DETAILED ACTION
The present office action represents a nonfinal action on the merits.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/30/2025 has been entered.
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 .
Priority
This application claims the priority date of foreign application JP2022-003873 of January 13, 2022.
Status of Claims
Claims 1 and 12 are amended and claims 1-4 and 6-13 are pending.
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 1-4 and 6-13 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.
Claims 1-4 and 6-11 are drawn to a medical information processing apparatus comprising processing circuitry, which is within the four statutory categories (i.e., machine). Claims 12-13 are drawn to a medical information processing method, which is within the four statutory categories (i.e., process).
Claim 1 recites a medical information processing apparatus, comprising:
a memory that stores information on medical care targets and a prediction model for inferring medical treatment effects; and
processing circuitry configured to:
predict, by using the stored prediction model, a medical treatment effect of each of a plurality of options that are possibly selected as a medical treatment judgment for a medical care target;
compute, based on a prediction result of the medical treatment effect, a first importance degree relating to an effect common to the plurality of options and a second importance degree relating to a difference in effect between the plurality of options, with respect to each of one or more features that relate to information on a state of a patient and affect the medical treatment effect;
present the first importance degree and the second importance degree by one graph or one list with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect;
determine whether or not both the first importance degree and the second importance degree are equal to respective thresholds or more in regard to a first feature that is included in advance information that relates to a reliability of the first feature and to a judgment criterion of a user; and
notify, to the user in real time, and in response to determining that the first importance degree and the second importance degree of the first feature are equal to the respective thresholds or more, that the first feature is a feature that is to be reviewed with priority.
Claim 12 recites a medical information processing method, comprising:
storing, in a memory, information on medical care targets and a prediction model for inferring medical treatment effects;
predicting, by using the stored prediction model, a medical treatment effect of each of a plurality of options that are possibly selected as a medical treatment judgment for a medical care target:
computing, based on a prediction result of the medical treatment effect, a first importance degree relating to an effect common to the plurality of options and a second importance degree relating to a difference in effect between the plurality of options, with respect to each of one or more features that relate to information on a state of a patient and affect the medical treatment effect;
presenting the first importance degree and the second importance degree by one graph or one list with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect:
determining whether or not both the first importance degree and the second importance degree are equal to respective thresholds or more in regard to a first feature that is included in advance information that relates to a reliability of the first feature and to a judgment criterion of a user; and
notifying, to the user in real-time, and in response to determining that the first importance degree and the second importance degree of the first feature are equal to the respective thresholds or more, that the first feature is a feature that is to be reviewed with priority.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and/or mathematical concepts, but for the recitation of generic computer components (e.g., obtaining patient information; managing patient information, in this case a medical information processing apparatus and method). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity and/or mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-4, 6-11, and 13 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 12.
The additional elements from claim 1 include:
medical information processing apparatus comprising (apply it, MPEP 2106.05(f)).
a memory that stores (apply it, MPEP 2106.05(f)).
processing circuitry configured to (apply it, MPEP 2106.05(f)).
The additional elements from claim 1 include:
storing, in a memory (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims 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 “processing apparatus” and “processing circuitry”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g., see Specification Paragraphs [0022] and [0035] (See MPEP 2106.05(f)).
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 discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification Paragraphs [0022] and [0035] disclose that the additional elements (i.e., processing apparatus and processing circuitry) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare, predicts a medical treatment effect of each of a plurality of options that are possibly selected as a medical treatment judgment for a medical care target);
Relevant court decisions: The following example of court decision demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): Receiving or transmitting data, e.g., see Intellectual Ventures v. Symantec; Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention uses as input patient feature values.
Dependent claims 2-4, 6-11, and 13 include other limitations, but none of these functions are deemed significantly more than the abstract idea. 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 any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity and/or mathematical concepts, using a medical information processing apparatus and method to predicts a medical treatment effect of each of a plurality of options that are possibly selected as a medical treatment judgment for a medical care target, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-4 and 6-13 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 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, 6-10, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Vos (U.S. Pub. No. 2022/0230728 A1) in view of Molero (U.S. Pub. No. 2024/0006080 A1) and Abu El Ata (U.S. Pub. No. 2022/0076841 A1).
