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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on May 9, 2026 has been entered.
Notice to Applicant
Receipt of Applicant’s Amendment filed May 9, 2026 is acknowledged.
Response to Amendment
Claims 1, 7-8, 10, 14, and 20-21 have been amended. Claims 2, 4, 6, 11, 13, 15, and 17 have not been modified. Claims 3, 5, 9, 12, 16, 18-19, 22-26 have been cancelled. Claims 1-2, 4, 6-8, 10-11, 16, 18-19, and 20-21 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments filed May 9, 2026 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues 35 U.S.C. §101 Rejections, pg. 9 of Remarks:
Regarding Step 2A, Prong One, Applicant argues that the claims are not abstract as the claims recite “a particular machine-executed processing sequence” applied to perform the steps of the amended independent claim. 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. See MPEP 2106.04(a)(2)III, which states that claims that recite abstract steps being performed on a generic computing device may still be directed towards an abstract idea (Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Regarding the additional elements of the image processing, extraction/segmentation of structures of interest, machine-learning analysis, privacy-preserving filtering/anonymization/encryption, and CRL-side second-model analysis, these elements are analyzed under Prong Two.
Regarding Step 2A, Prong Two, Applicant argues that the claim is constrained to a technical workflow as the POC system first generated extract features through image preprocessing, segmentation, and feature extraction and then transmits those features with identifying, sensitive, or confidential data filtered out by applying anonymization or encryption. Examiner respectfully disagrees.
Regarding image preprocessing, segmentation, and feature extraction, Examiner notes that this feature is an insignificant pre-solution activity that is incidental to the primary process of “integrat[ing] POC systems and CRL-based diagnostic systems and processes to provide the advantages of both POC and CRL systems” identified by pg. 2, lines 10-12 of Applicant specification. Furthermore, this feature is known within the field. However, if support exists for an improved image processing algorithm over existing options, that may be a consideration in overcoming the 101 rejection.
Regarding the anonymization and encryption, this feature is taught at a high level of generality, such that no specific, technical improvements are being applied to data encryption or anonymization techniques. Rather, they are only applied to perform an abstract idea of filtering data. See MPEP 2106.04(a)(2)IIC, which describes filtering as being abstract: “Other examples of managing personal behavior recited in a claim include: i. filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis)”.
Regarding Step 2B, Applicant argues that the Office Action fails to consider the ordered combination of claim elements, as the claim recites a specific order of (1) image transformation, (2) local machine-learning inference, (3) conditional routing, (4) privacy-preserving feature transmission, and (5) CRL-side second model-analysis.
As noted above, (1) image transformation is considered a pre-solution activity as it is incidental to the primary process of the claim. Furthermore, it is a feature that is known within the field, as demonstrated by:
Rogowska; Jadwiga, Handbook of Medical Image Processing and Analysis, 24 Dec 2008, Elsevier, 61-80: pg. 69, “In medical imaging, segmentation is important for feature extraction, image measurements, and image display. In some applications it may be useful to classify image pixels into anatomical regions, such as bones, muscles, and blood vessels, while in others into pathological regions, such as cancer, tissue deformities, and multiple sclerosis lesions. In some studies the goal is to divide the entire image into subregions such as the white matter, gray matter, and cerebrospinal fluid spaces of the brain [67], while in others one specific structure has to be extracted, for example breast tumors from magnetic resonance [71]. A wide variety of segmentation techniques has been proposed” pg. 80, “Segmentation is an important step in many medical applications involving measurements, 3D visualization, registration, and computer-aided diagnosis.”
