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 1-12 and 14-18 are currently pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“A storage unit” recited in Claims 1-12;
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof, e.g. see [0033] of the as-filed Specification.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-12 and 14-18 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 1-12 and 14-18 are within the four statutory categories. Claims 1-12 and 16-18 are drawn to a device for predicting a patient prognosis, which is within the four statutory categories (i.e. machine). Claim 14 is drawn to a method for predicting a patient prognosis, which is within the four statutory categories (i.e. process). Claim 15 is drawn to a non-transitory medium for predicting a patient prognosis, 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: An information processing device for predicting a prognosis of a subject patient affected by a disease, comprising:
a storage unit storing a prognosis prediction model, time-series information indicating a time-series transition of factors of the disease, patient time-series information, and a prediction result; and
a processor coupled to the storage unit configured to:
acquire the prognosis prediction model, being a machine learning model trained using the time-series information indicating the time-series transition of factors of the disease as an input, and output a prognosis of the disease;
acquire the time-series information about the subject patient; and
execute prognosis prediction of the subject patient using the patient time-series information about the subject patient and the prognosis prediction model;
output a result of the prognosis prediction;
execute a hypothetical prognosis prediction of the subject patient using hypothetical information, in which a portion of the time-series information about the subject patient has been changed, and the prognosis prediction model; and
predict an effect of an intervention corresponding to the change based on an actual prognosis prediction result and a hypothetical prognosis prediction result; and
output a prediction result of the effect of the intervention.
The underlined limitations as shown above recite the abstract idea of a mental process and/or a certain method of organizing human activity because they recite a process that could be practically performed in the human mind (i.e. observations, evaluations, judgments, and/or opinions – in this case, the steps of acquiring a prognosis prediction model, acquiring time-series patient information, executing prognosis prediction for the patient using the acquired time-series patient information and prognosis prediction model, outputting the results of the prognosis prediction, executing a hypothetical prognosis prediction, predicting an effect of an intervention based on an actual prognosis prediction result and the hypothetical prognosis prediction result, and outputting a prediction result of the intervention recite at least evaluations, specifically collecting information, analyzing it, and displaying certain results of the collection and analysis) or using a pen and paper, but for the recitation of generic computer components (i.e. the various units comprising a computing device), and/or 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 acquiring a prognosis prediction model, acquiring time-series patient information, executing prognosis prediction for the patient using the acquired time-series patient information and prognosis prediction model, outputting the results of the prognosis prediction, executing a hypothetical prognosis prediction, predicting an effect of an intervention based on an actual prognosis prediction result and the hypothetical prognosis prediction result, and outputting a prediction result of the intervention recite at least following rules or instructions to make a patient prognosis and to evaluate a patient intervention), 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 and 15 is identical as the abstract idea for Claim 1, because the only difference between Claims 1, 14, and 15 is that Claim 1 recites a device, whereas Claim 14 recites a method, and Claim 15 recites a non-transitory recording medium.
Dependent Claims 2-12 and 16-18 include other limitations, for example Claims 2-8, 11-12, and 16-18 recite types of disease data, event data, environmental factors, and time-series information, and Claims 9-10 recites types of prognoses and data used to train the model, 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, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-12 and 16-18 nonetheless recite the same abstract idea as independent Claim 1.
Hence Claims 1-12 and 14-18 recite 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 model acquisition unit, the subject patient information acquisition unit, the prognosis prediction execution unit, and the fact that the prognosis prediction model is a machine learning model) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the model acquisition unit, the subject patient information acquisition unit, and the prognosis prediction execution unit, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0035] 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 fact that the prognosis prediction model is a machine learning model amounts to limiting the abstract idea to the field of machine learning, e.g. see MPEP 2106.05(h); and/or
Additionally, dependent Claims 2-12 and 16-18 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 2-8, 11-12, and 16-18), and/or 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 1-12 and 14-18 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 model acquisition unit, the subject patient information acquisition unit, the prognosis prediction execution unit, and the fact that the prognosis prediction model is a machine learning model), 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:
[0035] of the as-filed Specification discloses that the additional elements (i.e. the model acquisition unit, the subject patient information acquisition unit, and the prognosis prediction execution unit) 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 the prognosis prediction model and the time-series information over a network, e.g. see [0032]-[0033] of the as-filed Specification;
Dependent Claims 2-12 and 16-18 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 2-8, 11-12, and 16-18), and/or the limitations recited by the dependent claims 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 1-12 and 14-18 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 1-12 and 14-18 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, 7-8, 12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lanius (US 2021/0398677) in view of Mitsumori (US 2020/0365273).