Regarding claim 1, Vos discloses a medical information processing apparatus comprising:
a memory that stores information on medical care targets and a prediction model for inferring medical treatment effects (Paragraphs [0004] and [0086] discuss using a clinical model to determine at least one indicator related to an outcome of a first treatment, predicting an effectiveness of the first treatment and the memory may be used to store information, such as data relevant to calculations or determinations made by the processor of the system or from any other components of the system.); and
processing circuitry configured to (Paragraphs [0029], [0046], and [0087] discuss apparatus and processing units using patient data.):
predict, by using the stored prediction model, a medical treatment effect of each of a plurality of options that are possibly selected as a medical treatment judgment for a medical care target (Paragraphs [0004]-[0005] and [0013] discuss using a clinical model to determine at least one indicator related to an outcome of a first treatment, predicting an effectiveness of the first treatment and generating a graphical representation of a predicted effectiveness of a first treatment, second treatment and displaying the effectiveness to a user to select a treatment for a patient.);
compute, based on a prediction result of the medical treatment effect, a first value relating to an effect common to the plurality of options and a second value relating to a difference in effect between the plurality of options, with respect to each of one or more features that relate to information on a state of a patient and affect the medical treatment effect (Paragraphs [0004], [0009], [0013], [0018], [0036]-[0038], [0082], and FIGS. 2-3 discuss computer implemented method for generating a graphical representation of a predicted effectiveness of a first or second treatment for example, using a clinical model to determine at least one indicator related to an outcome of a first treatment and selecting a treatment from a plurality of treatment options whereby the selected treatment has the highest predicted effectiveness; using the clinical model to determine at least one indicator related to an outcome may comprise providing patient characteristics, the predicted effectiveness comprises a score derived from the outputs of the clinical model and summing values of the at least one indicator, relating to prostate cancer, examples of indicators of outcomes of the first treatment include, but are not limited to, measures of potency recovery at 6, 12 or 24 months, measures of continence recovery at 1, 3 or 12 months, lymph node invasion, Gleason score, and seminal vesicle invasion.) (Examiner is interpreting indicators to be features in the claim.);
present the first value and the second value degree by one graph or one list (Paragraphs [0068]-[0069], FIGS. 2, 4 discuss circular bar charts display predicted effectiveness of two types of surgery that may be used in treatment and each treatment option has a different impact on quality of life and curability of the disease, for example, in this way a medical professional may quickly gain an overview of the function and oncological effectiveness of the two prospective treatments in order to selection an optima balance between oncological and functional outcome and multiple clinical model outcomes can be compared in a single view.);
determine whether or not both the first value and the second value are equal to respective value in regard to a first feature that is included in advance information that relates to a reliability of the first feature and to a judgment criterion of a user (Paragraphs [0038], [0041], [0065], and [0068] discuss a clinical model may link patient characteristics such as age, height, weight of the patient as input features and output an indicator related to an outcome (e.g. a clinical outcome, or quality of life outcome) of the first treatment and the predicted effectiveness may comprise a score, rating or ranking derived from the at least one indicator (e.g. a combined score, rating or ranking derived from the output(s) of the clinical model(s), for example, predicting an effectiveness of the first treatment may comprise summing values of the at least one indicator and/or taking a weighted average of values of the at least one indicator. In such a way, a plurality of indicators may be combined to form a predictor of the overall efficacy of the treatment. Thus providing a combined prediction (e.g. score or ranking) for a medical professional to consider and this may be done for a second treatment and the predicted effectiveness compared.); and
notify, to the user in real time, and in response to determining that the first value and the second value of the first feature are calculated, that the first feature is a feature that is to be reviewed with priority (Paragraphs [0070]-[0073], [0078], and FIG, 3 discuss a user may be able to interact with the predicted effectiveness, clinical models and the underlying patient data used for training and testing the clinical models with real-time feedback and predicting a effectiveness of a second treatment, and displaying the effectiveness of the first treatment and the effectiveness of the second treatment in the first graphical representation. By displaying the first and second treatments in the same graphical representation, the user or medical professional may more quickly be able to compare the effectiveness of the two treatments and further, enables a medical professional to consider both the predicted effectiveness of the first treatment and the underlying indicators related to individual outcomes (e.g. clinical model outputs) when selecting a treatment for a patient, for example, the graph can represent the indicator “localized disease” of the first and second treatments respectively and the graphs shows where the values are equal and different.).
Vos does not explicitly disclose:
a first importance degree and a second importance degree;
a first importance degree and a second importance degree are equal to respective thresholds or more;
with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect.
