DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed on September 09, 2025.
Claims 16-30 are pending
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 .
Information Disclosure Statement(s)
The information disclosure statement (IDS) submitted on 09/10/2025 was considered by the examiner.
Claim Rejection - 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 16-30 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter the grounds set out in detail below:
Independent Claims 16, 26, 29, and 30:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 16 falls within the statutory category of a method.
Independent claim 26 falls within the statutory category of a machine.
Independent claims 29 and 30 do not fall within a statutory category as they recite signals per se. For example, the BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves). Examiner notes for purposes of advancing prosecution the claims are only presumed to fall within statutory category to complete the remainder of the 101 analysis.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 16, 26, 29, and 30 claimed invention recites an abstract idea as set forth below:
The claim elements in the independent claims (claim 16 as representative) which set forth the abstract idea are:
method for classification of an occurrence of a complication caused by preeclampsia within a predetermined time period, comprising:
providing (S1) a pre-recorded first dataset (DS1) of medical parameters of a patient;
at least partially transforming (S2) the first dataset (DS1) of medical parameters of the patient using a first algorithm (A1), in particular a multivariate data analysis method, into a reduced second dataset (DS2) of medical parameters of the patient;
applying (S3) …[…]…to the second dataset (DS2) of medical parameters of the patient to classify the occurrence of the complication caused by preeclampsia within the predetermined time period;
and outputting (S4) a class (K1, K2) representing the occurrence of the complication caused by preeclampsia within the predetermined time period.
which falls within “certain methods of organizing human activity” as it is following rules and instructions to classify an occurrence of the complication caused by preeclampsia within the predetermined time period. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): Independent Claims 16, 26, 29, and 30 do not integrate the judicial exception into a practical application.
In Claim 16 the additional elements are:
Computer
a second machine learning algorithm (A2)
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, computer, is recited executing the abstract idea and is recited as a general computer which applies the abstract idea as “apply-it” as it is merely applying the abstraction with a generic computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The additional element, a second machine learning algorithm (A2), is recited as “apply-it” as a computer tool or an equivalent (i.e. “applying”). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In Claim 26 the additional elements are:
at least one input device (14)
a first computing device (16)
a second computing device (18)
an output device (20)
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a)-(d) are recited as executing the abstract idea and is recited as a general computer which applies the abstract idea as “apply-it” as it is merely applying the abstraction generic computer tools. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In Claim 29 and Claim 30 the additional elements are:
Computer program with program code executed on a computer
Computer-readable medium with program code of a computer program executed on a computer
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element,(a) and (b), are recited executing the abstract idea and are recited as a general computer which applies the abstract idea as “apply-it” as it is merely applying the abstraction with a generic computer. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Accordingly, claims 16, 26, 29, and 30 do not integrate the abstract idea into a practical application.
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims 16, 26, 29, and 30 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as analyzed above in step 2A prong 2 above, these additional elements, whether viewed individually or as an ordered combination, amount to no more than applying the abstract idea thus insufficient to provide “significantly more”. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Dependent Claims 17-25 and 27-28:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 17-25 fall within the statutory category of method.
The dependent claims 27-28 fall within the statutory category of machine.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 17-25 and 27-28 claimed invention continue to recite the abstract idea.
Dependent claims 17-25 and 27-28 continue to limit the abstract idea in the independent claims by further limiting the first dataset in claims 17, 18, and 27 further limiting the classification in claims 19, 21, and 22-25 further limiting the second dataset in claim 20 and 28. Thus, inheriting the same abstract idea which falls within “certain methods of organizing human activity” as it is following rules and instructions to classify an occurrence of the complication caused by preeclampsia within the predetermined time period. See MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): Claims 17-25 and 27-28 do not integrate the judicial exception into a practical application.
In Claims 17-25 and 27-28 the additional elements not already recited in the independent claims are:
a user interface
a patient app
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, (a) and (b) are recited as tools or the equivalent which apply the abstract idea as “apply-it” as they are merely applying the abstract idea to gather and output data. 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. The claim is directed to an abstract idea.
Eligibility Step 2B (Does the claim amount to significantly more?): Dependent claims 17-25 and 27-28 do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons given in Prong 2A-2. Therefore, the claims do not amount to significantly more and the claims are ineligible.
Claim Rejections - 35 USC § 112(a)
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.