Jiang; J., Medical image analysis with artificial neural networks, Dec 2010, Computerized Medical Imaging and Graphics, Vol 34, Issue 8, pgs. 617-631: pg. 623, “Medical image segmentation and edge detection serve many useful purposes in medical imaging analysis. They can serve as a pre-processing step for further computer-aided diagnosis systems, or for human diagnosis. By classifying areas with similar properties more specialised diagnostic techniques can be applied with less risk of their misuse on non-relevant tissue. Identification of edges, particularly those of tumours and organs, can serve to simplify human diagnosis and reduce mistakes in the identification of image features. The above sections describe a wide variety of different approaches, from various network types to a wide choice of feature extraction and pre-processing techniques. The commonly used network types include Hopfield, Kohonen, SOM, MLP, CNN, QNN, etc., where fuzzy c-means and fuzzy clustering along with genetic algorithm, EM, and BP algorithm are used for training… This section describes a number of applications where ANNs have been successfully used for computer-aided diagnosis, detection and simulation.”
Regarding (2) local and (5) CRL-side machine learning inference, while the inference itself is considered abstract under certain methods of organizing human activity as doctors typically perform patient diagnosis based on medical image data, the claimed architecture of 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.”
(3) Conditional routing is considered an abstract idea as the conditions are based on a criterion regarding the status of the diagnosis, as noted by amended claim 1.
(4) Privacy-preserving feature transmission is an extra-solution activity as the courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). As noted above, the privacy-preservation features are taught at a high level of generality to perform an abstract idea of filtering data without any specific, technical improvements to the field of data security.
Applicant argues 35 U.S.C. §103 Rejections, pg. 11 of Remarks:
Applicant argues that Kunz in view of Min does not teach or suggest all of the features of amended independent claims. Applicant arguments are moot as new art is applied to teach the amended claim limitations.
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, 6-8, 10-11, 16, 18-19, 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, 6-8, 10-11, 16, 18-19, and 20-21). Accordingly, claims 1-2, 4, 6-8, 10-11, 16, 18-19, 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, the digital data including image data obtained by capturing images;
analyzing, by a first machine learning model executing on a first processor at the point of care, the received patient sample data by (i) preprocessing the received image data to segment structures of interest from a background or other structures and to extract features including one or more of shape, texture, intensity, or spatial properties, from the segmented structures, and (ii) inputting the extracted features into the first machine learning model at the point of care to generate a first result and a confidence level associated with the first result;
automatically evaluating, by the first processor, the first result and 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 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 the 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; and
in a case where the criterion for advanced analysis is met:
electronically transmitting, with identifying, sensitive, or confidential data filtered out by applying privacy-preserving techniques including data anonymization or encryption, the extracted features generated from the received patient sample data to the central reference laboratory over a network for the advanced analysis using a second machine learning model to analyze the transmitted extracted features;
receiving, at the point of care over the network, a second result of the advanced analysis of the extracted features 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”, “automatically evaluating,…, the first result and the confidence level associated with the first result to determine whether a criterion for advanced analysis at a central reference laboratory is met”, “transmitting,…, the received patient sample data… for the 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 may provide instructions to medical staff to analyze patient medical image data. 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:
programmed data processing device system (1,10-11,13-14,15,17,20-21); diagnostic analyzer (1,10,14); segment structures of interest (1,10,14); extract features (1,10,14); first machine learning model (1,4,7,10,13-14,17,20); privacy-preserving techniques (1,10,14); second machine learning model (1,6,8,10,14,21); network (1,10,14); display (1-2,10-11,14-15); memory (10); first processor (1,10-11,14-15); image segmentation (1,10,14); privacy-preserving techniques including data anonymization or encryption (1,10,14); second processor (14,20-21); first memory (14); 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 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 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.
Image segmentation is also taught at a high level of generality. Pg. 14, lines 7-15 recites: “cleaning and preprocessing the acquired images to enhance the quality and remove any noise or artifacts (for example, using resizing, cropping, denoising, and normalization); segmenting the structures of interest from the background or other structures using various segmentation techniques such as thresholding, edge detection, region growing, or machine learning-based methods; extracting relevant features, such as shape, texture, intensity, or spatial properties, from the segmented structures using morphological operations, statistical analysis, and image texture analysis; and inputting the extracted features into a machine learning-based POC system to obtain, as output from the POC system, classification data.” No specific, technical improvements are being made to image segmentation as a variety of generic segmentation techniques are applied to perform a pre-solution activity of pre-processing the data; MPEP 2106.05(g).
