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 .
Status of the Claims
Claims 1 and 3-15 are currently pending. Claim 2 is canceled in the Claims filed on March 19, 2026.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 3-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1 and 14-15, Claims 1 and 14-15 recite “date information associated with a medical practice,” “training data including the number of elapsed days from a day on which the medical practice is performed, medical information including date information associated with a medical practice.” The last recitation of “a” medical practice is unclear because it is unclear whether this recitation of “a” medical practice is referring to “the” medical practice previously recited or to some different medical practice. In the interest of compact prosecution, Examiner will interpret this as reciting “date information associated with the medical practice.”
Additionally, Claims 1 and 14-15 recite “[deriving] fall prediction information…using a prediction model that has been trained through machine learning to predict the fall of the target patient…wherein the prediction model is trained by using training data including…information on the presence or absence of the fall.” It is unclear how the system derives a fall prediction for a future fall based on the presence or absence of the fall. In the interest of compact prosecution, Examiner will interpret this as the presence or absence of past/previous falls and/or the presence or absence of a fall in historical fall data being used as training data in order to predict fall prediction information for a future fall.
Additionally, Claims 1 and 14-15 recite “[deriving] fall prediction information of the target patient on the scheduled hospital visit date.” Given the broadest reasonable interpretation, this language could be reciting that the fall prediction information be determined on a particular date, or this language could be reciting that the fall prediction information is determined for the particular date, wherein these interpretations are materially different from each other. In the interest of compact prosecution and in accordance with [0032] of the as-filed Specification, this language will be interpreted as reciting deriving the fall prediction information for the scheduled hospital visit date.
Appropriate correction is required.
Dependent Claims 3-13 are also rejected under 35 U.S.C. 112(b) due to their dependence from independent Claim 1.
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 and 3-15 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 and 3-15 are within the four statutory categories. Claims 1 and 3-13 are drawn to a device for predicting falls, which is within the four statutory categories (i.e. machine). Claim 14 is drawn to a method for predicting falls, which is within the four statutory categories (i.e. process). Claim 15 is drawn to a non-transitory medium for predicting falls, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A medical support device comprising:
a processor,
wherein the processor
acquires medical information including date information associated with a medical practice for a target patient and prediction date information which is a scheduled hospital visit date of the target patient for predicting a fall of the target patient,
derives fall prediction information of the target patient on the scheduled hospital visit date using a prediction model that has been trained through machine learning to predict the fall of the target patient based on the number of elapsed days, which is a period from the date information to the prediction date information, wherein the prediction model is trained by using training data including the number of elapsed days from a day on which the medical practice is performed, medical information including date information associated with a medical practice for a patient, and information on the presence or absence of the fall, and
notifies of the fall prediction information.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of deriving fall prediction information using a prediction model, and notifying of the fall prediction information includes following rules or instructions for diagnosing a patient and notifying users of the diagnosis), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 14-15 is identical as the abstract idea for Claim 1, because the only difference between Claims 1 and 14-15 is that Claim 1 recites a device, whereas Claim 14 recites a method, and Claim 15 recites a non-transitory computer-readable medium.
Dependent Claims 3-13 include other limitations, for example Claims 3-12 recite various types of data processed by the system to ultimately obtain the fall prediction information, and Claim 13 recites notifying a medical worker of the fall prediction information, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Hence dependent Claims 3-13 are nonetheless directed towards fundamentally the same abstract idea as independent Claim 1.
Hence Claims 1 and 3-13 are directed towards the aforementioned abstract idea.
Prong 2 of Step 2A
Claims 1 and 14-15 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, the step of acquiring the medical information, and the fact that the prediction model is trained using machine learning) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the processor, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0026] and [0066] of the as-filed Specification, and see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language specifying that the prediction model is trained using machine learning and the types of training data, which amounts to limiting the abstract idea to the field of machine learning/artificial intelligence, e.g. see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, the recitation of acquiring the medical information, which amounts to mere data gathering, e.g. see MPEP 2106.05(g).
