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 .
Claims 7-19 are pending and under consideration. Claims 1-6 were canceled by preliminary amendment. Claims 13-19 were added by preliminary amendment.
This application is a National Stage Application filed under 37 CFR 371, of KR2021/017030, 11/18/2021, which claimed priority to two KR applications. Copies of the KR priority documents have been provided by the IB. The effective filing date for the examined claims appears to be the English Language document of KR-10-2020-159098, filed 24 November 2020. It is noted that no certified translation of KR-10-2021-0156076, filed 6 December 2021, is of record. The Examiner has reviewed all filed PCT – related documents.
This application has published as US PG-Pub 2024/0011996 A1.
The preliminary amendment, filed 5/4/2023, to the specification and claims has been entered.
The Drawings as filed are suitable for examination.
Two IDS forms have been entered and considered.
Claim Objections
Claim 14 is objected to because of the following informalities: Claim 14 does not end with a period. Appropriate correction is required.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) 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.
In claim 11, “wherein the machine learning algorithm model is learned by…” is interpreted as “wherein the machine learning algorithm is trained by…”
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 7-19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, the claims are drawn to statutory categories: Processes.
With respect to step (2A) (1): YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
7. (Currently Amended) A method of diagnosing prostate cancer, the method comprising
measuring, in a biological sample isolated from a subject, an expression level of one or more proteins selected from the group consisting of ANXA3 (annexin A3), PSMA (prostate-specific membrane antigen), ERG (erythroblast transformation-specific related gene protein), and ENG (endoglin), or genes encoding the same.
(EIA: measuring expression levels is a step of data gathering. JE: the claim recites a natural law: the naturally occurring correlation between the naturally occurring genetic information/encoded protein information of an individual, and the naturally occurring phenotype of prostate cancer. MPEP 2106.04(b).)
8. (Original) The method of claim 7, further comprising: measuring, in a biological sample isolated from a control group, an expression level of one or more proteins selected from the group consisting of ANXA3, PSMA, ERG, and ENG, and genes encoding the same; and comparing the expression levels of the subject and the control group.
(EIA: measuring control group data is a step of data gathering. Comparing expression levels is a Mental Process of observing the levels of each group, and making a judgement or conclusion about any differences. Alternatively, the mathematic concept of one data value being greater than, or less than another. MPEP 2106.04(a)(2).)
9. (Currently Amended) The method of claim 8, further comprising, when the expression level of the subject is higher or lower than the control group, determining the subject as having developed prostate cancer or predicting risk of developing prostate cancer at a high level.
(Mental Process of observing whether the level is higher or lower than a control level, and making a judgement as to whether cancer is present, or a mathematic concept of calculating a degree of risk of developing cancer. MPEP 2106.04(a)(2).)
10. (Original) The method of claim 7, further comprising applying the measured expression level of the proteins or the genes encoding the same to a machine learning algorithm model.
(Mathematic concept of applying the expression level values as inputs to a generically stated ML model. The ML Model is an element in addition to the JE, however it more of a field of use type limitation. The ML is generically stated, with no details as to how it was trained, or how it acts on the measured data to determine the desired results. MPEP 2106.04(a)(2); MPEP 2106.05(h).)
11. (Original) The method of claim 10, wherein the machine learning algorithm model is learned by setting, as input values, 1) an expression level of one or more proteins selected from the group consisting of ANXA3, PSMA, ERG, and ENG, or genes encoding the same, in a prostate cancer patient and 2) an expression level of one or more proteins selected from the group consisting of ANXA3, PSMA, ERG, and ENG, and genes encoding the same, in a control group.
(Mathematic concept of training the ML, spelling out two inputs. The nature of the ML is unlimited, and the claim does not set forth how the trained ML works to determine the desired results. MPEP 2106.04(a)(2).)
12. (Original) The method of claim 10, wherein the applying to the machine learning algorithm model comprises inputting the expression level of the proteins or the genes encoding the same measured in the subject to the machine learning algorithm model to output, as an output value, whether the subject has developed prostate cancer or is at risk of developing prostate cancer.
(Mathematic concept of training the ML, spelling out inputs and a desired output. The nature of the ML is unlimited, and the claim does not set forth how the trained ML works to determine the desired results. MPEP 2106.04(a)(2).)
13. (New) The method of claim 7, the method comprising measuring, in a biological sample isolated from a subject, an expression level of ERG and ENG, or genes encoding the same.
(EIA- a step of data gathering. MPEP 2106.05(g).)
14. (New) The method of claim 7, the method not comprising measuring an expression level of ANXA3 protein or gene encoding the same, to improve diagnosis accuracy
(EIA- a negative limitation excluding specific data, related to data gathering. “To improve diagnosis accuracy” is an intended use of the exclusion. MPEP 2106.05(g).)
15. (New) The method of claim 7, the method is performed using an electrochemical biosensor.
(EIA: a biosensor is used to gather the data. The biosensor is generically stated, and represents a field of use limitation. MPEP 2106.05(h).)
16. (New) The method of claim 12, wherein an input value entered into the model is obtained by quantifying the expression level of the one or more proteins selected from the group consisting of ANXA3, PSMA, ERG, and ENG, or the genes encoding the same, and wherein the input value is a voltage shift value measured by using the electrochemical biosensor.