Molero teaches:
a first importance degree and a second importance degree (Paragraphs [0035], [0267], and [0292]-[0293] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features.).
a first importance degree and a second importance degree are equal to respective thresholds or more (Paragraphs [0010], [0189], [0292]-[0293], and [0301] discuss feature importance and scores assigned to features and the treatment outcomes can be segmented into, for example, categories, thresholds, or ranges, such as a percentage range of increase or decrease in gene expression value after a target therapy treatment is performed and various data elements may be differentially weighted in this search (e.g., in accordance with predefined data element weightings, user input that indicates an importance of matching various data elements, and/or a prevalence of particular data element values across a subject record set). When searching across a set of records for potential matches, some records may lack values for various data elements. In these cases, it may be determined that (for example) the data element values do not match and/or the data.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include a first importance degree and a second importance degree and a first importance degree and a second importance degree are equal to respective thresholds or more, as taught by Molero, in order to validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Abu El Ata teaches:
with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include, with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect, as taught by Abu El Ata, in order to enable clinicians to diagnosis and prescribe therapeutics with higher certainty and provide more optimal patient outcomes. (Abu El Ata Paragraphs [0003]).
Regarding claim 2, Vos discloses wherein the processing circuitry is further configured to display the first value and the second value as a cumulative bar graph in regard to each of the one or more features (Paragraphs [0033], [0068]-[0070], and FIGS. 2-3 discuss a clinical model may link patient characteristics to an outcome of the treatment for the patient, it takes patient characteristics such as age, height, weight of the patient as input features and output an indicator related to an outcome (e.g. a clinical outcome, or quality of life outcome) of the first treatment and bar charts of the predicted effectiveness, for example, of two types of surgery used to treat cancer and each treatment option has a different impact on quality of life and curability of the disease and a medical profession can quickly gain an overview of the functional and oncological effectiveness, which are calculated by summing of all indicators that can be compared of the two prospective treatments in order to select an optimal balance between oncological and functional outcome as the treatment options are displayed in the same graphical representation.).
Vos does not explicitly disclose:
the first importance degree and the second importance degree as a cumulative bar graph.
Molero teaches:
the first importance degree and the second importance degree as a cumulative bar graph (Paragraphs [0035], [0267], and [0292]-[0293] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include the first importance degree and the second importance degree as a cumulative bar graph, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Regarding claim 3, Vos discloses wherein the processing circuitry is further configured to switch, according to a user instruction, such that the first value or the second value is displayed singly (Paragraphs [0068]-[0077] FIGS. 2-6 discuss graphical representations, outcomes can be compared in different views, for example, clicking on first level details may reveal further details of the underlying clinical models that provide the indicators that are used to produce the predicted effectiveness or alternatively, for example, the indicators produced by the clinical models may be accompanied by a color code, for example, green, yellow and red corresponding to a discriminating performance larger than 80% (good performance), between 70% and 80% (moderate performance) and below 70% (poor performance), respectively.).
Vos does not explicitly disclose:
the first importance degree or the second importance degree.
Molero teaches:
the first importance degree or the second importance degree (Paragraphs [0035] and [0292]-[0293] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include the first importance degree or the second importance degree, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Regarding claim 6, Vos discloses wherein the processing circuitry is further configured to:
determine whether or not changes in the height of different bar charts) and thus increases the speed and effectiveness with which a user can determine the most effective treatment for their patient. The user is thus able to repeatedly update the parameters and compare the results of different options.); and
notify, in response to determining that the first feature is present, that the first feature is a feature that is to be reviewed (Paragraphs [0002], [0031], [0060], [0070], [0080] discuss when selecting appropriate treatment the medical professional may use various considerations to try to quantify the expected outcome of the selected treatment, including patient age, fitness for treatment, expected improvement in quality of life, disease aggressiveness and the user may update (e.g. change) which indicators are associated with which categories, and the medical professional may be able to configure the predicted effectiveness according to their needs and preferences and the user is able to obtain a summary and make informed and quicker treatment selections when the graphical representation is displayed to compare effectiveness of the treatments.).
Vos does not explicitly disclose:
first importance degree equal to the respective threshold or more is present;
a feature that is to be reviewed with priority.
