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 .
Remarks
In response to communications sent August 8, 2025, claim(s) 1-3 and 5-19 is/are pending in this application; of these claim(s) 1 and 9 is/are in independent form. Claim(s) 4 is/are cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 8, 2025 has been entered.
Response to Arguments
Applicant’s arguments, see page 6 lines 6-9, filed August 8, 2025, with respect to claims 4 and 10-19 have been fully considered and are persuasive. The rejection of June 10, 2025 has been withdrawn.
Regarding 35 U.S.C. § 101: Applicant's arguments filed August 8, 2025 have been fully considered but they are not persuasive. The improvement to “prediction” is an improvement to the judicial exception itself. In response to applicant's argument regarding “a particular machine architecture…” rather than a general purpose computer, it is noted that the features upon which applicant relies (i.e., “ConvLSTM”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
For compact prosecution, the Examiner suggests reciting the element of the ConvLSTM in the claims, as the basis for temporal image analysis (i.e. taking into account “time changes”). See Applicant’s Remarks on page 6 line 22 to page 7 line 18 sent August 8, 2025. Note that Applicant’s arguments were intended to pertain to 35 U.S.C. § 101, but the ConvLSTM in the context of the Specification at Para [0031] and [0044] could be recited in the claims to advance prosecution regarding the rejection under 35 U.S.C. § 103 below.
Applicant’s arguments, see page 3 line 20, filed August 8, 2025, with respect to the rejection(s) of claim(s) 1-3 and 5-19 under 35 U.S.C. § 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. § 103 over US-20030198316-A1 (“Dewaele”) and US 5769074 A (“Barnhill”). Note that in the current rejection of record:
The Examiner now cites Dewaele Para [0035] for quantitative computer tomography images, which by definition involves multiple images of the same person at different times.
The Examiner cites to Barnhill Col 20 lines 28-41 for the element of blood-based or urine-based diagnostics.
The Examiner clarifies that the medical images are not directly used to train the neural network, but instead produce intermediate data to train the neural network. Hence, the neural network indirectly “uses” the images via the intermediate values.
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 1-3 and 5-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mental process, specifically the mental process of prediction. This judicial exception is not integrated into a practical application because the additional elements are mere data gathering and output, which are necessary pre-solution activity for the abstract idea . The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements, alone and in combination, are well-understood, routine, and conventional according to the legal precedent. See MPEP § 2106.05(d).II.i. regarding sending and receiving data over a network. That section recites:
“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added))”
See below for specific clarification of how claim elements are interpreted and analyzed:
1. A disease predicting system comprising:
a prediction unit comprising a neural network trained using a plurality of sets of a medical image and a hormone agent corresponding to the medical image and supervised data comprising a known disease corresponding to each set of medical image and corresponding hormone agent, wherein the plurality of sets includes two or more sets corresponding to a same person, each of the two or more sets corresponding to a different time (a mental process on applied on a general purpose computer and general purpose neural network);
an input unit comprising an input device configured to receive input information comprising first information including a medical image of a subject used in diagnosis of a disease and second information comprising a measurement value of a hormone agent in blood or in urine of the subject is input (inputting is not the mental process; nevertheless, it is not integrated into a practical application or significantly more than an abstract idea; see MPEP § 2106.05(g), which instructs that mere data gathering insignificant pre-solution activity: “Mere Data Gathering: i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989)”; courts have found receiving data over a network to be well-understood routine and conventional according to MPEP § 2106.05(d).II.i.); and
a control unit comprising a processor configured to control the prediction unit to analyze the first information of the subject and the second information of the subject to predict a future onset of the disease for the subject from the input information (a mental process applied on a general purpose computer).
2. The disease predicting system according to claim 1, further comprising
an output unit comprising a display device configured to display a prediction result predicted by the control unit (outputting is insignificant post-solution activity; the Examiner interprets this as sending data over a network and therefore well-understood routine and conventional; see MPEP § 2106.05(d).II.i.).
3. The disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (part of the mental process).
5. The disease predicting system according to claim l, wherein
the prediction is a change over time (limitations to the functionality of the mental process).
6. The disease predicting system according to claim l, wherein
the input information comprises third information relating to a health status of the subject (limitations to the data contemplated by the mental process).
