DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
2. This communication is in response to the communication filed 10/2/2025. Claims 10-11 and 39 are cancelled. Claim 1, 12, 16, 20, 25, 30, and 37 are currently amended. Claims 1-9, 12-38, and 40-44 are currently pending.
Claim Rejections - 35 USC § 101
3. 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.
3.1. Claims 1-9, 12-38, and 40-44 are rejected under 35 U.S.C. § 101 because while the claims (1) are to a statutory category (i.e., process, machine, manufacture or composition of matter, the claims (2A1) recite an abstract idea (i.e., a law of nature, a natural phenomenon); (2A2) do not recite additional elements that integrate the abstract idea into a practical application; and (2B) are not directed to significantly more than the abstract idea itself.
In regards to (1), the claims are to a statutory category (i.e., statutory categories including a process, machine, manufacture or composition of matter). In particular, independent claims 1, 36 and 37, and their respective dependent claims are directed, in part, to methods for predicting liver cancer recurrence in liver transplant patients.
In regards to (2A1), the claims, as a whole, recite and are directed to an abstract idea because the claims include one or more limitations that correspond to an abstract idea including mental processes and/or certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer. For example, independent claims 1, 36 and 37, as a whole, are directed to predicting liver cancer recurrence in liver transplant patients by, in part, receiving gene expression data related to a liver tissue sample for a subject having a liver cancer, inputting the data into a trained machine learning model, and predicting, using the model, a risk of recurrence of the liver cancer in the subject after liver transportation which are human activities and/or interactions and therefore, certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer. The dependent claims include all of the limitations of their respective independent claims and thus are directed to the same abstract idea identified for the independent claims but further describe the elements and/or recite field of use limitations.
Furthermore, assuming arguendo, the claims are not directed to certain methods of organizing human activities, the claims, nevertheless, are directed to an abstract idea because the claims, except for certain limitations (* identified below in bold), under the broadest reasonable interpretation, can be reasonably and practically performed in the human mind and/or with pen and paper using observation, evaluation, judgment and/or opinion. That is, other than reciting the certain additional elements, nothing in the claims precludes the limitations from being practically performed in the mind and/or with pen and paper.
CLAIM 1:
A computer-implemented method comprising:
training a machine learning model executing on a computing device to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer;
receiving gene expression data related to a liver tissue sample for a subject having a liver cancer;
inputting the gene expression data into the trained machine learning model executing on the computing device, wherein the gene expression data comprises respective gene expression levels for a top-q pairs of differently expressed genes, where q is 43; and
predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation.
CLAIM 2:
The computer-implemented method of claim 1, wherein the trained machine learning model is a supervised machine learning model.
CLAIM 3:
The computer-implemented method of claim 1, wherein the trained machine learning model is configured to predict the risk of recurrence as a probability score.
CLAIM 4:
The computer-implemented method of claim 1, wherein the trained machine learning model is configured to predict the risk of recurrence by classifying the subject into one of a plurality of categories.
CLAIM 5:
The computer-implemented method of claim 1, wherein the trained machine learning model is a support vector machine (SVM), a random forest model, a logistic regression model, or a k-top scoring pairs (k-TSP) model.
CLAIM 6:
The computer-implemented method of claim 1, wherein the gene expression data comprises respective gene expression levels for a top-n differentially expressed genes, where n is an integer greater or equal to 10.
CLAIM 7:
The computer-implemented method of claim 6, wherein n is greater than or equal to 50.
CLAIM 8:
The computer-implemented method of claim 6, wherein the top-n differentially expressed genes comprise one or more of HOOK1, EFCAB7, CDC7, NUF2, UBE2T, HELLS, RRM1, SYT12, KIF21A, RACGAP1, PRIMI, PITGES3, YEATS4, CCT2, PARPBP, PPPICC, KNTC1, TMED2, CDKN3, DLGAP5, BUBIB, NUSAP1, CCNB2, KIF23, FANCI, PRC1, CDC6, TOP2A, KPNA2, NDC80, RBBP8, NARS, BUB1, TOPBP1, SMC4, NCAPG, CENPE, PLK4, CENPU, CENPQ, TTK, FBXO5, ANLN, MELK, DYNLT3, ZNF674, KIF4A, AMMECR1, ZNF449, and BRCC3.
CLAIM 9:
The computer-implemented method of claim 6, wherein the trained machine learning model is a random forest model.
