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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/01/2024, 06/14/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is considered by examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
Claim 13
An “input interface” is described as “wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof” (¶ [0072]) with the input described as “the diagnostic image is represented by a pulmonary CT image, and the lesion is represented by a pulmonary nodule” (¶ [0046]) and further described in prose in at least ¶ [0046]-[0047]).
A “machine learning algorithm” is described as element 1NN “a module of a computer program which can run on any suitable platform and which comprises program units to perform the steps of the inventive computer-implemented method to classify an imaged lesion, to identify blood markers suited to adjudicate the imaged lesion, and to output a blood test panel comprising the identified blood markers” [0014] and specifically includes specific blood markers (¶ [0024], [0025]), shown as 1NN in Figure 5 (training), Figure 6 (inference) and algorithm is described in prose in at least ¶ [0046]-[0058] (Fig 1-7), performed on a computer processing device (¶ [0074]).
An “output device” is described as element 3 of Figure 1, 5, 6 and described as “a blood test panel 3 comprising the set of blood markers 30 that is most likely to return a conclusive result regarding the malignancy (or not) of the imaged lesion” ¶ [0047], with the described output described in prose in at least ¶ [0050]-[0052] (Fig 2-4).
Under 35 U.S.C. § 112(f), the broadest reasonable interpretation of the claims each incorporate particular detailed computer processing operations that are considered an improvement upon existing technological processes and therefore are statutory eligible. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336-37, 118 USPQ2d 1684, 1689-90 (Fed. Cir. 2016) and MPEP § 2106(II).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, each are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101 – Non-statutory subject matter
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.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because data per se and/or computer programs do not fall into one of the four categories of statutory invention (machine, process, manufacture, composition). More specifically, claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability (i.e., laws of nature, natural phenomena, and abstract ideas). Alice Corp. v. CLS Bank Int'l, 573 U. S. 208 (2014).
Regarding claim 14, the claim is drawn towards a “computer program product.” The “computer program product” claimed by the applicant is described as “program units to perform the steps of the inventive computer-implemented method” (specification ¶ [0013]) and is understood to one of ordinary skill in the art as software (software, specification ¶ [0036], [0073], [0075]-[0076]). As described in MPEP § 2106, data per se and computer programs do not fall into one of the four statutory categories. Therefore, since claim 14 is drawn towards non-eligible subject matter, the claim is not eligible for patent protection.
Claim Rejections - 35 USC § 101 – Abstract Idea
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 1-12, 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a computer-implemented method for use in adjudicating an imaged lesion (the mind of a medical professional can process visual data, including an anatomical medical image with a lesion), the computer-implemented method (computer is claimed broadly and generically; see MPEP § 2106.05(a)) comprising:
obtaining a diagnostic image showing a lesion (considered pre-solution data gathering activity; see MPEP § 2106.05(g));
inputting the diagnostic image to a machine learning algorithm (“machine learning algorithm” is claimed broadly and generically; see MPEP § 2106.05(a)) previously trained to classify the lesion and to propose, based on a lesion class for the lesion, a blood test panel including markers suited to adjudicate the lesion (a medical professional can view an image and determine a particular set of biologic markers to run in a blood panel for further assessing a given lesion identified in the medical image); and
outputting the blood test panel to a user (the mind of the medical profession (user) can determine the given preferred test to further diagnose a given lesion; limitation can also be considered insignificant post-solution output data; see MPEP § 2106.05(g)).
Claim 2 recites the computer-implemented method according to claim 1 (as described above), wherein the blood test panel comprises markers including at least one of: circulating tumor DNA, circulating tumor cells, proteins, circulating RNA, metabolites, sugars, or exosomes (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed).
Claim 3 recites the computer-implemented method according to claim 1 (as described above), wherein the machine learning algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed when the lesion cannot be visually determined to represent a particular classification from visual representation).