Regarding Claim 1, Lanius discloses the following: An information processing device for predicting a prognosis of a subject patient affected by a disease, comprising:
a storage unit storing a prognosis prediction model, time-series information indicating a time-series transition of factors of the disease, patient time-series information, and a prediction result; and
a processor coupled to the storage unit configured to:
a model acquisition unit that acquires a prognosis prediction model (The system includes a machine learning model database and an inference system that obtains machine learning models from the machine learning model database, e.g. see Lanius [0026]-[0027].), being a machine learning model that takes time-series information indicating a time-series transition of factors of the disease as an input, and outputs a prognosis of the disease (The system obtains time series data in the form of patient physiological data, e.g. see Lanius [0024], wherein the time series data is used as training data to train the machine learning models to generate output indicating a change in the stage of a medical condition, e.g. see Lanius [0026] and [0031], Fig. 2.);
a subject patient information acquisition unit that acquires the time-series information about the subject patient (The inference system obtains time series data of a patient including vital signs and laboratory data, e.g. see Lanius [0027] and [0047]-[0048], Fig. 3.); and
a prognosis prediction execution unit that executes prognosis prediction of the subject patient using the time-series information about the subject patient and the prognosis prediction model, and outputs a result of the prognosis prediction (The inference system infers changes in the stage of medical conditions of patients based on the patient time series data, utilizing the trained machine learning models, e.g. see Lanius [0027] and [0049]-[0050], Fig. 3.).
But Lanius does not teach and Mitsumori teaches the following:
execute a hypothetical prognosis prediction of the subject patient using hypothetical information, in which a portion of the time-series information about the subject patient has been changed, and the prognosis prediction model (The system enables a user to select a patient parameter to change, for example shortening a hospital admission to discharge period, e.g. see Mitsumori [0068], Fig. 6.); and
predict an effect of an intervention corresponding to the change based on an actual prognosis prediction result and a hypothetical prognosis prediction result (The system estimates a change in a test result due to the changed parameters, e.g. see Mitsumori [0073], Fig. 6.); and
output a prediction result of the effect of the intervention (The system displays the changed data accordingly, e.g. see Mitsumori [0073], Fig. 6.).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify Lanius to incorporate the hypothetical information and modifying the prediction based on the change as taught by Mitsumori in order to assist in an efficient discussion by a plurality of doctors for discussing medical treatment policy and to provide an efficient simulation, e.g. see Mitsumori [0119] and [0124].
Regarding Claim 7, the combination of Lanius and Mitsumori teaches the limitations of Claim 1, and Lanius further teaches the following:
The information processing device according to claim 1, wherein the time-series information is information that specifies values of the factors of the disease at a fixed time interval (The system receives patient data in order to output a predicted change in the patient condition, wherein the prediction process may be repeated at predetermined intervals, e.g. see Lanius [0046]-[0050], Fig. 3.).
Regarding Claim 8, the combination of Lanius and Mitsumori teaches the limitations of Claim 7, and Lanius further teaches the following:
The information processing device according to claim 7, wherein the time-series information includes information that specifies values of the factors of the disease at least every month (The system receives patient data in order to output a predicted change in the patient condition, wherein the prediction process may be repeated at predetermined intervals, such as every few hours, e.g. see Lanius [0046]-[0050], Fig. 3.).
Regarding Claim 12, the combination of Lanius and Mitsumori teaches the limitations of Claim 1, and Lanius further teaches the following:
The information processing device according to claim 1, wherein the disease is a disease of a respiratory system or a circulatory system (The patient conditions predicted by the system include acute kidney injury (i.e. a disease of a circulatory system), e.g. see Lanius [0002]-[0003], [0005], and [0031]-[0032].).
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 recording medium. Specifically pertaining to Claims 14-15, Examiner notes that Lanius teaches a method and a non-transitory computer-readable storage media performing the claimed functions, e.g. see Lanius [0001] and [0010], and hence the grounds of rejection provided above for Claim 1 are similarly applied to Claims 14-15.
Claims 2-6 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Lanius and Mitsumori in view of Clifford (US 2021/0398683).
Regarding Claim 2, the combination of Lanius and Mitsumori teaches the limitations of Claim 1, but does not teach and Clifford teaches the following:
The information processing device according to claim 1, wherein the factors of the disease include an environmental factor (The system generates an estimated health-status score for a user utilizing a trained machine learning model, e.g. see Clifford [0028] and [0065], wherein the data utilized includes device sensor data comprising environmental data, e.g. see Clifford [0034].).
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 Lanius and Mitsumori to incorporate utilizing the device sensor data to ultimately determine the health prediction as taught by Clifford in order to improve patient monitoring and provide further understanding of physiological and behavioral determinants of events and factors associated with such events, e.g. see Clifford [0027].
