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 .
This communication is in response to the amendment received on 12/22/2025. Claims 1-15 remain pending in this application.
The objection to the drawings, the 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph and the 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph have been withdraw in light of the amendments and Remarks filed on 12/22/2025.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-14 are drawn to a method which is within the four statutory categories (i.e. process). Claim 15 is drawn to a system which is within the four statutory categories (i.e. machine).
Step 2A, Prong 1:
Claim 1 has been amended to recite:
“providing (S1) the first data set (DS 1);
providing a plurality of third data sets (DS3) of medical parameters of further other patients;
applying (S2) a machine learning algorithm (A1) to the first data set (DS 1) and to the plurality of third data sets (DS3) to classify or suggest an implantable medical device, a treatment and/or drug clinically associated with the first data set (DS1) of medical parameters of the patient; and
outputting (S3) a second data set (DS2) comprising at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS1)”,
Claim 14 has been amended to recite:
“providing (S1’) a first training data set comprising a first data set (DS1) of medical parameters of a patient;
providing (S2’) a second training data set comprising a second data set (DS2) comprising at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS 1); and
training (S3’) the machine learning algorithm (Al) by an optimization algorithm which calculates a threshold value of a loss function for classifying or suggesting at least one implantable medical device, the at least one treatment, and/or at least one drug clinically associated with the first data set (DS 1)”, and
Claim 15 has been amended to recite:
“means for providing a first data set (DS1) of medical parameters of a patient;
means for providing a plurality of third data sets (DS3) of medical parameters of other patients;
means for applying a machine learning algorithm (A1) to the first data set (DS1) and to the plurality of third data sets (DS3); and
means for outputting a second data set (DS2) comprising at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS1)”.
These limitations correspond to performing mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Claims 2-13 are ultimately dependent from claim 1 and include all the limitations of claim 1. Therefore, claims 2-13 recite the same abstract idea. Claims 2-13 describe a further limitation regarding the basis for classifying or suggesting a medical device, a treatment and/or drug clinically associated with the first data set (medical data of the patient). These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “wherein additionally a rule-based algorithm is applied to the first data set (DS1) and to the plurality of third data sets (DS3) to classify or suggest an implantable medical device, wherein the rule-based algorithm (A2) compares the first data set (DS1) with the plurality of third data sets (DS3) wherein each of the third data sets (DS3) is related to or comprises at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or at least one drug”- claim 3, “wherein the machine learning algorithm (A1) outputs the at least one class (C) with the first data set (DS 1) having a closest match to the plurality of third data sets (DS3)”- claim 4, “wherein the at least one class (C) is outputted by the machine learning algorithm (A1) in an order of similarity to the plurality of third data sets (DS3)”- claim 5, “wherein the machine learning algorithm (A1) comprises a first algorithm (A 1a; A2a) applied to the text-based medical data (DS 1a, DS3a) and a second algorithm (Alb; A2b) applied to the image-based medical data (DS1b, DS3b), wherein the first algorithm (Ala; A2a) outputs at least a first numeric value to which a first score is assigned, and wherein and the second algorithm (A1b; A2b) outputs at least a second numeric value to which a second score (16) is assigned”- claim 6, “wherein the second data set (DS2) is calculated by forming a weighted average from a sum product comprising a first product of the first numeric value and the first score, and a second product of the second numeric value and the second score”- claim 7, “wherein the machine learning algorithm (A1) outputs a third numeric value representing a number of patients using the implantable medical device, the treatment, and/or the drug clinically associated with the first data set (DS1) and text-based information indicating a patient outcome using the implantable medical device, the treatment, and/or the drug for a predetermined amount of time”- claim 8, “wherein the machine learning algorithm (A1) outputs a fourth numeric value representing a probability of a successful patient outcome using the medical device, the treatment, and/or the drug clinically associated with the first data set (DS1)”- claim 9, “wherein the machine learning algorithm (A1) outputs a fifth numeric value representing if the at least one implantable medical device, the at least one treatment, and/or the at least one drug is suitable for a current stage of a health condition of the patient”- claim 10, “wherein, in response to outputting the second data set (DS2), a medical practitioner information request (R1) is triggered requesting if the medical practitioner agrees or disagrees with using the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS 1)”- claim 11, “wherein if in response to the medical practitioner information request (R1) disagreement with using the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS1) of medical parameters of the patient for patient treatment is expressed, a further medical practitioner information request (R2) is triggered requesting to provide reasons for the disagreement”- claim 12, “wherein a response to the medical practitioner information request (R1) is used to train the machine learning algorithm (A1)”- claim 13.