(EIA- data gathering limitations, and a mathematic concept of “quantifying” the measured data. MPEP 2106.04(a)(2), MPEP 2106.05(g).)
17. (New) The method of claim 12, wherein an output value outputted from the model is a result of determining whether the subject has developed prostate cancer or is at risk of developing prostate cancer, wherein the output value may be outputted as a predictor value expressed as a number between 0 and 1, wherein the predictor value is 0.5 or more, the subject may be determined to have developed prostate cancer or be at high risk of developing prostate cancer, and wherein the predictor value is less than 0.5, the subject may be determined to have not developed prostate cancer or be at low risk of developing prostate cancer.
(Mathematic concept of calculating predictor values, and comparing those values to certain thresholds. MPEP 2106.04(a)(2).)
18. (New) The method of claim 17, wherein the predictor value is closer to 0 or 1, the certainty of the algorithm prediction is increased.
(Mathematic concept of calculating predictor values, and comparing those values to certain thresholds. MPEP 2106.04(a)(2).)
19. (New) The method of claim 18, wherein the machine learning algorithm model is learned by setting, as output values, whether prostate cancer is development in the prostate cancer patient group and the control group as previously inputted.
(Mathematic concept of describing the calculated output values of the trained model. MPEP 2106.04(a)(2).)
Natural law embraced by claim(s) 7-19:
The claims embrace the naturally occurring correlations between naturally occurring changes in genetic or protein material in an individual, and a naturally occurring phenotype of prostate cancer. MPEP 2106.04(b).
With respect to step 2A (2): NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)):
“Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).”
Claim(s) 7, 8, 13-14 recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: using biomarker data to diagnose cancer. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Claim(s) 10, 15, 16 recite the additional non-abstract elements (EIA) of:
1) a machine learning model
2) electrochemical biosensors
The machine learning model which carries out the JE does not provide details about a particular ML, it is used generally, covering every mode of implementing the JE using ML. It does not provide details about how the ML operates to diagnose prostate cancer. The “trained ML” provides nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)). The limitations “to diagnose” or to “determine risk” describe desired outcomes without setting forth how to actually achieve those outcomes. The claim omits any details as to how the ML solves a technical problem, and instead recites only the idea of a solution or outcome. Also, the claim invokes a generic ML merely as a tool for making the recited mathematical calculation rather than purporting to improve the technology or a computer. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to apply the judicial exception on a computer.
The claimed biosensors are described at a high level of generality, and are used to generate the data values in the data gathering steps. The biosensors do not carry out the JE, nor do they affect how the JE is carried out. The biosensors represent a generic tool for performing extra-solution activity: data gathering. MPEP 2106.05(b, g, h).
The biosensor elements of the claims do not provide improvements to the functioning of a computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The biosensor elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The biosensor elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Dependent claim(s) 8-12, 16-19 recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 7, 8, 13, 14: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
The EIA of data gathering requires measuring the expression level of one or more of four named genes or their encoded proteins:
Clarke (US 2007/0099209 A1) discloses measuring gene expression levels of ANXA3 (Table 4); PSMA (Table 4), ERG (Table 4), and ENG (Table 4), for use in diagnosing cancers, including prostate cancer [0023].
Bowden (US 10,900,086 B1) discloses measuring gene expression levels of ANXA3 (Table 88), PSMA (col 348, col 2), ERG (Table 0086) and ENG (Table 88) for use in diagnosing prostate cancer (title).
McClelland (US 2011/0236903 A1) discloses measuring gene expression levels of ANXA3 (Table 34), PSMA (Table 18), ERG (Table 8A) and ENG (Table 35) for use in diagnosis and prognosis of prostate cancer (title).
Kang (Kang et al. (2015) Diagnosis of prostate cancer via nanotechnological approach. International Journal of Nanomedicine, vol 10, p6555-6569.) discloses use of ANXA3, PSMA and endoglin gene expression information (p6562, and Table 2) to diagnose prostate cancer.
Velonas (Velonas et al. (2013) Current status of biomarkers for prostate cancer. International Journal of Molecular Sciences, vol 14, p11034-11060.) discloses the of one or more of: TMPRSS2-ERG a fusion protein of the ENG gene (Table 1), Endoglin (CD105), PSMA, and ANXA3 (all in Table 2), for the diagnosis or prognosis of prostate cancer.
Spetzler (US 2014/0141986 A1) provides trained machine learning models for the diagnosis of prostate cancer, which use as input, gene expression information of at least PSMA (FOLH1), TMPRSS2-ERG, ERG, endoglin (CD105) and AXNA3.
Davincioni (US 10,513,737 B2) provides trained ML models for diagnosis of cancer, including prostate cancer, using gene expression information of at least ANXA3, PSMA (FOLH1), and ERG.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide an inventive concept. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.)
The data gathering steps constitute a general link to a technological environment: the gene expression levels are intended to be applied to prostate cancer diagnosis. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
Therefore, simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claim(s) 10, 15-16: the limitations identified above as non-abstract elements (EIA) of: 1) a machine learning model and 2) electrochemical biosensors.
