for example “an artificial intelligence (AI) model,” to perform the claim limitations. The additional elements in each of the steps are recited at a high-level of generality (i.e., the system includes any type of processor and storage media as they relate to general purpose computer components and includes ML model that can be any type of machine learning model (e.g., generative model, neural network, deep learning, NLP, support vector machine ("SVM"), random forests, gradient boosting, large language model ("LLM") etc.) that is trained by training data (Application Specification [0019], [0029], [0032]])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using, for example “an artificial intelligence (AI) model,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component. (i.e., the system includes any type of processor and storage media as they relate to general purpose computer components and includes ML model that can be any type of machine learning model (e.g., generative model, neural network, deep learning, NLP, support vector machine ("SVM"), random forests, gradient boosting, large language model ("LLM") etc.) that is trained by training data (Application Specification [0019], [0029], [0032]])). Mere instructions to apply an exception using a generic computer component or other machinery in its ordinary capacity cannot provide an inventive concept. See MPEP 2106.05(f)(2). The claim is not patent eligible.
Dependent claims 2-8 and 16 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract ideas. Dependent claim 7 recites the additional element of “wherein the Al model comprises a generative Al model,” however, these elements are recited at a high level such that they amount to using generic computer components or other machinery in their ordinary capacity to perform the abstract idea. See MPEP 2106.05(f)(2); Application Specification at [0029], [0032]. Dependent claims 8 and 16 each recites the additional element of “using a cloud infrastructure for providing healthcare information, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN,” however, these elements are recited at a high level such that they amount to using generic computer components or other machinery in their ordinary capacity to perform the abstract idea. See MPEP 2106.05(f)(2). The above additional element is also extra-solution activity that is well-understood, routine, and conventional activity. See US 2022/0247639 A1 at [0120]. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the dependent claims are rejected under 35 U.S.C. § 101.
Claims 9-15 are directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to “a computer-readable medium.” Independent claim 9 as well as the Application Specification, does not exclude the claimed medium from being transitory. See Application Specification at [0020], [0028] (“implemented by software stored in memory or other computer readable or tangible medium, and executed by a processor”); however, tangibility is not sufficient to exclude the concept of a transitory signal.
When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to a computer-readable storage medium that covers signal per se, which the USPTO must reject under 35 U.S.C. §101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer-readable storage medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C.§101 by adding the limitation "non-transitory" to the claim. Cf. Animals- Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. §101 ). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signal per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
Independent Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 9 recites
A computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to provide healthcare information to a plurality of healthcare providers, the providing healthcare information comprising:
receiving first historical information corresponding to the plurality of healthcare providers;
receiving second historical information corresponding to external health care information sources;
training an artificial intelligence (AI) model using the first historical information and the second historical information;
receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and
in response to the current information, generating one or more healthcare suggestions by the Al model and delivering the suggestions to one or more of the plurality of healthcare providers.
The claim limitations, as drafted, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components. That is, other than reciting the above bolded language, for example a “computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to,” and “an artificial intelligence (AI) model,” nothing in the claim precludes the limitations from being directed to managing personal behavior or interactions between people through rules or instructions. For example, but for the above bolded language, receiving first historical information corresponding to the plurality of healthcare providers; receiving second historical information corresponding to external health care information sources; receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and in response to the current information, generating one or more healthcare suggestions and delivering the suggestions to one or more of the plurality of healthcare providers in the context of this claim encompasses rules or instructions for managing personal behavior or interactions between people for providing healthcare information to a plurality of healthcare providers. The limitations If a claim limitation, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using, for example, a “computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to,” and “an artificial intelligence (AI) model,” to perform the claim limitations. The additional elements in each of the steps are recited at a high-level of generality (i.e., the system includes any type of processor and storage media as they relate to general purpose computer components and includes ML model that can be any type of machine learning model (e.g., generative model, neural network, deep learning, NLP, support vector machine ("SVM"), random forests, gradient boosting, large language model ("LLM") etc.) that is trained by training data (Application Specification [0019], [0029], [0032]])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements of using, for example “computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to,” and “an artificial intelligence (AI) model,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component. (i.e., the system includes any type of processor and storage media as they relate to general purpose computer components and includes ML model that can be any type of machine learning model (e.g., generative model, neural network, deep learning, NLP, support vector machine ("SVM"), random forests, gradient boosting, large language model ("LLM") etc.) that is trained by training data (Application Specification [0019], [0029], [0032]])). Mere instructions to apply an exception using a generic computer component or other machinery in its ordinary capacity cannot provide an inventive concept. See MPEP 2106.05(f)(2). The claim is not patent eligible.