Claim 4 recites the computer-implemented method according to claim 1 (as described above), wherein the blood test panel includes one or more of: an operating point along a receiver operating characteristic curve of the blood test panel, a decision threshold of a blood marker, or a weight of a blood marker (the medical professional can mentally remember certain features of particular biomarkers analyzed, such as commonly analyzed protein weights).
Claim 5 recites the computer-implemented method according to claim 1 (as described above), wherein the machine learning algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed and the best laboratory assay to perform the detection).
Claim 6 recites the computer-implemented method according to claim 5 (as described above), wherein the at least one laboratory analysis technique is: a next generation sequencing technique, a polymerase chain reaction technique, a mass spectrometry technique, an immunoassay technique, a fluorescence in-situ hybridization technique, or an electrochemical sensing technique (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed and the best laboratory assay to perform the detection).
Claim 7 recites the computer-implemented method according to claim 1 (as described above), 7. The computer-implemented method according to claim 1, wherein the machine learning algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed).
Claim 8 recites the computer-implemented method according to claim 7 (as described above), wherein a further biological marker is obtainable from a nasal swab, urine, a bronchial swab, or saliva (considered insignificant data gathering activity; see MPEP § 2106.05(g)).
Claim 9 recites the computer-implemented method according to claim 1 (as described above), wherein the diagnostic image is a computed tomography image (considered pre-solution data gathering activity; see MPEP § 2106.05(g)).
Claim 10 recites the computer-implemented method according to claim 1 (as described above), wherein the lesion is a pulmonary lesion, an adrenal lesion, a renal lesion, a hepatic lesion, a pancreatic lesion, or a mammary lesion (a medical professional can view an image and determine a particular set of biologic markers to run in a blood panel for further assessing a given lesion identified in the medical image).
Claim 11 recites the computer-implemented method according to claim 1 (as described above), wherein the machine learning algorithm is a two-step classifier configured to classify the lesion into one of a plurality of defined classes, identify a number of blood markers suited to adjudicate the lesion, and output the blood test panel including the blood markers (a medical professional can view a medical image to identify and classify a lesion (step 1) and determine a particular set of biologic markers to run in a blood panel for further assessing a given lesion identified in the medical image (step 2)).
Claim 12 recites a method of adjudicating an imaged lesion (the mind of a medical professional can process visual data, including an anatomical medical image with a lesion), the method comprising:
obtaining a diagnostic image showing a lesion (considered pre-solution data gathering activity; see MPEP § 2106.05(g));
processing the diagnostic image using the computer-implemented method according to claim 1 (as described above) and receiving the blood test panel (a medical professional can view an image and determine a particular set of biologic markers to run in a blood panel for further assessing a given lesion identified in the medical image); and
obtaining a blood sample and performing laboratory processing of the blood sample according to the blood test panel (considered insignificant post-solution activity based on the image data processing; see MPEP § 2106.05(g)).
Claim 14 recites a recites a non-transitory computer program product comprising instructions (computer is claimed broadly and generically; see MPEP § 2106.05(a)) which, when executed by a computer (computer is claimed broadly and generically; see MPEP § 2106.05(a)), cause the computer to carry out the computer-implemented method according to claim 1 (as described above).
Claim 15 recites a non-transitory computer-readable storage medium storing computer-executable instructions (computer is claimed broadly and generically; see MPEP § 2106.05(a)) that, when executed at a computer (computer is claimed broadly and generically; see MPEP § 2106.05(a)), cause the computer to carry out the computer-implemented method of claim 1 (as described above).
Claim 16 recites the computer-implemented method according to claim 2 (as described above), wherein the machine learning algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups (the medical professional will mentally consider options for what blood test panel biomarkers should be analyzed when the lesion cannot be visually determined to represent a particular classification from visual representation).
Claim 17 recites the computer-implemented method according to claim 16 (as described above), wherein the machine learning algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed and the best laboratory assay to perform the detection).