With respect to the machine learning model, or the trained model:
Spetzler (US 2014/0141986 A1) provides trained machine learning models for the diagnosis of prostate cancer, which use as input, gene expression information of at least PSMA (FOLH1), TMPRSS2-ERG, ERG, endoglin (CD105) and AXNA3.
Brown (US 2016/0041153 A1) provides trained ML models for diagnosing diseases, including prostate cancer, using gene expression information of at least ERG, PSMA.
Davincioni (US 10,513,737 B2) provides trained ML models for diagnosis of cancer, including prostate cancer, using gene expression information of at least ANXA3, PSMA (FOLH1), and ERG.
Schettini (US 2015/0301058 A1) discloses trained ML models for diagnosing cancers such as prostate cancer, using gene expression information of at least PSMA (FOLH1), Endoglin (CD-105), ERG, and ANXA3.
Dittamore (US 10,527,624 B2) discloses trained ML models for diagnosing prostate cancer, using gene expression information of ERG.
Klee (US 10,407,731 B2) discloses trained ML models for diagnosing and prognosing prostate cancer, using gene expression information of at least one of PSMA, and ERG.
With respect to the biosensors:
Clarke (US 2007/0099209 A1) discloses measuring gene expression levels of biomarkers, using sensors, biosensors, or electrochemical signaling [0282], for at least one of: ANXA3 (Table 4); PSMA (Table 4), ERG (Table 4), and ENG (Table 4), for use in diagnosing cancers, including prostate cancer [0023].
Brown (US 2016/0041153 A1) provides trained ML models for diagnosing diseases, including prostate cancer, using gene expression information of at least ERG, PSMA, measured using sensors, biosensors, or electrochemical signaling [0483].
Davincioni (US 10,513,737 B2) provides trained ML models for diagnosis of cancer, including prostate cancer, using gene expression information of at least ANXA3, PSMA (FOLH1), and ERG, measured by biosensors, sensors, or electrochemical signaling.
McClelland (US 2011/0236903 A1) discloses measuring gene expression levels of ANXA3 (Table 34), PSMA (Table 18), ERG (Table 8A) and ENG (Table 35), by use of biosensors, for the diagnosis and prognosis of prostate cancer (title).
Spetzler (US 2014/0141986 A1) provides trained machine learning models for the diagnosis of prostate cancer, which use as input, gene expression information of at least PSMA (FOLH1), TMPRSS2-ERG, ERG, endoglin (CD105) and AXNA3, measured by electrochemical signaling, sensors or biosensors [0872, 0878].
Schettini (US 2015/0301058 A1) discloses trained ML models for diagnosing cancers such as prostate cancer, using gene expression information of at least PSMA (FOLH1), Endoglin (CD-105), ERG, and ANXA3, measured using electrochemical signaling, biosensors or sensors [0535, 0540].
As such, the prior art recognizes that these elements are routine, well understood and conventional in the art.
The claims do not provide any details of how specific structures of the elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int’l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
Dependent claim(s) 8-12, 16-19 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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 7-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. The omitted steps are: in claim 7, no steps of comparing the measured levels of the biomarkers to any positive or negative reference data are provided, no error estimates or validation are recited, and no steps which provide the diagnosis of prostate cancer are recited. The subject is not limited to subjects susceptible to prostate cancer (i.e. males with a prostate). Merely measuring a gene / protein expression level, without any normalization, comparison, and analytics fails to provide the necessary and sufficient positive active method steps required to achieve a prostate cancer diagnosis.
While claim 8 provides some comparison to a “control group” it is unclear if this is a healthy (negative) control group, or a Prostate Cancer (positive) control group. Further, there are no consequences of the comparison to the control group: i.e. if the levels are higher than the control, then cancer, but if the levels are lower than the control, then no cancer. This fails to provide the necessary and sufficient steps to diagnose prostate cancer. This applies also to claim 11.
Claim 9 attempts to provide consequences, however it has been amended to cover both sides, wherein if “the expression level… is higher or lower than the control group…” which fails to provide the required steps for diagnosis of cancer.
Claims 10 and 12 attempt to provide applying the data to a generic ML model, however how the model acts is not set forth, nor are any steps explaining how the diagnosis of prostate cancer is achieved. Claims 10 and 12 fail to provide any reference information, normalization, or analytics necessary to diagnose prostate cancer.
In claim 13, no steps of comparing the measured levels of the biomarkers to any positive or negative reference data are provided, no error estimates or validation are recited, and no steps which provide the diagnosis of prostate cancer are recited. The subject is not limited to subjects susceptible to prostate cancer (i.e. males with a prostate). Merely measuring a gene / protein expression level, without any normalization, comparison, and analytics fails to provide the necessary and sufficient positive active method steps required to achieve a prostate cancer diagnosis.
In claim 14, the negative limitation excluding ANXA3 measurement still fails to provide steps of comparing the measured levels of the biomarkers to any positive or negative reference data, no error estimates or validation are recited, and no steps which provide the diagnosis of prostate cancer are recited. The subject is not limited to subjects susceptible to prostate cancer (i.e. males with a prostate). Merely measuring a gene / protein expression level, without any normalization, comparison, and analytics fails to provide the necessary and sufficient positive active method steps required to achieve a prostate cancer diagnosis.
