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 .
Priority
Acknowledgment is made of applicant’s claim for priority in view of the U.S. Provisional Application 63/152318, filed on 2/22/2021. As such the effective filing date of claims 1-20 is 2/22/2021.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 8/21/2023 and 12/19/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. The claims recite a method, system, and CRM for managing the controlling a laboratory information system. The judicial exception is not integrated into a practical application because while claims 1-20 attempt to integrate the exception into a practical application, said application is either generically recited computer elements that do not add a meaningful limitation to the abstract idea, or it is insignificant extra solution activity and simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements only store and retrieve information in memory as well as perform basic calculations that are known to be well-understood, routine and conventional computer functions as recognized by the decisions listed in MPEP § 2106.05(d).
Framework with which to Analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? [see MPEP § 2106.03]
Claims are directed to statutory subject matter, specifically a system (claims 1-16), a method (claims 17-18), and a CRM (claims 19-20).
Step 2A Prong One: Do the claims recite a judicially recognized exception, i.e., an abstract idea, a law of nature, or a natural phenomenon? [see MPEP § 2106.04(a)]
The claims herein recite abstract ideas, mental processes and mathematical concepts.
With respect to the Step 2A Prong One evaluation, the instant claims are found herein to recite abstract ideas that fall into the grouping of mental processes and mathematical concepts.
Claims 1, 17, and 19: Extracting clinically significant data, is a process of comparing/contrasting, identifying, selecting, and calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process. The one or more messages comprising information about a clinical workflow is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 2: Identifying a stage of the clinical workflow, determining a quantity of time between two or more successive stages, and determining one or more corrective actions are processes of comparing/contrasting, identifying, selecting, and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 3: Modifying a scheduling of one or more activities, and adjusting an allocation of resources associated with the one or more activities are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and str therefore abstract ideas, specifically mental processes.
Claim 4: The stage of the clinical workflow including the specified information is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claims 5, 18, and 20: The clinical workflow comprising the specified testing/assays is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 6: Determining to allocate a resource to the LIS is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 7: The resource including an antimicrobial indicating the presence of a microbe is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 8: Determining a subsequent stage of the clinical workflow and a time for the subsequent stage, and scheduling a quantity of resources required for the subsequent stage are processes of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and are therefore abstract ideas, specifically mental processes.
Claim 10: Determining if the messages are actionable is a process of comparing/contrasting and calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 11: Receiving the one or more messages comprising receiving a sequence of messages and the output value indicating whether the sequence of messages are actionable are merely further limiting the data itself which are abstract ideas, specifically a mental processes.
Claim 12: The output value indicating that a message of of the sequence of messages is associated with a first actionable event and a second actionable event is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 13: Determining the message to be actionable is a process of calculating information that can be done via pen and paper or within the human mind and is therefore an abstract idea, specifically a mental process.
Claim 14: The device comprising the specified parts is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 15: Controlling the device comprising transmitting at least one message to adjust the medical device is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Claim 16: The machine learning model comprising one of those specified is merely further limiting the data itself which is an abstract idea, specifically a mental process.
Step 2A Prong Two: If the claims recite a judicial exception under prong one, then is the judicial
exception integrated into a practical application? [see MPEP § 2106.04(d) and MPEP § 2106.05(a)-(c) & (e)-(h)]
Because the claims do recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they integrate the abstract ideas into a practical application.
The following claims recite the following additional elements in the form of non-abstract elements:
Claims 1, 17, and 19: A system, computer, CRM, data processor, memory, and instructions are all generic and nonspecific elements of a computer that do not improve the functioning of any computer or technology described herein [See MPEP § 2106.04(d)(1) and MPEP § 2106.05(d)]. Receiving one or more messages for a patient, and outputting at least one output value are insignificant extra solution activities specifically, mere data gathering and necessary data outputting (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827- 28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)]. Controlling at least one medical device associated with the patient to perform one or more tasks is mere instructions to apply an exception and therefore amounts to no more than a recitation of the words “apply it” [See MPEP § 2106.05(f)].
Claim 9: Inputting the one or more message into the machine learning model(s), and outputting the clinically significant data are insignificant extra solution activities specifically, mere data gathering and necessary data outputting (See Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827- 28 (Fed. Cir. 1989) and Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis)) [See MPEP § 2106.05(g)].
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? [see MPEP § 2106.05]
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept.