CLAIM 12:
A computer-implemented method comprising:
training a machine learning model executing on a computing device to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer;
receiving gene expression data related to a liver tissue sample for a subject having a liver cancer;
inputting the gene expression data into the trained machine learning model executing on the computing device, wherein the gene expression data comprises respective gene expression levels for a top-q pairs of differentially expressed genes, an integer greater or equal to 10; and
predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation,
wherein the top-q pairs of differentially expressed genes comprise one or more of BUB1B and SSH3, MCM8 and OGDHL, NUSAPI and FNDC4, KIF21A and RAB43, CDC7 and CNGA1, MORF4L2 and ETFB, HELLS and HAMP, PPIL1 and ZCCHC24, MELK and GALNT15, BRCC3 and CCDC69, CCT6A and ASL, CDKN3 and SYT12, RBBP8 and TMPRSS2, KIF23 and LMF1, KPNA2 and SUN2, SMC4 and FXYD1, PPPICC and FTCD, NUCB2 and NDRG2, PARP2 and IL11RA, VBP1 and AGTRI, TOP2A and TSPANY, KTN1 and COLI8A1, NCAPG and ADAMTS13, STT3B and CD14, SECLIC and C8G, CCNA2 and ADRAIA , CENPQ and UROC1, TTK and PLCH2, FANCI and SHBG, DEK and EGR1, RFC5 and APOF, PTGES3 and TAT, SNX7 and PGLYRP2, CCT2 and PIGR, PRC1 and MGMT, NARS and MASP1, RRM1 and MGLL, TOPBP1 and CTSF, F2 and ITIH4, ANLN and ZNF674, PRIM1 and SULTIAI1, RARRES2 and HRG, and CENPU and NNMT.
CLAIM 13:
The computer-implemented method of claim 10, wherein the trained machine learning model is a k-TSP model.
CLAIM 14:
The computer-implemented method of claim 1, further comprising: receiving mutation data related to the liver tissue sample; and inputting the mutation data into the trained machine learning model.
CLAIM 15:
The computer-implemented method of claim 14, wherein the mutation data comprises a number of somatic mutations present in the liver tissue sample from the subject.
CLAIM 16:
A computer-implemented method comprising:
training a machine learning model executing on a computing device to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer;
receiving gene expression data related to a liver tissue sample for a subject having a liver cancer;
inputting the gene expression data into the trained machine learning model executing on the computing device;
receiving mutation data related to the liver tissue sample; and
inputting the mutation data into the trained machine learning model executing on the computing device; and
predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation,
wherein the mutation data comprises a number of somatic mutations present in a top-m mutation pathways, where m is an integer greater or equal to 5.
CLAIM 17:
The computer-implemented method of claim 16, wherein m is 5.
CLAIM 18:
The computer-implemented method of claim 16, wherein the top-m mutation pathways comprise one or more of GO_ENDONUCLEASE_ ACTIVITY _ACTIVE_ WITH_EITHER_RIBO_OR_DEOXYRIBON UCLEIC_ACIDS_AND_PRODUCING_3 PHOSPHOMONOESTERS, GO_GLUCOSE_BINDING, GO_PALMITOYL_COA_HYDROLASE_ ACTIVITY, GO_PEPTIDE_N_ACETYLTRANSFERASE ACTIVITY, and GO_DOPAMINE BINDING.
CLAIM 19:
The computer-implemented method of claim 16, wherein the trained machine learning model is a random forest model.
CLAIM 20:
A computer-implemented method comprising:
training a machine learning model executing on a computing device to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer;
receiving gene expression data related to a liver tissue sample for a subject having a liver cancer;
inputting the gene expression data into the trained machine learning model executing on the computing device;
receiving mutation data related to the liver tissue sample; and
inputting the mutation data into the trained machine learning model executing on the computing device; and
predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation,
wherein the mutation data comprises a number of somatic mutations present in a top-r mutation pathway pairs, where r is an integer greater or equal to 3.
CLAIM 21:
The computer-implemented method of claim 20, wherein r is 3.
CLAIM 22:
The computer-implemented method of claim 20, wherein the top-r mutation pathways pairs comprise one or more of GO_SYNTAXIN_BINDING and GO_N_ACYLTRANSFERASE ACTIVITY, REACTOME_ GOLGI ASSOCIATED_VESICLE_ BIOGENESIS and GO_N_ACETYLTRANSFERASE ACTIVITY, and GO_REGULATION_OF_HORMONE_ METABOLIC PROCESS and GO_PEPTIDE_N_ACETYLTRANSFERASE ACTIVITY.