Claims 15-16 add biosensors, without providing any other required step, as set forth for claims 7, 13, and 14.
Claims 17-19 provide details about analysis of the output predictor value, without providing the necessary and sufficient steps to diagnose prostate cancer. Claim 17 depends from claim 12, as analyzed above.
Claims 7-19 are rejected on the basis that it contains an improper Markush grouping of alternatives. See In re Harnisch, 631 F.2d 716, 721-22 (CCPA 1980) and Ex parte Hozumi, 3 USPQ2d 1059, 1060 (Bd. Pat. App. & Int. 1984). A Markush grouping is proper if the alternatives defined by the Markush group (i.e., alternatives from which a selection is to be made in the context of a combination or process, or alternative chemical compounds as a whole) share a “single structural similarity” and a common use. A Markush grouping meets these requirements in two situations. First, a Markush grouping is proper if the alternatives are all members of the same recognized physical or chemical class or the same art-recognized class, and are disclosed in the specification or known in the art to be functionally equivalent and have a common use. Second, where a Markush grouping describes alternative chemical compounds, whether by words or chemical formulas, and the alternatives do not belong to a recognized class as set forth above, the members of the Markush grouping may be considered to share a “single structural similarity” and common use where the alternatives share both a substantial structural feature and a common use that flows from the substantial structural feature. See MPEP § 2117.
The Markush grouping of “proteins… or genes encoding the same” is improper because the alternatives defined by the Markush grouping do not share both a single structural similarity and a common use for the following reasons: the elements to be measured, proteins or “genes” are different types of chemical molecules overall, each with differing chemical structures, biochemical attributes and biochemical activities. Proteins are polypeptides, made up of amino acids. “genes” generally refers to the genomic DNA sequence of an open reading frame, and DNA is made up of nucleotides. Polypeptides and polynucleotides are separate chemical classes, each separately classified, and each separately treated in the prior art.
Reagents that can measure a protein level, such as antibodies, do not share both a single structural similarity and a common use with reagents that can measure a level of an mRNA produced by transcription of a gene, such as a polynucleotide probe.
To overcome this rejection, Applicant may set forth each alternative (or grouping of patentably indistinct alternatives) within an improper Markush grouping in a series of independent or dependent claims and/or present convincing arguments that the group members recited in the alternative within a single claim in fact share a single structural similarity as well as a common use.
Claims 7-19 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.
The metes and bounds of claims 7-9 are unclear with respect to the how the diagnosis is made using the recited protein/gene expression levels. It is unclear if, as a group all levels are expected to be higher in a prostate cancer patient; if the mere increase in the level of one of the listed proteins is sufficient to diagnose prostate cancer; if as a group all levels are expected to be lower in a prostate cancer group, or if the mere decrease in the level of one biomarker is sufficient to diagnose prostate cancer. This claim is particularly unclear in view of the rejection above regarding missing essential steps.
Claims 10-12, 17-19 fail to particularly point out and distinctly claim how the generically recited machine learning model was trained, on particular reference data, and how the trained model acts on the data provided to make the specific diagnosis of prostate cancer, or “at risk of developing prostate cancer.” The claims fail to set forth the training data (positive and/or negative controls), normalization, the particular ML structure, and how, specifically, the model acts on the test data after training. There is no differential analysis between the diagnosis of prostate cancer, and steps which specifically calculate the risk level for developing prostate cancer. These would appear to require differing steps in the modeling and analysis of the output. In claim 17-19 it is unclear how the model of claim 12 outputs a “predictor value”.
The metes and bounds of claim 14 are entirely unclear. Claim 14 excludes the measurement of ANXA3 to “improve diagnosis accuracy” without any explanation or positive active method steps by which the accuracy is calculated or improved. No accuracy limitations are present in any preceding claim. It is entirely unclear how to carry out this dependent claim if ANXA3 was the one protein measured from claim 7, which then is excluded from the analysis.
In claim 19, the phrase “wherein the machine learning algorithm model is learned by setting as output values whether prostate cancer is development in the prostate cancer group and the control group” is entirely unclear. The phrase is grammatically incorrect, and the examiner is unclear how to interpret this limitation. It would appear to possibly indicate the use of positive and negative control information; however, these limitations cannot be read into the claim.
Further in claim 19, the metes and bounds of “as previously inputted” is entirely unclear, with respect to how it further modifies the ML model of claim 18, which already contains all the limitations of claims 17, 12, 10, and 7.
OVERALL: Applicant is strongly encouraged to review the specification, and what Applicant considers their invention, to provide amendments to the claims which provide the necessary and sufficient positive active method steps to achieve the desired results.
Claims 7-19 are rejected under 35 U.S.C. 112, first paragraph, because the specification, while being enabling for methods of measuring all 4 named biomarkers (ANXA3, PSMA, ENG, ERG), in urine samples, from individuals at risk for prostate cancer, using electrochemical biosensors, applying the levels to a machine learning model comprising a random forest model or a neural network, to diagnose prostate cancer, does not enable use of any single expression level alone (without control groups, ML modeling or other elements) to diagnose prostate cancer or calculate a specific risk of developing prostate cancer in all subjects, with all sample types. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make or use the invention commensurate in scope with these claims.