Regarding Claim 3, the combination of Lanius, Mitsumori, and Clifford teaches the limitations of Claim 2, and Clifford further teaches the following:
The information processing device according to claim 2, wherein the factors of the disease include an environmental factor of a place of residence of the subject patient (The system generates an estimated health-status score for a user utilizing a trained machine learning model, e.g. see Clifford [0028] and [0065], wherein the data utilized includes device sensor data comprising environmental data and location data, e.g. see Clifford [0034], wherein the device location data may be used to determine a home location, e.g. see Clifford [0031] and [0049]-[0050].).
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 Lanius and Mitsumori to incorporate utilizing the device location and home data to ultimately determine the health prediction as taught by Clifford in order to improve patient monitoring and provide further understanding of physiological and behavioral determinants of events and factors associated with such events, e.g. see Clifford [0027].
Regarding Claim 4, the combination of Lanius, Mitsumori, and Clifford teaches the limitations of Claim 3, and Clifford further teaches the following:
The information processing device according to claim 3, wherein the environmental factor of the place of residence of the subject patient is an environmental factor of a point within a straight line distance of 200 km from a current address of the subject patient (The system generates an estimated health-status score for a user utilizing a trained machine learning model, e.g. see Clifford [0028] and [0065], wherein the data utilized includes device sensor data comprising environmental data and location data, e.g. see Clifford [0034], wherein the device location data may be used to determine a home location, e.g. see Clifford [0031] and [0049]-[0050], and wherein the location data further includes a Haversine distance between locations, wherein the Haversine distance refers to the shortest distance between two coordinates over the surface of the Earth, e.g. see Clifford [0050].).
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 Lanius and Mitsumori to incorporate utilizing the device location and home data to ultimately determine the health prediction as taught by Clifford in order to improve patient monitoring and provide further understanding of physiological and behavioral determinants of events and factors associated with such events, e.g. see Clifford [0027].
Regarding Claim 5, the combination of Lanius, Mitsumori, and Clifford teaches the limitations of Claim 2, and Clifford further teaches the following:
The information processing device according to claim 2, wherein the environmental factor includes at least one of a presence status of an environmental pollutant, and a meteorological parameter (The system generates an estimated health-status score for a user utilizing a trained machine learning model, e.g. see Clifford [0028] and [0065], wherein the data utilized includes device sensor data comprising environmental data and location data, wherein the environmental data includes temperature, humidity, and/or air pollution, e.g. see Clifford [0034].).
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 Lanius and Mitsumori to incorporate utilizing the environmental data to ultimately determine the health prediction as taught by Clifford in order to improve patient monitoring and provide further understanding of physiological and behavioral determinants of events and factors associated with such events, e.g. see Clifford [0027].
Regarding Claim 6, the combination of Lanius, Mitsumori, and Clifford teaches the limitations of Claim 2, and Clifford further teaches the following:
The information processing device according to claim 2, wherein the time-series information includes information indicating an amount of change in the environmental factor (The system generates an estimated health-status score for a user utilizing a trained machine learning model, e.g. see Clifford [0028] and [0065], wherein the data utilized includes device sensor data comprising environmental data, e.g. see Clifford [0034], wherein the data includes time series data, e.g. see Clifford [0064], and wherein the system monitors for alert conditions, wherein an alert condition corresponds to when a result is above a predefined threshold, e.g. see Clifford [0036].).
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 Lanius and Mitsumori to incorporate monitoring device data changes over time as taught by Clifford in order to improve patient monitoring and provide further understanding of physiological and behavioral determinants of events and factors associated with such events, e.g. see Clifford [0027].
Regarding Claims 16-17, the limitations of Claims 16-17 are substantially similar to those claimed in Claims 5-6 respectively, with the sole difference being that Claims 5-6 depend from Claim 2, whereas Claims 16-17 depend from Claim 3. Specifically pertaining to Claims 16-17, Examiner notes that the combination of Lanius and Clifford teaches the limitations of Claims 2-3 and 5-6 as shown above, and hence the grounds of rejection provided above for Claims 5-6 are similarly applied to Claims 16-17.
Claims 9-11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Lanius and Mitsumori in view of Cottin (US 2022/0293270).