Claim 18 recites the computer-implemented method according to claim 16 (as described above), wherein the machine learning algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed).
Claim 19 recites the computer-implemented method according to claim 2 (as described above), wherein the machine learning algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed and the best laboratory assay to perform the detection).
Claim 20 recites the computer-implemented method according to claim 19 (as described above), wherein the machine learning algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion (the medical professional can mentally consider options for what blood test panel biomarkers should be analyzed).
Examiner Note regarding Claim 13, as interpreted under 35 U.S.C. § 112(f), the broadest reasonable interpretation of the claims each incorporate particular detailed computer processing operations that are considered an improvement upon existing technological processes and therefore are statutory eligible. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336-37, 118 USPQ2d 1684, 1689-90 (Fed. Cir. 2016) and MPEP § 2106(II).
The limitations of analyzing a lesion of a medical image using generically-claimed machine learning are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, regarding the method or apparatus, other than reciting generic placeholder-related computer components, such as machine learning or generically claimed computer components, nothing in the claim elements precludes the steps from practically being performed in the mind. For example in claim 1, language of “obtaining” in the context of the claims is a pre-solution data gathering activity; “inputting” is to analyze the image for a lesion and determining a next step diagnostic; and “outputting” is post-solution activity of outputting the proposed diagnostic. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, these claims each recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the method claims do not recite any elements which could not be performed in the mind and the claims only recite generic placeholder-related computer components, including machine learning and generic computer components. The computer components are recited at a high-level of generality (i.e., generic machine learning for performing a general function of lesion identification and classification, which is described with a high level of generality of automating a manual operation) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, the computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the aforementioned claims are directed to abstract ideas.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic placeholder-related computer components, the machine learning and computer, used to classify lesions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an invention concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3, 16-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 3, 16 each recite the limitation ““large effect size” and is identified as indefinite for failing to claim a limitation without precision such that one of ordinary skill in the art would not clearly understand the metes and bounds of the limitation.
The specification is not clear in defining what is a “large effect size” and guidance is not provided in the specification but rather broadly states “a small effect size” and “large effect size” (specification ¶ [0010], [0052]). However, without specific biomarkers claimed and by covering any type of lesion, a “large effect size” is indefinite for lacking a standard for the measure of “large effect size”.
According to MPEP § 2173.05(b), a claim is held indefinite when “the specification lacked some standard for measuring the degrees intended” and it is unclear that the claim limitation for a “large effect size” within the context of the claim and specification is clear to one of ordinary skill in the art. Therefore, claim 3, 16 are rejected as indefinite for the claim limitation “large effect size.”
Claim 17-18 are rejected as being dependent on claim 16.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 9-11, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Holzer et al (US 2021/0383174) in view of Jaber et al (US 2022/0375602) and official notice of facts.
Regarding Claim 1, Holzer et al teach a computer-implemented method for use in adjudicating an imaged lesion (AI system 100 application incorporated into a computer system (¶ [0028]) for image analysis of a medical image to detect lesion associated with anatomical structure with workflow 200; Fig 1, 2 and ¶ [0020]-[0020], [0028]), the computer-implemented method comprising:
obtaining a diagnostic image showing a lesion (a medical image 102 is segmented 104 and registered 106 as a normal image or potentially abnormal if a lesion is present; Fig 1 and ¶ [0021]);
inputting the diagnostic image to a machine learning algorithm previously trained to classify the lesion (the image 102 segmented 106 with the lesion is input to a lesion-specific classifier 110 (machine learning algorithm ¶ [0025]), the classifier 110 was trained to identify and classify the type of lesions; Fig 1 and ¶ [0023]-[0025], [0029]).
Holzer et al does not teach to propose, based on a lesion class for the lesion, a blood test panel including markers suited to adjudicate the lesion; and outputting the blood test panel to a user. Note Holzer et al teaches an adjudication algorithm 112 to take a variety of different actions based on the output from the lesion-specific classifier 110 (¶ [0024]).