In In re Wands (8 USPQ2d 1400 (CAFC 1988)) the CAFC considered the issue of enablement in molecular biology. The CAFC summarized eight factors to be considered in a determination of "undue experimentation". These factors include: (a) the quantity of experimentation necessary; (b) the amount of direction or guidance presented; (c) the presence or absence of working examples; (d) the nature of the invention; (e) the state of the prior art; (f) the relative skill of those in the art; (g) the predictability of the art; and (h) the breadth of the claims.
In considering the factors for the instant claims:
a) In order to practice the claimed invention one of skill in the art must make a definitive diagnosis of prostate cancer based solely on measuring one named biomarker level in an unspecified sample, in an unspecified subject. For the reasons discussed below, there would be an unpredictable amount of experimentation required to practice the claimed invention.
b), c) The specification provides background and historical information about prostate cancer, including affected individuals, standards for diagnosis including biopsies, and the use of PSA (prostate-specific antigen) (p1-2). The specification points out the problematic nature of the single biomarker indicator PSA as set forth at page 2. PSA cannot be considered diagnostic on its own for the presence of prostate cancer: it is an indicator that can drive further medical procedures such as biopsy, MRI, or ultrasound, which then lead to a diagnosis. The specification summarizes aspects of the invention at pages 2-3, and various definitions at pages 3-14. A description of the drawings is provided at pages 14-16.
The specification provides working examples for the detection of the four named biomarkers in urine samples of individuals at risk for prostate cancer beginning at page 16. The biosensors (DGFET) are required to provide the technical solution to the issue that the concentration of biomarkers in urine can be very low. Figure 1 and its description at page 17 exemplify the measurement of all four biomarkers, using DGFET, in urine samples of patients at risk for prostate cancer. The measured expression levels of all four biomarkers were input into a “trained ML” algorithm.
Examples 1 and 2, pages 17-18 are directed to the biosensor manufacture, and use for measuring.
Example 3, p19, begins to discuss the analysis of the measurements. Two separate machine learning embodiments are disclosed: a random forest model and a neural network.
“In the present disclosure, RF decision trees and feedforward neural networks were used for the NN algorithm. RF consists of 80 random decision trees with binary classifications for cancer and normal cases, and majority-voted results are collected for all biomarker panels. In the case of NN, a feedfoward neural network with three hidden layers of three nodes was used. The NN model was implemented by using Keras with a TensorFlow framework. To train these two algorithms, a supervised learning method was used, wherein the algorithms were repeatedly trained by randomly assigning 70 % of the total dataset.”
Test data is then applied to the trained ML to output a “predictor value.”
“To evalute the signal combinations and generate the predictor values, the voltage shifts from the four biomarker sensing channels were input into both RF and NN algorithms. When calculating the accuracy, the predictor value of 0.5 was used as a threshold value (0.5 for cancer, <0.5 for normal). Each urine sample was measured three times, and an average of the measured predictor values was used to conclude a final decision on the PCa screening.”
Experimental example 1 begins at the bottom of page 19. Experimental example 1 is directed to determining whether the four named proteins can be detected by the DGFET sensors.
Experimental example 2, p 20, is directed to whether the four named proteins can be detected in urine samples by the DGFET sensors.
Experimental example 3 measures PSA (not one of the four named proteins) and prostate volume in three groups of patients at risk of developing prostate cancer: normal individuals, including individuals with benign prostatic hyperplasia, pre-DRE patients, and post-DRE patients. Table 1 summarizes the characteristics of the subjects.
Experimental example 4-1, p22 is directed to how well the biosensor detects each individual named protein in urine samples from the patients. ANXA3 had low accuracy in the association with prostate cancer, while pre-or post-DRE, PSMA, ERG and ENG were accurately sensed.
Experimental example 4-2, p23, attempts to provide the specificity, selectivity and accuracy of the use of each individual named protein in the diagnosis of prostate cancer. ANXA3 had poor sensitivity and specificity as well as poor accuracy. PSMA, ERG and ENG had acceptable specificity and accuracy. At page 24 the specification states that the use of only a single biomarker to diagnose missed the presence of prostate cancer in “about half” the tested patients with prostate cancer.
“In the experimental results, the high level of specificity was resulted, but about half of the patients with PCa were missed. That is, the current single biomarker test can minimize unnecessary biopsies, but due to a high rate of false negatives, a significant portion of the patients with PCa may be missed. Therefore, there is a need to establish a strategy to increase the specificity without damaging the sensitivity.”
This is an indication that no single biomarker expression level is sufficient to provide the specific and selective diagnosis of prostate cancer.
Experimental example 4-3, p24, establishes combinations of the four named biomarkers, and their correlative value. This example notes that all four biomarkers “properly blended” provide the best results.
