DETAILED ACTION
The present office action represents a final action on the merits.
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 a foreign application JP2022-082495 dated May 19, 2022.
Status of Claims
Claims 1-7 are amended, claim 8 is new, and claims 1-8 are 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 clustering unit ”, in claim 1, “a first feature amount generation unit”, in claims 1 and 4-5, “an estimation unit”, in claims 1, 2, and 4-5, “a second feature amount generation unit” in claims 2 and 5.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The Examiner has reviewed the as-filed disclosure and has made the following findings: “a clustering unit” – an embodiment of the structure is described at the Specification Paragraphs [0009], [0016], [0020], [0053], [0060], and [0107]-[0108], indicates that the various recited components are components of a general-purpose computer. The Examiner finds that there is sufficient algorithmic description of the claimed functionality such that sufficient structure (computer + algorithm) is disclosed. Further, “a first feature amount generation unit” – an embodiment of the structure is described at the Specification Paragraphs [0009], [0016], [0020], [0053], [0060], and [0107]-[0108], indicates that the various recited components are components of a general-purpose computer. The Examiner finds that there is sufficient algorithmic description of the claimed functionality such that sufficient structure (computer + algorithm) is disclosed. “[A]n estimation unit”– an embodiment of the structure is described at the Specification Paragraphs [0009], [0016], [0020], [0053], [0060], and [0107]-[0108], indicates that the various recited components are components of a general-purpose computer. The Examiner finds that there is sufficient algorithmic description of the claimed functionality such that sufficient structure (computer + algorithm) is disclosed. Further, “a second feature amount generation unit” – an embodiment of the structure is described at the Specification Paragraphs [0009], [0016], [0020], [0053], [0060], and [0107]-[0108], indicates that the various recited components are components of a general-purpose computer. The Examiner finds that there is sufficient algorithmic description of the claimed functionality such that sufficient structure (computer + algorithm) is disclosed.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them 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 limitations recite 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 1-8 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 1-6 and 8 are drawn to an information processing system, which is within the four statutory categories (i.e., machine). Claim 7 is drawn to an information processing method by a computer, which is within the four statutory categories (i.e., process).
Claims 1-6 and 8 recite an information processing system comprising:
a clustering model configured to classify behavior patterns of a plurality of depression patients into a plurality of clusters;
a wearable sensor configured to measure data related to an activity state of an object person;
a clustering unit that inputs, to the clustering model, behavior record information in which time of behavior performed by the object person during a first period is recorded for each behavior type, and classifies behavior patterns of the object person into any of the plurality of clusters wherein the first period is a first determined period since a measurement of the data related to the activity state is started;
a first feature amount generation unit that, based on measurement information including an activity amount and a sleep time of the object person measured during a second period, generates a first feature amount indicating an activity state of the object person for each partial period included in the second period wherein the first feature amount for the each partial period included in the second period is time-series feature amount in units of partial periods, wherein the second period is a second determined period since the measurement of the data related to the activity state is started, and wherein the second determined period is greater than the first determined period;
an estimation model constructed by performing machine learning; and
an estimation unit that estimates, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount by using the estimation model prepared for each of the plurality of clusters,
wherein the information processing system is configured to predict psychological stress of the object person using the estimation model after the second period, and to predict recurrence and worsening of depression symptoms of the object person in advance.
Claim 7 recites an information processing method by a computer, the information processing method comprising:
generating a clustering model to classify behavior patterns of a plurality of depression patients into a plurality of clusters;
configuring a wearable sensor to measure data related to an activity state of an object person;
clustering by inputting, to the clustering model, behavior record information in which time of behavior performed by the object person during a first period is recorded for each behavior type, and classifies behavior patterns of the object person into any of the plurality of clusters, wherein the first period is a first determined period since a measurement of the data related to the activity state is started;
generating a first feature amount, based on measurement information including an activity amount and a sleep time of the object person measured during a second period, by generating a first feature amount indicating an activity state of the object person for each partial period included in the second period, wherein the first feature amount for the each partial period included in the second period is time-series feature amount in units of partial periods, wherein the second period is a second determined period since the measurement of the data related to the activity state is started, and wherein the second determined period is greater than the first determined period;
constructing an estimation model by performing machine learning; and
estimating, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount, by using the estimation model prepared for each of the plurality of clusters,
wherein the information processing system is configured to predict psychological stress of the object person using the estimation model after the second period, and to predict recurrence and worsening of depression symptoms of the object person in advance.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity and mathematical concepts, but for the recitation of generic computer components. The underlined limitations are not part of the identified abstract idea (the method of organizing human activity or mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-6 and 8 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.