CLAIM 23:
The computer-implemented method of any one of claims 20-22 claim 20, wherein the trained machine learning model is a k-top scoring pairs (k-TSP) model.
CLAIM 24:
The computer-implemented method of claim 14, wherein the trained machine learning model is a support vector machine (SVM), a random forest model, a logistic regression model, or a k-top scoring pairs (k-TSP) model.
CLAIM 25:
A computer-implemented method of claim 1, further comprising:
training a machine learning model executing on a computing device to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer;
receiving gene expression data related to a liver tissue sample for a subject having a liver cancer;
inputting the gene expression data into the trained machine learning model executing on the computing device;
receiving a radiology-based parameter related to the liver cancer;
inputting the radiology-based parameter into the trained machine learning model; and
predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation.
CLAIM 26:
The computer-implemented method of claim 25, wherein the radiology-based parameter is based on a size or number of tumor nodules associated with the liver cancer.
CLAIM 27:
The computer-implemented method of claim 25 or 26, wherein the radiology-based parameter is Milan criteria.
CLAIM 28:
The computer-implemented method of any one of claims 25-27 claim 25, wherein the trained machine learning model is a k-top scoring pairs (k-TSP) model.
CLAIM 29:
The computer-implemented method of claim 25, wherein the trained machine learning model is a support vector machine (SVM), a random forest model, or a logistic regression model.
CLAIM 30:
A computer-implemented method comprising:
training a machine learning model executing on a computing device to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer;
receiving gene expression data related to a liver tissue sample for a subject having a liver cancer;
inputting the gene expression data into the trained machine learning model executing on the computing device;
receiving mutation data related to the liver tissue sample and a radiology-based parameter related to the liver cancer;
inputting the mutation data and the radiology-based parameter into the trained machine learning model executing on the computing device; and
predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation.
CLAIM 31:
The computer-implemented method of claim 30, wherein the trained machine learning model is a random forest model.
CLAIM 32:
The computer-implemented method of claim 30, wherein the trained machine learning model is a support vector machine (SVM), a logistic regression model, or a k- top scoring pairs (k-TSP) model.
CLAIM 33:
The computer-implemented method of claim 1, further comprising providing a treatment recommendation based on the prediction.
CLAIM 34:
The computer-implemented method of claim 33, wherein the treatment recommendation is to perform a liver transplant procedure on the subject.
CLAIM 35:
The computer-implemented method of claim 1, wherein the liver cancer is hepatocellular carcinoma (HCC).
CLAIM 36:
A method comprising:
predicting a risk of recurrence of a liver cancer in a subject after liver transplantation according to claim 1;
recommending the subject as a candidate for a liver transplant procedure based on the prediction; and
performing the liver transplant procedure on the subject.
CLAIM 37:
A system comprising:
a trained machine learning model executing on a computing device, wherein the trained machined learning model is trained to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer; and
the computing device comprising a processor and a memory, the memory having computer-executable instructions stored thereon that, when executed by the processor, cause the processor to:
receive gene expression data related to a liver tissue sample for a subject having a liver cancer;
input the gene expression data into the trained machine learning model; and
receive a radiology-based parameter related to the liver cancer;
input the radiology-based parameter into the trained machine learning model; and
receive a risk of recurrence of the liver cancer in the subject after liver transplantation, wherein the risk of recurrence is predicted by the trained machine learning model.
CLAIM 38:
The system of claim 37, wherein the memory has further computer-executable instructions stored thereon that, when executed by the processor, cause the processor to: receive mutation data related to the liver tissue sample; and input the mutation data into the trained machine learning model.
CLAIM 40:
The system of any one of claim 37, wherein the trained machine learning model is configured to predict the risk of recurrence as a probability score, or predict the risk of recurrence by classifying the subject into one of a plurality of categories.
CLAIM 41:
The system of any one of claim 37, wherein the trained machine learning model is a support vector machine (SVM), a random forest model, a logistic regression model, or a k-top scoring pairs (k-TSP) model.
CLAIM 42:
The system of any one of claim 37, wherein the memory has further computer-executable instructions stored thereon that, when executed by the processor, cause the processor to provide a treatment recommendation based on the prediction.
CLAIM 43:
The system of claim 42, wherein the treatment recommendation is to perform a liver transplant procedure on the subject.
CLAIM 44:
The system of claim 37, wherein the liver cancer is hepatocellular carcinoma (HCC).