Jaber et al is analogous art pertinent to the technological problem addressed in this application and teaches to propose, based on a lesion class for the lesion, a blood test panel (“blood test panel” described as a “single blood test” applicant’s specification ¶ [0008]) (the image 102 region of interest 702 is classified by the tumor analysis model and from the classification a recommended blood test is output; Fig 7 and ¶ [0095]); and outputting the blood test panel to a user (the blood tests recommended are determined based on the region of interest 702 and output for consideration to the individual (patient) 700 and/or healthcare provider; Fig 7 and ¶ [0095]-[0096]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Holzer et al with Jaber et al including to propose, based on a lesion class for the lesion, a blood test panel; and outputting the blood test panel to a user. By recommending a blood test in response to the medical image analysis performed by the classifier, the testing recommended is a less aggressive means to further characterize an image-diagnosed tumor (¶ [0095]) and such region of interest classifier analysis enable high-quality pathology results with reduced cost and time for medical diagnostic purposes, as recognized by Jaber et al (¶ [0106]).
Holzer et al in view of Jaber et al do not explicitly teach the blood test panel includes markers suited to adjudicate the lesion.
Official notice is taken as to the fact that it is well known a proposed blood test panel would include markers suited to adjudicate an identified lesion, such as a blood test that provide blood biomarkers for a more informative result to assist in answering a specific clinical question, as described by the applicant (applicant specification ¶ [0008]). One of ordinary skill in the art would recognize the benefits of using a blood test panel with markers suited to adjudicate a lesion identified with a patient’s medical image data. As recognized by Jaber et al, blood testing provide a means to further characterize a tumor identified in an image and allows for detailed biologic predictions, such as metastasis and tumor behavior and well as provide more informed medical treatment options (Jaber et al ¶ [0004]). Therefore, it would have been obvious for Jaber et al to propose the blood test panel includes markers suited to adjudicate the lesion (Jaber et al ¶ [0095]). It would have been obvious to modify Holzer et al by Jaber et al because Holzer et al teaches “an adjudication algorithm 112” which is used to determine what next steps are to be taken by a radiologist in response to the lesion-specific classifier response to the medical image (Holzer et al ¶ [0024]), including providing treatment, diagnosis and assessing diseases (Holzer, et al ¶ [0002]).
Regarding Claim 9, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), wherein the diagnostic image is a computed tomography image (Holzer et al, the medical image analyzed is a CT image; Fig 1 and ¶ [0015], [0021]).
Regarding Claim 10, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), wherein the lesion is a pulmonary lesion, an adrenal lesion, a renal lesion, a hepatic lesion, a pancreatic lesion, or a mammary lesion (Holzer et al, the medical image lesion may be associated with pulmonary lesions (lung cancer), blood ; ¶ [0003], [0020], [0022]).
Regarding Claim 11, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), wherein the machine learning algorithm is a two-step classifier (Holzer et al, the AI system 100 includes the lesion-specific classifier followed by an adjudication algorithm; Fig 1 and ¶ [0023]-[0024]) configured to classify the lesion into one of a plurality of defined classes (Holzer et al, the lesion-specific classifier classifies the lesion; Fig 1 and ¶ [0023]), identify a number of blood markers suited to adjudicate the lesion (Holzer et al, the adjudication algorithm 112 is to perform a given action based on the classifier 110 output; Fig 1 and ¶ [0024]); (Jaber et al teaches the recommended blood test output; Fig 7 and ¶ [0095] and notice of facts is given that a blood test is based on markers suited to further analyze the lesion), and output the blood test panel including the blood markers (Jaber et al, the recommended blood tests are output to the individual and/or healthcare provider; Fig 7 and ¶ [0095]-[0096]).