Privacy-preserving techniques are also taught at a high level of generality. Pg. 19, lines 11-13 recites: “Privacy-preserving techniques, such as data anonymization, encryption, or differential privacy, can be applied to protect individual data privacy while still contributing to the training process.” No specific, technical improvements are being made to privacy-preserving techniques as a variety of generic techniques are applied to perform an abstract idea of filtering data; MPEP 2106.05(g).
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 merely outputting result data.
Claims 4, 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.
Claim 6: This claim recites wherein the second 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: selecting, at the point of care, a portion of a plurality of patient sample data for transmission to the central reference laboratory as new training data based on an uncertainty sampling strategy using one or more of prediction entropy, margin, or confidence scores derived from an output of the first machine learning model; transmitting the portion of the plurality of patient sample data to the central reference laboratory; training, at the central reference laboratory, the first machine learning model using the portion of 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 an abstract idea of mathematical concepts by utilizing an uncertainty sampling strategy. This claim further teaches training a machine learning model at a high level of generality, such that no specific, technical improvements are made to how the training is performed, and deploying the model to perform the abstract idea of analyzing patient data.
Claims 8 and 21: These claims recite further including: selecting, at the point of care, a portion of a plurality of patient sample data for transmission to the central reference laboratory as new training data based on an uncertainty sampling strategy using one or more of prediction entropy, margin, or confidence scores derived from an output of the first machine learning model; transmitting the portion of the plurality of patient sample data to the central reference laboratory; training , at the central reference laboratory, the second machine learning model using the plurality of patient sample data; and analyzing the transmitted extracted features at the central reference laboratory using the trained second machine learning model; which teaches an abstract idea of mathematical concepts by utilizing an uncertainty sampling strategy. This claim further teaches training a machine learning model at a high level of generality, such that no specific, technical improvements are made to how the training is performed, and deploying the model to perform the abstract idea of analyzing patient data.
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 known 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.”
Image segmentation to preprocess images for computer diagnostic analysis is also known:
Rogowska; Jadwiga, Handbook of Medical Image Processing and Analysis, 24 Dec 2008, Elsevier, 61-80: pg. 69, “In medical imaging, segmentation is important for feature extraction, image measurements, and image display. In some applications it may be useful to classify image pixels into anatomical regions, such as bones, muscles, and blood vessels, while in others into pathological regions, such as cancer, tissue deformities, and multiple sclerosis lesions. In some studies the goal is to divide the entire image into subregions such as the white matter, gray matter, and cerebrospinal fluid spaces of the brain [67], while in others one specific structure has to be extracted, for example breast tumors from magnetic resonance [71]. A wide variety of segmentation techniques has been proposed” pg. 80, “Segmentation is an important step in many medical applications involving measurements, 3D visualization, registration, and computer-aided diagnosis.”
Jiang; J., Medical image analysis with artificial neural networks, Dec 2010, Computerized Medical Imaging and Graphics, Vol 34, Issue 8, pgs. 617-631: pg. 623, “Medical image segmentation and edge detection serve many useful purposes in medical imaging analysis. They can serve as a pre-processing step for further computer-aided diagnosis systems, or for human diagnosis. By classifying areas with similar properties more specialised diagnostic techniques can be applied with less risk of their misuse on non-relevant tissue. Identification of edges, particularly those of tumours and organs, can serve to simplify human diagnosis and reduce mistakes in the identification of image features. The above sections describe a wide variety of different approaches, from various network types to a wide choice of feature extraction and pre-processing techniques. The commonly used network types include Hopfield, Kohonen, SOM, MLP, CNN, QNN, etc., where fuzzy c-means and fuzzy clustering along with genetic algorithm, EM, and BP algorithm are used for training… This section describes a number of applications where ANNs have been successfully used for computer-aided diagnosis, detection and simulation.”