7. The disease predicting system according to claim l, wherein
the input information comprises fourth information relating to intervention for the subject (limitations to the data contemplated by the mental process).
8. The disease predicting system according to claim l, wherein
the processor of the control unit is configured to predict a first result based on the input information excluding fourth information and a second result based on the input information including the fourth information (limitations to the functionality of the mental process).
9. A non-transitory computer-readable medium storing a program executable by a disease predicting system comprising a control unit, an input unit, and an output unit, the program causing the control unit to execute (general purpose computer):
acquiring, via the input unit, input information comprising first information including a medical image of a subject used in diagnosis of a disease and second information comprising a measurement value of a hormone agent in blood or urine of the subject (inputting is not mental process; but it is not integrated into a practical application or significantly more than an abstract idea; see MPEP § 2106.05(g), which instructs that mere data gathering insignificant pre-solution activity: “Mere Data Gathering: i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989)”; courts have found receiving data over a network to be well-understood routine and conventional according to MPEP § 2106.05(d).II.i.);
predicting a future onset of the disease for the subject from the input information using an artificial neural network trained using a plurality of sets of a medical image and a hormone agent corresponding to the medical image and supervised data comprising a known disease corresponding to each set of medical image and corresponding hormone agent, wherein the plurality of sets includes two or more sets corresponding to a same person, each of the two or more sets corresponding to a different time (a mental process on applied on a general purpose computer and general purpose neural network); and
outputting a prediction result predicted by the control unit (outputting is insignificant post-solution activity; the Examiner interprets this as sending data over a network and therefore well-understood routine and conventional; see MPEP § 2106.05(d).II.i.).
10. The disease predicting system according to claim 1, wherein two or more diseases are simultaneously predicted (mental process).
11. The disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (limitations to the functionality of the mental process)., and
the prediction is a change over time (limitations to the functionality of the mental process)..
12. The disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (limitations to the functionality of the mental process), and
the prediction is a change over time (limitations to the functionality of the mental process).
13. The disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (limitations to the functionality of the mental process), and
the input information comprises third information relating to a health status of the subject (limitations to the data contemplated by the mental process).
14. The disease predicting system according to claim 1, wherein two or more diseases are simultaneously predicted (limitations to the functionality of the mental process), and the input information comprises third information relating to a health status of the subject (limitations to the data contemplated by the mental process).
15. The disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (limitations to the functionality of the mental process),
the prediction is a change over time (limitations to the data contemplated by the mental process), and
the input information comprises third information relating to a health status of the subject (limitations to the data contemplated by the mental process).
16. The disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (limitations to the functionality of the mental process), and
the input information comprises fourth information relating to intervention for the subject (limitations to the data contemplated by the mental process).
17. The disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (limitations to the functionality of the mental process), and
the input information comprises fourth information relating to intervention for the subject (limitations to the data contemplated by the mental process).
18. The disease predicting system according to claim 1, wherein two or more diseases are simultaneously predicted (limitations to the functionality of the mental process), the prediction is a change over time (limitations to the functionality of the mental process), and the input information comprises fourth information relating to intervention for the subject (limitations to the data contemplated by the mental process).
19. The disease predicting system according to claim I, wherein
two or more diseases are simultaneously predicted (limitations to the functionality of the mental process),
the prediction is a change over time, the input information comprises third information relating to a health status of the subject (limitations to the functionality of the mental process), and
the input information comprises fourth information relating to intervention for the subject (limitations to the data contemplated by the mental process).
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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) 1-3 and 5-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US-20030198316-A1 (“Dewaele”) in view of US 5769074 A (“Barnhill”).