Molero teaches:
a first importance degree equal to the respective threshold or more (Paragraphs [0010], [0035], [0292]-[0293], and [0322] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features and upon determining that at least one of the similarity weights outputted by the similarity model is within a threshold, the computer-implemented method can include identifying one of the other subjects based on the determination and assigning the treatment outcome of the identified other subject as the predicted treatment outcome for the particular subject.).
a feature that is to be reviewed with priority (Paragraphs [0189], [0292], and [0294] discuss various data elements may be differentially weighted, user input indicates an importance of matching various data elements and the system detects feature importance and the system outputs the guideline classification “meets guideline.”)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include a first importance degree equal to the respective threshold or more and a feature that is to be reviewed with priority, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Regarding claim 7, Vos discloses wherein the processing circuitry is further configured to:
determine whether or not a first feature with the second value is present, in a case where a user places importance on the difference in effect between the options (Paragraphs [0033], [0040], [0042], and [0072]-[0073], FIG. 3 discuss a clinical model may link patient characteristics to an outcome of the treatment for the patient, it takes patient characteristics such as age, height, weight of the patient as input features and output an indicator related to an outcome (e.g. a clinical outcome, or quality of life outcome) of the first treatment and combining or summarizing one or more indicators into the predicted effectiveness and the number of categories and associated name of the category may be determined by the user (e.g. by receiving user input) and the user may update (e.g. change) which indicators are associated with which categories, therefore, for example, the medical professional may be able to configure the predicted effectiveness according to their needs and preferences; for the bar chart, portions are overlain for direct comparison such that it can easily be seen that the predicted functional effectiveness of the first treatment is better than that of the second treatment for indicators.); and
notify, in response to determining that the first feature is present, that the first feature is a feature that is to be reviewed (Paragraphs [0002], [0031], [0060], and [0070] discuss when selecting appropriate treatment the medical professional may use various considerations to try to quantify the expected outcome of the selected treatment, including patient age, fitness for treatment, expected improvement in quality of life, disease aggressiveness and the user may update (e.g. change) which indicators are associated with which categories, and the medical professional may be able to configure the predicted effectiveness according to their needs and preferences and the user is able to obtain a summary and make informed and quicker treatment selections when the graphical representation is displayed to compare effectiveness of the treatments.).
Vos does not explicitly disclose:
the second importance degree equal to the respective threshold or more is present;
a feature that is to be reviewed with priority.
Molero teaches:
the second importance degree equal to the respective threshold or more is present (Paragraphs [0010], [0035], [0292]-[0293] and [0322] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features and upon determining that at least one of the similarity weights outputted by the similarity model is within a threshold, the computer-implemented method can include identifying one of the other subjects based on the determination and assigning the treatment outcome of the identified other subject as the predicted treatment outcome for the particular subject.).
a feature that is to be reviewed with priority (Paragraphs [0189], [0292], and [0294] discuss various data elements may be differentially weighted, user input indicates an importance of matching various data elements and the system detects feature importance and the system outputs the guideline classification “meets guideline.”)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include the second importance degree equal to the respective threshold or more is present and a feature that is to be reviewed with priority, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Regarding claim 8, Vos discloses wherein the processing circuitry is further configured to determine, in response to determining that the first feature with the first value and the second value that are present, that the first feature is an unnecessary feature in the medical treatment judgment (Paragraphs [0033], [0042], [0068], [0072], and [0078]-[0081], FIGS. 2-3 discuss a clinical model may link patient characteristics to an outcome of the treatment for the patient, it takes patient characteristics such as age, height, weight of the patient as input features and output an indicator related to an outcome (e.g. a clinical outcome, or quality of life outcome) of the first treatment and the user may update (e.g. change, adding and/or removing) which indicators are associated with which categories, therefore, for example, the medical professional may be able to configure the predicted effectiveness according to their needs and preferences and the user input may be used in real time to update the at least one indicator and thus update the predicted effectiveness of the first treatment and thus modify a patient characteristic and observe in real-time the impact on the predicted effectiveness; for the bar chart, portions are overlain for direct comparison such that it can easily be seen that the predicted functional effectiveness of the first treatment is better than that of the second treatment for indicators; for example, the predicted effectiveness measures (quality of life and oncological) are calculated by summing of all indicators relating to quality of life and oncological outcome respectively. It will be appreciated that it depends on the context of a predicted effectiveness as to whether a large circle is associated with a good outcome. In the example in FIG. 2, the quality of life portions should be maximal and oncological portions should be minimal. However, the skilled person will appreciate that this depends on how the predicted effectiveness is defined. For example, in other embodiments, a scale could be defined whereby the oncological portion should be maximized.).
Vos does not explicitly disclose:
the first importance degree and the second importance degree that are less than the respective thresholds.
