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 .
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1 includes an extra space after the “execute a call…to the different parties” limitation and the comma at the end.
Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-6 are drawn to a system for a medication management system, which is within the four statutory categories (i.e. machine). Claims 7 are drawn to a method for managing medication for a patient, which is within the four statutory categories (i.e. process). Claims 13-20 are drawn to an apparatus for medication management, which is within the four statutory categories (i.e. manufacture).
Step 2A | Prong One
Claim 1 (of Group I, Claims 1-6) recites a medication management system for managing medication for a patient, comprising:
a display device (MPEP § 2106.05(f), apply it);
a memory that stores a program (MPEP § 2106.05(f), apply it); and
a processor configured to execute the program to (MPEP § 2106.05(f), apply it):
acquire patient information about the patient, a first test value of a laboratory test that was performed on the patient, and medicine information including a dose of a medicine to be administered to the patient,
execute a call to a machine learning model with the patient information, the first test value, and the medicine information to determine a second test value expected at a predetermined time after the medicine is administered, the machine learning model having been trained with test values obtained before and after administration of the medicine to different patients, and medicine information including a dose of the medicine to be administered to the different patients,
determine a recommended dose or a preferred range of doses for the medicine based on the second test value,
generate data of a graph showing a relationship between doses of the medicine and test values, and
control the display device (MPEP § 2106.05(f), apply it) to display the graph and the recommended dose or the preferred range on the graph.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity because it recites fundamental economic practices, commercial or legal interactions, and/or managing personal behavior or relationships or interactions between people. Any limitations not identified above as part of the abstract idea are underlined and are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Group II (Claims 7-12) and Group III (Claims 13-20) is identical as the abstract idea for Claims 1-6 (Group I), because the only difference is they are directed towards difference statutory categories.
Dependent Claims 2-6, 8-12 and 14-20 include other limitations, for example Claims 2 and 8 recite the processor is configured to execute the program to control the display device to display the second test value together with the graph, Claims 3 and 9 recite wherein the machine learning model has been trained to output a possibility of complication after administration of the medicine, and the processor is configured to execute the program to control the display device to further display the possibility of complication, Claims 4 and 10 recite wherein the processor is configured to execute the program to control the display device to display the patient information together with the graph, Claims 5 and 11 recite wherein the memory stores a table (MPEP § 2106.05(f), apply it; MPEP § 2106.05(g), insignificant extra-solution activity) associating differences in test values before and after administration of the medicine with recommended doses or preferred ranges of doses for the medicine, and the processor is configured to execute the program to acquire a difference between the first and second test values, and determine the recommended dose or the preferred range based on the difference and the table stored in the memory, Claims 6 and 12 recite wherein the processor is configured to execute the program to control the display device to display the preferred range together with the graph, Claim 14 recites wherein the first test value is acquired through a laboratory test that was performed within a predetermined time from a scheduled administration start time of the medicine, Claim 15 recites wherein the information that is output from each of the machine learning models indicates a condition of the patient, Claim 16 recites wherein the information that is output from said one of the machine learning models indicates a second test value expected at a predetermined time after the medicine is administered, and the processor is configured to execute the program to determine a recommended dose of the medicine based on the second test value, Claim 17 recites wherein the medicine is heparin, and said at least a first test value is an activated partial thromboplastin time (APTT), an activated clotting time (ACT), or a combination thereof, Claim 18 recites wherein each machine learning model further receives, as input, a time from the laboratory test to administration of the medicine, Claim 19 recites wherein each machine learning model further receives, as input, a time from the laboratory test to administration of the medicine, and Claim 20 recites an interface circuit connectable to a display device (MPEP § 2106.05(f), apply it), wherein the processor is configured to execute the program to output the generated information to the display device and cause the display device to display the generated information together with the patient information and the medicine information, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1,7 and 13.
Step 2A | Prong Two
Furthermore, Claims 1-20 are not integrated into a practical application because the additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a display device, memory, and processor, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraph [0032] of the present Specification, see MPEP 2106.05(f); and
add insignificant extra-solution activity to the abstract idea – for example, the recitation of storing data, which amounts to an insignificant application, see MPEP 2106.05(g).
Step 2B
Furthermore, the Claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e. the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraph [0032] of the Specification discloses that the additional elements (i.e. a display device, memory, processor) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. storing data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the current invention merely recites the storing of instruction and table data on a database and/or electronic memory.