As to claim 1, Dewaele teaches a disease predicting system (Dewaele Para [0041]: prognostic risk prediction) comprising:
a prediction unit comprising an artificial neural network (Dewaele Para [0068]: an artificial neural network) trained using a plurality of sets of a medical image (Dewaele Para [0022]: trained using images by deriving predictors from the plane of the images; for example, Dewaele teaches that the measurements are from images involving quantitative computer tomograph) and a hormone agent corresponding to the medical image (Dewaele Para [0041]-[0042]: hormone replacement therapy corresponding to mineral density) and supervised data comprising a known disease corresponding to each set of medical image and corresponding hormone agent (Dewaele Para [0068]: learning from the data by using the neural network, a supervised learning technique) wherein the plurality of sets includes two or more sets corresponding to a same person, each of the two or more sets corresponding to a different time (Dewaele Para [0035]: quantitative computer tomography images are the source, which by definition involves multiple images of the same person at different times);
an input unit comprising an input device configured to receive input information comprising first information including a medical image of a subject used in diagnosis of a disease (Dewaele Para [0042]: information used for diagnosis, including age and lifestyle factors) and second information comprising a measurement value of a hormone agent (Dewaele Para [0042]: information about hormone replacement therapy by the subject) … ; and
a control unit comprising a processor configured to control the prediction unit to analyze the first information of the subject and the second information of the subject to predict a future onset of the disease for the subject (Dewaele Para [0041]: a prediction in the form of a risk score using the neural network of Para [0068] and the information in Para [0042]).
However, Dewaele does not teach that the hormone agent is in blood or urine of the subject. (Dewaele does teach at Para [0042]: the “intake” amount of a hormonal replacement, which is generally coincident with hormone’s presence in the body, blood, and urine; the Examiner interprets that the measurement is the intake quantity, and the presence of the hormone-like agent in the blood or urine is a property of the hormone for which the intake value is determined).
Nevertheless, Barnhill expressly teaches a hormone agent is in blood or urine of the subject (see Barnhill column 20 lines 28-41 in the context of diagnostic methods for modeling; Barnhill column 11 lines 30-39 teaches that the measurements might be hormone measurements.)
Dewaele and Barnhill are in the same field of bioinformatics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Dewaele to include the teachings of Barnhill because blood and urine is a minimally invasive way to detect hormone levels to facilitate multi-access diagnostics using existing knowledge and data about the patient (See Barnhill, abstract). There would be a reasonable expectation of success because the diagnostics measurements for blood and urine would involve observations that feed directly into a neural network ready for learning from those values (see Barnhill, abstract).
As to claim 2, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, further comprising
an output unit comprising a display device configured to display a prediction result predicted by the control unit (Dewaele Para [0090] the output of the prediction is used in the context of mass screening).
As to claim 3, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”).
As to claim 5, Dewaele in view of Barnhill teaches the disease predicting system according to claim l, wherein
the prediction is a change over time (Dewaele Para [0041]: a prediction that is a Cox proportional hazards regression, which is a model for shift in changes over time using a survival function).
As to claim 6, Dewaele in view of Barnhill teaches the disease predicting system according to claim l, wherein
the input information comprises third information relating to a health status of the subject (Dewaele Para [0043]: information about a body mass index of a subject, which is a health status).
As to claim 7, Dewaele in view of Barnhill teaches the disease predicting system according to claim l, wherein
the input information comprises fourth information relating to intervention for the subject (Dewaele Para [0043]: information about calcium intake, which is an intervention).
As to claim 8, Dewaele in view of Barnhill teaches the disease predicting system according to claim l, wherein processor of the control unit is configured to predict a first result based on the input information excluding fourth information and a second result based on the input information including the fourth information (Dewaele Para [0041]: risk factors, serving as independent variables, are selected for use in a Cox proportional hazards regression model for prediction based on statistical criteria for inclusion of the variables; hence the variables in Para [0043], such as the forth information for calcium intake, produce different statistical prediction results for the Cox model depending on whether they are included in the model).
As to claim 9, Dewaele teaches a non-transitory computer-readable medium storing a program executable by a disease predicting system (Dewaele Para [0041]: prognostic risk prediction) comprising a control unit, an input unit, and an output unit, the program causing the control unit (Dewaele Para [0040]: entered into a computer) to execute :
acquiring, via the input unit, input information comprising first information including a medical image of a subject used in diagnosis of a disease (Dewaele Para [0042]: information used for diagnosis, including age and lifestyle factors) and second information comprising a measurement value of a hormone agent (Dewaele Para [0042]: information about hormone replacement therapy by the subject) …;
predicting a future onset of the disease for the subject from the input information using a artificial neural network (Dewaele Para [0068]: an artificial neural network) trained using a plurality of sets of a medical image Dewaele Para [0022]: trained using images by deriving predictors from the plane of the images; for example, Dewaele teaches that the measurements are from images involving quantitative computer tomograph) and a hormone agent corresponding to the medical image (Dewaele Para [0041]-[0042]: hormone replacement therapy corresponding to mineral density) and supervised data comprising a known disease corresponding to each set of medical image and corresponding hormone agent (Dewaele Para [0068]: learning from the data by using the neural network, a supervised learning technique) wherein the plurality of sets includes two or more sets corresponding to a same person, each of the two or more sets corresponding to a different time (Dewaele Para [0035]: quantitative computer tomography images are the source, which by definition involves multiple images of the same person at different times); and
outputting a prediction result predicted by the control unit (Dewaele Para [0090] the output of the prediction is used in the context of mass screening).