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, 6-8, 11, 13, 15, 17, and 20-21 additional limitations which amount to elements that have been recognized as known activities in particular fields, claims 2, 4, 6-8, 11, 13, 15, 17, and 20-21, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 2, 4, 6-8, 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, 6-8, 10-11, 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, 10-11, 13-15, 17, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zangenehpour (US 20260011126) in view of Hu (US 20220004899).
Regarding claim 1, Zangenehpour teaches a method executed by a programmed data processing device system, ([0031], “a computer program executing on a computing device that is accessible to a user”) the method comprising:
receiving, from an analyzer at a point of care, patient sample data comprising digital data, the digital data including image data obtained by capturing images ([0031], “the computing device sends image data captured by these image sensors to the pose monitoring platform for computer vision analysis… users could be connected with healthcare professionals such as physical therapists, physicians, nurses, counselors, etc. For example, the pose monitoring platform may generate interfaces through which a coach can serve as a guide, partner, or “cheerleader” for a user as she completes sessions in accordance with a program. Similarly, the pose monitoring platform may generate interfaces through which a healthcare professional can obtain or rely on advice regarding symptoms, treatment, and the like.”). Examiner interprets the patient’s location as being a point of care, as they may interface with a healthcare professional regardless of where they are.
analyzing, by a first machine learning model executing on a first processor at the point of care, the received patient sample data by (i) preprocessing the received image data to segment structures of interest from a background or other structures and to extract features including one or more of shape, texture, intensity, or spatial properties, from the segmented structures ([0066], “The body pose module 224 can extract one or more feature maps from the image data. In one embodiment, the body pose module 224 segments the image data into contiguous regions of pixels. Each contiguous region of pixels may be associated with a portion of the environment. In some embodiments, the body pose module 224 segments the image data based on objects shown in the image data.”), and
(ii) inputting the extracted features into the first machine learning model at the point of care to generate a first result and a confidence level associated with the first result ([0101], “the first machine learning model can be a lightweight model with a computational budget such that the model is able to operate, in real time, upon receipt of digital images.” [0067], “The body pose module 224 can apply the neural network 226 to each extracted feature map.” [0073], “where the computing devices can include local versions of machine learning models for pose estimation.” [0102], “At step 406, the analysis module 218 (e.g., through autolabeling module 230), can compute a confidence metric for the digital image and corresponding estimated pose. For example, the autolabeling module 230 can generate, for the first estimated pose, a first confidence metric that is indicative of a likelihood that the first estimated pose corresponds to an actual pose of the user, as described above… the autolabeling module 230 can calculate the confidence metric utilizing one or more machine learning models and/or the neural network 226.”);
automatically evaluating, by the first processor, the first result and the confidence level associated with the first result to determine whether a criterion for advanced analysis at a central reference laboratory is met ([0109], “At step 410, the autolabeling module 230 can provide the image to a second machine learning model (e.g., a second machine learning model associated with the body pose module 224 and/or one or more neural networks 226), if the image is determined to have a low confidence estimated pose.”),
wherein the criterion 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 the confidence level associated with the first result is less than a second threshold ([0109], “in response to a determination that the first confidence metric is less than the threshold value, the autolabeling module 230 can transmit or provide the digital image to a second machine learning model within the body pose module 224 as part of an inferencing operation, so as to obtain a second estimated pose of the user that is produced by the second machine learning model as output. For example, the autolabeling module 230 can provide these digital images to heavyweight machine learning model, which can provide improved estimated poses when compared to a lightweight machine learning model, as discussed above in relation to FIG. 2B.”),
(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; and
in a case where the criterion for advanced analysis is met:
electronically transmitting, with identifying, sensitive, or confidential data filtered out by applying privacy-preserving techniques including data anonymization or encryption, the extracted features generated from the received patient sample data to the central reference laboratory over a network for the advanced analysis using a second machine learning model to analyze the transmitted extracted features ([0049], “some user data may be stored on, and processed by, her own computing device for security and privacy purposes. This information may be processed (e.g., encrypted or obfuscated) before being transmitted to the server system 108.” [0029], “a pose estimation model that relies on a complex model stored on a network-accessible server system—commonly referred to as the “cloud”” [0109], “the autolabeling module 230 can transmit or provide the digital image to a second machine learning model within the body pose module 224 as part of an inferencing operation, so as to obtain a second estimated pose of the user that is produced by the second machine learning model as output.” [0076], “The heavyweight model that includes a neural network 226 can include more model parameters or hidden layers than a lightweight model, for example. By including a machine learning model with a larger computational budget than a lightweight model, the pose monitoring platform 212 enables improved accuracy and robustness for inferences (e.g., pose estimation operations) in comparison to a lightweight model.”). Examiner interprets the server system to encompass the central reference laboratory, as it houses the second machine learning model, which performs advanced analysis.