Regarding Claim 9, the combination of Lanius and Mitsumori teaches the limitations of Claim 1, but does not teach and Cottin teaches the following: The information processing device according to claim 1, wherein
the prognosis of the disease includes an occurrence of a plurality of events that are in a competing risk relationship (The system includes a machine-learning process that receives an input of covariate medical data and outputs a probability-based multi-state model of an illness (i.e. a prognosis), e.g. see Cottin [0087] and [0090], wherein the states of illness include an initial non-illness state (“state 0”), an intermediate illness state (“state 1”), and an absorbing death state (“state 2”), e.g. see Cottin [0096]. Furthermore, the system tracks state transitions, where a patient may transition from state 0 to state 1, state 0 to state 2, and from state 1 to state 2, wherein the transitions from state 0 to state 1 and state 0 to state 2 are competing transitions, e.g. see Cottin [0101].), and
the prognosis prediction model is a model trained using a machine learning algorithm corresponding to the plurality of events that are in a competing risk relationship (The machine-learning process is trained based on the input dataset including covariates such as illness-death and time-to-event data for a set of patients, e.g. see Cottin [0087]-[0088] and [0108].).
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 Lanius and Mitsumori to incorporate the competing risk relationship between patient states as taught by Cottin in order to improve the accuracy of determining patient data with respect to a multi-state model of an illness, e.g. see Cottin [0088].
Regarding Claim 10, the combination of Lanius, Mitsumori, and Cottin teaches the limitations of Claim 9, and Cottin further teaches the following: The information processing device according to claim 9,
wherein the prognosis prediction model is a model that outputs, as the prognosis of the disease, an index value representing a possibility of an event occurring among the plurality of events that are in a competing risk relationship (The system includes a machine-learning process that receives an input of covariate medical data and outputs a probability-based multi-state model of an illness (i.e. a prognosis), e.g. see Cottin [0087] and [0090], wherein the states of illness include an initial non-illness state (“state 0”), an intermediate illness state (“state 1”), and an absorbing death state (“state 2”), e.g. see Cottin [0096]. Furthermore, the system tracks state transitions, where a patient may transition from state 0 to state 1, state 0 to state 2, and from state 1 to state 2, wherein the transitions from state 0 to state 1 and state 0 to state 2 are competing transitions, e.g. see Cottin [0101]. Furthermore, the output is a distribution of probabilities for each time interval and transition, e.g. see Cottin [0087]-[0090] and [0092].).
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 Lanius and Mitsumori to incorporate determining the probability of patient states including the competing risk relationship between patient states as taught by Cottin in order to improve the accuracy of determining patient data with respect to a multi-state model of an illness, e.g. see Cottin [0088].
Regarding Claim 11, the combination of Lanius, Mitsumori, and Cottin teaches the limitations of Claim 9, and Cottin further teaches the following: The information processing device according to claim 9, wherein
the plurality of events that are in a competing risk relationship include acute exacerbation and death (The system includes a machine-learning process that receives an input of covariate medical data and outputs a probability-based multi-state model of an illness (i.e. a prognosis), e.g. see Cottin [0087] and [0090], wherein the states of illness include an initial non-illness state (“state 0”), an intermediate illness state (“state 1”), and an absorbing death state (“state 2”), e.g. see Cottin [0096]. Furthermore, the system tracks state transitions, where a patient may transition from state 0 to state 1, state 0 to state 2, and from state 1 to state 2, wherein the transitions from state 0 to state 1 and state 0 to state 2 are competing transitions, e.g. see Cottin [0101]. Furthermore, the output is a distribution of probabilities for each time interval and transition, e.g. see Cottin [0087]-[0090] and [0092].).
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 Lanius and Mitsumori to incorporate determining the probability of patient states including the competing risk relationship between patient states as taught by Cottin in order to improve the accuracy of determining patient data with respect to a multi-state model of an illness, e.g. see Cottin [0088].
Regarding Claim 18, the limitations of Claim 18 are substantially similar to those claimed in Claim 11, with the sole difference being that Claim 18 depends from Claim 10, whereas Claim 11 depends from Claim 9. Specifically pertaining to Claim 18, Examiner notes that the combination of Lanius and Mitsumori teaches the limitations of Claims 9-11 as shown above, and hence the grounds of rejection provided above for Claim 11 are similarly applied to Claim 18.
Response to Arguments
Applicant’s arguments, see Remarks, filed July 24, 2026, with respect to the rejections of Claims 1-18 under 35 U.S.C. 112(b) have been fully considered and, in combination with the claim amendments, are persuasive. The rejections of Claims 1-12 and 14-18 under 35 U.S.C. 112(b) have been withdrawn.
Applicant’s arguments, see Remarks, filed July 24, 2026, with respect to the rejections of Claims 1-18 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants allege that the claimed invention is patent eligible because it recites a process that cannot be practically performed mentally and now requires specific computing hardware and a trained machine learning prognosis model, e.g. see pg. 9 of Remarks – Examiner disagrees.