* The limitations that are in bold are considered “additional elements” that are further analyzed below in subsequent steps of the 101 analysis. The limitations that are not in bold are abstract and/or can be reasonably and practically performed in the human mind and/or with pen paper.
In regards to (2A2), the claims do not recite additional elements that integrate the abstract idea into a practical application. The additional elements in the claims (i.e., * identified above in bold) do not integrate the abstract idea into a practical application because the additional elements merely add insignificant extra-solution activity to the abstract idea; merely link the use of the judicial exception to a particular technological environment or field of use; and/or simply append technologies and functions, specified at a high level of generality, to the abstract idea (i.e., the additional elements do not amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer).
Here, the additional elements (e.g., computer, model, trained machine learning model, computing device, processor, memory, computer executable instructions, etc.) are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea using generic computer technologies. Moreover, the claims recite “using the model”, “cause the processor to”, etc. devoid of any meaningful technological improvement details and thus, further evidence the additional elements are merely being used to leverage generic technologies to automate what otherwise could be done manually. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Furthermore, the additional elements do not recite improvements to the functioning of a computer, or to any other technology or technical field—the additional elements merely recite general purpose computer technology; the additional elements do not recite applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition—there is no actual administration of a particular treatment; the additional elements do not recite applying the judicial exception with, or by use of, a particular machine—the additional elements merely recite general purpose computer technology; the additional elements do not recite limitations effecting a transformation or reduction of a particular article to a different state or thing—the additional elements do not recite transformation such as a rubber mold process; the additional elements do not recite applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment—the additional elements merely leverage general purpose computer technology to link the abstract idea to a technological environment.
In regards to (2B), the claims, individually, as a whole and in combination with one another, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of (A) a generic computer structure(s) that serves to perform computer functions that serve to merely link the abstract idea to a particular technological environment (i.e., computers); and/or (B) functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Here, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer technologies. Mere instructions to apply an exception using generic computer technologies cannot provide an inventive concept.
Moreover, paragraphs [0082]-[0083] of applicant's specification (US 2024/0404707) recites that the system/method is implemented using a computing device such as a well-known computing system including personal computers, servers, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, network personal computers (PCs), minicomputers, mainframe computers, embedded systems, and/or distributed computing environments including a plurality of any of the above systems or devices which are well-known general purpose or generic-type computers and/or technologies. The use of generic computer components recited at a high level of generality to process information through an unspecified processor/computer does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Furthermore, the additional elements are merely well-known general purpose computers, components and/or technologies that receive, transmit, store, display, generate and otherwise process information which are akin to functions that courts consider well-understood, routine, and conventional activities previously known to the pertinent industry, such as, performing repetitive calculations; receiving or transmitting data over a network; electronic recordkeeping; retrieving and storing information in memory; and sorting information (See, for example, MPEP § 2106).
Therefore, the claims are not patent-eligible under 35 U.S.C. § 101.
Allowable Subject Matter
4. Claims 1-9, 12-38, and 40-44 are objected to as being rejected under 35 U.S.C. 101, but appear to be allowable if rewritten to overcome the 35 U.S.C. 101 issues.
4.1. The following is an examiner’s statement of reasons for allowance:
The first exemplary prior art Cao et al. (CN 111554402) teaches a system that has a model training and parameter adjusting module for training and adjusting a model parameter based on a primary postoperative recurrence risk data processing model based on machine learning on training data. A model evaluation module utilizes test data to test the primary postoperative recurrence risk data processing model based on the machine learning for evaluating a data processing result to determine accuracy of the primary postoperative recurrence risk data processing model based on the machine learning. A data processing module performs primary postoperative recurrence risk data processing by using the constructed primary postoperative recurrence risk data processing model based on the machine learning.
The next exemplary prior art Chen et al. (TW 201339310) teaches a gene set for predicting post-surgery recurrence or metastasis risk in cancer patients and a method of using said genes to predict post-surgery recurrence or metastasis risk in cancer patients, especially in liver cancer. The precision rate of predicting post-surgery recurrence in patients with liver cancer according to the present invention is 76.47%; and the precision rate of predicting post-surgery metastasis in patients with liver cancer is 82.61%. Thereby, the best successive treatment method can be evaluated by precisely predicting the recurrence and metastasis risk in patients with liver cancer.