Additionally, dependent Claims 3-13 include other limitations, but these limitations do not include any additional elements beyond those already recited in independent Claim 1, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1 and 3-15 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1 and 14-15 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, the step of acquiring the medical information, and the fact that the prediction model is trained using machine learning), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0026] and [0066] of the as-filed Specification discloses that the additional elements (i.e. the processor) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the additional elements recite acquiring medical information over a network, e.g. see [0020] and [0069]-[0070] of the as-filed Specification;
Performing repetitive calculations, e.g. see Parker v. Flook, and/or Bancorp Services v. Sun Life – similarly, the additional elements recite performing basic calculations (i.e. the high level recitation of the prediction model being trained using machine learning) and does not impose meaningful limits on the scope of the claims;
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the additional elements merely recite the creating and maintaining of medical infomration;
Dependent Claims 3-13 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements do not recite any additional elements not already recited in independent Claim 1, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1 and 3-15 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. 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 and 3-15 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.
Claims 1, 3-5, 7-8, 10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Drake (US 2023/0020908) in view of Burwinkel (US 2023/0404490).
Regarding Claim 1, Drake teaches the following: A medical support device comprising:
a processor (The system includes a processor, e.g. see Drake [0005].),
wherein the processor
acquires medical information including date information associated with a medical practice for a target patient and prediction date information which is a scheduled hospital visit date of the target patient for predicting a fall of the target patient (The system tracks features that have predicted value impact on a likelihood score generated by a model for a patient, for example a number of days since a last outpatient visit, a number of days since a last inpatient visit, a number of hospital stay days, and/or a number of ICU admission days (i.e. date information and a scheduled hospital visit date), e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116], wherein the likelihood score is for a likelihood of a patient fall, e.g. see Drake [0094] and [0115]-[0116], wherein the likelihood of a patient fall may be for a specified future time period, e.g. see Drake [0090], [0099], [0103], [0105], and [0132].),
derives fall prediction information of the target patient using a prediction model that has been trained through machine learning to predict the fall of the target patient based on the number of elapsed days, which is a period from the date information to the prediction date information (The system includes machine learning model data used for training one or more machine learning models used to generate predictions including a fall risk (i.e. fall prediction information), wherein the machine learning model data may include historical feature vector inputs, e.g. see Drake [0032], [0050], [0062], [0090]-[0091], and [0133]-[0134]. Furthermore, the fall prediction may be for a future fall of the patient, for example within the next six months and/or the next year (i.e. elapsed days between the date information to the prediction date information), e.g. see Drake [0090], [0099], [0103], [0105], and [0132].), wherein the prediction model is trained by using training data including the number of elapsed days from a day on which the medical practice is performed, medical information including date information associated with a medical practice for a patient, and information on the presence or absence of the fall (The data used for training the machine learning models includes patient data including a number of days since a last inpatient or outpatient event, a last discharge disposition, and historical data indicating whether the patient has previously fallen, wherein features for a training dataset may be only appropriately dated information, e.g. see Drake [0032], [0037]-[0038], [0053], and [0111]-[0113].), and
notifies of the fall prediction information (The system displays (i.e. notifies) a list of the highest fall risk patients, e.g. see Drake [0107].).
But Drake does not teach and Burwinkel teaches the following:
wherein the fall prediction information of the target patient is on the scheduled hospital visit date (The system evaluates a patient’s fall risk, e.g. see Burwinkel [0004], wherein present clinical techniques assess a patient’s fall risk in a hospital, e.g. see Burwinkel [0018], and wherein the system evaluates a patient’s fall risk for a future time, for example a time when a patient will be walking to an appointment (i.e. a scheduled hospital visit) within the next hour, e.g. see Burwinkel [0036].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Drake to incorporate predicting the patient’s fall risk for a time when the patient will be visiting a hospital as taught by Burwinkel in order to ensure that the patient makes it to the appointment safely, e.g. see Burwinkel [0036].