Dependent claims 10-15 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract ideas. Dependent claim 15 recites the additional element of “wherein the Al model comprises a generative Al model,” however, these elements are recited at a high level such that they amount to using generic computer components or other machinery in their ordinary capacity to perform the abstract idea. See MPEP 2106.05(f)(2); Application Specification at [0029], [0032].Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the dependent claims are rejected under 35 U.S.C. § 101.
Claims 17-20 are drawn to a cloud based system for providing healthcare information to a plurality of healthcare providers, which is within the four statutory categories (i.e. machine).
Independent Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 17 recites
17. A cloud based system for providing healthcare information to a plurality of healthcare providers, the system comprising: an artificial intelligence (AI) model; one or more processors coupled to the Al model and configured to:
receive first historical information corresponding to the plurality of healthcare providers;
receive second historical information corresponding to external health care information sources;
train the Al model using the first historical information and the second historical information;
receive current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and
in response to the current information, generate one or more healthcare suggestions by the Al model and delivering the suggestions to one or more of the plurality of healthcare providers.
The claim limitations, as drafted, is a machine that, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components. That is, other than reciting the above bolded language, nothing in the claim precludes the limitations from being directed to managing personal behavior or interactions between people through rules or instructions. For example, but for the above bolded language, receiving first historical information corresponding to the plurality of healthcare providers; receiving second historical information corresponding to external health care information sources; receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and in response to the current information, generating one or more healthcare suggestions and delivering the suggestions to one or more of the plurality of healthcare providers in the context of this claim encompasses rules or instructions for managing personal behavior or interactions between people for providing healthcare information to a plurality of healthcare providers. The limitations If a claim limitation, under its broadest reasonable interpretation, covers rules or instructions for managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the above bolded additional elements of using, for example, a “cloud based system for providing healthcare information to a plurality of healthcare providers, the system comprising: an artificial intelligence (AI) model; one or more processors coupled to the Al model and configured to,” to perform the claim limitations. The additional elements in each of the steps are recited at a high-level of generality (i.e., the system includes any type of processor and storage media as they relate to general purpose computer components and includes ML model that can be any type of machine learning model (e.g., generative model, neural network, deep learning, NLP, support vector machine ("SVM"), random forests, gradient boosting, large language model ("LLM") etc.) that is trained by training data (Application Specification [0019], [0029], [0032]])). As such, the limitations amount to no more than mere instructions to implement an abstract idea on a computer or other machinery in its ordinary capacity, or merely uses a computer or other machinery in its ordinary capacity as a tool to perform an abstract idea. See MPEP 2106.05(f)(2). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the above bolded additional elements of using, for example a “cloud based system for providing healthcare information to a plurality of healthcare providers, the system comprising: an artificial intelligence (AI) model; one or more processors coupled to the Al model and configured to,” to perform the claim limitations amounts to no more than mere instructions to apply the exception using a generic computer component. (i.e., the system includes any type of processor and storage media as they relate to general purpose computer components and includes ML model that can be any type of machine learning model (e.g., generative model, neural network, deep learning, NLP, support vector machine ("SVM"), random forests, gradient boosting, large language model ("LLM") etc.) that is trained by training data (Application Specification [0019], [0029], [0032]])). Mere instructions to apply an exception using a generic computer component or other machinery in its ordinary capacity cannot provide an inventive concept. See MPEP 2106.05(f)(2). The claim is not patent eligible.