The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are generic, conventional, nonspecific, or insignificant extra solution activity. These additional elements include:
The additional elements of a system, computer, CRM, data processor, memory, and instructions are all generic and nonspecific elements of a computer that are well-understood, routine and conventional within the art and therefore do not improve the functioning of any computer or technology described therein (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(d)(II)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of receiving one or more messages for a patient, outputting at least one output value, inputting the one or more message into the machine learning model(s), and outputting the clinically significant data are all insignificant extra solution activities, specifically mere data gathering and necessary data outputting (See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) [See MPEP § 2106.05(g)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
The additional elements of controlling at least one medical device associated with the patient to perform one or more tasks (Conventional: Specification paragraph [0020] “the at least one medical device may include a diagnostic device”, Kiourti et al. 2013 abstract “In this paper, we attempt to comparatively review the current status and challenges of IIMDs with wireless telemetry functionalities. Full solutions of commercial IIMDs are also recorded. The objective is to provide a comprehensive reference for scientists and developers in the field”) is mere instructions to apply an exception and therefore amounts to no more than a recitation of the words “apply it” (See Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017), and Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)) [See MPEP § 2106.05(f)]. Therefore, taken both individually and as a whole, the additional elements do not amount to significantly more than the judicial exception by providing an inventive concept.
Therefore, claims 1-20, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
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 1, 5-6, 9, and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Idemen et al. (Springer International Publishing (2020) 1284-1291) and Lee et al. (Journal of Biomedical and Health Informatics (2020) 536-546).
Claim 1 is directed to a system that receives data from an LIS and uses a machine learning model to extract information to control medical devices for patients.
Claim 17 is directed to a method that receives data from an LIS and uses a machine learning model to extract information to control medical devices for patients.
Claim 19 is directed to a CRM that receives data from an LIS and uses a machine learning model to extract information to control medical devices for patients.
Idemen et al. teaches in the abstract “With the development of technologies such as Internet of Things, Cloud Computing, Big Data and Machine Learning, created business models and information systems based on these business models have the need for restructuring. Medical Laboratories are environments where technical devices are located. New generation devices also have the IoT infrastructure. Thus, these devices support the transfer of device data to the relevant health information system through cloud computing. Monitoring the operating conditions, estimated maintenance periods and calibration settings for the specified devices on a common platform will increase the quality and reliability of the measurement results to be determined through the device. In this paper, a new generation LIS architecture, named LabHub, is proposed. With the platform which will be developed on LabHub architecture, traceability of the devices in all public and private sector, domestic and international medical laboratories will be provided with a cloud application that serves as a software principle… Methods based on machine learning algorithms will be used to identify contradictory situations. Thus, the infrastructure for both the measurement of the desired quality of the devices and the reliable recording of the measurement results will be developed”, on page 1287, paragraph 4 “Machine learning technologies, which will ensure that the data collected from different devices are used for possible device and test result anomaly determinations, will both speed up the diagnosis process and support the effective, accurate and reliable monitoring of the patient. Not only testing but also predicting anomaly of the device will have a cost reducing effect”, on page 1288, paragraph 1 “LabHub is designed as a platform that includes big data aware processes, which are designed for anomaly detection services and preventive maintenance services with supervised learning artificial algorithms, and IoT-based cloud technologies”, page 1289, paragraph 2 “In addition to the managing the big data of the medical laboratories, machine learning techniques will be used for anomaly detection and device/environment preventive maintenance recommendation. Machine learning technologies will be used in the development of these services”, reading on a system/method/CRM, comprising: at least one data processor; and at least one memory storing instructions which, when executed by the at least one data processor, cause the system to perform operations comprising: receiving, at a machine learning model, from one or more laboratory information systems, one or more messages for a patient as at least one input value to the machine learning model, wherein the one or more messages provide information about a clinical workflow; based on the machine learning model receiving the one or more messages as the at least one input value to the machine learning model, outputting, by the machine learning model, at least one output value indicating that the one or more messages are actionable; extracting, from the one or more messages, clinically significant data.
Idemen et al. does not teach the controlling of the devices based on this information.
Lee et al. teaches in the abstract “The automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system. Methods: A bioinspired RL designing method was developed for automated insulin infusion. This method has reward functions that imply the temporal homeostatic objective and discount factors that reflect an individual specific pharmacological characteristic. The proposed method was applied to a training method using an RL algorithm and was evaluated in virtual patients from the FDA approved UVA/Padova simulator with unannounced meal intakes”, reading on controlling, based at least on the clinically significant data, at least one medical device associated with the patient to perform one or more tasks.