Regarding Claim 14, Holzer et al teach a non-transitory computer program product comprising instructions (AI system 100 incorporated into a computer system implementing a workflow queue based on computer program products to computer system 801; Fig 1-3 and ¶ [0028], [0036]), which, when executed by a computer (computer program products executed by processor; ¶ [0036]), cause the computer to carry out the computer-implemented method according to claim 1 (as described above).
Regarding Claim 15, Holzer et al teach a non-transitory computer-readable storage medium storing computer-executable instructions (AI system 100 incorporated into a computer system implementing a workflow queue with such instructions stored on a non-transitory computer-readable storage medium in the computer system 801; Fig 1-3 and ¶ [0028], [0036]) that, when executed at a computer (processor enables the computer system 801 to perform method of AI system; ¶ [0036]), cause the computer to carry out the computer-implemented method of claim 1 (as described above).
Claims 2, 4-8, 12, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Holzer et al (US 2021/0383174) in view of Jaber et al (US 2022/0375602), official notice of facts and Herath et al (The Role of Circulating Biomarkers in Lung Cancer).
Regarding Claim 2, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), including the blood test panel (Jaber et al, the image 102 region of interest 702 is classified by the tumor analysis model and from the classification a recommended blood test is output; Fig 7 and ¶ [0095]).
Holzer et al in view of Jaber et al and official notice of facts do not explicitly teach wherein the blood test panel comprises markers including at least one of: circulating tumor DNA, circulating tumor cells, proteins, circulating RNA, metabolites, sugars, or exosomes.
Herath et al is analogous art and teaches wherein the blood test panel comprises markers including at least one of: circulating tumor DNA, circulating tumor cells, proteins, circulating RNA, metabolites, sugars, or exosomes (note only one marker is required from conjunction “or”; a liquid biopsy (including a blood test panel) is used to detect tumor biomarkers, including circulating tumor DNA, micro RNA, exosomes and circulating tumor cells; Fig 1, 2 and Liquid Biopsy ¶ 1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to modify the teaching of Holzer et al in view of Jaber et al and official notice of facts with Herath et al including wherein the blood test panel comprises markers including at least one of: circulating tumor DNA, circulating tumor cells, proteins, circulating RNA, metabolites, sugars, or exosomes. By performing a liquid biopsy (blood test panel), early diagnosis with a higher sensitivity may be performed with a less-invasive and cost-effective process to improve the diagnosis accuracy and identify best-suited therapeutic treatments, as recognized by Herath et al (Liquid Biopsy ¶ 2).
Regarding Claim 4, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), including the blood test panel (Jaber et al, the image 102 region of interest 702 is classified by the tumor analysis model and from the classification a recommended blood test is output; Fig 7 and ¶ [0095]).
Holzer et al in view of Jaber et al and official notice of facts do not explicitly teach wherein the blood test panel includes one or more of: an operating point along a receiver operating characteristic curve of the blood test panel, a decision threshold of a blood marker, or a weight of a blood marker.
Herath et al is analogous art and teaches wherein the blood test panel includes one or more of: an operating point along a receiver operating characteristic curve of the blood test panel, a decision threshold of a blood marker, or a weight of a blood marker (note only one marker is required from conjunction “or”; a liquid biopsy (including a blood test panel) is used to detect tumor biomarkers, and tumor-derived DNA may be separated based on fragment size (weight of a blood marker); also noted Fig 2 describes protein expression and phosphorylation (Western blot assays based on weight of the marker); Fig 1, 2 and Liquid Biopsy ¶ 1-3, cfDNA and ctDNA ¶ 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to modify the teaching of Holzer et al in view of Jaber et al and official notice of facts with Herath et al including wherein the blood test panel includes one or more of: an operating point along a receiver operating characteristic curve of the blood test panel, a decision threshold of a blood marker, or a weight of a blood marker. By performing a liquid biopsy (blood test panel) and performing laboratory evaluations of biomarkers with established techniques, early diagnosis with a higher sensitivity may be performed with a less-invasive and cost-effective process resulting in improving the early diagnosis accuracy and identify best-suited therapeutic treatments with output from the assay, as recognized by Herath et al (Liquid Biopsy ¶ 2).
