DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
Receipt of Applicant’s Amendment filed December 19, 2025 is acknowledged.
Response to Amendment
Claims 1, 4, 6-8, 10, 13-14, 17, and 20-21 have been amended. Claims 2, 9, 11, and 15 have not been modified. Claims 3, 5, 12, 16, 18-19, 22-26 have been cancelled. Claims 1-2, 4, 6-11, 13-15, 17, and 20-21 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed December 19, 2025 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues 35 U.S.C. §112 Rejections, pg. 7 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the §112 rejection.
Applicant argues 35 U.S.C. §101 Rejections, pg. 7 of Remarks:
Regarding Applicant argument that the claims do not recite an abstract idea, Applicants argue that the claims recite computer-implemented features. Examiner notes that such features are analyzed under Prong Two of Step 2A. The claims, as a whole, are directed towards determining if a sample requires advanced analysis and transmitting sample data to a reference laboratory for the advanced analysis to be performed. As such, the claims are characterized under certain methods of organizing activity, as the object of the method performed by the computer features can be performable by a clinician who is seeking to diagnose patients based on patient data.
Applicant further argues that the claims do not merely describe mathematical calculations, data collection, or generic information processing. While the claims were not previously characterized under mathematical concepts, Examiner agrees with Applicant that the amendments introduce an abstract idea of mathematical processes as evaluating confidence levels and comparing said confidence levels to a predetermined threshold to determine if the criterion is met encompasses statistical analysis. Data collection is considered to be an insignificant extra-solution activity performed by the diagnostic analyzer under Prong Two of Step 2A; please see MPEP 2106.05(g). The information processing is considered abstract as the result of the processing leads to the abstract ideas of diagnosis and determining if the sample meets the advanced analysis criterion.
Regarding Applicant argument that the claims recite additional elements that integrate the abstract idea into a practical application, Examiner respectfully disagrees. Although Applicant argues that the claimed system is directed towards a hybrid approach that balances the speed of POC systems with the analytical capabilities of CRL systems to improve diagnostic systems, the independent claims recite wherein all patient data is first analyzed with the diagnostic analyzer (POC system) before it is optionally sent to a CRL system based on statistical analysis. Thus, there is no balancing of benefits of individual systems. Rather, the only improvement is to the abstract idea of determining if a sample requires further data analysis. Additionally, no specific, technical improvements are made to the diagnostic systems themselves as two pre-trained machine learning models are simply applied to perform the abstract idea of diagnostic analysis (pg. 13, lines 24-26, “These algorithms analyze the data from the patient sample using pre-trained machine learning models and report the results to the user (e.g., a clinician).” Pg. 35, lines 23-24, “analyze the reflexed patient sample data at the central reference laboratory using the trained second machine learning model.”).
Applicant draws parallels to Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc. and SRI International, Inc. v. Cisco Systems, Inc. However, the fact patterns of those cases are not analogous to the instant application as they are specific to the field of technology to which their claimed technical improvements are directed towards (improved graphical user interfaces and computer network security, respectively).
Regarding Step 2B, Applicant argues that the Office Action fails to consider the ordered combination of claim elements, as the claim recites a specific architecture of transmitting data to a CRL based on confidence levels determined at a first machine learning algorithm. Examiner notes that confidence levels are not considered an additional element, as they are simply math. Thus, the ordered combination of elements recites two separate systems comprising machine learning models, wherein the systems may communicate data over a network. Such a combination is known, as demonstrated by:
Fig. 1 of Szeto (US 20180018590) depicts separate systems (hospital, clinic, laboratory) that each house their own machine learning model. These systems may communicate through a network.
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Applicant argues 35 U.S.C. §102 Rejections, pg. 9 of Remarks:
Applicant argues that Kunz does not teach or suggest all of the features of amended independent claims.
Regarding the confidence level, Examiner respectfully disagrees. [0078-0079] of Kunz recites: “1. Rules for routing may be defined as: a. Users specify categorical fields for executing the rule on the radiology data. They list the conditions that the radiological data may need to satisfy for the rule to be executed, which may be the form of disease, the tissue type, the location of the sample, the pathologist assigned to review it at the originating institution, and/or the output of an AI-based system not being able to make the diagnosis on the data with an adequate level of confidence, etc.”