Claims 16-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The as-filed disclosure does not provide any description as to how a classification of an occurrence of the complication caused by preeclampsia is determined by a first algorithm and a second machine learning algorithm. The elements do appear in the as filed disclosure but purely disclosed as providing the result “an occurrence of the complication caused by preeclampsia” with no further understanding of how the algorithms achieve the result. As such, applicant was not in possession of the claimed invention at the time of filing. The Examiner prospectively notes that this written description rejection is not based on whether one skilled in the art would know how to program a computer to perform any form of a classification of an occurrence of the complication caused by preeclampsia (i.e., an enablement rejection), but rather is directed to the Applicant' s lack of specificity as to how the classification of an occurrence of the complication caused by preeclampsia is specifically performed with respect to the Applicant' s claimed invention (i.e., a written description-possession rejection).
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.
Claims 16, 20, 21, 22, 23, 24, 25, 26, and 27 are rejected under 35 U.S.C. 102(a)(1) based upon a public use or sale or other public availability of the invention. Schmidt, Leon J., et al. "A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes." American journal of obstetrics and gynecology 227.1 (2022): 77-e1. (Year: 2022)
As per claim 16, Schmidt teaches:
a. Computer-implemented method for classification of an occurrence of a complication caused by preeclampsia within a predetermined time period (i.e. any time up until visit up until 14 days post delivery) , comprising: (page 1 objective discloses, “This study aimed to develop, train, and test an automated machine-learning model for the prediction of adverse outcomes in patients with suspected preeclampsia.” And see page 2 study outcome discloses, “This study’s main target variable was the occurrence of a composite fetal or maternal AO at any time after a visit, up until 14 days after delivery of the neonate, if not specified otherwise.”)
b. providing (S1) a pre-recorded first dataset (DS1) of medical parameters of a patient; (see table 1 study population characteristics e.g. proteinuria, systolic blood pressure, diastolic blood pressure, an sFlt-1 value, a PIGF value)
c. at least partially transforming (S2) the first dataset (DS1) of medical parameters of the patient using a first algorithm (A1), in particular a multivariate data analysis method, into a reduced second dataset (DS2) of medical parameters of the patient; (see page 3 algorithms which discloses, “Thoroughly following best practice guidelines for the development of ML systems, we chose 2 different ML algorithms, random forest (RF) classifier and gradient-boosted tree (GBTree), for examination of our dataset and focused on methods based on decision tree learning using classification trees.” and see Concrete values for each features’ mean absolute Shapley values can be found in the appendix (Supplemental Table 2) along with an exemplary illustration of Shapley values’ interpretation for a specific example (Supplemental Figure 1).” / examiner notes that random forest classifier is algorithm A1 and the population characteristics transformed into illustrated mean Shapley values e.g. supplementary figure 1 is based on PHOSITA definition multivariate analysis as features are considered simultaneously to understand which one has the most impact)
d. applying (S3) a second machine learning algorithm (A2) to the second dataset (DS2) of medical parameters of the patient to classify the occurrence of the complication caused by preeclampsia within the predetermined time period; (see page 3 algorithms which discloses, “Thoroughly following best practice guidelines for the development of ML systems, we chose 2 different ML algorithms, random forest (RF) classifier and gradient-boosted tree (GBTree), for examination of our dataset and focused on methods based on decision tree learning using classification trees.” And see And see page 7 interpreting the model which discloses, “The highest impact features derived by Shapley values were the gestational age (0.725 0.073), measurement of sFlt-1etoePlGF ratio outside of the 95th percentile (0.237 0.021), sFlt-1e to ePlGF ratio MoM (0.144 0.025), sFlt-1 MoM (0.184 0.023), PlGF deviation from median (0.157 0.028), and height (0.14 0.032) (Figure 2). fig. 2 and see supplemental table 4 / examiner notes GBTree is the algorithm A2 and fig. 2 is an example of reduced shapley value dataset D2)
e. and outputting (S4) a class (K1, K2) representing the occurrence of the complication caused by preeclampsia within the predetermined time period. (see supplemental table 4 and see supplemental table 8 and table 9 which disclose a classification of AO events and examples of when the model predicted the AO event in counter to negative biomarkers)
7. As per claim 20, Schmidt further teaches:
a. Computer-implemented method according to claim 16, wherein the transformed second dataset (DS2) comprises the patient's age (see supplemental table 2 page 16) , body mass index before pregnancy (see fig. 2 BMI), multiples of the median of sFlt-1 (see fig. 2 sFlt LAB multiple of median) , multiples of the median of PIGF, (see fig.2) multiples of the median of the ratio of sFlt-1 and PIGF (see fig. ) , a deviation from the mean systolic blood pressure (see fig. 2) , and a deviation from the mean diastolic blood pressure. (see supplemental table 2 page 16)
8. As per claim 21, Schmidt further teaches:
a. Computer-implemented method according to claim 20, wherein the second machine learning algorithm (A2) and/or a third machine learning algorithm (A3) applied to the second dataset (DS2) of medical parameters of the patient outputs a classification of a delivery caused by preeclampsia within a predetermined time period. (see supplemental table 4 page 32 e.g. AO and delivery before 34 weeks gestation and see supplementary table 8 and 9 on pages 26-27 e.g. days to delivery and GBtree adverse outcome prediction and see page 2 study population discloses, “All women presented without the manifest clinical diagnosis of preeclampsia.”)