Dependent claims 18-20 include limitations of the independent claim and are directed to the same abstract idea as discussed above and incorporated herein. The dependent claims are rejected under 35 U.S.C. § 101 because they are directed to non-statutory subject matter. These additional claims recite what the data is and how it is analyzed. These information characteristics do not integrate the judicial exception into a practical application, and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract ideas. Dependent claim 20 recites the additional element of “using a cloud infrastructure for providing healthcare information, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN,” however, these elements are recited at a high level such that they amount to using generic computer components or other machinery in their ordinary capacity to perform the abstract idea. See MPEP 2106.05(f)(2). The above additional element is also extra-solution activity that is well-understood, routine, and conventional activity. See US 2022/0247639 A1 at [0120]. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the dependent claims are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 9-15, and 17-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. 2025/0087342 A1 (hereinafter “Khosla et al.”).
RE: Claim 1 Khosla et al. teaches the claimed:
1. A method of providing healthcare information to a plurality of healthcare providers, the method comprising: receiving first historical information corresponding to the plurality of healthcare providers ((Khosla et al., [0043],m [0050], [0059]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data; The historical healthcare delivery information 148 can include historical information recorded for past patients regarding any monitored parameters associated with their course of care. In one or more embodiments, this can include patient data 104, workflow data 106, clinician data 108 and/or facility data 110 associated with a past patient's course of care. For example, for respective past patients, the historical healthcare delivery information 148 can identify a condition or diagnosis of the patient, information identifying or describing the physiological state/condition of the patient at the time of arrival at the healthcare facility ( or at the time when treatment was initiated), detailed parameters regarding what treatment was provided to the patient, including clinical actions that were taken in association with treatment and when, the clinical reactions or outcomes of the respective actions, what clinicians performed respective actions, states of the clinicians at the time of performance of the respective actions, physiological states of the patient associated with respective actions, contextual state of the healthcare facility over the course of care (e.g., adequately staffed or not, number of patients admitted, status of medical resources available, etc.), and the like));
receiving second historical information corresponding to external health care information sources ((Khosla et al., [0043], [0050]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data));
training an artificial intelligence (Al) model using the first historical information and the second historical information ((Khosla et al., [0106]) (system can employ supervised or semi-supervised machine learning to develop one or more guidance models prior to application of system to facilitate reasoning about and providing responses to the live feedback information. In this regard, the one or more guidance models can include one or more mathematical models that have been trained and developed by the model development component using training data and the information provided by the healthcare information sources and the historical healthcare delivery information. The training data can include the same or similar data as the live feedback information));
receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources ((Khosla et al., [0060]) (The reception component 126 can also receive and/or retrieve live feedback information 154 from one or more dynamic healthcare delivery data sources 102 regarding various dynamic aspects of healthcare delivery at a healthcare facility. The information provided by the dynamic healthcare delivery data sources 102 can be considered dynamic because at least some of the information can dynamically change over a course of patient care)); and
in response to the current information, generating one or more healthcare suggestions by the Al model and delivering the suggestions to one or more of the plurality of healthcare providers ((Khosla et al., [0036]) (the AI system can further be configured to determine or infer the action using machine learning analysis of the feedback in view of the information provided by the various data sources. The AI system can further recommend the action for performance by an appropriate clinician)).
RE: Claim 2 Khosla et al. teaches the claimed:
2. The method of claim 1, wherein the first historical information comprises one or more of: data on the healthcare providers; information related to healthcare personnel; patient demography; clinical information or patient outcomes ((Khosla et al., [0043],m [0050], [0059]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data; The historical healthcare delivery information 148 can include historical information recorded for past patients regarding any monitored parameters associated with their course of care. In one or more embodiments, this can include patient data 104, workflow data 106, clinician data 108 and/or facility data 110 associated with a past patient's course of care. For example, for respective past patients, the historical healthcare delivery information 148 can identify a condition or diagnosis of the patient, information identifying or describing the physiological state/condition of the patient at the time of arrival at the healthcare facility ( or at the time when treatment was initiated), detailed parameters regarding what treatment was provided to the patient, including clinical actions that were taken in association with treatment and when, the clinical reactions or outcomes of the respective actions, what clinicians performed respective actions, states of the clinicians at the time of performance of the respective actions, physiological states of the patient associated with respective actions, contextual state of the healthcare facility over the course of care (e.g., adequately staffed or not, number of patients admitted, status of medical resources available, etc.), and the like)).