Molero teaches:
the first importance degree and the second importance degree that are less than the respective thresholds (Paragraphs [0010], [0035], [0292]-[0293] and [0322], [0334] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features and upon determining that at least one of the similarity weights outputted by the similarity model is within a threshold, the computer-implemented method can include identifying one of the other subjects based on the determination and assigning the treatment outcome of the identified other subject as the predicted treatment outcome for the particular subject, and upon determining that none of the similarity weights outputted by the similarity model are within the threshold, identifying another set of subjects having been diagnosed with a different type of cancer than the particular subject to search for a mutation order that is similar to the mutation order of the particular subject; further features can be removed as being characterized as noise.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include, the first importance degree and the second importance degree that are less than the respective thresholds, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Regarding claim 9, Vos discloses wherein the processing circuitry is further configured to generate a prediction model to which a value relating to the one or more features is input and which outputs a medical treatment effect of each of the plurality of options, by using, as training data, a value relating to the one or more features in regard to a medical care target, an option selected for the medical care target, and a medical treatment result by the selected option (Paragraphs [0007], [0033] discuss the machine learning model may be trained to predict a survival outcome, a pathological outcome and/or a functional outcome of the first treatment and a clinical model to predict an indicator related to an outcome of the first treatment, a clinical model may link patient characteristics to an outcome of the treatment for the patient, it takes patient characteristics such as age, height, weight of the patient as input features and output an indicator related to an outcome (e.g. a clinical outcome, or quality of life outcome) of the first treatment.
Vos does not explicitly disclose:
a medical care target in a past, an option selected for the medical care target in the past.
Molero teaches:
a medical care target in a past, an option selected for the medical care target in the past (Paragraphs [0264] and [0297] discuss training data can include clinical data, including treatments, treatment responses, diagnoses, medical history, history of medication, etc.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include a medical care target in a past, an option selected for the medical care target in the past, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Regarding claim 10, Vos discloses wherein the processing circuitry is further configured to generate, based on the prediction model, a first value prediction model that outputs the first value, and a second value prediction model that outputs the second value (Paragraphs [0030]-[0033], [0042], [0044], and [0047] discuss clinical models include any model or framework that may be used to determine (e.g. predict or calculate) an indicator related to an outcome of the first treatment. For example, a clinical model may link patient characteristics to an outcome of the treatment for the patient and take patient characteristics such as age, height, weight of the patient as input features and output an indicator related to an outcome (e.g. a clinical outcome, or quality of life outcome) of the first treatment; the outputs of clinical models may be used as input features to a machine learning model and the machine learning model used to predict the effectiveness of the first treatment from input features comprising indicator(s) (e.g. outputs) from a clinical model; the medical professional may determine the number of categories and names of the category and update which indicators are associated with which categories and configure the predicted effectiveness according to their needs and preferences.).
Vos does not explicitly disclose:
a first importance degree prediction model that outputs the first importance degree, and a second importance degree prediction model that outputs the second importance degree.
Molero teaches:
a first importance degree prediction model that outputs the first importance degree, and a second importance degree prediction model that outputs the second importance degree (Paragraphs [0035] and [0292]-[0293] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include a first importance degree prediction model that outputs the first importance degree, and a second importance degree prediction model that outputs the second importance degree, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Regarding claim 12, Vos discloses a medical information processing method comprising (Paragraph [0004] discusses a method for generating a graphical representation of a predicted effectiveness of a treatment.):
storing, in a memory, information on medical care targets and a prediction model for inferring medical treatment effects (Paragraphs [0004] and [0086] discuss using a clinical model to determine at least one indicator related to an outcome of a first treatment, predicting an effectiveness of the first treatment and the memory may be used to store information, such as data relevant to calculations or determinations made by the processor of the system or from any other components of the system.);
predicting, by using the stored prediction model, a medical treatment effect of each of a plurality of options that are possibly selected as a medical treatment judgment for a medical care target (Paragraphs [0004]-[0005] and [0013] discuss using a clinical model to determine at least one indicator related to an outcome of a first treatment, predicting an effectiveness of the first treatment and generating a graphical representation of a predicted effectiveness of a first treatment, second treatment and displaying the effectiveness to a user to select a treatment for a patient.);