It would have been obvious at the time of first filing to have modified the teachings of Idemen et al. for the LIS and medical device system that uses machine learning to collect data and monitor devices, with the teachings of Lee et al. for the automation of a medical device using machine learning as the former is an LIS specifically designed to incorporate medical devices and their information via machine learning as a way to monitor the devices, whereas the latter is a medical device that is designed to be monitored and controlled using machine learning and states within the abstract “In the in silico trial with a variation of insulin sensitivity and dawn phenomenon, the policy achieved a mean glucose of 124.72 mg/dL and percentage time in the normal range of 89.56%. The layer-wise relevance propagation provides interpretable information on AI-driven decision for robustness to sensor noise, automated postprandial regulation, and insulin stacking avoidance. Conclusion: The AP algorithm based on the bioinspired RL approach enables fully automated blood glucose control with unannounced meal intake. Significance: The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties”. One would have had a reasonable expectation of success given that this would merely be a substitution of one known component, the general medical devices in Idemen el al., for another, the specific medical device of Lee et al., whose outcomes are both known. Furthermore, Lee et al. specifically states that “The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties”, suggesting a similar methodology can be used with other medical devices and systems. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 5 is directed to the system of claim 1 but further specifies that the clinical workflow comprises microbial or virus testing.
Claim 18 is directed to the method of claim 17 but further specifies that the clinical workflow comprises microbial or virus testing.
Claim 20 is directed to the CRM of claim 19 but further specifies that the clinical workflow comprises microbial or virus testing.
Idemen et al. teaches on page 1285, paragraph 2 “When the individual’s first consultation to physician for any health problem, the first medical method usually applied is the request for a medical test. This health practice can be routinely requested not only in acute problems but also in the monitoring of chronic diseases. The rather complex and big healthcare field is divided into subfields to be worked better. This has also been the case for medical tests, and medical laboratories are divided into different subfields such as biochemistry, hematology, and microbiology. Laboratory Information Systems (LIS) have been developed in order to transmit the results of the analysis made in medical laboratories to the Hospital Information Management System and to be seen results by the doctor who made the request and by the patient”, and it would be obvious that the use of microbial testing would be used in the field of microbiology within the narrow field of healthcare for medical tests, thereby reading on wherein the clinical workflow comprises a microbial testing workflow [[and/]]or a virology assay.
Claim 6 is directed to the system of claim 1 but further specifies that a determination is made to allocate a resource to the LIS.
Idemen et al. teaches on page 1289, paragraph 3 “The main purpose of LabHub is to develop a big data management platform that will collect, transfer, manage and process the data of the medical devices in the clinical laboratories and the environment in which these devices are located, and provide the services to analyze these data for anomaly detection and predictive maintenance through the cloud”, reading on wherein the one or more tasks include: determining, based at least on the clinically significant data, to allocate a resource to the one or more laboratory information systems.
Claim 9 is directed to the system of claim 1 but further specifies the inputting of messages into the machine learning model and the model outputting clinically significant data.
Idemen et al. teaches on page 1289, paragraph 2 “In addition to the managing the big data of the medical laboratories, machine learning techniques will be used for anomaly detection and device/environment preventive maintenance recommendation”, and in paragraph 3 of the same page “LabHub, an intelligent new generation laboratory information system architecture is proposed. The main purpose of LabHub is to develop a big data management platform that will collect, transfer, manage and process the data of the medical devices in the clinical laboratories and the environment in which these devices are located, and provide the services to analyze these data for anomaly detection and predictive maintenance through the cloud”, reading on wherein extracting the clinically significant data comprises inputting the one or more messages into one or more of the machine learning model or a different machine learning model, the one or more of the machine learning model or the different machine learning model outputting the clinically significant data.
Claim 11 is directed to the system of claim 1 but further specifies receiving a sequence of messages and the output indicates whether the sequence is clinically significant.
Idemen et al. teaches on page 1288, paragraph 3 “The Medical Laboratory Monitoring Service will collect any required data for the devices and also from the environment where the corresponding device located to produce an effective and accurate result through IoLT (Internet of Laboratory Thing) plug-in…In addition to the managing the big data of the medical laboratories, machine learning techniques will be used for anomaly detection and device/environment preventive maintenance recommendation. Machine learning technologies will be used in the development of these services. Anomaly Detection Service will use the supervised classification method which will developed by the kNN technique to detect unwanted anomalies. It will activate the necessary warning mechanism in any violation. Although the system is capable of stopping the test process, the current legislation does not allow the test period to be stopped autonomously. Therefore, it is necessary to use warning mechanism. Preventive Maintenance Service will also use supervised learning method for classification will be carried out by LSTM technique. The classification will include four levels: emergency, high, and medium and low. This service will give suggestions to the healthcare institution for preventive maintenance and calibration about test devices in the laboratory across the LabHub platform”, reading on wherein receiving the one or more messages comprises receiving a sequence of messages, and wherein the at least one output value indicates whether the sequence of messages are actionable.