Regarding the point of care where the medical data originates, Examiner notes that this is not a functional element as the diagnostic analyzer being at a point of care has no functional bearing on its operation. However, [0003] of Kunz recites: “in histopathology, glass slides may have to be physically moved so that an expert may review them. If the preferred expert is outside of the originating hospital or clinic, there may be significant delay before an expert may receive the glass slides, or a non-preferred expert may be selected because they are physically closer to the originating institution.” It would be obvious to one of ordinary skill in the art that the originating institution produces the glass slides, which would require the physical presence of the patient to be acquired. Under the broadest reasonable interpretation, Examiner interprets such a hospital or clinic to encompass a point of care.
Regarding the two-tier machine learning architecture, Examiner agrees that Kunz alone does not teach the claimed system. Thus, Min is applied to address the amended claim limitations.
Regarding Min, Applicant argues that neither Kunz nor Min provide any motivation for a POSITA to design a distributed two-tier machine learning architecture as Min’s system performs no local analysis of the patient data. Examiner notes that Kunz, not Min, is relied upon to teach local analysis of the patient data. Min is only applied to teach an external system that may process patient data using an in-house developed machine learning model. By combining the local processing and routing to external systems, as taught by Kunz, and the transmission of medical data to external systems that use machine learning models trained on large datasets to analyze said medical data, as taught by Min, for the advantage of “communicating with large databases, performing high volume transaction processing, and generating reports from large databases” (Min; [0241]), it would be obvious to one of ordinary skill in the art to arrive at the claimed system.
Examiner respectfully maintains the rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4, 6-11, 13-15, 17, and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process and a machine
(claims 1-2, 4, 6-11, 13-15, 17, and 20-21). Accordingly, claims 1-2, 4, 6-11, 13-15, 17, and 20-21 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method claim 1 as the claim that represents the claimed invention for analysis; system claim 10 and system claim 14 being similar to method claim 1.
Claim 1:
A method executed by a programmed data processing device system, the method comprising:
receiving, from a diagnostic analyzer at a point of care, patient sample data comprising digital data;
analyzing, by a first machine learning model executing on a first processor at the point of care, the received patient sample data to generate a first result and a confidence level associated with the first result;
automatically evaluating, by the first processor, the confidence level associated with the first result to determine whether a criterion for advanced analysis at a central reference laboratory is met, wherein the criterion is met when the confidence level is less than a predetermined threshold; and
in a case where the criterion for advanced analysis is met:
electronically transmitting the received patient sample data to the central reference laboratory over a network for the advanced analysis using a second machine learning model;
receiving, at the point of care over the network, a second result of the advanced analysis from the central reference laboratory; and
displaying at least one of the first result or the second result on a display at the point of care.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity under managing personal behaviors of people. The claim elements are directed towards “analyzing the received patient sample data”, “evaluating a first result of analyzing the received patient sample data”, “reflexing the received patient sample data… for advanced analysis”, and “receiving,.., a second result of the advanced analysis”, or aiding in patient diagnoses. Diagnosing a patient condition falls under the abstract concept of managing personal behaviors of people as the claimed steps could otherwise be performed by clinicians collecting and analyzing patient samples. This is supported by pg. 1, lines 22-28 of Applicant specification, which recites: “Alternatively, the clinician can collect samples from a patient and send them to a central reference laboratory (CRL) for diagnostic analysis, potentially including manual evaluation of data obtained from the samples. In some cases, the POC system may include a diagnostic analyzer where some of the data (patient sample) is collected and analyzed locally; and the same data is sent to a CRL for professional clinical analysis.” Examiner further notes that a clinician could also otherwise evaluate the result and determine whether a criterion for advanced analysis is met.
The above limitations further cover performance of the limitation as mathematical processes. The claim elements recite “evaluating,…, the confidence level associated with the first result…, wherein the criterion is met when the confidence level is less than a predetermined threshold”. Calculating a confidence level and comparing said level with a threshold is a mathematical process of statistical analysis.
Accordingly, the claim recites at least one abstract idea.