The dependent claims include additional limitations but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 7.
The additional elements from claim 1 include:
a wearable sensor (apply it, MPEP 2106.05(f)).
an information processing system comprising (apply it, MPEP 2106.05(f)).
a clustering unit (apply it, MPEP 2106.05(f)).
a first feature amount generation unit (apply it, MPEP 2106.05(f)).
an estimation unit (apply it, MPEP 2106.05(f)).
The additional elements from claim 7 include:
a computer (apply it, MPEP 2106.05(f)).
a wearable sensor (apply it, MPEP 2106.05(f)).
Additional elements found in dependent claims:
a second feature amount generation unit (apply it, MPEP 2106.05(f)).
Claims 1-8 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a “an information processing system comprising”, “a clustering unit”, “a first feature amount generation unit”, “an estimation unit”, “a computer”, “a second feature amount generation unit”, and “ a wearable sensor”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see Specification Paragraphs [0017]-[0019], [0033]-[0036], [0050]-[0055], [0060]-[0062], [0069]-[0073], and [0094] (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., Paragraphs [0017]-[0019], [0033]-[0036], [0050]-[0055], [0060]-[0062], [0069]-[0073], and [0094] of the Specification discloses that the additional elements (i.e., an information processing system, a wearable sensor, a clustering unit, a first feature amount generation unit, an estimation unit, a computer, a second feature amount generation unit) 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).
Dependent claims 2-6 and 8 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims and additionally, “a second feature amount generation unit”.
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 predicting recurrence and worsening of depression symptoms in advance or improves any other technology.
Therefore, whether taken individually or as an ordered combination, claims 1-8 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 1-2 and 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Kashiwagi (U.S. Pub. No. 2023/0284983 A1) in view of Rau (U.S. Pub. No. 2021/0106265 A1).
Regarding claim 1, Kashiwagi discloses an information processing system comprising:
a clustering model configured to classify behavior patterns of a plurality of depression patients into a plurality of clusters (Paragraphs [0057]-[0058] discuss select features for clustering in accordance with a degree of importance of features that are used to generate an identifier through the machine learning in the machine learning to generate the identifier model, and generate a clustering classifier and provides information related to selection of a therapy for a first subject with depression.);
a clustering unit that inputs, to the clustering model
a first feature amount generation unit that, based on measurement information including an activity amount of the object person measured during a second period, (Examiner interprets “second period” as a predetermined period. See Specification Paragraph [0033].) generates a first feature amount indicating an activity state of the object person for each partial period included in the second period, wherein the first feature amount for the each partial period included in the second period is time-series feature amount in units of partial periods, wherein the second period is a second determined period since the measurement of the data related to the activity state is started, and wherein the second determined period is greater than the first determined period (Examiner notes that the prior art does not explicitly state “second period”, however, Examiner interprets time sequentially measuring to include a second period.) (Paragraphs [0021], [0063]-[0066], [0279], [0686], and [0714] discuss the clustering device receives, from a plurality of brain activity measurement devices that are measured while the subject is performing some physical activity, information that represents time correlation of brain activities among a plurality of predetermined brain area pairs for each of the plurality of second subjects, including time-sequentially picked-up images – real-time fMRI is used as the brain activity detecting apparatus for time-sequentially measuring brain activities by functional brain imaging; brain activities of traveling subject are measured while the subject travels from measurement site to measurement site, though not limiting, in a prescribed period (for example, in a one-year period) and the fMRI measurement data of the traveling subject, attribute data of the subject, and measurement parameters are collected from respective measurement sites to storage device of data center.);
an estimation model constructed by performing machine learning (Paragraphs [0057], [0500], [0696] discuss execute machine learning, through supervised learning, to generate an identifier model for discriminating presence or absence of the diagnosis label on the basis of the features and an algorithm that, assuming that a plurality of objects to be clustered arise in accordance with a certain probability distribution in each cluster, performs clustering to estimate the “probability distribution.”.);