These limitations correspond to mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Accordingly, these 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. The claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements that are recited in the claims amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception cannot provide an inventive concept.
The claims are not patent eligible.
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.
Claims 1-2, 4-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rapaka et al. (hereinafter Rapaka) (US 2018/0315182 A1).
Claim 1 has been amended to recite a computer implemented method for classifying or suggesting at least one implantable medical device, at least one treatment, and/or at least one drug clinically associated with a first data set (DS1) of medical parameters of a patient, comprising the steps of:
providing (S1) the first data set (DS 1) (Rapaka discloses “The medical scan data is acquired for a given patient ….” in [0029], “…the medical system obtains other data for the patients . The data is obtained by access to a memory , loading from memory , or transfer through an interface device . The other data is in one or more memories and / or from one or more sensors . The data is obtained from a computerized medical record , billing database , department records , picture archiving and communications system ( PACS ) , or another source …” in [0030]);
providing a plurality of third data sets (DS3) of medical parameters of further other patients (Rapaka discloses “In act 34 , the medical system provides decision support . Rather than just providing the predicted results , additional information is provided . For example , past cases for other patients similar to the current patient are identified . Similarity may be may be measured with least square difference , vector dot product , binary matching , or other comparison of known values . The search may be limited by the predicted results , such as limiting to patients with a same condition . Based on the identified cases of other patients , variation in treatment and outcome may be calculated . This distribution of treatments and outcomes is presented to guide decision making for treating the patient .” in [0080]);
applying (S2) a machine learning algorithm (A1) to the first data set (DS 1) and to the plurality of third data sets (DS3) to classify or suggest an implantable medical device, a treatment and/or drug clinically associated with the first data set (DS1) of medical parameters of the patient (Rapaka discloses “Based on the identified cases of other patients, variation in treatment and outcome may be calculated. This distribution of treatments and outcomes is presented to guide decision making for treating the patient” in [0080] and “In another embodiment , a sub - set of data for a current patient is identified . A sub - set of the medical scan data , other data , or both is determined based on the output of the machine learnt model or as the output of the machine learnt model . For example , the machine learnt model is trained to determine , based on current symptoms , the rel evant past events of the patients that may help the clinician to take decisions faster . By selecting this sub - set of the data , a reduction of the consultation time , which is a critical aspect for clinicians , may result . By outputting the selected sub - set of data for review , the time that a clinician allocates In to a single patient may be decreased .” in [0081]); and
outputting (S3) a second data set (DS2) comprising at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS 1) (Rapaka discloses “Based on the identified cases of other patients, variation in treatment and outcome may be calculated. This distribution of treatments and outcomes is presented to guide decision making for treating the patient” in [0080] and “In another embodiment , a sub - set of data for a current patient is identified . A sub - set of the medical scan data , other data , or both is determined based on the output of the machine learnt model or as the output of the machine learnt model . For example , the machine learnt model is trained to determine , based on current symptoms , the rel evant past events of the patients that may help the clinician to take decisions faster . By selecting this sub - set of the data , a reduction of the consultation time , which is a critical aspect for clinicians , may result . By outputting the selected sub - set of data for review , the time that a clinician allocates In to a single patient may be decreased .” in [0081]).
Claim 2 recites the computer implemented method of claim 1, wherein the first data set (DS1) comprises text-based medical data (DS1la) and image-based medical data (DS1b), wherein the plurality of third data sets (DS3) comprises text- based medical data (DS3a) and image-based medical data (DS3b) and medical treatment data (DS3c), wherein the text-based medical data (DS 1a, DS3a) comprise an age, a gender, a weight, comorbidities, a drug prescription history, a treatment history, a heart rate, a blood pressure, an activity profile and/or symptoms of the patient, and wherein the image-based medical data (DS 1b, DS3b) comprises a CT-scan, a MRI-scan, an angiograph and/or at least one ultrasound-image, and wherein the medical treatment data (DS3c) comprises data identifying a medical device, a treatment, and/or a drug (Rapaka; [0031]-[0032]).
Claim 4 has been amended to recite the computer implemented method of claim 3, wherein the machine learning algorithm (A1) outputs the at least one class (C) with the first data set (DS 1) having a closest match to the plurality of third data sets (DS3) (Rapaka; [0066], [0082]).
Claim 5 has been amended to recite the computer implemented method of claim 4, wherein the at least one class (C) is outputted by the machine learning algorithm (A1) in an order of similarity to the plurality of third data sets (DS3) (Rapaka; [0080]).