Examiner notes that the mere presence of computing hardware (e.g. a processor and a memory) does not guarantee that a claim will be found eligible. For example, an invention may nonetheless recite a mental process even if the mental process is performed on a generic computer, in a computer environment, and/or using a computer as a tool to perform the mental process, e.g. see MPEP 2106.04(a)(2)(III)(C). With regards to the claimed invention, the current claim language merely recites a storage unit, a processor, and an already trained machine learning model recited at a high level of generality, and hence the aforementioned limitations are equivalent to reciting that the abstract idea be performed by a generic computer, in a computer environment, and/or using the computer as a tool to perform the mental process. Additionally, as shown above, Examiner notes that the machine learning model, as presently claimed, merely generally links the abstract idea of a mental process to the technological environment/field of use of machine learning, e.g. see MPEP 2106.05(h). Hence, the claimed invention recites an abstract idea.
Applicant further alleges that the claimed invention is patent eligible because it applies a trained machine learning prognosis prediction model in a specific way to solve a particular problem in computer-based prognosis prediction, e.g. see pg. 10 of Remarks – Examiner disagrees.
The invention of McRO claimed the use of particular rules to set morph weights and transitions through phenomes to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters, e.g. see MPEP 2106.05(a)(II). In contrast, the problem of “evaluating how a prospective intervention is expected to affect a subject patient’s prognosis” represents a problem that has existed since long before the advent of any type of computer technology, and hence does not represent a technological problem but rather represents a problem in healthcare relating to the diagnosis/evaluation of a patient. Additionally, Examiner 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 particular 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.
For the aforementioned reasons, Claims 1-12 and 14-18 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed July 24, 2026, with respect to the rejections of Claims 1, 7-8, 12, and 14-15 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, 7-8, 12, and 14-15 under 35 U.S.C. 102(a)(1) have been withdrawn. However, for the reasons disclosed above, and as will be further explained below, Claims 1-12 and 14-18 are nonetheless rejected under 35 U.S.C. 103.
Applicant’s arguments, see Remarks, filed July 24, 2026, with respect to the rejections of Claims 1-12 and 14-18 under 35 U.S.C. 103 have been fully considered but are not persuasive.
Applicant alleges that the rejections presented under 35 U.S.C. 103 are improper because the combination of Lanius and Mitsumori does not teach the limitations pertaining to the prediction and outputting of the prediction result for the hypothetical prognosis, e.g. see pgs. 11-12 of Remarks – Examiner disagrees.
Regarding Lanius, as shown above, Examiner agrees that Lanius does not teach the steps of executing the hypothetical prognosis prediction, predicting an effect of an intervention corresponding to a change, and outputting the prediction result of the effect of the intervention. However, as Applicants acknowledge, Lanius teaches utilizing machine-learning models to process patient time-series data to predict a patient prognosis, e.g. see pg. 11 of Remarks.
Additionally, the steps of executing the hypothetical prognosis prediction, predicting an effect of an intervention corresponding to a change, and outputting the prediction result of the effect of the intervention are taught by Mitsumori. As shown by the citations above, and as Applicants acknowledge, Mitsumori teaches performing a simulation to estimate a resulting change based on an operator-input change, e.g. see pgs. 11-12 of Remarks and Mitsumori [0073], Fig. 6. Furthermore, regarding the specific architecture and the use of a machine learning model, Mitsumori teaches storage that stores a learned model, e.g. see Mitsumori [0026] and [0034], and a processor to execute the functions of the system, e.g. see Mitsumori [0092], wherein the learned model employs known methods or machine learning or deep learning, e.g. see Mitsumori [0054]. Hence Mitsumori teaches the limitations it is cited for.
Additionally, even assuming, arguendo, that Mitsumori did not teach the specific architecture of the machine learning model, Examiner notes that the hardware limitations and a machine learning model is taught by Lanius, e.g. see Lanius [0024]-[0027] and [0030]. That is, even assuming, arguendo, that Mitsumori did not teach the hardware and the machine learning model, it nonetheless would have been obvious to modify Lanius’ hardware and machine learning model to perform the simulation operations as taught by Mitsumori in order to assist in an efficient discussion by a plurality of doctors for discussing medical treatment policy and to provide an efficient simulation, e.g. see Mitsumori [0119] and [0124].
Hence, the combination of Lanius and Mitsumori is not deficient to teach the features it is cited for.
For the aforementioned reasons, Claims 1-12 and 14-18 are rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
Musara (US 2020/0286294) – teaches a system that simulates a patient condition based on patient data, wherein the patient data indicates changing conditions over time
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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