displaying at least one of the first result or the second result on a display at the point of care ([0077], “After performing such processing, the body pose module 224 may cause the display mechanism 206 to display the indication, allowing the user to move her body parts if she is aiming for a different pose. In some embodiments, the body pose module 224 may send indications to the GUI module 220 for display via the display mechanism 206, rather than directly causing the display mechanism 206 to display indications or other information.”).
Zangenehpour does not teach wherein the analyzer is diagnostic and receiving, at the point of care over the network, a second result of the advanced analysis of the extracted features from the central reference laboratory.
However, Hu does teach wherein the analyzer is diagnostic ([0022], “the collector 130 may collect physical characteristic data of a user for medical diagnosis, such as heart rate, blood pressure, blood glucose concentration, diastolic blood pressure, age, etc.” [0024], “the controller 140 inputs the characteristic data into a terminal-side exception diagnosis model to obtain a terminal-side exception judgment result.”) and
receiving, at the point of care over the network, a second result of the advanced analysis of the extracted features from the central reference laboratory ([0007], “obtaining a terminal-side exception judgment result by inputting the characteristic data into a terminal-side exception diagnosis model; in response to the terminal-side exception judgment result indicating that the characteristic data is abnormal, transmitting the characteristic data to a cloud server; receiving diagnostic information from the cloud server;” [0051], “The controller 220 inputs the characteristic data into a cloud-side exception diagnosis model to obtain a cloud-side exception judgment result.” [0070], “if the diagnostic information indicates that a cloud-side exception judgment result of the cloud server is the same as the terminal-side exception judgment result, the diagnostic information indicating that the characteristic data is abnormal is transmit to the user terminal apparatus 100, to indicate that the characteristic data is indeed the exception data through a more accurate judgment.”). Examiner interprets the cloud server to encompass the central reference laboratory, as it houses the second machine learning model, which performs advanced analysis (more accurate judgment).
Zangenehpour in view of Hu are considered analogous to the claimed invention because they are in the field of transmitting health data for machine learning analysis, as supported by [0003-0004] of Zangenehpour (“Exercise therapy is an intervention technique that utilizes physical activity as the principal treatment method for addressing the symptoms of musculoskeletal (MSK) conditions, such as acute physical ailments and chronic physical ailments… Therefore, a better approach is needed for monitoring pose to ensure that users are able to achieve lasting improvement in terms of MSK function. The benefits of improved performance of poses are not limited to exercise therapy programs.”). 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 Zangenehpour with Hu for the advantage of including analyses from “us[ing] information from the cloud server 200 to determine whether the user terminal apparatus 100 accurately identified the characteristic data as abnormal” (Hu; [0040]).