Claim 6 has been amended to recite the computer implemented method of claim 2, wherein the machine learning algorithm (A1) comprises a first algorithm (A 1a; A2a) applied to the text-based medical data (DS 1a, DS3a) and a second algorithm (Alb; A2b) applied to the image-based medical data (DS1b, DS3b), wherein the first algorithm (Ala; A2a) outputs at least a first numeric value to which a first score is assigned, and wherein and the second algorithm (A1b; A2b) outputs at least a second numeric value to which a second score (16) is assigned (Rapaka; [0037]).
Claim 7 recites the computer implemented method of claim 6, wherein the second data set (DS2) is calculated by forming a weighted average from a sum product comprising a first product of the first numeric value and the first score, and a second product of the second numeric value and the second score (Rapaka; [0037]-[0038]).
Claim 8 has been amended to recite the implemented method of claim 6, wherein the machine learning algorithm (A1) outputs a third numeric value representing a number of patients using the implantable medical device, the treatment, and/or the drug clinically associated with the first data set (DS1) and text-based information indicating a patient outcome using the implantable medical device, the treatment, and/or the drug for a predetermined amount of time (Rapaka; [0037]-[0038]).
Claim 9 has been amended to recite the computer implemented method of claim 8, wherein the machine learning algorithm (A1) outputs a fourth numeric value representing a probability of a successful patient outcome using the implantable medical device, the treatment, and/or the drug clinically associated with the first data set (DS1) (Rapaka; [0068]).
Claim 10 has been amended to recite the implemented method of claim 9, wherein the machine learning algorithm (A1) outputs a fifth numeric value representing if the at least one implantable medical device, the at least one treatment, and/or the at least one drug is suitable for a current stage of a health condition of the patient (Rapaka; [0068]).
Claim 11 has been amended to recite the computer implemented method of claim 1, wherein, in response to outputting the second data set (DS2), a medical practitioner information request (R1) is triggered requesting if the medical practitioner agrees or disagrees with using the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS 1) (Rapaka; [0069]).
Claim 12 has been amended to recite the computer implemented method of claim 11, wherein if in response to the medical practitioner information request (R1) disagreement with using the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS1) of medical parameters of the patient for patient treatment is expressed, a further medical practitioner information request (R2) is triggered requesting to provide reasons for the disagreement (Rapaka; [0069]).
Claim 13 has been amended to recite the computer implemented method of claim 11, wherein a response to the medical practitioner information request (R1) is used to train the machine learning algorithm (A1) (Rapaka; [0069]).
Claim 14 has been amended to recite a computer implemented method for providing a trained machine learning algorithm (A1) for classifying or suggesting at least one medical device, at least one treatment, and/or at least one drug clinically associated with a first data set (DS1) of medical parameters of a patient, comprising the steps of:
providing (S1’) a first training data set comprising a first data set (DS1) of medical parameters of a patient (Rapaka discloses “For a given patient , the machine learnt classifier determines the results from the input feature vector . For example , the condition and / or risk due to the condition are determined . Once trained , the machine learnt classifier is instantiated as a matrix or matrices . The matrix maps the values of the input features to values of the result ( e . g . , condition ) . …” in [0068]);
providing (S2’) a second training data set comprising a second data set (DS2) comprising at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS 1) (Rapaka discloses “For a given patient , the machine learnt classifier determines the results from the input feature vector . For example , the condition and / or risk due to the condition are determined . Once trained , the machine learnt classifier is instantiated as a matrix or matrices . The matrix maps the values of the input features to values of the result ( e . g . , condition ) . …” in [0068]); and
training (S3’) the machine learning algorithm (Al) by an optimization algorithm which calculates a threshold value of a loss function for classifying or suggesting at least one implantable medical device, the at least one treatment, and/or at least one drug clinically associated with the first data set (DS 1) (Rapaka discloses “As the machine learnt model is used , the machine learnt model may be updated . The results for a given patient are verified by a physician . The model being correct or not may be used to trigger whether to retrain the model . The verified results for a patient may be used as ground truth , providing another sample for training . …Once sufficient additional data is collected , the new data is combined with the existing data to train a new version of the machine learnt model , which is then deployed in the clinical setting …” in [0069]).