Claims 10 and 14 are found to be abstract for the same reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
Additional elements cited in the claims:
A programmed data processing device system (1,10-11,13-14,15,17,20-21); a diagnostic analyzer (1,10,14); a first machine learning model (1,4,7,10,13-14,17,20); a second machine learning model (1,6,8,10,14,21); a network (1,10,14); a display (1-2,10-11,14-15); a memory (10); a first processor (1,10-11,14-15); a second processor (14,20-21); a first memory (14); a second memory (14)
Any computing devices that would be able to perform the method (programmed data processing device system, processor, first processor, second processor) are taught at a high level of generality such that the claim elements amounts to no more than mere instructions to apply the exception using any generic component capable of performing the claim limitations. Pg. 10, lines 27-32 of Applicant specification recites: “Each of the phrases “data processing device,” “data processor,” “processor,” and “computer” is intended to include any data processing device, such as a central processing unit (“CPU”), a circuit, a field programmable gate array (FPGA), a desktop computer, a laptop computer, a mainframe computer, a tablet computer, a personal digital assistant, a cellular phone, and any other device configured to process data, manage data, or handle data, whether implemented with electrical, magnetic, optical, biological components, or the like.” No specific, technical improvements are being made to computing devices as generic devices are simply being used to perform the abstract idea.
Memory devices (memory, first memory, second memory) are also taught at a high level of generality. Pg. 11, lines 8-16 of Applicant specification recites: “Each of the phrases “processor-accessible memory” and “processor-accessible memory device” is intended to include any processor-accessible data storage device, whether volatile or nonvolatile, electronic, magnetic, optical, or otherwise, including but not limited to, registers, floppy disks, hard disks, Compact Discs, DVDs, flash memories, ROMs (Read-Only Memory), and RAMs (Random Access Memory). In some aspects of the disclosure, each of the phrases “processor-accessible memory” and “processor-accessible memory device” is intended to include a non-transitory computer-readable storage medium. In some aspects of the disclosure, the memory device system 130 can be considered a non-transitory computer-readable storage medium system.” No specific, technical improvements are being made to computer readable mediums as any generic storage medium is simply applied to perform the insignificant extra-solution activity of storing data.
Machine learning (machine learning model, first machine learning model, second machine learning model, convolution neural network) is also taught at a high level of generality. Pg. 26, lines 25-29 of Applicant specification recites: “In some aspects of the disclosure. the machine learning model is a convolution neural network. Convolutional neural networks (CNNs) are a type of deep learning model specifically designed for processing and analyzing visual data, such as medical images. Inspired by the human visual system, CNNs utilize convolutional layers to extract local patterns and hierarchical representations from the input data.” No specific, technical improvements are being made to the field of machine learning, as any generic pre-trained convolutional neural network is applied to perform the abstract idea.
The diagnostic analyzer is taught at a high level of generality, such that it only serves to perform the insignificant extra-solution activity of receiving data and performing the abstract idea of the diagnostic analysis, as described in the independent claims.
The network is also taught at a high level of generality, such that it only serves to perform the insignificant extra-solution activity of transmitting data.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 2, 11, and 15: These claims recite the method further including, in a case where the criterion for advanced analysis is not met, displaying the first result on the display at the point of care; which teaches an insignificant extra-solution activity of simply outputting data.
Claims 4, 6, 13, and 17: These claims recite wherein the first machine learning model is a convolution neural network; which only serves to narrow the type of model that is used.
Claims 7 and 20: These claims recite further including: receiving a plurality of patient sample data at the central reference laboratory; training the first machine learning model using the plurality of patient sample data; and deploying the trained first machine learning model at the point of care system to analyze the patient sample data received at the point of care; which teaches training a machine learning model at a high level of generality and deploying the model to perform an abstract idea.
Claims 8 and 21: These claims recite further including: receiving a plurality of patient sample data at the central reference laboratory; training the second machine learning model using the plurality of patient sample data; and analyzing the reflexed patient sample data at the central reference laboratory using the trained second machine learning model; teaches an abstract idea of analyzing the received patient sample data. This claim teaches training a machine learning model at a high level of generality.