Regarding Claim 3, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 1,
wherein the processor
acquires the prediction date information (The data used to determine the patient’s fall risk includes a number of days spent in the hospital and/or a number of days since a last inpatient visit, e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116].), and
sets, as the target patient, a patient for whom the prediction date information is associated as a scheduled hospital visit date (The fall risk prediction is generated for patients, and is used to identify high risk (i.e. target) patients, e.g. see Drake [0032], [0050], [0062], [0090]-[0091], and [0133]-[0134].).
Regarding Claim 4, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 1,
wherein the date information is at least one of a prescription date of a drug for the target patient, an examination date for the target patient, a surgery date for the target patient, a hospitalization date of the target patient, a discharge date of the target patient, or a diagnosis date for the target patient (The data used to determine the patient’s fall risk includes a number of days spent in the hospital and/or a number of days since a last inpatient visit, e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116], wherein the aforementioned patient data may be used when the data is appropriately dated within a time period, e.g. see Drake [0051]-[0053] and [0113].).
Regarding Claim 5, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 1,
wherein, in a case in which a plurality of pieces of the date information are associated with the medical practice for the target patient in the medical information (The data used to determine the patient’s fall risk includes data for patients and practices, including a number of days spent in the hospital and/or a number of days since a last inpatient visit, e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116].), the processor
acquires specific date information closest to the prediction date information among the plurality of pieces of date information (The data may be selected based on the data falling within an appropriate time period prior to some designated reference time, e.g. see Drake [0051]-[0053] and [0113].), and
derives the fall prediction information of the target patient using the prediction model based on the number of elapsed days derived from the specific date information and the prediction date information (The data used to determine the patient’s fall risk includes data for patients and practices, including a number of days spent in the hospital and/or a number of days since a last inpatient visit, e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116].).
Regarding Claim 7, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 5,
wherein the processor derives the fall prediction information weighted based on the number of days on which the medical practice is performed within a predetermined period from the specific date information or the prediction date information (The data used to determine the patient’s fall risk includes a number of days spent in the hospital and/or a number of days since a last inpatient visit, e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116], wherein the aforementioned patient data may be used when the data is appropriately dated within a time period, e.g. see Drake [0051]-[0053] and [0113], wherein the data in each layer of the machine learning model may be weighted, e.g. see Drake [0069].).
Regarding Claim 8, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 1,
wherein the processor compares the number of elapsed days with a predetermined first threshold value, and derives the fall prediction information based on the number of elapsed days that is equal to or smaller than the first threshold value (The data used to determine the patient’s fall risk includes a number of days spent in the hospital and/or a number of days since a last inpatient visit, e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116], wherein the aforementioned patient data may be used when the data is appropriately dated within a time period, e.g. see Drake [0051]-[0053] and [0113] – that is, the data being within the time period means the data satisfies a threshold value in terms of date.).
Regarding Claim 10, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 1,
wherein the medical information includes at least one of basic patient information, disease information, drug information, surgery information, examination information, or keyword information (The features tracked by the system include a number of days since a last outpatient visit, and a number of days since a last inpatient visit, a number of hospital stay days, and/or a number of ICU admission days (i.e. any of the foregoing may be interpreted as at least “basic patient information,” “surgery information,” and/or “examination information”), e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116].),
the date information is included in at least one of the basic patient information, the disease information, the drug information, the examination information, or the surgery information (The features tracked by the system include a number of days since a last outpatient visit, and a number of days since a last inpatient visit, a number of hospital stay days, and/or a number of ICU admission days (i.e. any of the foregoing may be interpreted as “date information”), e.g. see Drake [0036]-[0037], [0057], and [0115]-[0116].), and
the processor derives the fall prediction information using the prediction model based on the number of elapsed days and at least one of the basic patient information, the disease information, the drug information, the surgery information, the examination information, or the keyword information (The system includes machine learning model data used for training one or more machine learning models used to generate predictions including a fall risk (i.e. fall prediction information), wherein the machine learning model data may include historical feature vector inputs (i.e. the number of elapsed days from the date information and the prediction date information), e.g. see Drake [0032], [0050], [0062], [0090]-[0091], and [0133]-[0134].).