Dependent Claims 2-6, 8-12, 14-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than electronic recordkeeping (e.g. the storing feature of dependent Claims 5 and 11).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above-identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claim 1-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vodencarevic (U.S. Pub. No. 2022/0310261 A1) in view of Takazawa (U.S. Pub. No. 2022/0110584 A1).
Regarding claim 1, Vodencarevic discloses a medication management system for managing medication for a patient, comprising:
a display device (Paragraph [0019] discusses a display device used as an output interface.);
a memory that stores a program (Paragraphs [0019], [0022-0023] and [0046] discuss a memory being used to store data and programs in the computing unit.); and
a processor configured to execute the program to (Paragraphs [0046-0047] discuss the system including a processor that runs the software.):
acquire patient information about the patient, a first test value of a laboratory test that was performed on the patient, and medicine information including a dose of a medicine to be administered to the patient (Paragraphs [0014], [0018] and [0023] discuss obtaining information about a patient, including lab data, and using a clinical decision support system to determine drug-related factors, including dosing.),
execute a call to a machine learning model with the patient information, the first test value, and the medicine information to determine a second test value expected at a predetermined time after the medicine is administered (Paragraphs [0016], [0018] and [0023] discuss using a plurality of different trained predictions to provide an estimate of a drug-related treatment optimization risk probably within a future time period using the inputted data, including drugs taken by the patient, construed as including after medication is administered.), the machine learning model having been trained with test values obtained before and after administration of the medicine to different patients, and medicine information including a dose of the medicine to be administered to the different patients (Paragraphs [0016], [0018] and [0020-0021] discuss using the trained prediction models including drug-related treatment optimization risk probability, which includes the dosage of the drug the patient is using.),
determine a recommended dose or a preferred range of doses for the medicine based on the second test value (Paragraphs [0021] and [0079-0082] discuss determining a recommended drug dosage for the patient based on the input data, which includes side effects after administration of the drug.);
but Vodencarevic does not appear to explicitly disclose:
generating data of a graph showing a relationship between doses of the medicine and test values, and
controlling the display device to display the graph and the recommended dose or the preferred range on the graph.
Takazawa teaches:
generating data of a graph showing a relationship between doses of the medicine and test values (Paragraph [0080] discusses figure 2, which shows a display of the dosage regimen support screen.), and
controlling the display device to display the graph and the recommended dose or the preferred range on the graph (Paragraph [0073] discusses generating a suggestion based on the previous dosage about a future dosage to be displayed on a screen. See also figures 2-5 showing the dosage regimen support screen.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to modify Vodencarevic to include Takazawa, in order to “make an appropriate treatment plan while considering side effects that individually occur in a patient (Takazawa, Paragraph [0003]).”
Regarding claim 2, Vodencarevic does not appear to explicitly disclose, wherein
the processor is configured to execute the program to control the display device to display the second test value together with the graph.
Takazawa teaches wherein the processor is configured to execute the program to control the display device to display the second test value together with the graph (Paragraphs [0038] and [0092-0095] discuss displaying the test values with the graph.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to modify Vodencarevic to include Takazawa, in order to “make an appropriate treatment plan while considering side effects that individually occur in a patient (Takazawa, Paragraph [0003]).”
Regarding claim 3, Vodencarevic discloses wherein the machine learning model has been trained to output a possibility of complication after administration of the medicine, and the processor is configured to execute the program to control the display device to further display the possibility of complication (Paragraphs [0078], [0084] and [0120] discuss the prediction models being trained to determining if there a risk of drug adverse events and outputting the results.).
Regarding claim 4, Vodencarevic does not appear to explicitly disclose wherein the processor is configured to execute the program to control the display device to display the patient information together with the graph.
Takazawa teaches wherein the processor is configured to execute the program to control the display device to display the patient information together with the graph (Paragraphs [0038] and [0092-0095] discuss displaying the test values with the graph and a recommend dosage.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to modify Vodencarevic to include Takazawa, in order to “make an appropriate treatment plan while considering side effects that individually occur in a patient (Takazawa, Paragraph [0003]).”
Regarding claim 5, discloses wherein the memory stores a table associating differences in test values before and after administration of the medicine with recommended doses or preferred ranges of doses for the medicine, and the processor is configured to execute the program to acquire a difference between the first and second test values, and determine the recommended dose or the preferred range based on the difference and the table stored in the memory (Paragraphs [0023-0024] and [0117-0120] discusses storing the data for the selection scheme in a table in the memory which is used to analyze the difference between the values and to recommend a dosage.).