Claim 12 is directed to the system of claim 11, and thus claim 1, but further specifies the output to be associated with first and second actionable events.
Idemen et al. teaches on page 1288, paragraph 3 “The Medical Laboratory Monitoring Service will collect any required data for the devices and also from the environment where the corresponding device located to produce an effective and accurate result through IoLT (Internet of Laboratory Thing) plug-in…In addition to the managing the big data of the medical laboratories, machine learning techniques will be used for anomaly detection and device/environment preventive maintenance recommendation. Machine learning technologies will be used in the development of these services. Anomaly Detection Service will use the supervised classification method which will developed by the kNN technique to detect unwanted anomalies. It will activate the necessary warning mechanism in any violation. Although the system is capable of stopping the test process, the current legislation does not allow the test period to be stopped autonomously. Therefore, it is necessary to use warning mechanism. Preventive Maintenance Service will also use supervised learning method for classification will be carried out by LSTM technique. The classification will include four levels: emergency, high, and medium and low. This service will give suggestions to the healthcare institution for preventive maintenance and calibration about test devices in the laboratory across the LabHub platform”, reading on wherein the at least one output value indicates that a message of the sequence of messages is associated with a first actionable event, and wherein the at least one output value further indicates that the sequence of messages are associated with a second actionable event.
Claim 13 is directed to the system of claim 12, and thus claim 1, but further specifies that the machine learning model determines the message to be actionable as part of the sequence of messages.
Idemen et al. teaches on page 1288, paragraph 3 “The Medical Laboratory Monitoring Service will collect any required data for the devices and also from the environment where the corresponding device located to produce an effective and accurate result through IoLT (Internet of Laboratory Thing) plug-in…In addition to the managing the big data of the medical laboratories, machine learning techniques will be used for anomaly detection and device/environment preventive maintenance recommendation. Machine learning technologies will be used in the development of these services. Anomaly Detection Service will use the supervised classification method which will developed by the kNN technique to detect unwanted anomalies. It will activate the necessary warning mechanism in any violation. Although the system is capable of stopping the test process, the current legislation does not allow the test period to be stopped autonomously. Therefore, it is necessary to use warning mechanism. Preventive Maintenance Service will also use supervised learning method for classification will be carried out by LSTM technique. The classification will include four levels: emergency, high, and medium and low. This service will give suggestions to the healthcare institution for preventive maintenance and calibration about test devices in the laboratory across the LabHub platform”, reading on wherein the machine learning model determines the message to be actionable as part of the sequence of messages.
Claim 14 is directed to the system of claim 1 but further specifies the medical device comprise one of those specified.
Idemen et al. teaches on page 1287, paragraph 4 “Machine learning technologies, which will ensure that the data collected from different devices are used for possible device and test result anomaly determinations, will both speed up the diagnosis process and support the effective, accurate and reliable monitoring of the patient”, reading on wherein the at least one medical device comprises one or more of a diagnostic device, an infusion pump, a dispensing cabinet, or a wasting station.
Claim 15 is directed to the system of claim 1 but further specifies transmitting to the medical device a message to adjust one or more of an operational state or functional element.
Lee et al. teaches in the abstract “automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system”, and on page 543, column 2, paragraph 2 “In this paper, we proposed the bioinspired RL designing approach for a fully automated AP system. To mimic the natural automatic BG control of a healthy pancreas by using an RL algorithm, we approached the insulin secretion mechanism of β-cells from a temporal perspective. As key components of the RL structure, the reward function and discount rate reflect the intent and temporal attention horizon of the optimization problem. In bioinspired RL, we designed the reward function as a combination of long and short-term rewards. The long-term reward evaluates the BG level and encourages the RL algorithm to learn the pancreatic basal insulin secretion. The short-term reward evaluates the rate of change in BG, encouraging the algorithm to learn the under- and over-basal regulating functions of a healthy pancreas”, reading on wherein controlling the at least one medical device comprises transmitting, to the at least one medical device, at least one message to adjust one or more of an operational state or a functional element of the at least one medical device.
Claim 16 is directed to the system of claim 1 but further specifies that the machine learning model comprise one of those specified.