computing, based on a prediction result of the medical treatment effect, a first value relating to an effect common to the plurality of options and a second value relating to a difference in effect between the plurality of options, with respect to each of one or more features that relate to information on a state of a patient and affect the medical treatment effect (Paragraphs [0004], [0009], [0013], [0018], [0036]-[0038], [0082], and FIGS. 2-3 discuss computer implemented method for generating a graphical representation of a predicted effectiveness of a first or second treatment for example, using a clinical model to determine at least one indicator related to an outcome of a first treatment and selecting a treatment from a plurality of treatment options whereby the selected treatment has the highest predicted effectiveness; using the clinical model to determine at least one indicator related to an outcome may comprise providing patient characteristics, the predicted effectiveness comprises a score derived from the outputs of the clinical model and summing values of the at least one indicator, relating to prostate cancer, examples of indicators of outcomes of the first treatment include, but are not limited to, measures of potency recovery at 6, 12 or 24 months, measures of continence recovery at 1, 3 or 12 months, lymph node invasion, Gleason score, and seminal vesicle invasion.) (Examiner is interpreting indicators to be features in the claim.);
presenting the first value and the second value by one graph or one list (Paragraphs [0068]-[0069] and FIGS. 2, 4 discuss circular bar charts display predicted effectiveness of two types of surgery that may be used in treatment and each treatment option has a different impact on quality of life and curability of the disease, for example, in this way a medical professional may quickly gain an overview of the function and oncological effectiveness of the two prospective treatments in order to selection an optima balance between oncological and functional outcome and multiple clinical model outcomes can be compared in a single view.);
determining whether or not both the first value and the second value are equal to respective value in regard to a first feature that is included in advance information that relates to a reliability of the first feature and to a judgment criterion of a user (Paragraphs [0038], [0041], [0065], and [0068] discuss a clinical model may link patient characteristics such as age, height, weight of the patient as input features and output an indicator related to an outcome (e.g. a clinical outcome, or quality of life outcome) of the first treatment and the predicted effectiveness may comprise a score, rating or ranking derived from the at least one indicator (e.g. a combined score, rating or ranking derived from the output(s) of the clinical model(s), for example, predicting an effectiveness of the first treatment may comprise summing values of the at least one indicator and/or taking a weighted average of values of the at least one indicator. In such a way, a plurality of indicators may be combined to form a predictor of the overall efficacy of the treatment. Thus providing a combined prediction (e.g. score or ranking) for a medical professional to consider and this may be done for a second treatment and the predicted effectiveness compared.); and
notifying, to the user in real-time, and in response to determining that the first value and the second value of the first feature are calculated, that the first feature is a feature that is to be reviewed with priority (Paragraphs [0070]-[0073], [0078], and FIG, 3 discuss a user may be able to interact with the predicted effectiveness, clinical models and the underlying patient data used for training and testing the clinical models with real-time feedback and predicting a effectiveness of a second treatment, and displaying the effectiveness of the first treatment and the effectiveness of the second treatment in the first graphical representation. By displaying the first and second treatments in the same graphical representation, the user or medical professional may more quickly be able to compare the effectiveness of the two treatments and further, enables a medical professional to consider both the predicted effectiveness of the first treatment and the underlying indicators related to individual outcomes (e.g. clinical model outputs) when selecting a treatment for a patient, for example, the graph can represent the indicator “localized disease” of the first and second treatments respectively and the graphs shows where the values are equal and different.).
Vos does not explicitly disclose:
a first importance degree and a second importance degree;
a first importance degree and a second importance degree are equal to respective thresholds or more;
with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect.
Molero teaches:
a first importance degree and a second importance degree (Paragraphs [0035] and [0292]-[0293] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features.).
a first importance degree and a second importance degree are equal to respective thresholds or more (Paragraphs [0010], [0189], [0292]-[0293], and [0301] discuss feature importance and scores assigned to features and the treatment outcomes can be segmented into, for example, categories, thresholds, or ranges, such as a percentage range of increase or decrease in gene expression value after a target therapy treatment is performed and various data elements may be differentially weighted in this search (e.g., in accordance with predefined data element weightings, user input that indicates an importance of matching various data elements, and/or a prevalence of particular data element values across a subject record set). When searching across a set of records for potential matches, some records may lack values for various data elements. In these cases, it may be determined that (for example) the data element values do not match and/or the data.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include a first importance degree and a second importance degree and a first importance degree and a second importance degree are equal to respective thresholds or more, as taught by Molero, in order to validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Abu El Ata teaches:
with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect.
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include, with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect, as taught by Abu El Ata, in order to enable clinicians to diagnosis and prescribe therapeutics with higher certainty and provide more optimal patient outcomes. (Abu El Ata Paragraphs [0003]).