an estimation unit that estimates, for each of the plurality of clusters, a value of a feature of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount, by using the estimation model prepared for each of the plurality of clusters (Paragraphs [0063]-[0066], [0663]-[0664], [0714] discuss a statistical model and in order to evaluate the magnitude of each factor's effect, estimation is done with variables; the clustering device receives, from a plurality of brain activity measurement devices that are measured while the subject is performing some physical activity, information that represents time correlation of brain activities among a plurality of predetermined brain area pairs for each of the plurality of second subjects, including time-sequentially picked-up images – real-time fMRI is used as the brain activity detecting apparatus for time-sequentially measuring brain activities by functional brain imaging.);
wherein the information processing system is configured to predict a feature of the object person after the second period, and to predict symptoms of the object person in advance (Paragraphs [0295] and [0695] discuss disease identifier (diagnosis marker) capable of predicting whether a subject is healthy or has a disease, the brain activity measuring devices (fMRI devices) measure data of brain activities obtained at a plurality of measuring sites, and generation of biomarkers and estimation (prediction) of diagnosis labels by the biomarkers are realized based on the brain activity data by distributed processing.).
Kashiwagi does not explicitly disclose:
a wearable sensor configured to measure data related to an activity state of an object person;
a sleep time of the object person measured during a second period;
a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount,
predict psychological stress of the object person after the second period, and to predict recurrence and worsening of depression symptoms of the object person in advance.
Rau teaches:
a wearable sensor configured to measure data related to an activity state of an object person (Paragraph [0011] discusses provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns.);
a sleep time of the object person measured during a second period (Paragraphs [0011] discuss provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns over a specific time period.);
an estimation unit that estimates, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount (Paragraphs [0069], [0107], [0131], and [0499]-[0500] discuss an algorithm that, assuming that a plurality of objects to be clustered arise in accordance with a certain probability distribution in each cluster, performs clustering to estimate the “probability distribution” depending on the features; the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels, providing real-time information with higher specificity.);
predict psychological stress of the object person after the second period, and to predict recurrence and worsening of depression symptoms of the object person in advance (Paragraphs [0069], [0107], [0131], and [0499]-[0500] discuss an algorithm that, assuming that a plurality of objects to be clustered arise in accordance with a certain probability distribution in each cluster, performs clustering to estimate the “probability distribution” depending on the features; the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels, providing real-time information with higher specificity.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, a wearable sensor configured to measure data related to an activity state of an object person, a sleep time of the object person measured during a second period, and an estimation unit that estimates, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount, and predict psychological stress of the object person after the second period, and to predict recurrence and worsening of depression symptoms of the object person in advance, as taught by Rau, in order to provide for improved methods and apparatus for treatment and monitoring of mental health patients. (Rau Paragraph [0003].).
Regarding claim 2, Kashiwagi does not explicitly disclose further comprising a second feature amount generation unit that generates a second feature amount indicating a behavior pattern of the object person for each partial period included in the second period from behavior record information in which time of behavior performed by the object person during the second period is recorded for each behavior type,
wherein the estimation unit estimates, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on the object person information, the cluster into which the object person is classified, the first feature amount, and the second feature amount.