Claim 15 has been amended to recite a system for classifying or suggesting at least one implantable medical device, at least one treatment, and/or at least one drug clinically associated with a first data set (DS1) of medical parameters of a patient, comprising:
means for providing a first data set (DS1) of medical parameters of a patient (Rapaka; [0029] [0030]);
means for providing a plurality of third data sets (DS3) of medical parameters of other patients (Rapaka; [0080]);
means for applying a machine learning algorithm (A1) to the first data set (DS1) and to the plurality of third data sets (DS3) (Rapaka; [0081]); and
means for outputting a second data set (DS2) comprising at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS1) (Rapaka; [0081]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Rapaka et al. (hereinafter Rapaka) (US 2018/0315182 A1) in view of Casey et al. (hereinafter Casey) (US 11,376,076 B2).
Claim 3 has been amended to recite the computer implemented method of claim 1, wherein additionally a rule-based algorithm is applied to the first data set (DS1) and to the plurality of third data sets (DS3) to classify or suggest an implantable device, wherein the rule-based algorithm (A2) compares the first data set (DS1) with the plurality of third data sets (DS3) wherein each of the third data sets (DS3) is related to or comprises at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or at least one drug.
Rapaka discloses “Based on the identified cases of other patients, variation in treatment and outcome may be calculated. This distribution of treatments and outcomes is presented to guide decision making for treating the patient” in [0080], but fails to expressly teach “a rule-based algorithm is applied to the first data set (DS1) and to the plurality of third data sets (DS3) to classify or suggest an implantable device”. However, this feature is well known in the art, as evidenced by Casey.
In particular, Casey discloses “…Al techniques can include, but are not limited to, case - based reasoning, rule - based systems,…” in col. 11, lines 17-25 and “To generate a treatment plan, the patient data set 108 can be input into the trained machine learning model(s). Additional data, such as the selected subset of reference patient data sets and/or similar patient data sets, and/or treatment data from the selected subset, can also be input into the trained machine learning model(s). The trained machine learning model(s) can then calculate whether various candidate treatment procedures and/or medical device designs are likely to produce a favorable outcome for the patient…” in col. 11, line 64 to col. 12, line 5.
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include the aforementioned limitation as disclosed by Casey with the motivation of providing favorable outcome for the patient (Casey; col. 12, lines 2-5).
Response to Arguments
Applicant's arguments filed 12/22/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claims are not directed to an abstract idea, the claims recite “providing structured medical data; applying an algorithm to these datasets, generating a structured output dataset containing a medical device/treatment/drug class”, and these limitations are directed to more than an abstract calculation.
In response, Examiner submits that claims recite “providing (S1) the first data set (DS 1); providing a plurality of third data sets (DS3) of medical parameters of further other patients; applying (S2) a machine learning algorithm (A1) to the first data set (DS 1) and to the plurality of third data sets (DS3) to classify or suggest an implantable medical device, a treatment and/or drug clinically associated with the first data set (DS1) of medical parameters of the patient; and outputting (S3) a second data set (DS2) comprising at least one class (C) representing the at least one implantable medical device, the at least one treatment, and/or the at least one drug clinically associated with the first data set (DS1)” (classifying datasets by applying a machine learning algorithm and outputting data), which corresponds to mathematical calculations, therefore these limitations fall within the “mathematical concept” grouping of abstract ideas (see MPEP §2106.04(a)(2)).
Applicant argues that the claims recite more than an abstract idea, for instance, claims are directed to a computer-implemented data processing pipeline with defined inputs and outputs based upon specific data sets, that the steps cannot be performed mentally.
In response, Examiner submits that claim limitations are directed to mathematical concepts and not directed to mental processes. The MPEP recites “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” in § 2106.04(a)(2). Also, see the example “using an algorithm for determining the optimal number of visits by a business representative to a client” (In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979)).
Arguments about 35 USC 102 rejection:
Applicant argues that Rapaka does not teach “the output class represents at least one implantable medical device, a treatment, or a drug”.
In response, Examiner submits that claim limitation recites “applying (S2) a machine learning algorithm (A1) to the first data set (DS 1) and to the plurality of third data sets (DS3) to classify or suggest an implantable medical device, a treatment and/or drug clinically associated with the first data set (DS1) of medical parameters of the patient” and Rapaka discloses “Based on the identified cases of other patients, variation in treatment and outcome may be calculated. This distribution of treatments and outcomes is presented to guide decision making for treating the patient” in [0080]. The claim limitation requires classifying or suggesting an implantable medical device, a treatment and/or drug, so the implantable device is one of the options recited in the claims, and the reference does not have to teach all of the options. Rapaka discloses “variation in treatment and outcome may be calculated”, which involves a treatment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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/DILEK B COBANOGLU/ Primary Examiner, Art Unit 3687
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