Regarding Claim 12, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 1,
wherein the fall prediction information is at least one of presence or absence of a fall risk or a probability of the fall risk (The system generates predictions including a fall risk (i.e. fall prediction information), wherein the fall risk may be evaluated against a threshold, e.g. see Drake [0090]-[0091] and [0099].),
the fall prediction information is used for determining whether or not the fall risk is present or the fall risk is equal to or greater than a second threshold value (The calculated fall risk is compared to a threshold, e.g. see Drake [0099].), and
the processor notifies of the fall prediction information based on a result of the determination (The system notifies a care provider when it is determined that the fall risk exceeds the threshold, e.g. see Drake [0099].).
Regarding Claim 13, the combination of Drake and Burwinkel teaches the limitations of Claim 1, and Drake further teaches the following: The medical support device according to claim 12,
wherein the processor notifies a medical worker associated with the target patient of the fall prediction information (The system notifies a care provider (i.e. a medical worker) when it is determined that the fall risk exceeds the threshold, to enable the care provider to perform additional assessments for the patient, e.g. see Drake [0099].).
Regarding Claims 14-15, the limitations of Claims 14-15 are substantially similar to those claimed in Claim 1, with the sole difference being that Claim 1 recites a device whereas Claim 14 recites a method, and Claim 15 recites a non-transitory computer-readable storage medium executed by a computer. Specifically pertaining to Claims 14-15, Examiner notes that Drake teaches a method embodied as instructions stored in a non-transitory computer-readable medium, e.g. see Drake [0140] and [0151]-[0152], and hence the grounds of rejection provided above for Claim 1 are similarly applied to Claims 14-15.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Drake and Burwinkel in view of Hanson (US 2012/0314901), further in view of Sudharsan (US 2015/0006462).
Regarding Claim 6, the combination of Drake and Burwinkel teaches the limitations of Claim 5, but does not teach and Hanson teaches the following: The medical support device according to claim 5,
wherein the processor derives the fall prediction information weighted based on the number of days on which the medical practice is performed from the specific date information (The system analyzes various data to generated a weighted aggregate score indicative of the confidence of a possible patient fall, wherein the data includes medication adherence (i.e. a number of days on which a medical practice is performed), e.g. see Hanson [0069] and [0073].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Drake and Burwinkel to incorporate factoring in medication adherence as part of the fall risk calculation as taught by Hanson in order to provide more accurate fall detection with a lower false positive rate, e.g. see Hanson [0069].
But the combination of Drake, Burwinkel, and Hanson does not teach and Sudharsan teaches the following:
wherein the number of days is the number of consecutive days (The system monitors patient compliance based on a daily medication adherence for one or more medications, wherein certain medications may be weighted based on their relevance or importance, e.g. see Sudharsan [0053].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Drake, Burwinkel, and Hanson to incorporate daily medication adherence monitoring as taught by Sudharsan in order to enable physicians and patients to make clinical decisions and enhance the accuracy of the clinical decisions, e.g. see Sudharsan [0065].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Drake and Burwinkel in view of Saeed (US 2011/0029248).
Regarding Claim 9, the combination of Drake and Burwinkel teaches the limitations of Claim 8, but does not teach and Saeed teaches the following: The medical support device according to claim 8,
wherein the first threshold value is set to a different value for each classification of the medical information associated with the date information (The system enables setting a plurality of thresholds based on rules, e.g. see Saeed [0052], wherein the thresholds may be different based on the rules varying based on time windows and types of patients, e.g. see Saeed [0056].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Drake and Burwinkel to incorporate setting the thresholds based on different patient types and timing data as taught by Saeed in order to enable the system to predict patient conditions for a variety of different types of patients, e.g. see Saeed [0010] and [0056].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Drake and Burwinkel in view of Chung (US 2020/0411149).