The cited prior art, however, does not reasonably disclose, teach and/or suggest the combination of features as presented in the claims including, inter alia,
“inputting the gene expression data into the trained machine learning model executing on the computing device, wherein the gene expression data comprises respective gene expression levels for a top-q pairs of differently expressed genes, where q is 43”, as recited in amended independent claim 1; “inputting the gene expression data into the trained machine learning model executing on the computing device, wherein the gene expression data comprises respective gene expression levels for a top-q pairs of differentially expressed genes, an integer greater or equal to 10”, as recited in amended independent claim 12;
“receiving mutation data related to the liver tissue sample; and inputting the mutation data into the trained machine learning model executing on the computing device; and predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation, wherein the mutation data comprises a number of somatic mutations present in a top-m mutation pathways, where m is an integer greater or equal to 5”, as recited in amended independent claim 16;
“receiving mutation data related to the liver tissue sample; and inputting the mutation data into the trained machine learning model executing on the computing device; and predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation, wherein the mutation data comprises a number of somatic mutations present in a top-r mutation pathway pairs, where r is an integer greater or equal to 3”, as recited in amended independent claim 20;
“receiving a radiology-based parameter related to the liver cancer; inputting the radiology-based parameter into the trained machine learning model; and predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation”, a recited in amended independent claim 25;
“receiving mutation data related to the liver tissue sample and a radiology-based parameter related to the liver cancer; inputting the mutation data and the radiology-based parameter into the trained machine learning model executing on the computing device; and predicting, using the trained machine learning model executing on the computing device, a risk of recurrence of the liver cancer in the subject after liver transplantation”, as recited in amended independent claim 30;
“receive a radiology-based parameter related to the liver cancer; input the radiology-based parameter into the trained machine learning model; and receive a risk of recurrence of the liver cancer in the subject after liver transplantation, wherein the risk of recurrence is predicted by the trained machine learning model”, as recited in amended independent claim 37.
Response to Arguments
5. Applicant's arguments filed 10/2/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 10/2/2025.
5.1. Applicant argues, on pages 12-13 of the response, that the claims, as a whole, are not directed to an abstract idea and/or the abstract idea is integrated into a practical application; and “training a machine learning model executing on a computing device to predict recurrence of a cancer in liver transplant patients based on alterations of gene expressions and/or genomes in the cancer” cannot be practically performed in the human mind and/or with pen and paper.
In response, it is noted that the claims, as a whole, are directed to predicting liver cancer recurrence in liver transplant patients by receiving data related to the patient, such as, gene expression data of a liver tissue sample; and using the data to predict a risk of recurrence of the liver cancer in the patient after liver transplantation and thereby improve the clinical outcomes of (hepatocellular carcinoma (HCC) patients. These are human activities and/or interactions and therefore, properly categories under the abstract category of certain methods of organizing human activity which encompasses both certain activity of a single person, certain activity that involves multiple people, and certain activity between a person and a computer [emphasis added], as detailed in section 3, supra. Furthermore, nothing in the claims precludes a human from receiving data and processing the data to predict a risk of recurrence of liver cancer in the patient after liver transplantation—these steps can be reasonably and practically performed in the human mind and/or with pen and paper and thus, also be properly categorized under the abstract grouping of mental processes, as detailed in section 3, supra, and incorporated herein. While the claims may recite certain additional elements, such as, “training a machine learning model…” that cannot be reasonably and practically performed in the human mind and/or with pen and paper, these additional elements are analyzed in subsequent prongs of the 101 analysis.
Moreover, these claim recited additional elements--identified above in bold—do not integrate the abstract idea into a practical application because these additional elements merely add insignificant extra-solution activity to the abstract idea; merely link the use of the judicial exception to a particular technological environment or field of use; and/or simply append technologies and functions, specified at a high level of generality, to the abstract idea (i.e., the additional elements do not amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer). For example, the additional elements (e.g., computing device, trained machine learning model, etc.) are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the abstract idea using generic computer technologies to automate what otherwise could be done manually. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
In short, it is respectfully submitted that the pending claims, as a whole, are directed to and recite an abstract idea; the additional elements do not integrate the abstract idea into a practical application; the additional elements do not amount to significantly more than the abstract idea itself; and therefore, the pending claims are not patent-eligible subject matter under 35 U.S.C. § 101.
Conclusion
6. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Tomaszewski whose telephone number is (313)446-4863. The examiner can normally be reached M-F 5:30 am - 2:30 pm.
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/MICHAEL TOMASZEWSKI/Primary Examiner, Art Unit 3681