Experimental example 5, beginning at the bottom of p24, applies raw DGFET data to either a random forest algorithm, or a neural network. Each network was trained on 70% of the experimental data, and tested on the remaining 30%. The type of learning is supervised learning. The designed output was binary: either cancer was, or was not present. “both algorithms learn the raw data of the DGFET to consider even minor characteristics caused by urine that is not expected to have a significant impact.”
Experimental example 5-1, p25, assesses each named protein individually for how “important” that protein is, for the random forest algorithm to make the cancer/no cancer call.
“As a result, the PSMA, ERG, and ENG biomarkers that showed similar sensing performance in Examples above exhibited significantly different importance in the RF algorithm (see FIG. 9). PSMA and ERG showed the relative importance at a similar level (0.23 for both biomarkers), but ERG was found to have higher accuracy than PSMA, indicating that the relative importance does not necessarily mean the accuracy. In addition, in a similar manner as in the results of FIG. 6, ANXA3 showed the lowest importance (7 % only), suggesting that some biomarkers barely contribute to the diagnostic performance.”
Experimental example 5-2, p25, studies combinations of the four named proteins.
“the accuracy increased in both algorithms as the number of biomarkers in the panel increased (see FIG. 10). In detail, the accuracy of the RF and NN algorithms using a single biomarker was 73.2 % and 66.3 %, respectively. As the number of biomarkers increased, the average accuracy remarkably improved to 97.1 % in RF and 94.2 % in NN. Based on these results, referring to the improved average accuracy with increasing number of biomarkers in two different ML algorithms, it was confirmed that the multimarker approach provided more accurate clinical results.”
This supports the assertion that no single biomarker expression level is sufficient to provide specific and selective diagnosis of prostate cancer.
Experimental example 5-3, p26, assesses the output of the “predictor value” associated with the cancer/ no cancer call from the trained ML. The use of the predictor value in making the cancer/ no cancer call improved the validation results (fewer false positive/false negative results). This indicates information on how to make the call based on the predictor value is required to achieve the desired goal.
Experimental example 5-4, p26-27, further assesses the results of the ML. Various subsets of protein combinations were tested and analyzed.
“as the number of biomarkers increased, the ROC curves approached close to each axis, meaning an increase in accuracy (see FIGS. 13A to 13D). In addition, as the number of biomarkers increased, variations in the ROC curves decreased. Such a trend was also observed in the NN algorithm (see FIGS. 14A to 14D).”
“it was confirmed that an AUROC value also increased as the number of biomarkers increased (see FIG. 15A). In detail, when a single biomarker was used, the AUROC value was the largest with ENG, followed by ERG, PSMA, and ANXA3. In addition, the AUROC value was significantly changed depending on a combination of biomarkers, and in this regard, the combination was analyzed to be more important than the number of biomarkers. For example, ENG+ ERG had better sensing performance than PSMA +ERG+ ANXA3 (see FIG. 15B). It was also confirmed that the inclusion of ANXA3 resulted in degraded sensing performance in both the RN and NN algorithms, and thus it can be seen that there is an optimal biomarker combination. For example, in consideration of the AUROC value, the optimal sensing results were achieved by ENG + ERG + PSMA in RF and ENG + ERG in NN.”
This supports the assertion that no single biomarker expression level is sufficient to provide specific and selective diagnosis of prostate cancer. Further, the best combination also depends on the particular ML algorithm employed.
Experimental example 6, p27-28, analyzes the rate of false positive and false negative results, with respect to how they drive patient treatment, and affect prostate cancer diagnosis. “As a result, it was confirmed that the false rate decreased as the number of biomarkers increased. For example, when a combination of four biomarkers was used, only two false panels (see FIGS. 16A to 16E).”
d) The invention is drawn to measuring at least one gene expression level or protein expression level from a set of listed genes, for the purpose of diagnosing prostate cancer. The broadest claim does not require any data analytics, or validation. The broadest claim does not apply trained ML to act on the test data, to provide the diagnosis. The sample is not a urine sample, and the measurement is not performed by the named DGFET biosensors.
e) The determination of a minimum essential set of gene/ protein expression data required to make the calculations for the specific and selective diagnosis of prostate cancer requires significant inventive decision making and skill. One of skill must identify genes induced or repressed in the appropriate subject groups, with and without the named cancer. From that extensive list, a minimally sufficient list must be determined of genes which are most significant to the diagnosis, in the particular patient group and sample type, and useful in the calculation the predictor value used for the diagnosis.
The prior art notes that single biomarker expression level diagnostics for prostate cancer which historically have used PSA, have been shown not to provide a specific and selective diagnosis of the presence of prostate cancer.
Lomas et al. (Lomas, D. J. et al. (June, 2020) All change in the prostate cancer diagnostic pathway. Nature Reviews in Clinical Oncology, vol 17, p372-381) summarizes the issue with PSA:
“Following detection of high levels of serum prostate- specific antigen, many men are advised to have transrectal ultrasound- guided biopsy in an attempt to locate a cancer. This nontargeted approach lacks accuracy and carries a small risk of potentially life- threatening sepsis. Worse still, it can detect clinically insignificant cancer cells, which are unlikely to be the origin of advanced- stage disease. The detection of these indolent cancer cells has led to overdiagnosis, one of the major problems of contemporary medicine, whereby many men with clinically insignificant disease are advised to undergo unnecessary radical surgery or radiotherapy.” (abstract).
