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 .
Claims 1, 7 and 13 have been amended. Claims 19 and 20 have been cancelled. Claims 21 and 22 are newly added.
Claims 1-18 and 21-22 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 4 and 10 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 4 recites “wherein the processor is configured to execute the program to control the display device to display the patient information together with the graph,” which does not further limit the subject matter of claim 1. Claim 10 is rejected for similar reasons. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-18 and 21-22 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 and 21-22 are drawn to a system for a medication management system, which is within the four statutory categories (i.e. machine). Claims 7-12 are drawn to a method for managing medication for a patient, which is within the four statutory categories (i.e. process). Claims 13-18 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 and 21-22) 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 and a plurality of machine learning models, each of which is configured to receive a different test value of a laboratory test performed on a patient, and has been trained with (MPEP § 2106.05(f), apply it):
different test values obtained before and after administration of a medicine to different patients, and medicine information including a dose of the medicine administered to the different patients; 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,
determine a type of the first test value included in the patient information, and select, according to the determined type, one of the plurality of machine learning models that can accept an input of the first test value,
execute a call to the selected 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,
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 to concurrently display, on a single screen of the display device (MPEP § 2106.05(f), apply it):
the patient information that was input into the selected machine learning model, in a first region of the single screen,
one or more prediction values that were output from the selected machine learning model and that include the second test value, in a second region of the single screen, and
to display the graph with 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 13-18) is identical as the abstract idea for Claims 1-6 and 21-22 (Group I), because the only difference is they are directed towards difference statutory categories.
Dependent Claims 2-6, 8-12, 14-18, and 21-22 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 (MPEP § 2106.05(f), apply it) 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 (MPEP § 2106.05(f), apply it) 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 21 recites wherein the processor is configured to execute the program to automatically control, based on the recommended dose, an administration mechanism of an administration device that administers the medicine to the patient, and Claim 22 recites wherein the plurality of machine learning models have been trained with (MPEP § 2106.05(f), apply it) pieces of training data that are grouped, every predetermined time period, according to an elapsed time from a laboratory test to administration of the medicine, and the processor (MPEP § 2106.05(f), apply it) is configured to execute the program to select said one of the plurality of machine learning models further according to an elapsed time from the laboratory test that produced the first test value to administration of the medicine, 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 1-18 and 21-22 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, machine learning model and processor, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraph [0032-0033] and [0036] 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:
paragraphs [0032-0033] and [0036] of the Specification discloses that the additional elements (i.e. a display device, memory, processor, machine learning models) 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, 14-18 and 21-22 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 either repeat those already found in the independent claims or 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 1-18 and 21-22 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free from Prior Art
The prior art of record, including Vodencarevic (U.S. Pub. No. 2022/0310261 A1), Takazawa (U.S. Pub. No. 2022/0110584 A1) and Howard (U.S. Pub.
No. 2012/0166222 A1), does not disclose or render obvious the combination of limitations found in the independent claims.
Takazawa teaches controlling the display device to display the graph and the recommended dose or the preferred range on the graph, but does not explicitly teach concurrently displaying the graph with “(i) the patient information that was input into the selected machine learning model, in a first region of the single screen” and “(ii) one or more prediction values that were output from the selected machine learning model and that include the second test value, in a second region of the single screen,” in combination with the limitations found in the independent claims.
Claims 2-6 and 21-22 are free from prior art as they depend on claim 1. Claims 7-12 and 13-18 are free from prior art for similar reasons as claim 1 and its dependents.
Response to Arguments
Applicant's arguments filed 09/10/2026 have been fully considered.
Claim Rejections – 35 U.S.C. § 101
With respect to Step 2A, Prong One, Applicant asserts that the “limitations set forth specific operations performed by a computer system on stored machine learning models and a display device” and that the “operations are not fundamental economic practices, are not commercial or legal interactions, and do not manage personal behavior or relationships or interactions between people (Remarks, page 10).” Examiner maintains that the computer and display device are merely used as a tool to implement the recited abstract idea. The medication management system is automating a process performed by medical professionals in the care of a patient who is receiving a medication.
Regarding Step 2A, Prong Two, Applicant asserts that the claims “reflect an improvement in the functioning of the claimed medication management system itself” and that “the specification explains that ‘some medical facilities and clinical departments measure only a subset of these test parameters’ and that, under such circumstances, ‘a predictive system that uses test parameters such as APTT as explanatory variables may be less versatile, because it cannot be applied to patients whose data are not complete (Remarks, page 10).’” This is not a technical problem. The claims do not solve a problem particular to machine learning and the machine learning model is not improved as a result of the claimed invention. The specification states that machine learning model can be neural network, decision tree, a support vector machine, or another learning algorithm. The machine learning is recited at “apply it” level and therefore, does not result in an improvement to machine learning or any technology. The improvement, if any, is to the abstract idea itself.
Applicant further asserts that “amended claim 1 recites a specific, structured manner of presenting a defined set of information on the display device” as it recites “control[ling] the display device to concurrently display, on a single screen of the display device” items (i), (ii) and (iii). The display device is not improved as a result of what is being displayed on it. There is not a technical problem with displaying data. The output of the data is considered part of the abstract idea. The display device that is used does not rise to the level of a practical application as it is recited an “apply it” level.
Applicant asserts that the “recited determination and selection determine the content of the recited display, and together these limitations define a specific system in which the prediction pathway is selected according to the determined type of the available first test value and the basis of the resulting recommendation is presented on a single screen (Remarks, page 12).” Applicant is arguing that portions of the abstract idea result in a practical application. Examiner maintains that the additional elements fail to result in a practical application and that the abstract idea cannot be used to integrate itself into a practical application.
Therefore, the claims remain rejected to an abstract without a practical application or significantly more.
Claim Rejections – 35 U.S.C. § 103
With respect to the prior art rejection, the arguments have been considered, but are moot as the art rejection has been withdrawn in view of the amendments.
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 Rachelle Reichert whose telephone number is (303)297-4782. The examiner can normally be reached M-F 9-5 MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham can be reached at (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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