Rau teaches:
further comprising a second feature amount generation unit that generates a second feature amount indicating a behavior pattern of the object person for each partial period included in the second period from behavior record information in which time of behavior performed by the object person during the second period is recorded for each behavior type (Paragraph [0011] discusses provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns over a specific time period.),
wherein the estimation unit estimates, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on the object person information, the cluster into which the object person is classified, the first feature amount, and the second feature amount (Paragraphs [0011], [0069], [0107], and [0131] discuss provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns over a specific time period and the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels. These data elements are organized into a matrix configuration to perform inferential analytics and severity indices for different illnesses. This objective patient measurement data from the present invention helps the clinicians by providing real-time information with higher specificity.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, further comprising a second feature amount generation unit that generates a second feature amount indicating a behavior pattern of the object person for each partial period included in the second period from behavior record information in which time of behavior performed by the object person during the second period is recorded for each behavior type and wherein the estimation unit estimates, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on the object person information, the cluster into which the object person is classified, the first feature amount, and the second feature amount, as taught by Rau, in order to provide for improved methods and apparatus for treatment and monitoring of mental health patients. (Rau Paragraph [0003].).
Regarding claim 4, Kashiwagi discloses wherein the estimation unit estimates the magnitude of the feature by inputting the object person information and the first feature amount generated by the first feature amount generation unit to an estimation model prepared for a cluster into which a behavior pattern of the object person is classified by machine learning using teacher data in which the object person information and the first feature amount are used as explanatory variables (Paragraphs [0022], [0055], [0057], and [0066] discuss brain activity analysis of the brain by the fMRI enables the estimation of stimulus input or the state of recognition from spatial patterns of the brain activities and a clustering device includes a processor that executes the clustering process, the processor selects features for clustering in accordance with a degree of importance of features that are used to generate an identifier through the machine learning on the basis of measurement data on brain activities and that provide information related to selection of a therapy for a subject with depression symptoms on the basis of the results of measurement of brain activities of the subject using the discriminator (identifier) or the classifier as a biomarker.).
Kashiwagi does not explicitly disclose:
wherein the estimation unit estimates the magnitude of the psychological stress by inputting the object person information and the magnitude of the psychological stress is used as an objective function.
Rau teaches:
wherein the estimation unit estimates the magnitude of the psychological stress by inputting the object person information and the magnitude of the psychological stress is used as an objective function (Paragraphs [0011], [0069], [0107], and [0131] discuss provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns over a specific time period and the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels. These data elements are organized into a matrix configuration to perform inferential analytics and severity indices for different illnesses. This objective patient measurement data from the present invention helps the clinicians by providing real-time information with higher specificity.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, wherein the estimation unit estimates the magnitude of the psychological stress by inputting the object person information and the magnitude of the psychological stress is used as an objective function, as taught by Rau, in order to provide for improved methods and apparatus for treatment and monitoring of mental health patients. (Rau Paragraph [0003].).
Regarding claim 5, Kashiwagi does not explicitly disclose wherein the estimation unit estimates the magnitude of the psychological stress by inputting the object person information, the first feature amount generated by the first feature amount generation unit, and the second feature amount generated by the second feature amount generation unit to an estimation model prepared for a cluster in which a behavior pattern of the object person is classified by machine learning using 26 teacher data in which the object person information, the first feature amount, and the second feature amount are used as explanatory variables and the magnitude of the psychological stress is used as an objective function.
Rau teaches:
wherein the estimation unit estimates the magnitude of the psychological stress by inputting the object person information, the first feature amount generated by the first feature amount generation unit, and the second feature amount generated by the second feature amount generation unit to an estimation model prepared for a cluster in which a behavior pattern of the object person is classified by machine learning using 26 teacher data in which the object person information, the first feature amount, and the second feature amount are used as explanatory variables and the magnitude of the psychological stress is used as an objective function (Paragraphs [0011], [0069], [0107], and [0131] discuss provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns over a specific time period and the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels. These data elements are organized into a matrix configuration to perform inferential analytics and severity indices for different illnesses. This objective patient measurement data from the present invention helps the clinicians by providing real-time information with higher specificity.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, wherein the estimation unit estimates the magnitude of the psychological stress by inputting the object person information, the first feature amount generated by the first feature amount generation unit, and the second feature amount generated by the second feature amount generation unit to an estimation model prepared for a cluster in which a behavior pattern of the object person is classified by machine learning using 26 teacher data in which the object person information, the first feature amount, and the second feature amount are used as explanatory variables and the magnitude of the psychological stress is used as an objective function, as taught by Rau, in order to provide for improved methods and apparatus for treatment and monitoring of mental health patients. (Rau Paragraph [0003].).