RE: Claim 3 Khosla et al. teaches the claimed:
3. The method of claim 1, wherein the second historical information comprises one or more of: latest therapeutic protocols and drugs; opportunities for continuing medical education; local conditions; disease outbreaks and accidents; or natural disasters and calamities ((Khosla et al., [0051]) (In this regard, the SOP data 114 can include information that identifies and/or defines one or more standardized or defined protocols for following in association with performance of a procedure, treating a patient with a condition, and/or responding to a clinical scenario)).
RE: Claim 4 Khosla et al. teaches the claimed:
4. The method of claim 1, wherein the suggestions comprise one or more of: latest approved protocols for treatment; new generation of drugs available; or contact information of other physicians who have treated similar conditions successfully ((Khosla et al., [0036]) (the AI system can further be configured to determine or infer the action using machine learning analysis of the feedback in view of the information provided by the various data sources. The AI system can further recommend the action for performance by an appropriate clinician; the AI system can consider information regarding historical reactions performed in same or similar clinical scenarios involving the event or condition, in view of one or more relevant SOPs and further in view of variable factors relevant to the current context, such as but not limited to, factors associated with the current patient as found in the patient EMR (e.g., other conditions of the patient, allergies, preferences, etc.), available resources (e.g., medical supplies and instruments that may be needed), available clinicians, capabilities of the clinicians, fatigue levels of the clinicians (e.g., determinations or inferences can be made using for example: facial analysis, focus of attention, posture, eyes, voice, gaze, hours already worked, wearables that monitor vitals . . . ) and other possible factors relevant to the current context of the entire healthcare facility and the patient and the like)).
RE: Claim 5 Khosla et al. teaches the claimed:
5. The method of claim 1, wherein the suggestions comprise one or more of: details of available training; recommended associations of doctors to join; or research articles to review ((Khosla et al., [0054], [0238], [0239]) (The medical literature 118 can include can include various sources of electronic literature providing medical information … medical research data; the procedure training module can tailor the simulation to focus on areas where the clinician needs practice or improvement. For example, the procedure training module 1502 can employ information regarding aspects of a clinical scenario, procedure, course of care and the like, that the clinician tends to perform incorrectly or poorly. The procedure training module 1502 can then tailor subsequent simulations to present the clinician with information pertaining to similar clinical events, conditions, contexts, etc., that the clinician is deficient in)).
RE: Claim 6 Khosla et al. teaches the claimed:
6. The method of claim 1, wherein the suggestions comprise one or more of: an indication of drugs to immediately procure; latest information regarding a disease outbreak; or resources for emergency procedure training ((Khosla et al., [0095]) (a response can include a recommended action for performance by a machine, such as an IMD, a medical instrument, a medical device and the like, that can be configured to perform automated actions in response to control commands ( e.g., dispensing medication, applying a medical treatment, moving a blade or needle relative to a body of a patient, etc.))).
RE: Claim 7 Khosla et al. teaches the claimed:
7. The method of claim 1, wherein the Al model comprises a generative Al model Once patient data has been anonymized, the stripped or cleaned data can be input to the generative AI component 570 to generate a set of recommendations in connection with provisioning of treatment));
further comprising: retraining the Al model in response to actions taken by the plurality of healthcare providers in response to the suggestions ((Khosla et al., [0107]) (the model development component 304 can continuously or regularly optimize or adapt the one or more guidance models 306 based on the newly logged historical healthcare delivery information. Accordingly, as more cases are logged, the live healthcare delivery guidance module 132 can continue learning and tailoring inferences and decisions about what constitutes a significant event or condition, and how to respond to that significant event or condition to facilitate reducing adverse outcomes and improving the accuracy and specificity of clinical decisions)).