Regarding Claim 11, the combination of Drake and Burwinkel teaches the limitations of Claim 10, but does not teach and Chung teaches the following: The medical support device according to claim 10,
wherein the processor derives a contribution degree of the medical information and the number of elapsed days input to the prediction model to the fall prediction information (The system identifies fall risk factors including high and low risk factors, e.g. see Chung [0069]-[0070] and [0081], Fig. 17.), and
notifies of at least one of the medical information or the number of elapsed days based on the contribution degree together with the fall prediction information (The system generates (i.e. notifies) a report that shows the patient risk factors contributing to the risk of a patient fall, e.g. see Chung Fig. 17.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of Drake and Burwinkel to incorporate identifying and displaying the fall risk factors as taught by Chung in order to enable a better understanding of fall risk factors to minimize hospital costs for treatment of injuries from falls, e.g. see Chung [0003].
Response to Arguments
Applicant’s arguments, see Remarks, filed March 19, 2026, with respect to the rejections of Claims 1 and 3-15 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant alleges that the claimed invention is patent eligible because it does not recite an abstract idea but instead recites a “specific technological solution to a particular technological problem,” and recites a “concrete technical implementation,” e.g. see pg. 9 of Remarks – Examiner disagrees.
Examiner initially notes that the absence of complete preemption does not guarantee that a claim will be eligible, and further notes that preemption is not a stand-alone test for patentability, but rather is inherent in the two-part Alice/Mayo framework, e.g. see MPEP 2106.04. That is, even assuming, arguendo, that the claimed invention recites a specific configuration for an abstract idea, a narrow abstract idea nonetheless recites an abstract idea, and the broadness/narrowness of the abstract idea is not, by itself, dispositive of the eligibility of the claim. Furthermore, as shown above, Examiner has provided evidence demonstrating that the present invention is directed towards at least one court-identified abstract idea that is not integrated into a practical application, and further that the additional elements of the present invention (i.e. any elements not identified as part of the abstract idea) do not represent significantly more than the abstract idea, and hence has addressed any concerns arising from preemption.
Furthermore, [0004] and [0008] of the as-filed Specification discloses that the problem addressed by the claimed invention are “[predicting] a fall with high accuracy while reducing a burden on patients and medical institutions.” That is, the claimed invention makes the fall prediction more convenient for patients and medical institutions, which represents a problem that has existed since long before the advent of any type of computer technology, and hence is not properly characterized as a technical/technological problem.
For the aforementioned reasons, Claims 1 and 3-15 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed March 19, 2026, with respect to the rejections of Claims 1-5, 7-8, 10, and 12-15 under 35 U.S.C. 102(a)(1) have been fully considered and, in combination with the claim amendments, are persuasive. The rejections of Claims 1-5, 7-8, 10, and 12-15 under 35 U.S.C. 102(a)(1) have been withdrawn. However, for the reasons shown above, Claims 1 and 3-15 are nonetheless rejected under 35 U.S.C. 103.
Applicant’s arguments, see Remarks, filed March 19, 2026, with respect to the rejections of the Claims 1 and 3-15 under 35 U.S.C. 103 have been fully considered but are not persuasive.
Applicant alleges that Drake is deficient because it does not teach calculating the number of elapsed days up to a specific date, in order to predict a patient’s condition at that specific date, e.g. see pgs. 10-11 of Remarks – Examiner disagrees.
As stated above, Drake teaches that a prediction for a fall may be for a future period of time, for example in the next six months and/or the next year, e.g. see Drake [0103], and hence teaches a number of elapsed days up to a specific date. Furthermore, as shown above, Drake is not cited to teach that the fall prediction is for the specific scheduled hospital visit date, and instead Burwinkel is cited to teach this feature. Hence, any arguments pertaining to this feature as it relates to Drake are moot.
For the aforementioned reasons, Claims 1 and 3-15 are rejected under 35 U.S.C. 103.
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 JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
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/JOHN P GO/Primary Examiner, Art Unit 3681
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