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. DETAILED ACTION Claims 1-13 are pending. This action is a non-final rejection. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-13 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without more. Claim 1-11 are method claims, claim 12 is a system and claim 13 is a computer program product which is interpreted as a non-transitory computer readable medium. (ignoring “can be loaded” which is more of intended use. Thus the claims are directed to statutory classes of invention. Step 1 Yes Independent claim 1 is being analyzed as representative. The limitations under their broadest reasonable interpretation cover “mental processes” that can be conducted in the human mind. The abstract limitations executing the following steps via a … :- obtaining for a patient (P) respective values of a plurality of clinical data (p); - generating a training dataset via the steps of - estimating by means of a non-linear classifier a respective risk class (v) for the values of said plurality of clinical data (p) of said patient (P);- adding the values of said plurality of clinical data (p) of said patient (P) and the respective estimated risk class (v) to said training dataset ;- generating a plurality of modified datasets by modifying the values of said plurality of clinical data (p) of said patient (P);- estimating (1408) by means of said non-linear classifier a respective risk class (v) for the values of each modified dataset;- adding the values of each modified dataset and the respective estimated risk class (v) to said training dataset ;- determining a risk class (v) among said risk classes (v) estimated for said modified datasets indicating a lower risk than said risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P), - training a linear classifier configured to estimate said risk class (v) as a function of the values of said plurality of clinical data (p) by using said training dataset ;- determining a separation plane of said linear classifier separating the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) from said risk class (v) indicating a lower risk;- using said separation plane to calculate the minimum change (MP) required to the values of said plurality of clinical data (p) of said patient (P) to change the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) in said risk class (v) indicating a lower risk; and - providing a support for clinical decisions by showing said change (MP) to the values of said plurality of clinical data (p) of said patient (P) on a … . Here the claims are directed to a mental process. Aside from the initial rendition of computer and “display” at the end of claim 1, there are no tangible elements and the process does not see to improve the state of AI or training data. The recitation of generic computing elements does not preclude a claim from reciting an abstract idea. Step 2A prong 1 yes, the claims recite an abstract idea) The judicial exception is not integrated into a practical application. As disclosed a computer and a display are not indicative of more than a generic computer, thus to amounting to instructions to apply the generic computer component. Claims 12 and 13 are similar. Thus the claims 1-13 are directed to an abstract idea without a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and as an ordered combination they do not add significantly more known as inventive concept to the exception. As discussed above with resect to integration fo the abstract idea into a practical application the additional element of a computer and display amounts to no more than mere instructions to apply a generic computer. The specification does not provide any specific technical improvement that could be applied on first review. However, applicant might be able to show an improvement in the field of machine learning. Step 2B No, the claims do not provided significantly more. The dependent claims 2-11 do not improve the analysis for claim 1. Claim Objections Claim 13 is objected to because of the following informalities: In regards to claim 13, the format should be non-transitory computer readable medium. “ can be loaded” is more of intended use. Appropriate correction is required. Objection – Claim 1 “a patient respective values” followed by “the values of said plurality of clinical data”, “the values, …. “ the values of each modified data set” This should be “the patient respective values of each modified data sets…” “ said modified data sets”. Each modified dataset, said modified data sets are inconsistently presented. Later in claim 5 “a respective modified data set” is followed by “said modified dataset” However, claim 5 is dependent on claim 1 and thus “a respective modified data set” re introduces what was already introduced. Please review the antecedent basis for “the values” and “modified sets” so that each instance of “a” has a the but, Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim (s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 2022/0208305 to Bontrager in view of US Patent Publication to T ell 20210343372 As per claim 1 Bontrager discloses; obtaining for a patient (P) respective values of a plurality of clinical data (p ); Bontrager (fig. 1) generating a training dataset via the steps of – estimating by means of a non-linear classifie r Bontrager( 0074, according to google a neural network could be a form of non-linear classification) a respective risk class (v) for the values of said plurality of clinical data (p) of said patient (P );- Bontrager( 0084) adding the values of said plurality of clinical data (p) of said patient (P) and the respective estimated risk class (v) to said training dataset ; Bontrager ( 0105 classifiers of different types… ) generating a plurality of modified datasets by modifying the values of said plurality of clinical data (p) of said patient (P); Bontrager ( 0108) estimating by means of said non-linear classifier a respective risk class (v) Bontrager( 0074 , according to google a neural network could be a form of non-linear classification ) for the values of each modified dataset ; - adding the values of each modified dataset and the respective estimated risk class (v) to said training dataset; determining a risk class (v) among said risk classes (v) estimated for said modified datasets indicating a lower risk than said risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P), Bontrager( 0213, risk classifications) training a linear classifier configured to estimate said risk class (v) as a function of the values of said plurality of clinical data (p) by using said training dataset; determining a separation plane of said linear classifier separating the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) from said risk class (v) indicating a lower risk; Bontrager( 0105) the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) in said risk class (v) indicating a lower risk; Bontrager( 0213) and - providing a support for clinical decisions by showing said change (MP) to the values of said plurality of clinical data (p) of said patient (P) on a display. Bontrager( 0149,… clinical decision support) As per claim 2, Bontrager discloses; The method according to Claim 1, comprising the following steps during a training phase: - obtaining an additional training dataset comprising for each of a plurality of reference patients (PR) respective values for said plurality of clinical data (p) and a respective value identifying a risk class (v); - training said non-linear classifier using said additional training dataset, wherein said non-linear classifier is configured to estimate said risk class (v) as a function of the values of said plurality of clinical data (p). Bontrager ( 0104 , 0107 ) Bontrager does not explicitly disclose what Tell teaches; using said separation plane to calculate the minimum change (MP) required to the values of said plurality of clinical data (p) of said patient (P) to change (0167) It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the AI driven therapy analysis of Bontrager with the Biopsy analysis of Tell for the motivation of “providing clinical support for cancer treatment” As per claim 3, Bontrager discloses; The method according to Claim 1 or Claim 2, wherein said risk class (v) indicating a lower risk than said risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) corresponds to the risk class (v) among said risk classes (v) estimated for said modified datasets indicating a minimum risk. Bontrager( 0213) As per claim 4 Bontrager discloses; The method according to Claim 3, wherein said linear classifier is a multi-class classifier, and said separation plane of said linear classifier separates said risk class (v) indicating a minimum risk from all other risk classes (v) estimated for said modified datasets. Bontrager( 0105) As per claim 5, Bontrager discloses; The method according to Claim 3, comprising: - replacing the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) with a first value; - associating the risk class (v) indicating a minimum risk with a second value; - replacing each risk class (v) estimated for a respective modified dataset with said first value if the respective risk class indicates a risk being greater than said risk class (v) indicating a minimum risk and said second value if the respective risk class corresponds to said risk class (v) indicating a minimum risk, - training said linear classifier, whereby said linear classifier is a binary classifier configured to classify the values of said plurality of clinical data (p) either with said first value or said second value, wherein said separation plane of said linear classifier separates said risk class (v) indicating a minimum risk from all other risk classes (v) estimated for said modified datasets . Bontrager (0105) As per claim 6, Bontrager discloses; The method according to any of the previous claimsClaim1, wherein said using said separation plane to calculate the minimum modification (MP) required comprises: - determining the clinical data (p) to be modified by calculating the normal of said separation plane; and - determining the extent of the modification to said clinical data (p) to be modified by calculating the minimum distance between the values of said plurality of clinical data (p) of said patient (P) and said separation plane. Bontrager (0109) As per claim 7 Bontrager discloses; The method according Claim 1, comprising receiving one or more constraints (CS) for modifications to the values of said plurality of clinical data (p) of said patient (P), and at least one of: - generating said plurality of modified datasets by modifying the values of said plurality of clinical data (p) of said patient (P) as a function of said one or more constraints (CS ); and/or - verifying whether the minimum modification (MP) required to the values of said plurality of clinical data (p) of said patient (P) satisfies said one or more constraints (CS). Bontrager (0085 constraint in the feature set) As per claim 8, Bontrager discloses; The m ethod according to Claim 7, wherein said one or more constraints (CS) comprise at least one of: - a blacklist containing clinical data which cannot be modified; and/or - a list containing the definitions of the range of acceptable values for one or more of said clinical data (p); and/or - data specifying one or more correlation matrices, wherein each correlation matrix specifies the correlation between a plurality of clinical data (p). Bontrager( 0209, blacklist) As per claim 9 Bontrager discloses; The method according to Claim 7, wherein said one or more constraints (CS) comprise a list of actions , such as taking a given drug and/or a given medical intervention and/or a given physical activity, wherein each action specifies a change to one or more of said clinical data (p), and wherein said method comprises: - determining whether one of said actions is compatible with said minimum modification (MP) required by verifying whether the direction of the changes to said one or more clinical data (p) corresponds substantially to the direction of said minimum modification (MP) required; and - in case an action is compatible with said minimum modification (MP) required, showing said action on said display. Bontrager( 0107 , such as is a generic list…. More of a suggestion, in 0109 various suggestions for intervention are discussed ) As per claim 10, Bontrager discloses; The method according to Claim 2, wherein said value identifying a risk class (v) corresponds to: - information on whether the patient will develop a given disease; - data that indicate the seriousness of the disease that the patient has developed; - data that identify a disease-free survival time; - data that identify whether a stay in hospital envisaged; or - data that identify a risk of death, onset of complications or the likelihood of adverse events in the case of surgical operation. Bontrager( 0067) As per claim 11 Bontrager discloses; The method according to any of the previous 1 wherein said clinical data (p) comprise one or more of: omics data, such as transcriptomics, genomics, epigenomics, metabolomics, methylomics , and/or proteomics data, data obtained via laboratory analyses, such as blood tests, radiological data, and general clinical data, such as age, sex, history of smoking. Bontrager( fig. 1 Omics) Claims 12 and 13 are similar to claim 1. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. A Multi-Learning Training Approach for Distinguishing Low and High Risk Cancer Patients - IEEE 2021 Machine Learning Model for Breast Cancer Prediction , IEEE, 2020 Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT BRUCE I EBERSMAN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3442 . 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