RE: Claim 9 Khosla et al. teaches the claimed:
9. A computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to provide healthcare information to a plurality of healthcare providers, the providing healthcare information comprising ((Khosla et al., [0003]) (a system is provided that comprises a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory)):
receiving first historical information corresponding to the plurality of healthcare providers ((Khosla et al., [0043],m [0050], [0059]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data; The historical healthcare delivery information 148 can include historical information recorded for past patients regarding any monitored parameters associated with their course of care. In one or more embodiments, this can include patient data 104, workflow data 106, clinician data 108 and/or facility data 110 associated with a past patient's course of care. For example, for respective past patients, the historical healthcare delivery information 148 can identify a condition or diagnosis of the patient, information identifying or describing the physiological state/condition of the patient at the time of arrival at the healthcare facility ( or at the time when treatment was initiated), detailed parameters regarding what treatment was provided to the patient, including clinical actions that were taken in association with treatment and when, the clinical reactions or outcomes of the respective actions, what clinicians performed respective actions, states of the clinicians at the time of performance of the respective actions, physiological states of the patient associated with respective actions, contextual state of the healthcare facility over the course of care (e.g., adequately staffed or not, number of patients admitted, status of medical resources available, etc.), and the like));
receiving second historical information corresponding to external health care information sources ((Khosla et al., [0043], [0050]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data));
training an artificial intelligence (AI) model using the first historical information and the second historical information ((Khosla et al., [0106]) (system can employ supervised or semi-supervised machine learning to develop one or more guidance models prior to application of system to facilitate reasoning about and providing responses to the live feedback information. In this regard, the one or more guidance models can include one or more mathematical models that have been trained and developed by the model development component using training data and the information provided by the healthcare information sources and the historical healthcare delivery information. The training data can include the same or similar data as the live feedback information));
receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources ((Khosla et al., [0060]) (The reception component 126 can also receive and/or retrieve live feedback information 154 from one or more dynamic healthcare delivery data sources 102 regarding various dynamic aspects of healthcare delivery at a healthcare facility. The information provided by the dynamic healthcare delivery data sources 102 can be considered dynamic because at least some of the information can dynamically change over a course of patient care)); and
in response to the current information, generating one or more healthcare suggestions by the Al model and delivering the suggestions to one or more of the plurality of healthcare providers ((Khosla et al., [0036]) (the AI system can further be configured to determine or infer the action using machine learning analysis of the feedback in view of the information provided by the various data sources. The AI system can further recommend the action for performance by an appropriate clinician)).
RE: Claim 10 Khosla et al. teaches the claimed:
10. The computer readable medium of claim 9, wherein the first historical information comprises one or more of: data on the healthcare providers; information related to healthcare personnel; patient demography; clinical information or patient outcomes ((Khosla et al., [0043],m [0050], [0059]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data; The historical healthcare delivery information 148 can include historical information recorded for past patients regarding any monitored parameters associated with their course of care. In one or more embodiments, this can include patient data 104, workflow data 106, clinician data 108 and/or facility data 110 associated with a past patient's course of care. For example, for respective past patients, the historical healthcare delivery information 148 can identify a condition or diagnosis of the patient, information identifying or describing the physiological state/condition of the patient at the time of arrival at the healthcare facility ( or at the time when treatment was initiated), detailed parameters regarding what treatment was provided to the patient, including clinical actions that were taken in association with treatment and when, the clinical reactions or outcomes of the respective actions, what clinicians performed respective actions, states of the clinicians at the time of performance of the respective actions, physiological states of the patient associated with respective actions, contextual state of the healthcare facility over the course of care (e.g., adequately staffed or not, number of patients admitted, status of medical resources available, etc.), and the like)).
RE: Claim 11 Khosla et al. teaches the claimed:
11. The computer readable medium of claim 9, wherein the second historical information comprises one or more of: latest therapeutic protocols and drugs; opportunities for continuing medical education; local conditions; disease outbreaks and accidents; or natural disasters and calamities ((Khosla et al., [0051]) (In this regard, the SOP data 114 can include information that identifies and/or defines one or more standardized or defined protocols for following in association with performance of a procedure, treating a patient with a condition, and/or responding to a clinical scenario)).