Regarding claim 2, Zangenehpour in view of Hu teaches the method of claim 1. Zangenehpour 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 ([0077], “for each indication, the body pose module 224 may cause the display mechanism 206 to display an indication that the user is performing the estimated pose with the body part. The body pose module 224 may do so in near real time. For example, the body pose module 224 may receive and segment image data and apply the neural network 226 to determine a pose of a body part as the user is performing the pose in real time. After performing such processing, the body pose module 224 may cause the display mechanism 206 to display the indication, allowing the user to move her body parts if she is aiming for a different pose. In some embodiments, the body pose module 224 may send indications to the GUI module 220 for display via the display mechanism 206, rather than directly causing the display mechanism 206 to display indications or other information.” [0054], “The display mechanism 206 can be any mechanism that is operable to visually convey information to a user (e.g., a user). For example, the display mechanism 206 may be a panel that includes light-emitting diodes (LEDs), organic LEDs, liquid crystal elements, or electrophoretic elements.”). Examiner notes that the data is not transmitted to the second machine learning model in the above example.
Regarding claim 4, Zangenehpour in view of Hu teaches the method of claim 1. Zangenehpour further teaches wherein the first machine learning model is a convolution neural network ([0069], “the body pose module 224 can generate estimated poses using one or more machine learning models designed and trained for pose estimation (also called “pose estimation models” or simply “models”), which can include the neural network 226 or any other neural network, artificial intelligence, or computer-based analytical method… the machine learning model can be implemented as a convolutional neural network (or feed forward network, recurrent neural network, random forest, or xgboost model).” [0100], “At step 404, the monitoring module 216 can provide the image to a first machine learning model, such as a machine learning model within the analysis module 218 and/or the body pose module 224.”).
Regarding claim 6, Zangenehpour in view of Hu teaches the method of claim 1. Zangenehpour further teaches wherein the second machine learning model is a convolution neural network ([0069], “the body pose module 224 can generate estimated poses using one or more machine learning models designed and trained for pose estimation (also called “pose estimation models” or simply “models”), which can include the neural network 226 or any other neural network, artificial intelligence, or computer-based analytical method… the machine learning model can be implemented as a convolutional neural network (or feed forward network, recurrent neural network, random forest, or xgboost model).” [0109], “the autolabeling module 230 can provide the image to a second machine learning model (e.g., a second machine learning model associated with the body pose module 224 and/or one or more neural networks 226), if the image is determined to have a low confidence estimated pose.”).
Regarding claim 8, Zangenehpour in view of Hu teaches the method of claim 1. Zangenehpour further teaches the method further including:
selecting, at the point of care, a portion of a plurality of patient sample data for transmission to the central reference laboratory as new training data based on an uncertainty sampling strategy using one or more of prediction entropy, margin, or confidence scores derived from an output of the first machine learning model ([0014], “FIG. 5 depicts a flow diagram of a process leveraging confidence metrics to generate training data from pose estimation models.” [0134], “Having determined that the confidence metric 514 is above the threshold value, the autolabeling module 230 can generate the digital image 502 and the estimated pose 512 within a training data structure, such as training data 516B for further training of the first local pose estimator.”). Examiner notes choosing confidence metrics above a threshold value corresponds to the uncertainty sampling strategy identified by pg. 18, lines 26-28 of Applicant specification (“Common uncertainty sampling strategies include selecting instances with the highest prediction entropy, margin, or confidence scores.”).
transmitting the portion of the plurality of patient sample data to the central reference laboratory ([0122], “the second machine learning model can be trained based on training datasets from multiple computing devices corresponding to multiple users, as discussed in relation to FIG. 4C below.” [0123], “the computing device 200 can receive and append training data from external sources to the training dataset. For example, the pose monitoring platform 212 can receive an external training dataset from a source external to the computing device… The pose monitoring platform 212 (e.g., through training module 232) can append the external training dataset to the training dataset for tuning the second machine learning model.”);
training the second machine learning model using the plurality of patient sample data ([0112], “the training module 232 can provide the training data to the second machine learning model for further training of this machine learning model (e.g., the heavyweight model). For example, in response to the determination that the second confidence metric is greater than the threshold value, the training module 232 can provide the data structure (e.g., the training data structure 234) that is representative of the training dataset to the second machine learning model, so as to tune the second machine learning model.”); and
analyzing the transmitted extracted features at the central reference laboratory using the trained second machine learning model [0109], “the autolabeling module 230 can transmit or provide the digital image to a second machine learning model within the body pose module 224 as part of an inferencing operation, so as to obtain a second estimated pose of the user that is produced by the second machine learning model as output.” [0029], “a pose estimation model that relies on a complex model stored on a network-accessible server system—commonly referred to as the “cloud”” [0076], “The heavyweight model that includes a neural network 226 can include more model parameters or hidden layers than a lightweight model, for example. By including a machine learning model with a larger computational budget than a lightweight model, the pose monitoring platform 212 enables improved accuracy and robustness for inferences (e.g., pose estimation operations) in comparison to a lightweight model.”). Examiner interprets the server system to encompass the central reference laboratory, as it houses the second machine learning model, which performs advanced analysis.