PSA was not qualified by the FDA as diagnostic for prostate cancer, but as an indicator that disease may be present, and represents an important screening tool- in combination with TRUS- guided biopsy information or other pathologic findings. Lomas reviews the pros and cons of the current standard of diagnosis, as well as new emerging tools such as MRI image analysis and MRI guided biopsies.
Lomas also reviews the rise in biomarker analysis, as part of the screening process, beginning at page 375. Different sample types provide different biomarkers to be tested. The biomarkers available in blood are not necessarily the same as those available to be tested in urine, which are not necessarily the same as those that can be tested on biopsy slides or preserved biopsy tissue samples.
Lomas reviews approved biomarker screening panels for blood samples, and each requires more than one type of biomarker along with other pathologic or diagnostic information to make a specific and selective diagnosis of prostate cancer. These screening tools are largely used to direct patient management to further testing (biopsy) and not a stand-alone diagnostic tool.
Lomas reviews approved biomarker screening panels for urine samples, and each requires more than one type of biomarker along with other pathologic or diagnostic information to make a specific and selective diagnosis of prostate cancer. These screening tools are largely used to direct patient management to further testing (biopsy) and not a stand-alone diagnostic tool. One tool relevant to the claimed invention is discussed at p376:
“Mi- Prostate Score is another post- DRE urine test that can guide the decision on whether to perform biopsy sampling. The test measures the RNA levels of the prostate- specific gene fusion TMPRSS2–ERG and the noncoding RNA PCA3, which are combined with serum PSA levels to generate a score that provides the quantitative risk of having prostate cancer detected in a biopsy sample and that of having potentially metastatic prostate cancer96. A cut- off value to define a positive or negative result has not been defined; instead, the decision is taken by the health- care provider and/or the patient. In a validation cohort, however, cut- offs of 30% and 40% would have led to 35% and 47% of biopsies being avoided, respectively, while delaying the diagnosis of 1.0% and 2.3% of high- grade cancers96.”
Lomas notes that National and International oncology guidelines do not agree on the use of biomarker panels in determining whether biopsies should be obtained, or in the diagnosis of prostate cancer.
“This challenge might be one of the most important concerning the use of biomarkers in prostate cancer diagnosis; without clear recommendations on which specific test to use, health- care providers might be less likely to order them. In our view, guideline organizations will ultimately have to endorse multiple tests and let clinicians decide in order not to appear biased. More education for providers on the advantages and drawbacks of each biomarker panel is needed.” (p377)
f) The skill of those in the art of oncology, molecular biology and bioinformatics is high.
g) The prior art predicts that no single biomarker expression level, on its own, is sufficient to provide a specific and selective diagnosis of prostate cancer. The prior art biomarker panels used in the diagnostic pathway for patients utilize more than one biomarker expression level, as well as particular clinical information, other pathologic findings, and demographic information. The prior art notes that differing types of samples require the use of differing biomarker panels. The prior art notes that the appropriate subject groups must be examined, including proper reference and control populations. The prior art predicts that the statistical analysis of each biomarker requires careful attention, and validation. Technical details of the biomarker panel also require significant testing, and cost analysis, prior to implementation within the standard of care.
h) The claims are broad because they merely require the measurement of one named biomarker, to diagnose prostate cancer.
The skilled practitioner would first turn to the instant specification for guidance to determine whether any one of the four named proteins provides specific and selective diagnosis of prostate cancer. However, the instant specification notes that at least ANXA3 is unsuitable for such a use, and that various combinations of the four named proteins increased accuracy. The instant specification solely utilizes urine samples, where the biomarkers are measured using a particular type of sensor. The claims are not limited to these embodiments and embrace all sample types, all subject populations, and any type of measurement. The specification provides that specific analytics are required to make the determinations, which are not required by the claim. As such, the skilled practitioner would turn to the prior art for such guidance, however, the prior art shows that the identification of the genes which are necessary and sufficient for the specific and selective diagnosis of prostate cancer, in any sample, using any measurements and any analytics, is not predictable, and requires significant inventive skill and decision making. Finally, said practitioner would turn to trial-and-error experimentation to determine these embodiments. Such represents undue experimentation.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 7-19 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Spetzler (2014).
Spetzler, D. et al. Circulating Biomarkers. US 2014/0141986 A1, published 5/22/2014.
Spetzler (US 2014/0141986 A1) provides trained machine learning models for the diagnosis of prostate cancer, which use as input, gene expression information of at least PSMA (FOLH1), TMPRSS2-ERG, ERG, endoglin (CD105) and AXNA3, measured by electrochemical signaling, sensors or biosensors.
With respect to claim 7, Spetzler measures protein levels or gene expression levels of: PSMA [0022], ANXA3 [0791], an ERG-related protein: TMPRSS2-ERG fusion [0112], ERG (Table 5), and ENG (endoglin or CD105) [0797, 0801]. The expression levels are used in methods of diagnosing, or detecting prostate cancer [0019, 0031, et al.]. That meets all of the requirements of claim 7.