RE: Claim 12 Khosla et al. teaches the claimed:
12. The computer readable medium of claim 9, wherein the suggestions comprise one or more of: latest approved protocols for treatment; new generation of drugs available; or contact information of other physicians who have treated similar conditions successfully ((Khosla et al., [0036]) (the AI system can further be configured to determine or infer the action using machine learning analysis of the feedback in view of the information provided by the various data sources. The AI system can further recommend the action for performance by an appropriate clinician; the AI system can consider information regarding historical reactions performed in same or similar clinical scenarios involving the event or condition, in view of one or more relevant SOPs and further in view of variable factors relevant to the current context, such as but not limited to, factors associated with the current patient as found in the patient EMR (e.g., other conditions of the patient, allergies, preferences, etc.), available resources (e.g., medical supplies and instruments that may be needed), available clinicians, capabilities of the clinicians, fatigue levels of the clinicians (e.g., determinations or inferences can be made using for example: facial analysis, focus of attention, posture, eyes, voice, gaze, hours already worked, wearables that monitor vitals . . . ) and other possible factors relevant to the current context of the entire healthcare facility and the patient and the like)).
RE: Claim 13 Khosla et al. teaches the claimed:
13. The computer readable medium of claim 9, wherein the suggestions comprise one or more of: details of available training; recommended associations of doctors to join; or research articles to review ((Khosla et al., [0054], [0238], [0239]) (The medical literature 118 can include can include various sources of electronic literature providing medical information … medical research data; the procedure training module can tailor the simulation to focus on areas where the clinician needs practice or improvement. For example, the procedure training module 1502 can employ information regarding aspects of a clinical scenario, procedure, course of care and the like, that the clinician tends to perform incorrectly or poorly. The procedure training module 1502 can then tailor subsequent simulations to present the clinician with information pertaining to similar clinical events, conditions, contexts, etc., that the clinician is deficient in)).
RE: Claim 14 Khosla et al. teaches the claimed:
14. The computer readable medium of claim 9, wherein the suggestions comprise one or more of: an indication of drugs to immediately procure; latest information regarding a disease outbreak; or resources for emergency procedure training ((Khosla et al., [0095]) (a response can include a recommended action for performance by a machine, such as an IMD, a medical instrument, a medical device and the like, that can be configured to perform automated actions in response to control commands ( e.g., dispensing medication, applying a medical treatment, moving a blade or needle relative to a body of a patient, etc.))).
RE: Claim 15 Khosla et al. teaches the claimed:
15. The computer readable medium of claim 9, wherein the Al model comprises a generative Al model ((Khosla et al., [0142]) (Once patient data has been anonymized, the stripped or cleaned data can be input to the generative AI component 570 to generate a set of recommendations in connection with provisioning of treatment));
the providing healthcare information further comprising: retraining the Al model in response to actions taken by the plurality of healthcare providers in response to the suggestions ((Khosla et al., [0107]) (the model development component 304 can continuously or regularly optimize or adapt the one or more guidance models 306 based on the newly logged historical healthcare delivery information. Accordingly, as more cases are logged, the live healthcare delivery guidance module 132 can continue learning and tailoring inferences and decisions about what constitutes a significant event or condition, and how to respond to that significant event or condition to facilitate reducing adverse outcomes and improving the accuracy and specificity of clinical decisions)).