Claim 9: This claim recites wherein the criterion includes one or more of (i) a case where the first result indicates that the received patient sample data is abnormal, (ii) a case where a difference between the first result and a reference value is greater than a first threshold, (iii) a case where a confidence level associated with the first result is less than a second threshold, (iv) a case where the first result cannot be obtained by analysis at the point of care, or (v) a case where the first result indicates a diagnostic condition that requires advanced analysis at the central reference laboratory; which only serves to limit the abstract idea of determining if a criterion is met.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as 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), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Two separate systems comprising machine learning models, wherein the systems may communicate data over a network, is known, as demonstrated by:
Fig. 2 of Hu (US 20220004899) depicts a terminal-side diagnosis model (S120), which then transmits data to a cloud-side diagnosis model (S310) for further processing.
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[0013] of Heaton (US 20180000385) recites: “The wearable device is configured to: write motion data read from the motion sensor and ambient pressure data read from the ambient pressure sensor to a buffer; pass motion data and ambient pressure data into a compressed fall detection model stored locally to detect a fall event at a first time, the compressed fall detection model defining a compressed form of a complete fall detection model; and broadcast a corpus of sensor data from the buffer and a cue for confirmation of the fall event to a first local wireless hub in response to detecting the fall event, the first local wireless hub in the set of local wireless hubs and proximal the wearable device at the first time, the corpus of sensor data corresponding to a duration of time terminating at approximately the first time. The remote computer system is configured to: receive the corpus of sensor data from the first local wireless hub via a computer network; pass the corpus of sensor data into the complete fall detection model to confirm the fall event; and transmit a prompt to assist the resident to a computing device affiliated with a care provider within the facility in response to confirming the fall event.”
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2,4,6-9, 11, 13, 15, 17, and 20-21 additional limitations which amount to elements that have been recognized as activities in particular fields, claims 2,4,6-9, 11, 13, 15, 17, and 20-21, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2,4,6-9, 11, 13, 15, 17, and 20-21, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-2, 4, 6-11, 13-15, 17, and 20-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4, 6, 8-11, 13-15, 17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Kunz (US 20220051783) in view of Min (US 20230289963).
Regarding claim 1, Kunz teaches a method executed by a programmed data processing device system ([0008], “A computer system for providing automated routing of medical data comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to perform operations”), the method comprising:
receiving, from a diagnostic analyzer at a point of care, patient sample data comprising digital data ([0034], “The slide intake tool 103 may scan pathology images and convert them into a digital form, according to an exemplary embodiment. The slides may be scanned with slide scanner 104, and the slide manager 105 may process the images on the slides into digitized pathology images and store the digitized images in storage 106.” [0024], “Medical data may be medical records (e.g., text), medical images (e.g., digital microscopy, whole slide images, x-ray scans, MRI scans, CT scans, etc.), genetic testing, genomic testing, etc.” [0003], “in histopathology, glass slides may have to be physically moved so that an expert may review them. If the preferred expert is outside of the originating hospital or clinic, there may be significant delay before an expert may receive the glass slides, or a non-preferred expert may be selected because they are physically closer to the originating institution.”). It would be obvious to one of ordinary skill in the art that the originating institution produces the glass slides, which would require the physical presence of the patient to be acquired. Under the broadest reasonable interpretation, Examiner interprets such a hospital or clinic to encompass a point of care.
analyzing, by a first machine learning model executing on a first processor at the point of care, the received patient sample data to generate a first result and a confidence level associated with the first result ([0029], “AI-based assessment of the medical data.” [0078-0079], “1. Rules for routing may be defined as: … the output of an AI-based system not being able to make the diagnosis on the data with an adequate level of confidence”). Examiner notes that an AI-based system that routes medical data to external systems for advanced analysis based on diagnosis and confidence level would generate the result and confidence level associated with the first result.