However, Dewaele does not teach that the hormone agent is in blood or urine of the subject. (Dewaele does teach at Para [0042]: the “intake” amount of a hormonal replacement, which is generally coincident with hormone’s presence in the body, blood, and urine; the Examiner interprets that the measurement is the intake quantity, and the presence of the hormone-like agent in the blood or urine is a property of the hormone for which the intake value is determined).
Nevertheless, Barnhill expressly teaches a hormone agent is in blood or urine of the subject (see Barnhill column 20 lines 28-41 in the context of diagnostic methods for modeling; Barnhill column 11 lines 30-39 teaches that the measurements might be hormone measurements.)
Dewaele and Barnhill are in the same field of bioinformatics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Dewaele to include the teachings of Barnhill because blood and urine is a minimally invasive way to detect hormone levels to facilitate multi-access diagnostics using existing knowledge and data about the patient (See Barnhill, abstract). There would be a reasonable expectation of success because the diagnostics measurements for blood and urine would involve observations that feed directly into a neural network ready for learning from those values (see Barnhill, abstract).
As to claim 10, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”).
As to claim 11, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”), and
the prediction is a change over time (Dewaele Para [0041]: a prediction that is a Cox proportional hazards regression, which is a model for shift in changes over time using a survival function).
As to claim 12, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”), and
the prediction is a change over time (Dewaele Para [0041]: a prediction that is a Cox proportional hazards regression, which is a model for shift in changes over time using a survival function).
As to claim 13, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”), and
the input information comprises third information relating to a health status of the subject (Dewaele Para [0043]: information about a body mass index of a subject, which is a health status).
As to claim 14, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”), and the input information comprises third information relating to a health status of the subject (Dewaele Para [0043]: information about a body mass index of a subject, which is a health status).
As to claim 15, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”),
the second information comprises a measurement value of a hormone agent in blood or in urine of the subject, the prediction is a change over time (Dewaele Para [0042]: the “intake” amount of a hormonal replacement, which is generally coincident with hormone’s presence in the body, blood, and urine; the Examiner interprets that the measurement is the intake quantity, and the presence of the hormone-like agent in the blood or urine is a property of the hormone for which the intake value is determined), and
the input information comprises third information relating to a health status of the subject (Dewaele Para [0043]: information about a body mass index of a subject, which is a health status).
As to claim 16, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”), and
the input information comprises fourth information relating to intervention for the subject (Dewaele Para [0043]: information about calcium intake, which is an intervention).
As to claim 17, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”), and
the input information comprises fourth information relating to intervention for the subject (Dewaele Para [0043]: information about calcium intake, which is an intervention).
As to claim 18, Dewaele in view of Barnhill teaches the disease predicting system according to claim 1, wherein two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”), the prediction is a change over time, and the input information comprises fourth information relating to intervention for the subject (Dewaele Para [0043]: information about calcium intake, which is an intervention).
As to claim 19, Dewaele in view of Barnhill teaches the disease predicting system according to claim I, wherein
two or more diseases are simultaneously predicted (Dewaele Para [0002]: clarifies that the prediction of osteoporosis involves two disease predictions simultaneously, “loss of bone mass” and “deterioration of bone micro-architecture”),
the prediction is a change over time, the input information comprises third information relating to a health status of the subject (Dewaele Para [0043]: information about a body mass index of a subject, which is a health status), and
the input information comprises fourth information relating to intervention for the subject (Dewaele Para [0043]: information about calcium intake, which is an intervention).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Saturday, 10-5 ET.
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 October 31, 2025