Regarding claim 13, Vos discloses wherein the notifying step comprises displaying a list of features to be reviewed in order of priority, and the method further comprises receiving a selection of one feature of the displayed list of features, and in response, displaying detailed information of the selected feature (Paragraphs [0018], [0031], [0072]-[0077], and FIGS. 3-6 discuss use a clinical model to determine at least one indicator related to an outcome of a first treatment, predict and effectiveness of the first treatment, based on the at least one indicator, and send an instruction to the user interface to cause the user interface to display the effectiveness of the first treatment using a first graphical representation, indicators from different clinical models may be combined into a single overall prediction of an effectiveness of the treatment and the first graphical representation may be divided (or further divided) into portions corresponding to values of the at least one indicator. This enables a medical professional to consider both the predicted effectiveness of the first treatment and the underlying indicators related to individual outcomes (e.g. clinical model outputs) when selecting a treatment for a patient.).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Vos in view of Molero and Abu El Ata and in further view of Nash (U.S. Pub. No. 2021/01217516 A1).
Regarding claim 4, Vos discloses wherein the processing circuitry is further configured to display the one or more features in graph indicating the variable (Paragraphs [0002], [0033], FIG. 3 discuss when selecting an appropriate treatment various considerations may be used to quantify the expected outcome of the selected treatment, such as age, fitness for treatment, factors specific to the disease and output an indicator related to an outcome, the clinical models may link patient characteristics to an outcome of the treatment for the patient and display the features in a graphical representation.).
Vos does not explicitly disclose:
displays the features on two-dimensional coordinates with a first axis indicating the first importance degree and a second axis indicating the second importance degree.
Nash teaches:
displays the features on two-dimensional coordinates with a first axis and a second axis (Paragraph [0035] and FIG. 3C discuss capture data on an X-Y graph that captures data from a patient.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include displays the features on two-dimensional coordinates with a first axis and a second axis, as taught by Nash, in order determine the optimal intervention for a patient using objective data-driven criteria (Nash Paragraph [0018]).
Molero teaches:
the first importance degree and the second importance degree (Paragraphs [0035] and [0292]-[0293] discuss feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include the first importance degree and the second importance degree, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Vos in view of Molero and Abu El Ata and in further view of Watanabe (U.S. Pub. No. 2022/0284329 A1).
Regarding claim 11, Vos discloses wherein the processing circuitry is further configured to generate at least one of a first model of the first value prediction model, and a second model of the second value prediction model (Paragraphs [0043]-[0044] and [0047] discuss predicting an effectiveness of a treatment using a machine learning model based on at least one indicator and a machine learning model may be trained to predict the effectiveness of the first treatment based on the at least one indicator, thus the outputs of clinical models may be used as input features to a machine learning model, for each clinical model numerous classifiers are trained for predicting.).
Vos does not explicitly disclose:
first explanatory model that explains a basis for an inference result of the first importance degree prediction model, and a second explanatory model that explains a basis for an inference result of the second importance degree prediction model.
Molero teaches:
a first importance degree and a second importance degree (Paragraphs [0035], [0180], and [0292]-[0293] discuss models to generate predictive outputs and feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include a first importance degree and a second importance degree, as taught by Molero, in order validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy comply with disease treatment guidelines and improve personalized selection of lines of therapy for subjects, personalized assessments of side effects, and verification that lines of therapy comply with existing guidelines, so as to improve treatment efficacy. (Molero Paragraphs [0002] and [0009]).
Watanabe teaches:
first explanatory model that explains a basis for an inference result of the first importance degree prediction model, and a second explanatory model that explains a basis for an inference result of the second importance degree prediction model (Paragraphs [0034] discuss explanatory information output apparatus that performs inference on input data using a machine learning model and presents, to a user, explanatory information indicating the grounds for the inference.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Vos to include first explanatory model that explains a basis for an inference result of the first importance degree prediction model, and a second explanatory model that explains a basis for an inference result of the second importance degree prediction model, as taught by Watanabe, in order allow the user to easily understand and be satisfied with the prediction by the machine learning model. (Watanabe Paragraph [0127]).
Response to Arguments
Applicant’s arguments filed 12/30/2025 have been fully considered.
Rejections under 35 U.S.C. 101:
With respect to claim 1 and the Prong 1 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity and/or mathematical concepts. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Applicant argues, “the additional elements recited in amended Claim 1 integrate any purported abstract idea into a practical application. Further, Claim I recites a technical solution to a technical problem. As set forth in paragraph 4 of the published application, "... in a case where the importance degree in regard to each medical treatment option is displayed, since the amount of information is too large, there arises a problem that the interpretation of information is difficult. In addition, although there are needs to specify a factor contributing to a difference in effect between medical treatment options, and a factor contributing to an outcome, such as prognosis, regardless of medical treatment options, there is a problem that it is difficult for the doctor to select necessary information for judgment from an enormous amount of information.".” (Remarks, page 13). Examiner respectfully disagrees. Examiner maintains that the improvement is to the abstract idea. Applicant’s claims are managing personal behavior or relationships or interactions between people and/or mathematical concepts because the claims are directed to predicting a medical treatment effect. Predicting a medical treatment effect, is not a technical problem rooted in the technology and is directed to the abstract idea. As indicated above, the additional elements recited in the claims are recited at the apply it level, and are merely used as tools to implement the abstract idea. As such, the additional elements are not improved by the claimed invention.