automatically evaluating, by the first processor, the confidence level associated with the first result to determine whether a criterion for advanced analysis at a central reference laboratory is met, wherein the criterion is met when the confidence level is less than a predetermined threshold ([0078-0079], “1. Rules for routing may be defined as: a. Users specify categorical fields for executing the rule on the radiology data. They list the conditions that the radiological data may need to satisfy for the rule to be executed, which may be the form of disease, …, and/or the output of an AI-based system not being able to make the diagnosis on the data with an adequate level of confidence, etc.”); and
in a case where the criterion for advanced analysis is met:
electronically transmitting the received patient sample data to the central reference laboratory over a network for the advanced analysis ([0024], “routing medical data to an expert for assessment based on a set of criteria, a manual intervention, or an AI-based assessment of the medical data.” [0045], “Receivers may be internal or external to the originating institution. Receivers may be an individual or a group of individuals such as an entire medical department or a company. Receivers may be defined to have a specific skillset or expertise to receive the medical data for assessment.” [0036], “The data assessment tool 101, and one or more or its components, may transmit and/or receive digitized slide images and/or patient information to server systems 110, physician servers 121, hospital servers 122, clinical trial servers 123, research lab servers 124, and/or laboratory information systems 125 over an electronic network 120.”)”;
receiving, at the point of care over the network, a second result of the advanced analysis from the central reference laboratory ([0060], “After the recipient(s) reviews the medical data, their report may be sent back to the originating institution via the cloud, an internet connection, and/or a local area network, etc.”); and
displaying at least one of the first result or the second result on a display at the point of care ([0035], “The viewing application tool 108 may provide a user with a specimen property or image property information pertaining to digital pathology image(s), according to an exemplary embodiment. The information may be provided through various output interfaces (e.g., a screen, a monitor, a storage devices and/or a web browser, etc.).”).
Kunz does not teach wherein the advanced analysis uses a second machine learning model.
However, Min does teach wherein the advanced analysis uses a second machine learning model ([0122], “ One or more of the devices 130 may be at an imaging facility that generates images of a patient's arteries, a medical facility (e.g., a hospital, doctor's office, etc.) or may be the personal computing device of a patient or care provider. ” [0121], “the processing system 120 is located remotely from where the patient image data is generated or stored.” [0063], “the system can be configured to identify and/or characterize one or more regions of plaque within the medical image. In some embodiments, the system can be configured to utilize one or more AI and/or ML algorithms to automatically and/or dynamically identify and/or characterize one or more regions of plaque using image processing.” [0154], “The processing system 120 includes image information stored on a storage device 410,… The other patients' stored plaque information may be a collection of information from one, dozens, hundreds, thousands, tens of thousands, hundreds of thousands, or millions of patients, or more.” [0434], “the one or more AI and/or ML algorithms can be trained using a Convolutional Neural Network (CNN) on a set of medical images on which one or more regions of plaque have been identified and/or characterized. The set of medical images used for training the AI and/or ML algorithms ideally includes a large number of images in which regions of plaque have been identified and/or characterized. For example, at least hundreds of images, and more preferably thousands of images, tens of thousands images, hundreds of thousands of images, or more than hundreds of thousands of images (e.g., millions, tens of millions or more). The number of images used to train an AI and/or ML algorithm can increase over time to improve the AI and/or ML algorithm and increase the accuracy of the identification of regions of plaque and increase the accuracy of the characterization of regions of plaque.”). Examiner notes that the processing system is analogous to the central reference laboratory, as it is remotely located from the originating facility and has greater accuracy due to the large amounts of data that the processing system has at its disposal (pg. 15, lines 19-21 of Applicant specification).
Kunz in view of Min are considered analogous to the claimed invention because they are in the field of analyzing patient sample data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kunz with Min for the advantage of including analyses from “communicating with large databases, performing high volume transaction processing, and generating reports from large databases” (Min; [0241]).
Regarding claim 2, Kunz in view of Min teaches the method of claim 1. Kunz further teaches the method further including, in a case where the criterion for advanced analysis is not met, displaying the first result on the display at the point of care ([0035], “The viewing application tool 108 may provide a user with a specimen property or image property information pertaining to digital pathology image(s), according to an exemplary embodiment. The information may be provided through various output interfaces (e.g., a screen, a monitor, a storage devices and/or a web browser, etc.).”). Examiner notes that as Kunz displays the first result (specimen or image property information) regardless of whether or not the criterion is met, Kunz teaches both embodiments of display.
Regarding claim 4, Kunz in view of Min teaches the method of claim 1. Kunz does not teach wherein the first machine learning model is a convolution neural network.
However, Min does teach wherein the machine learning model is a convolution neural network ([0063], “in some embodiments, the one or more AI and/or ML algorithms can be trained using a Convolutional Neural Network (CNN) on a set of medical images on which one or more regions of plaque have been identified and/or characterized.” [0121], “the processing system 120 is located in the same geographic proximity as an imaging facility that images and stores patient image data.”). As Min teaches wherein a machine learning model is a convolution neural network, it would be obvious that the first machine learning model of Kunz may also be a convolution neural network.