Lee et al. teaches on page 537, column 1, paragraph 5 “In applying RL to the optimization problem, the first step is to formulate the problem as a Markov decision process (MDP), which is a mathematical framework of sequential decisions from interactions with systems having stochastic uncertainty”, and in Figure 4 “Each neural network consists of normalization block and two fully-connected hidden layer with ReLU activations”, reading on wherein the machine learning model comprises one or more of: a regression model, an instance-based model, a regularization model, a decision tree, a Bayesian model, a clustering model, an associative model, a neural network, a deep learning model, a dimensionality reduction model an ensemble model, a recurrent neural network (RNN), a hidden Markov model, a conditional random field (CRF) model, or a gated recurrent unit (GRU).
Claims 2-4, 7-8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Idemen et al. (Springer International Publishing (2020) 1284-1291) and Lee et al. (Journal of Biomedical and Health Informatics (2020) 536-546) as applied to claims 1, 5-6, 9, and 11-20 above, and further in view of Nguyen et al. (US 20190369126 A1).
Claim 2 is directed to the system of claim 1 but further specifies identifying a stage of the clinical workflow along with determining a quantity of time between two or more stages and corrective actions based on the exceeding of a threshold value.
Idemen et al. and Lee et al. teach the system, method and CRM of claims 1, 17, and 19 as previously described.
Idemen et al. and Lee et al. do not teach the identification of a stage of the clinical workflow along with determining a quantity of time between two or more stages and corrective actions based on the exceeding of a threshold value.
Nguyen et al. teaches in paragraph [0124] “LIS interchange (LI): The LI 114 monitors low-level connectivity, and processes order and result data to convert them into acceptable messages within the guidelines of ASTM and HL7 messaging standards. These guidelines include actions to drive the instrument's behavior to test requests, laboratory workflow, and communicate final result data to the Hospital Laboratory Information System”, in paragraph [0010] “In some examples, a decision support generator is provided which is configured (1) to avoid delayed administration of directed antimicrobial therapy (treatment modification in 0-6 hours, preferably 0-5 hours, preferably 0-4 hours, preferably 0-3 hours, preferably 0-3 hours, preferably 0-1 hours, preferably 0-0.5 hours, preferably 0.5-5 hours (2) to ensure compliance with antimicrobial stewardship teams' policies, procedures and guidelines, (3) to enforce diverse policies for different regions, hospitals and physician offices using the decision support generator to allow users to flexibly tailor, in real time, their own rules”, in paragraph [0049] “The communication system, such as the LIS communication system, may enable bi-directional reporting. Specifically, when a sample is collected, a physician may create a test order called a physician test order. The physician test order enables the physician to specify a sample stability time”, and in paragraph [0055] “When the LIS interchange receives the PTO and reformats it to a test order, the test order is auto published with information associated with the PTO/sample, such as patient identification, accession number (accession ID), test ordered (for example, BCID-GP, BCID-GN, and BCID-FP), patient type (for example, pediatric, intensive care, and maternity), patient location (for example, pediatrics, ER, maternity), and/or time stamps, such as sample collection, sample ordering, time received at central receiving, central receiving sort, transport to lab, and/or accession of sample. The PTO may include other identifying information such as ordering clinician, hospital, hospital network, which may be published in the test order. These time stamps provide real-time monitoring by the instrument software of pending test order turn-around time”, reading on wherein the one or more tasks include: identifying, based at least on the clinically significant data, a stage of the clinical workflow associated with the one or more messages; determining, based at least on a timestamp associated with the one or more messages, a quantity of time between two or more successive stages of the clinical workflow; and in response to the quantity of time between the two or more successive stages of the clinical workflow exceeding a threshold value, determining one or more corrective actions.
It would have been obvious at the time of first filing to have modified the teachings of Idemen et al. and Lee et al. for the system, method, and CRM of claims 1, 17, and 19, with the teachings of Nguyen et al. for a pathogenic detection device as the latter teaches the use of Laboratory information system for decision support in paragraphs [0048]-[0049], and Ideman et al. teaches the use of a LIS that uses machine learning for decision support, and both support the use/control of medical devices such as those taught within Lee et al., which also uses machine learning in developing automation. One would have had a reasonable expectation of success given that the bi-directional LIS and the LabHub LIS would merely be a substitution of known methods with expected outcomes. Therefore, it would have been obvious at the time of first filing to have modified the teachings of each and to be successful.
Claim 3 is directed to the system of claim 2, and thus claim 1, but further specifies that the corrective actions comprise one of those specified.