Practical application is a way to overcome the Prong 2 35 U.S.C 101 rejection, however, here, as written, the claims do not result in a practical application. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate that a practical application may be present where the claimed invention provides a technical solution to a technical problem. Applicant states, “paragraph 95 in the published application details the technical solution and the improvement noting that "[t]hereby, since important features, among many features, can be narrowed down and reviewed, the efficiency of the user's judgment is improved. In addition, by omitting the measurement of features of less importance degrees, a medical treatment judgment with equal precision to the case of performing complete examinations can be supported with a less number of examinations." Thus, one practical application of the invention recited in Claim 1 is that fewer examinations of patients are required.” (Remarks, page 14). Applicant states, “nowhere does the Office even attempt to consider the elements of Claim 1 as a whole, which the Revised Guidance states is critical. ” (Remarks, page 16). Applicant further states, “Claim 1 recites a similar model as recited in the Ex Parte Desjardins case, in which the Board notes that software can make non-abstract improvements to computer technology, just as hardware improvements can. On the contrary, the Office appears to only consider "processing circuitry" as an additional element, and does not consider how the "software" elements of Claim1can make non-abstract improvements.” (Remarks, page 17). Examiner respectfully disagrees. In the Application, predicting a medical treatment effect that results in fewer examinations of patients, is part of the abstract idea and the abstract idea cannot be used to integrate itself into a practical application. Further, predicting a medical treatment effect that results in fewer examinations of patients is not an additional element. Also, Ex Parte Desjardins is distinguishable from the Application. Here, the additional elements, including the medical information processing apparatus, do not result in a practical application or technical improvement, as they are recited at an apply it level, as stated above. Here, there is no improvement to the functioning of any additional element, the computer technology, or any other technology. The Application is an improvement to the abstract idea and does not improve any computer element.
All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. Individually and in combination, the additional elements do not provide significantly more than the abstract idea. There is no technological improvement to any additional element. For the reasons stated above, claim 12 similarly fails to overcome the 35 U.S.C. 101 rejection.
Rejections under 35 U.S.C. 103:
Applicant’s amendments overcome the previous rejection. Applicant’s arguments are well taken. Examiner withdraws the previous rejection in light of the amendments. Applicant states, “the '080 application fails to disclose processing circuitry configured to compute, based on a prediction result of the medical treatment effect, a first importance degree relating to an effect common to the plurality of options and a second importance degree relating to a difference in effect between the plurality of options, with respect to each of one or more features that relate to information on a state of a patient and affect the medical treatment effect, and present the first importance degree and the second importance degree by one graph or one list with an indication of a positive impact representing a positive contribution to the medical treatment effect and a negative impact representing a negative contribution to the medical treatment effect, as recited in amended Claim 1.” (Remarks page 11). Examiner agrees and the rejection has been amended.
Applicant states, “the 080 application fails to disclose that for each of the one or more features, both a first importance degree and a second importance degree are computed, as those importance degrees are defined in the claim. For example, the first importance degree, which is calculated for each of the features, relates to an effect common to the plurality of options that be selected as a medical treatment for the patient.” (Remarks, page 11). Molero discusses feature importance refers to a category of algorithms that assign scores to input features and the score represents the importance or degree of contribution that the input feature imposed on the output and a knowledge graph is created related to the importance of the features. See Paragraphs [0035], [0267], and [0292]-[0293]. Examiner interprets the prior art to include these limitations. Molero discusses predict subject-specific side effects of a candidate line of therapy for treating cancer and validate whether the reasons (e.g., represented by certain features in a subject record) that contributed to the selection of a particular line of therapy. See Paragraph [0002]. Applicant states, “the '080 application fails to disclose computing, for each of those features, for example, a first importance degree relating to an effect common to the plurality of options (surgery, medication, etc.).” (Remarks, page 11). Examiner respectfully disagrees. Molero discusses feature importance and scores assigned to features and the treatment outcomes Paragraphs [0010], [0189], [0292]-[0293], and [0301] . Applicant’s arguments with respect to claims 1 and 12 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim 1 and 12 amendments.
Conclusion
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/DAWN T. HAYNES/
Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686