RE: Claim 17 Khosla et al. teaches the claimed:
17. A cloud based system for providing healthcare information to a plurality of healthcare providers, the system comprising: an artificial intelligence (AI) model; one or more processors coupled to the Al model and configured to ((Khosla et al., [0044]) (The healthcare intelligence server 146 can be configured to provide various AI based tools for integrated healthcare organizations or environments to create actionable insight across the healthcare system and the care pathway, enabling better clinical and financial outcomes. In the embodiment shown, at least some of these tools can be provided by the live healthcare delivery guidance module 132. The components of the healthcare intelligence server 146 (e.g., reception component, the memory 128, the processor, the live healthcare delivery guidance module 132 can be provided at one or more dedicated computing devices (e.g., real or virtual machines))):
receive first historical information corresponding to the plurality of healthcare providers ((Khosla et al., [0043],m [0050], [0059]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data; The historical healthcare delivery information 148 can include historical information recorded for past patients regarding any monitored parameters associated with their course of care. In one or more embodiments, this can include patient data 104, workflow data 106, clinician data 108 and/or facility data 110 associated with a past patient's course of care. For example, for respective past patients, the historical healthcare delivery information 148 can identify a condition or diagnosis of the patient, information identifying or describing the physiological state/condition of the patient at the time of arrival at the healthcare facility ( or at the time when treatment was initiated), detailed parameters regarding what treatment was provided to the patient, including clinical actions that were taken in association with treatment and when, the clinical reactions or outcomes of the respective actions, what clinicians performed respective actions, states of the clinicians at the time of performance of the respective actions, physiological states of the patient associated with respective actions, contextual state of the healthcare facility over the course of care (e.g., adequately staffed or not, number of patients admitted, status of medical resources available, etc.), and the like));
receive second historical information corresponding to external health care information sources ((Khosla et al., [0043], [0050]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data));
train the Al model using the first historical information and the second historical information ((Khosla et al., [0106]) (system can employ supervised or semi-supervised machine learning to develop one or more guidance models prior to application of system to facilitate reasoning about and providing responses to the live feedback information. In this regard, the one or more guidance models can include one or more mathematical models that have been trained and developed by the model development component using training data and the information provided by the healthcare information sources and the historical healthcare delivery information. The training data can include the same or similar data as the live feedback information));
receive current information corresponding to the plurality of healthcare providers and/or the external health care information sources ((Khosla et al., [0060]) (The reception component 126 can also receive and/or retrieve live feedback information 154 from one or more dynamic healthcare delivery data sources 102 regarding various dynamic aspects of healthcare delivery at a healthcare facility. The information provided by the dynamic healthcare delivery data sources 102 can be considered dynamic because at least some of the information can dynamically change over a course of patient care)); and
in response to the current information, generate one or more healthcare suggestions by the Al model and delivering the suggestions to one or more of the plurality of healthcare providers ((Khosla et al., [0036]) (the AI system can further be configured to determine or infer the action using machine learning analysis of the feedback in view of the information provided by the various data sources. The AI system can further recommend the action for performance by an appropriate clinician)).
RE: Claim 18 Khosla et al. teaches the claimed:
18. The cloud based system of claim 17, wherein the first historical information comprises one or more of: data on the healthcare providers; information related to healthcare personnel; patient demography; clinical information or patient outcomes ((Khosla et al., [0043],m [0050], [0059]) (System 100 includes one or more dynamic healthcare delivery data sources 102, one or more healthcare information sources 112, historical healthcare delivery information 148; the healthcare information sources 112 can provide information including but not limited to: SOP data 114, EMR/EHR data 116, medical literature 118, human resources (HR) data 120, finance data 122 and inventory data; The historical healthcare delivery information 148 can include historical information recorded for past patients regarding any monitored parameters associated with their course of care. In one or more embodiments, this can include patient data 104, workflow data 106, clinician data 108 and/or facility data 110 associated with a past patient's course of care. For example, for respective past patients, the historical healthcare delivery information 148 can identify a condition or diagnosis of the patient, information identifying or describing the physiological state/condition of the patient at the time of arrival at the healthcare facility ( or at the time when treatment was initiated), detailed parameters regarding what treatment was provided to the patient, including clinical actions that were taken in association with treatment and when, the clinical reactions or outcomes of the respective actions, what clinicians performed respective actions, states of the clinicians at the time of performance of the respective actions, physiological states of the patient associated with respective actions, contextual state of the healthcare facility over the course of care (e.g., adequately staffed or not, number of patients admitted, status of medical resources available, etc.), and the like)).
RE: Claim 19 Khosla et al. teaches the claimed:
19. The cloud based system of claim 17, wherein the second historical information comprises one or more of: latest therapeutic protocols and drugs; opportunities for continuing medical education; local conditions; disease outbreaks and accidents; or natural disasters and calamities ((Khosla et al., [0051]) (In this regard, the SOP data 114 can include information that identifies and/or defines one or more standardized or defined protocols for following in association with performance of a procedure, treating a patient with a condition, and/or responding to a clinical scenario)).
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.
Claims 8, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0087342 A1 (hereinafter “Khosla et al.”) in view of U.S. 2022/0247639 A1 (hereinafter “Pieczul et al.”).