Regarding Claim 5, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), including the machine learning algorithm (Holzer et al, the adjudication algorithm 112 is to perform a given action based on the classifier 110 output; Fig 1 and ¶ [0024]).
Holzer et al in view of Jaber et al and official notice of facts do not explicitly teach the algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel.
Herath et al is analogous art and teaches wherein the blood test panel comprises markers including the algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel (a liquid biopsy (blood sample testing) for a given treatment strategy is determined based on the type of cancer detected (screening, localized, metastatic, refractory cancer); Fig 2 and Liquid Biopsy ¶ 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to modify the teaching of Holzer et al in view of Jaber et al and official notice of facts with Herath et al including the algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel. By performing a liquid biopsy (blood test panel) and performing laboratory evaluations of biomarkers with established techniques, early diagnosis with a higher sensitivity may be performed with a less-invasive and cost-effective process resulting in improving the early diagnosis accuracy and identify best-suited therapeutic treatments with output from the assay, as recognized by Herath et al (Liquid Biopsy ¶ 2).
Regarding Claim 6, Holzer et al in view of Jaber et al, official notice of facts and Herath et al teach the computer-implemented method according to claim 5 (as described above), wherein the at least one laboratory analysis technique is: a next generation sequencing technique, a polymerase chain reaction technique, a mass spectrometry technique, an immunoassay technique, a fluorescence in-situ hybridization technique, or an electrochemical sensing technique (Herath et al, PCR analysis is one assay that may be used for testing (Table 1); cfDNA and ctDNA ¶ 3).
Regarding Claim 7, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), including the machine learning algorithm (Holzer et al, the adjudication algorithm 112 is to perform a given action based on the classifier 110 output; Fig 1 and ¶ [0024]).
Holzer et al in view of Jaber et al and official notice of facts do not explicitly teach the algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion.
Herath et al is analogous art and teaches wherein the blood test panel comprises markers including the algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion (the liquid biopsy (including a blood test panel) is used to detect tumor biomarkers, with a number of target biomarkers; Fig 1, 2 and Liquid Biopsy ¶ 1-3, cfDNA and ctDNA ¶ 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to modify the teaching of Holzer et al in view of Jaber et al and official notice of facts with Herath et al including identify a number of further biological markers to assist in adjudicating the lesion. By performing a liquid biopsy (blood test panel) and performing laboratory evaluations of multiple biomarkers, early diagnosis with a higher sensitivity may be performed with a less-invasive and cost-effective process resulting in improving the early diagnosis accuracy and identifying best-suited therapeutic treatments from the assay output, as recognized by Herath et al (Liquid Biopsy ¶ 2).
Regarding Claim 8, Holzer et al in view of Jaber et al, official notice of facts and Herath et al teach the computer-implemented method according to claim 7 (as described above), wherein a further biological marker is obtainable from a nasal swab, urine, a bronchial swab, or saliva (liquid biopsy is a body fluid to perform biomarker screening, including sample types including all body fluids for exosomes (Table 1); Exosomes, Circulating miRAs).
Regarding Claim 12, Holzer et al teach a method of adjudicating an imaged lesion (method of using an AI system 100 for image analysis of a medical image to detect lesion associated with anatomical structure with workflow 200; Fig 1, 2 and ¶ [0007], [0020]-[0020], [0028]), the method comprising:
obtaining a diagnostic image showing a lesion (a medical image 102 is segmented 104 and registered 106 as a normal image or potentially abnormal if a lesion is present; Fig 1 and ¶ [0021]); and
processing the diagnostic image using the computer-implemented method according to claim 1 (as described above by Holzer et al in view of Jaber et al and official notice of facts).
Holzer et al does not teach receiving the blood test panel; and obtaining a blood sample and performing laboratory processing of the blood sample according to the blood test panel.