Regarding claim 6, Vodencarevic does not appear to explicitly disclose wherein the processor is configured to execute the program to control the display device to display the preferred range together with the graph.
Takazawa teaches wherein the processor is configured to execute the program to control the display device to display the preferred range together with the graph (Paragraph [0094] discusses the suggested dosage including the time range of the doses.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to modify Vodencarevic to include Takazawa, in order to “make an appropriate treatment plan while considering side effects that individually occur in a patient (Takazawa, Paragraph [0003]).”
2025Attorney Docket No. 317EP.001US01
Claim 7 recites substantially similar limitations as those already addressed in claim 1, and, as such, is rejected for similar reasons as given above.
Claim 8 recites substantially similar limitations as those already addressed in claim 2, and, as such, is rejected for similar reasons as given above.
Claim 9 recites substantially similar limitations as those already addressed in claim 3, and, as such, is rejected for similar reasons as given above.
Claim 10 recites substantially similar limitations as those already addressed in claim 4, and, as such, is rejected for similar reasons as given above.
Claim 11 recites substantially similar limitations as those already addressed in claim 5, and, as such, is rejected for similar reasons as given above.
Claim 12 recites substantially similar limitations as those already addressed in claim 6, and, as such, is rejected for similar reasons as given above.
Claim 13 recites substantially similar limitations as those already addressed in claim 1, and, as such, is rejected for similar reasons as given above. Claim 13 recites that the output includes the effectiveness of the medicine, which is disclosed by Vodencarevic in at least paragraphs [0069] and [0071-0073].
Claim 14 recites substantially similar limitations as those already addressed in claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 15, Vodencarevic discloses wherein the information that is output from each of the machine learning models indicates a condition of the patient (Paragraphs [0023], [0070] and [0076] discuss that the output is based on the patient’s diagnosis/condition, construed as including the output indicating the condition of the patient, such as if the patient is sustained remission or experiencing flares.).
Claim 16 recites substantially similar limitations as those already addressed in claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 19, Vodencarevic discloses wherein the processor is configured to execute the program to determine whether any one of the machine learning models can receive the first test value as input (Paragraph [0016] discusses the data being received and used for various prediction models.).
Claim 20 recites substantially similar limitations as those already addressed in claim 4, and, as such, is rejected for similar reasons as given above. Vodencarevic discloses an interface circuit connectable to a display device for output in [0016] and [0019].
Claim 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vodencarevic in view of Takazawa, and in further view of Howard (U.S. Pub. No. 2012/0166222 A1).
Regarding claim 17, Vodencarevic does not appear to explicitly disclose wherein:
the medicine is heparin, and
said at least a first test value is an activated partial thromboplastin time (APTT), an activated clotting time (ACT), or a combination thereof.
Howard teaches wherein:
the medicine is heparin (Paragraphs [0001] and [0049] discuss the medications including heparin.), and
said at least a first test value is an activated partial thromboplastin time (APTT), an activated clotting time (ACT), or a combination thereof (Paragraph [0049] discusses that the recommended dosage is based on laboratory test results identifying the activated partial thromboplastin time (APTT) of the patient’s blood.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to modify Vodencarevic to include heparin as taught by Howard, in order to identify “potential risk factors based on one or more of an anticoagulant patient's medical history, symptoms and diagnosis of the patient's condition, laboratory test results, scheduled procedures, and other medications prescribed for the patient, among any other available data elements useful in identifying such risk factors (Howard, Paragraph [0003])” in identifying a new dosage of an anti-coagulant for a patient (Howard, Paragraph [0006]).”
Regarding claim 18, Vodencarevic does not appear to explicitly disclose wherein each machine learning model further receives, as input, a time from the laboratory test to administration of the medicine.
Howard teaches wherein each machine learning model further receives, as input, a time from the laboratory test to administration of the medicine (Paragraph [0056] and figure 4 show that the date and timestamp of labs and medication is included the input data.).
Therefore, it would have been obvious before the effective filing date of the claimed invention to modify Vodencarevic to include heparin as taught by Howard, in order to identify “potential risk factors based on one or more of an anticoagulant patient's medical history, symptoms and diagnosis of the patient's condition, laboratory test results, scheduled procedures, and other medications prescribed for the patient, among any other available data elements useful in identifying such risk factors (Howard, Paragraph [0003])” in identifying a new dosage of an anti-coagulant for a patient (Howard, Paragraph [0006]).”
Conclusion
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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