Kunz in view of Min are considered analogous to the claimed invention because they are in the field of analyzing patient sample data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kunz with Min for the advantage of “analyz[ing] information in a manner similar to high-level cognitive functions of a human mind” (Min; [0180]).
Regarding claim 6, Kunz in view of Min teaches the method of claim 1. Kunz does not teach wherein the second machine learning model is a convolution neural network.
However, Min does teach wherein the second machine learning model is a convolution neural network ([0063], “in some embodiments, the one or more AI and/or ML algorithms can be trained using a Convolutional Neural Network (CNN) on a set of medical images on which one or more regions of plaque have been identified and/or characterized.” [0121], “the processing system 120 is located remotely from where the patient image data is generated or stored.”).
Kunz in view of Min are considered analogous to the claimed invention because they are in the field of analyzing patient sample data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kunz with Min for the advantage of “analyz[ing] information in a manner similar to high-level cognitive functions of a human mind” (Min; [0180]).
Regarding claim 8, Kunz teaches the method of claim 1. Kunz does not teach the method further including: receiving a plurality of patient sample data at the central reference laboratory; training the second machine learning model using the plurality of patient sample data; and analyzing the reflexed patient sample data at the central reference laboratory using the trained second machine learning model.
However, Min does teach the method further including:
receiving a plurality of patient sample data at the central reference laboratory ([0030], “At block 510, a processing system may receive image information via a network 125 (FIG. 1), the image information including the image data.” [0154], “The processing system 120 includes image information stored on a storage device 410, which may come from the network 125 illustrated in FIG. 1. The image information may include image data, scan information, and/or patient data… The other patients' stored plaque information may be a collection of information from one, dozens, hundreds, thousands, tens of thousands, hundreds of thousands, or millions of patients, or more.”);
training the second machine learning model using the plurality of patient sample data ([0179], “In an example where a NN is used, the NN can be trained using information from a plurality of patients, where the information for each patient can include medical images and one or more patient characteristics.”); and
analyzing the reflexed patient sample data at the central reference laboratory using the trained second machine learning model ([0063], “the system can be configured to identify and/or characterize one or more regions of plaque within the medical image. In some embodiments, the system can be configured to utilize one or more AI and/or ML algorithms to automatically and/or dynamically identify and/or characterize one or more regions of plaque using image processing.”). Examiner notes that analyzing images for plaque encompasses an analysis of the sample data.
Kunz in view of Min are considered analogous to the claimed invention because they are in the field of analyzing patient sample data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kunz with Min for the advantage of “analyz[ing] information in a manner similar to high-level cognitive functions of a human mind” (Min; [0180]).
Regarding claim 9, Kunz in view of Min teaches the method of claim 1. Kunz further teaches wherein the criterion includes one or more of
(i) a case where the first result indicates that the received patient sample data is abnormal ([0071], “the workflow may include determining that the AI prediction is uncertain or lacking expertise in an identified disease, such as in the case of rare cancers.”),
(ii) a case where a difference between the first result and a reference value is greater than a first threshold,
(iii) a case where a confidence level associated with the first result is less than a second threshold ([0077], “the output of an AI-based system not being able to make the diagnosis on the data with an adequate level of confidence, etc.”),
(iv) a case where the first result cannot be obtained by analysis at the point of care ([0077], “if a dental center lacks the expertise for a condition, such as bone loss, unusual wisdom teeth, cavities, infections, and/or cysts or tumors, then the scans (e.g., x-rays from the patient) may be automatically detected by the AI system and routed to an expert within or outside of the medical center.”), or
(v) a case where the first result indicates a diagnostic condition that requires advanced analysis at the central reference laboratory ([0077], “if a dental center lacks the expertise for a condition, such as bone loss, unusual wisdom teeth, cavities, infections, and/or cysts or tumors, then the scans (e.g., x-rays from the patient) may be automatically detected by the AI system and routed to an expert within or outside of the medical center.”).