Regarding claims 10 and 14, these claims are rejected for the same reasons as claim 1, as described above. Zangenehpour 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 ([0151], “The computer programs typically comprise one or more instructions (e.g., instructions 1004, 1008, 1028) set at various times in various memory and storage devices in a computing device.”).
Zangenehpour 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 ([0151], “The computer programs typically comprise one or more instructions (e.g., instructions 1004, 1008, 1028) set at various times in various memory and storage devices in a computing device.”)
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 ([0048], “the pose monitoring platform 102 is executed entirely by a cloud computing service operated by, for example, Amazon Web Services®, Google Cloud Platform™, or Microsoft Azure®. In such embodiments, the pose monitoring platform 102 may reside on a server system 108 comprised of one or more computer servers that are accessible via a network (e.g., the Internet).” [0050], “the pose monitoring platform 212 is embodied as a computer program that is executed by another computing device (e.g., a computer server) to which the computing device 200 is communicatively connected.” [0057], “the pose monitoring platform 212 may be referred to as a computer program that resides within the memory 204.”).
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 Zangenehpour (US 20220051783) in view of Hu (US 20230289963) further in view of Morimoto (US 20210217523).
Regarding claim 7, Zangenehpour in view of Hu teaches the method of claim 1. Zangenehpour teaches the method further including:
selecting, at the point of care, a portion of a plurality of patient sample data for transmission to the central reference laboratory as new training data based on an uncertainty sampling strategy using one or more of prediction entropy, margin, or confidence scores derived from an output of the first machine learning model ([0014], “FIG. 5 depicts a flow diagram of a process leveraging confidence metrics to generate training data from pose estimation models.” [0134], “Having determined that the confidence metric 514 is above the threshold value, the autolabeling module 230 can generate the digital image 502 and the estimated pose 512 within a training data structure, such as training data 516B for further training of the first local pose estimator.”). Examiner notes choosing confidence metrics above a threshold value corresponds to the uncertainty sampling strategy identified by pg. 18, lines 26-28 of Applicant specification (“Common uncertainty sampling strategies include selecting instances with the highest prediction entropy, margin, or confidence scores.”); and
transmitting the portion of the plurality of patient sample data to the central reference laboratory ([0122], “the second machine learning model can be trained based on training datasets from multiple computing devices corresponding to multiple users, as discussed in relation to FIG. 4C below.” [0123], “the computing device 200 can receive and append training data from external sources to the training dataset. For example, the pose monitoring platform 212 can receive an external training dataset from a source external to the computing device… The pose monitoring platform 212 (e.g., through training module 232) can append the external training dataset to the training dataset for tuning the second machine learning model.”).
Zangenehpour does not teach the method further including: training, at the central reference laboratory, a first machine learning model using the portion of 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:
training, at the central reference laboratory, a first machine learning model using the portion of 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.” [0046], “trained model information is generated from a portion of the biological information, and the correct answer rate is obtained from the remaining portion.”); 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)”).
Zangenehpour in view of Hu 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 Zangenehpour in view of Hu 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID CHOI whose telephone number is (571)272-3931. The examiner can normally be reached M-Th: 8:30-5:30 ET.
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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