Idemen et al. and Lee et al. teach the system, method and CRM of claims 1, 17, and 19 as previously described.
Idemen et al. teaches on page 1289, paragraph 3 “The main purpose of LabHub is to develop a big data management platform that will collect, transfer, manage and process the data of the medical devices in the clinical laboratories and the environment in which these devices are located, and provide the services to analyze these data for anomaly detection and predictive maintenance through the cloud”, reading on wherein the one or more corrective actions include one or more of: 1) modifying a scheduling of one or more activities associated with the clinical workflow; or 2) adjusting an allocation of resources associated with the one or more activities.
Claim 4 is directed to the system of claim 2, and thus claim 1, but further specifies that the clinical workflow includes one of the specified stages.
Idemen et al. and Lee et al. teach the system, method and CRM of claims 1, 17, and 19 as previously described.
Idemen et al. and Lee et al. do not teach the clinical workflow includes one of the specified stages.
Nguyen et al. teaches in paragraph [0178] “The assays that are selectable may include, for example, a Respiratory, Blood Culture Identification-Gram Positive, Blood Culture Identification-Gram Negative”, reading on wherein the stage of the clinical workflow includes a start of a culturing process for a microbe, a gram positive or gram negative identification for the microbe, a species or organism identification for the microbe, or an antimicrobial susceptibility of the microbe.
Claim 7 is directed to the system of claim 6, and thus claim 1, but further specifies that the resource includes an antimicrobial indicating a presence of a microbe susceptible to the antimicrobial.
Idemen et al. and Lee et al. teach the system, method and CRM of claims 1, 17, and 19 as previously described.
Idemen et al. and Lee et al. do not teach that the resource includes an antimicrobial indicating a presence of a microbe susceptible to the antimicrobial.
Nguyen et al. teaches in paragraph [0178] “The assays that are selectable may include, for example, a Respiratory, Blood Culture Identification-Gram Positive, Blood Culture Identification-Gram Negative”, and paragraph [0038] “Examples disclosed herein include a method, a device, a kit, and/or a system of delivering test results along with templated comments guiding test result interpretation and antimicrobial prescribing”, reading on wherein the resource includes an antimicrobial based at least on the clinically significant data indicating a presence of a microbe susceptible to the antimicrobial.
Claim 8 is directed to the system of claim 6, and thus claim 1, but further specifies determining a subsequent stage/time of a clinical workflow and scheduling a quantity of resources required for the stage.
Idemen et al. and Lee et al. teach the system, method and CRM of claims 1, 17, and 19 as previously described.
Idemen et al. teaches on page 1289, paragraph 3 “The main purpose of LabHub is to develop a big data management platform that will collect, transfer, manage and process the data of the medical devices in the clinical laboratories and the environment in which these devices are located, and provide the services to analyze these data for anomaly detection and predictive maintenance through the cloud”.
Idemen et al. and Lee et al. do not teach the determination of a subsequent stage/time of a clinical workflow and scheduling a quantity of resources required for the stage.
Nguyen et al. teaches in paragraph [0124] “LIS interchange (LI): The LI 114 monitors low-level connectivity, and processes order and result data to convert them into acceptable messages within the guidelines of ASTM and HL7 messaging standards. These guidelines include actions to drive the instrument's behavior to test requests, laboratory workflow, and communicate final result data to the Hospital Laboratory Information System”, reading on wherein to allocate the resource comprises: determining, based at least on the clinically significant data, a subsequent stage of the clinical workflow and a time for the subsequent stage of the clinical workflow; and scheduling, in accordance with the time of the subsequent stage of the clinical workflow, a quantity of resources required for the subsequent stage of the clinical workflow.
Claim 10 is directed to the system of claim 1 but further specifies messages being actionable in response to a threshold of data being tagged as clinically significant.
Idemen et al. and Lee et al. teach the system, method and CRM of claims 1, 17, and 19 as previously described.
Idemen et al. and Lee et al. do not teach the messages being actionable in response to a threshold of data being tagged as clinically significant.
Nguyen et al. teaches in paragraph [0059] “A Sample Stability feature or a Sample Stability time enables the user to specify the stability time on a per assay basis. The software tracks PTO orders and sends an alert notification when an order has violated the threshold for sample stability”, reading on wherein the one or more messages are determined to be actionable in response to more than a threshold quantity of data in the one or more messages being tagged as clinically significant by the machine learning model.
Conclusion
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/K.N.A./Examiner, Art Unit 1687
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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