9. As per claim 22, Schmidt further teaches:
a. Computer-implemented method according to claim 21, wherein the second machine learning algorithm (A2) and/or the third machine learning algorithm (A3) applied to the second dataset (DS2) of medical parameters of the patient outputs the classification of a delivery caused by preeclampsia within the next 14 days for patients before the 34th week of pregnancy and the classification of a delivery caused by preeclampsia within the next 7 days for patients after the 34th week of pregnancy. (see supplemental table 4 page 32 e.g. AO and delivery before 34 weeks gestation and AO and delivery after 34 wk of gestation and see and see page 2 study population discloses, “All women presented without the manifest clinical diagnosis of preeclampsia.” /examiner notes MPEP § 2144.05, the claimed day ranges lie inside ranges disclosed by the prior art of before and after 34 weeks of pregnancy for delivery therefore a prima facie case of obviousness exists.)
10. As per claim 23, Schmidt further teaches:
a. Computer-implemented method according to claim 20, wherein the second machine learning algorithm (A2) and/or a fourth machine learning algorithm (A4) applied to the second dataset (DS2) of medical parameters of the patient outputs a classification of an occurrence of preeclampsia. (page 2 discloses, “All women presented without the manifest clinical diagnosis of preeclampsia.”/ examiner notes the occurrence is known if clinical diagnosis is preeclampsia )
11. As per claim 24, Schmidt further teaches:
a. Computer-implemented method according to claim 23, further comprising providing the classification generated by the second machine learning algorithm (A2), the third machine learning algorithm (A3), and/or the fourth machine learning algorithm (A4). (see supplemental table 4 page 32 e.g. AO and delivery before 34 weeks gestation and see supplementary table 8 and 9 on pages 26-27 e.g. days to delivery and GBtree adverse outcome prediction and see page 2 discloses, “All women presented without the manifest clinical diagnosis of preeclampsia.”/ examiner notes the occurrence is known if clinical diagnosis is preeclampsia)
12. As per claim 25, Schmidt further teaches:
a. Computer-implemented method according to claim 16, wherein the classification of the occurrence of the complication caused by preeclampsia within the predetermined time period comprises a first class (K1) representing a prediction of the occurrence of the complication caused by preeclampsia within the predetermined time period (i.e. e.g. Maternal death) , and wherein a second class (K2) of the classification represents a prediction of the non-occurrence (i.e. e.g. Maternal cerebral hemorrhage at 0) of the complication caused by preeclampsia within the predetermined time period. (see supplemental table 4 maternal death and maternal cerebral hemorrhage respectively)
13. As per claims 26 and 27 they are system claims which repeat the same limitations of claims 1 and 2, the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Schmidt disclose the underlying process steps that constitute the methods of claims 1 and 2 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 26 and 27 are rejected for the same reasons given above for claims 1 and 2.