[Language that suggest or makes optional (e.g. “or”) but does not require steps to be performed or does not limit a claim to a particular structure does not limit the scope of a claim or claim limitation (MPEP §2106 II C; In re Johnston, 77 USPQ2d 1788 (CA FC 2006); Intel Corp. v. Int'l Trade Comm'n, 20 USPQ2d 1161 (Fed. Cir. 1991)).]
Regarding claims 10 and 14, these claims are rejected for the same reasons as claim 1, as described above. Kunz further teaches a diagnostic system comprising: a memory configured to store instructions; and a processor communicatively connected to the memory and configured to execute the stored instructions ([0008], “A computer system for providing automated routing of medical data comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to perform operations”).
Kunz further teaches a hybrid diagnostic system comprising: a first memory configured to store first instructions; a first processor communicatively connected to the first memory and configured to execute the stored first instructions at a point of care ([0008], “A computer system for providing automated routing of medical data comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to perform operations”)
and a second memory configured to store second instructions; and a second processor communicatively connected to the second memory and configured to execute the stored second instructions at the central reference laboratory ([0027], “Recipients may be a specific person in the originating center, a department in the center, or an external entity (e.g., an individual or group of individuals at a different hospital or clinic).” [0115], “Communications interface 560 allows software and data to be transferred between device 500 and external devices.” [0029], “physician servers 121, hospital servers 122, clinical trial servers 123, research lab servers 124, and/or laboratory information systems 125, etc., may each be connected an electronic network 120, such as the Internet, through one or more computers, servers and/or handheld mobile devices.”).
Regarding claims 11, 13, 15, 17, and 21, these claims are rejected for the same reasons as claims 2, 4, 2, 4, and 8, respectively.
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kunz (US 20220051783) in view of Min (US 20230289963) further in view of Morimoto (US 20210217523).
Regarding claim 7, Kunz in view of teaches the method of claim 1. Kunz does not teach the method further including: receiving a plurality of patient sample data at the central reference laboratory; training a first machine learning model using the plurality of patient sample data; and deploying the trained first machine learning model in the point of care system to analyze the patient sample data received at the point of care.
However, Min does teach the method further including:
receiving a plurality of patient sample data at the central reference laboratory ([0154], “The processing system 120 includes image information stored on a storage device 410, which may come from the network 125 illustrated in FIG. 1… The other patients' stored plaque information may be a collection of information from one, dozens, hundreds, thousands, tens of thousands, hundreds of thousands, or millions of patients, or more.”).
Kunz in view of Min are considered analogous to the claimed invention because they are in the field of analyzing patient sample data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kunz with Min for the advantage of collecting “information from one, dozens, hundreds, thousands, tens of thousands, hundreds of thousands, or millions of patients, or more” (Min; [0154]).
Kunz in view of Min does not teach the method further including: training a first machine learning model using the plurality of patient sample data; and deploying the trained first machine learning model in the point of care system to analyze the patient sample data received at the point of care.
However, Morimoto does teach the method further including:
receiving a plurality of patient sample data at the central reference laboratory ([0030], “FIG. 1,... The electronic clinical record database 10 and the trained model generator 11 are arranged in a facility 1 such as a large hospital that a relatively large number of patients visit.” [0031], “The electronic clinical record database 10 stores the biological information about the patient group PF.”);
training a first machine learning model using the plurality of patient sample data ([0032], “The trained model generator 11 is configured to generate the trained model information M derived from a pattern included in the biological information about the patient group PF, by machine learning based on the biological information about the patient group PF stored in the electronic clinical record database 10.”); and
deploying the trained first machine learning model in the point of care system to analyze the patient sample data received at the point of care ([0042], “the determiner 21 receives the trained model information M generated by the trained model generator 11 via the external network 30. Furthermore, the determiner 21 is configured to determine the presence or absence of a disease in a patient P2 who is not included in the patient group PF based on the received trained model information M.” [0043], “the electronic clinical record database 20 of the facility 2 stores the electronic clinical record data of the patient P2. The patient P2 is a patient P2 who is not included in the patients P1 (patient group PF)”).
Kunz in view of Min further in view of Morimoto are considered analogous to the claimed invention because they are in the field of transmitting health data for machine learning analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kunz in view of Min with Morimoto for the advantage of providing a system wherein “trained model information is provided to the outside” (Morimoto; [0016]).
Regarding claim 20, this claim is rejected for the same reasons as claim 7.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684