Jaber et al is analogous art pertinent to the technological problem addressed in this application and teaches a blood test panel (“blood test panel” described as a “single blood test” applicant’s specification ¶ [0008]) (the image 102 region of interest 702 is classified by the tumor analysis model and from the classification a recommended blood test is output; Fig 7 and ¶ [0095]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Holzer et al with Jaber et al including receiving the blood test panel. By recommending a blood test in response to the medical image analysis performed by the classifier, the testing recommended is a less aggressive means to further characterize an image-diagnosed tumor (¶ [0095]) and such region of interest classifier analysis enable high-quality pathology results with reduced cost and time for medical diagnostic purposes, as recognized by Jaber et al (¶ [0106]).
Holzer et al in view of Jaber et al and official notice of facts do not explicitly teach obtaining a blood sample and performing laboratory processing of the blood sample according to the blood test panel.
Herath et al is analogous art and teaches obtaining a blood sample and performing laboratory processing of the blood sample according to the blood test panel (a liquid biopsy (including a blood test panel) is used to detect tumor biomarkers, including circulating tumor DNA, micro RNA, exosomes and circulating tumor cells based on an appropriate assay (Table 1); Fig 1, 2 and Liquid Biopsy ¶ 1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to modify the teaching of Holzer et al in view of Jaber et al and official notice of facts with Herath et al including obtaining a blood sample and performing laboratory processing of the blood sample according to the blood test panel. By performing a liquid biopsy (blood test panel), early diagnosis with a higher sensitivity may be performed with a less-invasive and cost-effective process to improve the diagnosis accuracy and identify best-suited therapeutic treatments, as recognized by Herath et al (Liquid Biopsy ¶ 2).
Regarding Claim 19, Holzer et al in view of Jaber et al, official notice of facts and Herath et al teach the computer-implemented method according to claim 2 (as described above), wherein the machine learning algorithm (Holzer et al, the adjudication algorithm 112 is to perform a given action based on the classifier 110 output; Fig 1 and ¶ [0024]) is trained to identify at least one laboratory analysis technique for analysis of the blood test panel (Herath et al, a liquid biopsy (blood sample testing) for a given treatment strategy is determined based on the type of cancer detected (screening, localized, metastatic, refractory cancer); Fig 2 and Liquid Biopsy ¶ 3).
Regarding Claim 20, Holzer et al in view of Jaber et al, official notice of facts and Herath et al teach the computer-implemented method according to claim 19 (as described above), wherein the machine learning algorithm (Holzer et al, the adjudication algorithm 112 is to perform a given action based on the classifier 110 output; Fig 1 and ¶ [0024]) is trained to identify a number of further biological markers to assist in adjudicating the lesion (Herath et al, the liquid biopsy (including a blood test panel) is used to detect tumor biomarkers, with a number of target biomarkers; Fig 1, 2 and Liquid Biopsy ¶ 1-3, cfDNA and ctDNA ¶ 4).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Holzer et al (US 2021/0383174) in view of Jaber et al (US 2022/0375602), official notice of facts and Cohen et al (US 2021/0256323).
Regarding Claim 3, Holzer et al in view of Jaber et al and official notice of facts teach the computer-implemented method according to claim 1 (as described above), including the machine learning algorithm (Holzer et al, the adjudication algorithm 112 is to perform a given action based on the classifier 110 output; Fig 1 and ¶ [0024]).
Holzer et al in view of Jaber et al and official notice of facts do not explicitly teach the algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups.