With respect to claim 8, Spetzler provides control groups or reference data at: [0033, 0108-0109, 0119 et al.]
With respect to claim 9, a level higher or lower than the control group can indicate the presence of prostate cancer [0033, 0108-0109, 0119 et al.].
With respect to claim 10, the use of machine learning algorithms is disclosed at [0358, 0360-0366, 1952-1956 et al.]
With respect to claim 11, the ML is trained using gene expression values of the same listed proteins and control group information. [0358, 0360-0366, 1952-1956 et al.]
With respect to claim 12, an output of “prostate cancer” or a “risk” is disclosed at [0358, 0360-0366, 1952-1956 et al.]
With respect to claim 13, Spetzler measures protein levels or gene expression levels of: an ERG-related protein: TMPRSS2-ERG fusion [0112], ERG (Table 5), and ENG (endoglin or CD105) [0797, 0801]. The expression levels are used in methods of diagnosing, or detecting prostate cancer [0019, 0031, et al.]. That meets all of the requirements of claim 13.
With respect to claim 14, ANXA3 is not required for diagnosis.
With respect to claim 15-16, Spetzler provides the use of biosensors, including electrochemical sensing, at least at [0872, 0878, 1284, 1570-1571, 1572, 1579] to measure any of the desired biomarkers.
With respect to claims 17-19, the values output by Spetzler appear to meet these limitations. Spetzler provides output values from the trained ML algorithm at least at: [0333-0334, 0350, 0864, 0904-0905, 0908, 1206, 1262, 1275-0176 et al.].
Claim(s) 7-9, 13-16 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Clarke (2007).
Clarke, M. F. et al. Compositions and methods for treating and diagnosing cancer. US 2007/0099209 A1, published 5/3/2007.
Clarke (US 2007/0099209 A1) discloses measuring gene expression levels of biomarkers, using sensors, biosensors, or electrochemical signaling.
With respect to claim 7, Clarke measures the expression levels of at least one of: ANXA3 (Table 4, Table 5, Table 7A, Table 7C); PSMA (FOLH1) (Table 4, Table 5, et al.), ERG (Table 4, Table 7B, Table 7D), and ENG (Table 4, Table 6). The measured expression levels are intended to diagnose cancer, including prostate cancer [0023]
With respect to claim 8, Clarke provides control groups or reference data at: [0091, 0126, 0246 et al.]
With respect to claim 9, a level higher or lower than the control group can indicate the presence of prostate cancer [0091, 0126, 0246 et al.].
With respect to claim 13, Clarke measures protein levels or gene expression levels of: ANXA3 (Table 4, Table 5, Table 7A, Table 7C); PSMA (FOLH1) (Table 4, Table 5, et al.), ERG (Table 4, Table 7B, Table 7D), and ENG (Table 4, Table 6). The expression levels are used in methods of diagnosing, or detecting prostate cancer [0023, et al.]. That meets all of the requirements of claim 13.
With respect to claim 14, ANXA3 is not required for diagnosis.
With respect to claim 15-16, Clarke provides the use of biosensors, including electrochemical sensing, at least at [0281] to measure any of the desired biomarkers.
Claim(s) 7-12, 14-19 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Davincioni (2019).
Davincioni, E. et al. Cancer diagnostics using non-coding transcripts. US 10,513,737 B2, published 12/24/2019.
Davincioni (US 10,513,737 B2) provides trained ML models for diagnosis of cancer, including prostate cancer, using gene expression information of at least ANXA3, PSMA (FOLH1), and ERG, measured by biosensors, sensors, or electrochemical signaling.
With respect to claim 7, Davincioni measures protein levels or gene expression levels of: PSMA (FOLH1) [Table 22], ANXA3 [Table 22], ERG (Table 22). The expression levels are used in methods of diagnosing, or detecting prostate cancer [column 1: background, column 9, etc.]. That meets all of the requirements of claim 7.
With respect to claim 8, Davincioni provides control groups or reference data at: [column 3-4, et al.]
With respect to claim 9, a level higher or lower than the control group can indicate the presence of prostate cancer [col 3-4 et al.].
With respect to claim 10, the use of machine learning algorithms is disclosed at [column 6, Tables 16 and 18, col 58-59 et al.]. Random forest algorithms, and neural networks are disclosed at least at col 58-59.
With respect to claim 11, the ML is trained using gene expression values of the same listed proteins and control group information. [column 6, Tables 16 and 18, col 58-59 et al.]
With respect to claim 12, an output of “prostate cancer” or a “risk” is disclosed at [col 6, Fig 18 and its description, col 41-42, col 59-60 et al.]
With respect to claim 14, ANXA3 is not required for diagnosis.
With respect to claim 15-16, Davincioni provides the use of biosensors, including electrochemical sensing, at least at [Col 18, 46, 48] to measure any of the desired biomarkers.
With respect to claims 17-19, the values output by Davincioni appear to meet these limitations. Davincioni provides output values from the trained ML algorithm at least at: [col 7, col 34, col 42, col 60, col 90 et al.]. “p-values” are disclosed throughout.
Conclusion
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/MARY K ZEMAN/Primary Examiner, Art Unit 1686
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