RE: Claim 8 Khosla et al. teaches the claimed:
8. The method of claim 1,
Khosla et al. fails to explicitly teach, but Pieczul et al. teaches the claimed:
further comprising: using a cloud infrastructure for providing healthcare information, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN ((Pieczul et al., [0120]) (The VCN can include a local peering gateway (LPG) that can be communicatively coupled to a secure shell (SSH) VCN via an LPG contained in the SSH VCN. The SSH VCN can include an SSH subnet, and the SSH VCN can be communicatively coupled to a control plane VCN via the LPG contained in the control plane VCN. Also, the SSH VCN can be communicatively coupled to a data plane VCN via an LPG. The control plane VCN and the data plane VCN can be contained in a service tenancy that can be owned and/or operated by the IaaS provider)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the virtual cloud network comprising a local peering gateway couple to a secure shell virtual cloud network as taught by Pieczul et al. within the method and system for employing artificial intelligence (AI) to facilitate healthcare delivery as taught by Khosla et al. with the motivation of providing improved techniques for analyzing and/or validating network policies for containerized applications (Pieczul et al., [0002]-[0003]).
RE: Claim 16 Khosla et al. teaches the claimed:
16. The method of claim 1.
Khosla et al. fails to explicitly teach, but Pieczul et al. teaches the claimed:
the providing healthcare information further comprising: using a cloud infrastructure for providing healthcare information, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN ((Pieczul et al., [0120]) (The VCN can include a local peering gateway (LPG) that can be communicatively coupled to a secure shell (SSH) VCN via an LPG contained in the SSH VCN. The SSH VCN can include an SSH subnet, and the SSH VCN can be communicatively coupled to a control plane VCN via the LPG contained in the control plane VCN. Also, the SSH VCN can be communicatively coupled to a data plane VCN via an LPG. The control plane VCN and the data plane VCN can be contained in a service tenancy that can be owned and/or operated by the IaaS provider)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the virtual cloud network comprising a local peering gateway couple to a secure shell virtual cloud network as taught by Pieczul et al. within the method and system for employing artificial intelligence (AI) to facilitate healthcare delivery as taught by Khosla et al. with the motivation of providing improved techniques for analyzing and/or validating network policies for containerized applications (Pieczul et al., [0002]-[0003]).
RE: Claim 20 Khosla et al. teaches the claimed:
20. The cloud based system of claim 17.
Khosla et al. fails to explicitly teach, but Pieczul et al. teaches the claimed:
wherein the system is executed on a cloud infrastructure, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN ((Pieczul et al., [0120]) (The VCN can include a local peering gateway (LPG) that can be communicatively coupled to a secure shell (SSH) VCN via an LPG contained in the SSH VCN. The SSH VCN can include an SSH subnet, and the SSH VCN can be communicatively coupled to a control plane VCN via the LPG contained in the control plane VCN. Also, the SSH VCN can be communicatively coupled to a data plane VCN via an LPG. The control plane VCN and the data plane VCN can be contained in a service tenancy that can be owned and/or operated by the IaaS provider)).
One of ordinary skill in the art at the time of the effective filing date would have found it obvious to combine the virtual cloud network comprising a local peering gateway couple to a secure shell virtual cloud network as taught by Pieczul et al. within the method and system for employing artificial intelligence (AI) to facilitate healthcare delivery as taught by Khosla et al. with the motivation of providing improved techniques for analyzing and/or validating network policies for containerized applications (Pieczul et al., [0002]-[0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. 2021/0295207 A1 teaches artificial intelligence system for generating educational inquiry responses from biological extractions (Abstract);
U.S. 2022/0122700 A1 teaches a unified and integrated medical data management tool to improve the nationwide level of medical education; evaluate the efficiency and appropriateness of physician diagnosis and treatment decisions and a knowledgebase to provide suggested alternatives (Abstract);
U.S. 2021/0265063 A1 teaches recommending a patient obtain a second medical opinion (SMO) from a second healthcare provider (Abstract); and
U.S. 2020/0273585 A1 teaches methods and systems for providing health professionals with continued education are based on performance gaps identified from patient data available in transactional systems of record (Abstract).
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/A.M.B./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682
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