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 17 are rejected under 35 U.S.C. 103 as being unpatentable over Schmidt, Leon J., et al. "A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes." American journal of obstetrics and gynecology 227.1 (2022): 77-e1. (Year: 2022) in view of Peri et. al (hereinafter Peri) (US20240038402A1)
14. As per claim 17, Schmidt further teaches:
a. Computer-implemented method according to claim 16, wherein the first dataset (DS1) comprises a first set of medical parameters of the patient recorded…[…]…, comprising protein levels in urine, systolic blood pressure, diastolic blood pressure, an sFlt-1 value, a PIGF value, a classification of thrombocytopenia, and a classification of elevated liver enzymes. (see table 1 study population characteristics e.g. proteinuria, systolic blood pressure, diastolic blood pressure, an sFlt-1 value, a PIGF value and see study population discloses, “We included all women aged 18years, with a singleton pregnancy, and a gestational age of 20 weeks with signs and symptoms of preeclampsia, available lab oratory values for the sFlt-1etoePlGF ratio, and complete outcome data. “Signs and symptoms” of preeclampsia were defined as elevated blood pressure (systolic blood pressure of 140 mm Hg and diastolic blood pressure of 90 mm Hg) or preexisting hypertension without concurring proteinuria; determined by urine dipstick reading >2þ in 2 separate tests at least 6 hours apart or 300 mg/L in 24-hour urine sampling without concurring hypertension; at least 1 preeclampsia-related symptom (headache, visual disturbances, progressive edema and/or excessive weight gain, or upper epigastric pain); abnormalities in specific laboratory values (isolated low platelet count or elevated liver enzymes);” / thrombocytopenia is interpreted by examiner as isolated low platelet count )
15. However, Schmidt does not explicitly teach:
a. …[…]…by a user interface (10) of a medical practitioner
16. However, Peri does teach:
a. …[…]…by a user interface (10) of a medical practitioner ([0039] discloses, “The interactive web interface comprises of (i) an input platform for input of data by Clinicians and pregnant women and (ii) a display platform for providing the insights regarding the maternal health condition rendered as risk scores in a webpage and (iii) a display platform for providing the insights regarding the maternal health condition rendered as graphical charts on the interactive dashboard using various risk indicators of pregnant women.”)
17. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Schmidt’s teachings as previously cited with Peri’s teachings as previously cited, the motivation being Schmidt utilizes machine learning which is a computer based math therefore it would be predictable for a computer to have an interface and simply choice data that is displayed and would improve the efficiency and transparency of viewing the data.
Claims 18, 19, and 28 is rejected under 35 U.S.C. 103 as being unpatentable over Schmidt, Leon J., et al. "A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes." American journal of obstetrics and gynecology 227.1 (2022): 77-e1. (Year: 2022) in view of Peri et. al (hereinafter Peri) (US20240038402A1) and in further view of Zhang et. al (hereinafter Zhang) (CN116994751A)
18. As per claim 18, Schmidt further teaches:
a. Computer-implemented method according to claim 17, wherein the first dataset (DS1) comprises a second set of medical parameters of the patient recorded…[…]…comprising the patient's age (see page 5 table 1), …[…]…height (see page 5 table 1), see a classification of hypertension before the current pregnancy, (see page 6 table 1), a classification of new onset hypertension during the current pregnancy, (see page 6 table 1), and gestational age, in particular in days (see page 5 table 1).
19. However, Schmidt does not explicitly teach:
a. …[…]… by a patient app (12) and/or the user interface (10) of the medical practitioner,…[…]…
20.. However, Peri does teach:
a. …[…]… by a patient app (12) and/or the user interface (10) of the medical practitioner,…[…]… ([0039] discloses, “The interactive web interface comprises of (i) an input platform for input of data by Clinicians and pregnant women and (ii) a display platform for providing the insights regarding the maternal health condition rendered as risk scores in a webpage and (iii) a display platform for providing the insights regarding the maternal health condition rendered as graphical charts on the interactive dashboard using various risk indicators of pregnant women.”)
21. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Schmidt’s teachings as previously cited with Peri’s teachings as previously cited, the motivation being Schmidt utilizes machine learning which is a computer based math therefore it would be predictable for a computer to have an interface and simply choice data that is displayed and would improve the efficiency and transparency of viewing the data.
22. However, Peri also does not teach:
a. weight before the current pregnancy
23. However, Zhang does teach:
a. weight before the current pregnancy (see page 4 step 1 discloses, “such as the weight before pregnancy,”)
24. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Schmidt’s teachings as previously cited and Peri’s teachings with Zhang’s teachings as previously cited, the motivation being Schmidt teaches tracking excessive weight gain (see page 2), thus it would be obvious that to track weight gain you must know the starting weight and is choice data which only improves precision of classification for complications.