Cohen et al is analogous art and teaches wherein the blood test panel comprises markers including the algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups (the machine learning model screening for radiographic pulmonary nodules (NN10 Fig 1B ¶ [0265]-[0266]) determines risk level and uses a categorization table or threshold value to determine risk and incorporates lung cancer biomarker panels (NN9 Fig 1B and ¶ [0264]) to distinguish benign from cancerous nodules in the radiographic data; Fig 1B and ¶ [0147]-[0151], [0157], [0235], [0265]-[0266]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to modify the teaching of Holzer et al in view of Jaber et al and official notice of facts with Cohen et al including the algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups. By utilizing biomarker data in addition to image data, and additional patient data, a complete analysis can be performed by the machine learning model to assess relevant grouping patterns and create a complete analysis in determining the risk of cancer and providing the best suggested approach for follow-up diagnostic testing and treatment, as recognized by Cohen et al (¶ [0003]-[0007]).
Claims 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Holzer et al (US 2021/0383174) in view of Jaber et al (US 2022/0375602), official notice of facts, Herath et al (The Role of Circulating Biomarkers in Lung Cancer) and Cohen et al (US 2021/0256323).
Regarding Claim 16, Holzer et al in view of Jaber et al, official notice of facts and Herath et al teach the computer-implemented method according to claim 2 (as described above), including the machine learning algorithm (Holzer et al, the adjudication algorithm 112 is to perform a given action based on the classifier 110 output; Fig 1 and ¶ [0024]).
Holzer et al in view of Jaber et al, official notice of facts and Herath et al do not explicitly teach the algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups.
Cohen et al is analogous art and teaches wherein the blood test panel comprises markers including the algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups (the machine learning model screening for radiographic pulmonary nodules (NN10 Fig 1B ¶ [0265]-[0266]) determines risk level and uses a categorization table or threshold value to determine risk and incorporates lung cancer biomarker panels (NN9 Fig 1B and ¶ [0264]) to distinguish benign from cancerous nodules in the radiographic data; Fig 1B and ¶ [0147]-[0151], [0157], [0235], [0265]-[0266]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to modify the teaching of Holzer et al in view of Jaber et al and official notice of facts and Herath et al with Cohen et al including the algorithm is trained to estimate a malignancy risk of the lesion, and to include at least one marker with a large effect size in the blood test panel when the malignancy risk is at a threshold between two risk groups. By utilizing biomarker data in addition to image data, and additional patient data, a complete analysis can be performed by the machine learning model to assess relevant grouping patterns and create a complete analysis in determining the risk of cancer and providing the best suggested approach for follow-up diagnostic testing and treatment, as recognized by Cohen et al (¶ [0003]-[0007]).
Regarding Claim 17, Holzer et al in view of Jaber et al, official notice of facts, Herath et al and Cohen et al teach the computer-implemented method according to claim 16 (as described above), wherein the machine learning algorithm is trained to identify at least one laboratory analysis technique for analysis of the blood test panel (Cohen et al, the biomarker analysis performs a biomarker velocity 145 to determine the biomarker value relative to time; Fig 1B and ¶ [0262], [0264]).
Regarding Claim 18, Holzer et al in view of Jaber et al, official notice of facts, Herath et al and Cohen et al teach the computer-implemented method according to claim 16 (as described above), wherein the machine learning algorithm is trained to identify a number of further biological markers to assist in adjudicating the lesion (Cohen et al, biomarker panel includes multiple marker types and the related velocity score to determine the presence of cancer; Fig 1B and ¶ [0264]).
Allowable Subject Matter
Claim 13 allowed based on the interpretation under 35 U.S.C. § 112(f) and in light of not identifying the entirety of the claimed invention found, as claimed by applicant, in the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cohen et al (US 2023/0223145) teach a classifier model for classifying a patient lesion image for a malignancy class and testing time ranges for follow-up to the given classification.
Dobak et al (US 2021/0330245) teach a classifier model for classifying lesions on skin to assist a physician with skin cancer detection and suggested telemedicine solution.
Li et al (Machine Learning for Lung Cancer Diagnosis, Treatment and Prognosis) teach the current state of the art for imaging and sequencing practices that combine machine learning, image processing and biomarker analysis for the identification and classification of lung cancer.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661