25. As per claim 19, Schmidt further teaches:
a. Computer-implemented method according to claim 18, wherein the second machine learning algorithm (A2) performs a further classification of the occurrence of the complication caused by preeclampsia within the predetermined time period based on the re-provision of medical parameters of the patient recorded (see supplemental table 3 e.g. new onset of hypertension this visit)
26. However, Schmidt does not teach:
a. …[…]…by the patient app (12)….[…]…
27. However, Peri does teach:
a. …[…]…by the patient app (12)….[…]… ([0039] discloses, “The interactive web interface comprises of (i) an input platform for input of data by Clinicians and pregnant women and (ii) a display platform for providing the insights regarding the maternal health condition rendered as risk scores in a webpage and (iii) a display platform for providing the insights regarding the maternal health condition rendered as graphical charts on the interactive dashboard using various risk indicators of pregnant women. / examiner notes pregnant woman dashboard is considered app as its on a device with ability to input information)
28. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Schmidt’s teachings as previously cited with Peri’s teachings for the same reasons as given in claim 17.
29. As per claim 28 it is a system claim which repeat the same limitations of claim 18, the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings of Schmidt, Peri, and Zhang as well as motivations to combine disclose the underlying process steps that constitute the methods of claim 18 it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 28 is rejected for the same reasons given above for claim 18.
30. As per claims 29 and 30 the teachings of Schmidt disclose the underlying process steps that constitute the method of claim 1 and the the executable instructions that perform the steps are cited below:
31. As per claim 29, Schmidt and Peri do not teach:
a. Computer program with program code to execute at least parts of a method according to claim 16, when the computer program is executed on a computer.
32. However, Zhang does teach:
a. Computer program with program code to execute at least parts of a method according to claim 16, when the computer program is executed on a computer. (page 4 discloses, “The device is implemented in whole or in part by software, hardware, firmware or any combination thereof. When used, it is implemented in whole or in part in the form of a computer program product, and the computer program product includes one or more computer instructions; When the computer program instructions are loaded or executed on a computer, the processes or functions described in this method are generated in whole or in part; The computer is a general-purpose computer, a special-purpose computer, a computer network, or other programmable device; The computer instructions are for storage in a computer-readable storage medium or for transmission from one computer-readable storage medium to another computer-readable storage medium (for example, the computer instructions may be transferred from a website, computer, server or data The center transmits to another website site, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (such as infrared, wireless, microwave, etc.) means).”)
33. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Schmidt’s teachings as previously cited and Peri’s teachings with Zhang’s teachings as previously cited, the motivation being Schmidt utilizes machine learning which is a computer based math therefore it would be predictable for a computer to have an software and a medium and simply choice data that is displayed and analyzed would improve the efficiency and transparency of viewing the data.
.
34. As per claim 30, Schmidt and Peri do not teach:
a. Computer-readable medium with program code of a computer program to execute at least parts of a method according to claim 16, when the computer program is executed on a computer.
35. However, Zhang does teach:
Computer-readable medium with program code of a computer program to execute at least parts of a method according to claim 16, when the computer program is executed on a computer. (page 7 and page 8 disclose, “the computer is a general purpose computer, special purpose computer, computer network, or other programmable apparatus; the computer instructions are for storage in a computer-readable storage medium or transmission from one computer-readable storage Method and device for constructing pre-eclampsia early-stage risk prediction model A the computer instructions are for storage in a computer-readable storage medium or transmission from one computer-readable storage medium to another computer-readable storage medium (e.g., the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer readable storage medium is any available medium that can be accessed by a computer or a data storage device such as a server, a data center, etc. that contains one or more integration of the available media; the usable medium is a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk).”)
36. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Schmidt’s teachings as previously cited and Peri’s teachings with Zhang’s teachings as previously cited, the motivation being Schmidt utilizes machine learning which is a computer based math therefore it would be predictable for a computer to have an software and a medium and simply choice data that is displayed and analyzed would improve the efficiency and transparency of viewing the data.
Prior Art Cited But Not Relied Upon
KOOIJMAN et. al (hereinafter KOOIJMAN) (EP4557307A1)
CSOBAN et. al (hereinafter CSOBAN) (WO2024153941A1)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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