Regarding claim 6, Kashiwagi discloses wherein the second period is a plurality of weeks, and the partial period is a plurality of days (Paragraphs [0146], [0686], and [0821] discuss time-sequentially measuring brain activities and data for persons treated 0th- sixth week’; brain activities of traveling subject are measured while the subject travels from measurement site to measurement site, though not limiting, in a prescribed period (for example, in a one-year period) and the fMRI measurement data of the traveling subject, attribute data of the subject, and measurement parameters are collected from respective measurement sites to storage device of data center.).
Regarding claim 7, Kashiwagi discloses information processing method by a computer, the information processing method comprising:
generating a clustering model to classify behavior patterns of a plurality of depression patients into a plurality of clusters (Paragraphs [0057]-[0058] discuss select features for clustering in accordance with a degree of importance of features that are used to generate an identifier through the machine learning in the machine learning to generate the identifier model, and generate a clustering classifier and provides information related to selection of a therapy for a first subject with depression.);
clustering by inputting, to the clustering model, behavior record information in which time of behavior performed by the object person during a first period is recorded for each behavior type, and classifies behavior patterns of the object person into any of the plurality of clusters, wherein the first period is a first determined period since a measurement of the data related to the activity state is started (Paragraphs [0057], [0063], [0072], [0074], [0144], [0146], and [0821] discuss the clustering device receives, from a plurality of brain activity measurement devices provided at a plurality of measurement sites, information that represents time correlation of brain activities among a plurality of predetermined brain area pairs for each of the plurality of subjects; time sequentially measuring brain activities and clustering by artificial intelligence using a model, and for classifying subjects having a specific disorder into a plurality of groups (sub-groups) in a manner allowing generalization at a plurality of facilities, based on time-correlation patterns.);
generating a first feature amount, based on measurement information including an activity amount of the object person measured during a second period, generating a first feature amount indicating an activity state of the object person for each partial period included in the second period, wherein the first feature amount for the each partial period included in the second period is time-series feature amount in units of partial periods, wherein the second period is a second determined period since the measurement of the data related to the activity state is started, and wherein the second determined period is greater than the first determined period (Paragraphs [0021], [0063]-[0066], [0279], [0686], and [0714] discuss the clustering device receives, from a plurality of brain activity measurement devices that are measured while the subject is performing some physical activity, information that represents time correlation of brain activities among a plurality of predetermined brain area pairs for each of the plurality of second subjects, including time-sequentially picked-up images – real-time fMRI is used as the brain activity detecting apparatus for time-sequentially measuring brain activities by functional brain imaging; brain activities of traveling subject are measured while the subject travels from measurement site to measurement site, though not limiting, in a prescribed period (for example, in a one-year period) and the fMRI measurement data of the traveling subject, attribute data of the subject, and measurement parameters are collected from respective measurement sites to storage device of data center.); and
constructing an estimation model by performing machine learning (Paragraphs [0057], [0500], [0696] discuss execute machine learning, through supervised learning, to generate an identifier model for discriminating presence or absence of the diagnosis label on the basis of the features and an algorithm that, assuming that a plurality of objects to be clustered arise in accordance with a certain probability distribution in each cluster, performs clustering to estimate the “probability distribution.”.);
estimating, for each of the plurality of clusters, a value of a feature of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount, by using the estimation model prepared for each of the plurality of clusters (Paragraphs [0063]-[0066], [0663]-[0664], [0714] discuss a statistical model and in order to evaluate the magnitude of each factor's effect, estimation is done with variables; the clustering device receives, from a plurality of brain activity measurement devices that are measured while the subject is performing some physical activity, information that represents time correlation of brain activities among a plurality of predetermined brain area pairs for each of the plurality of second subjects, including time-sequentially picked-up images – real-time fMRI is used as the brain activity detecting apparatus for time-sequentially measuring brain activities by functional brain imaging.);
wherein the information processing system is configured to predict a feature of the object person after the second period, and to predict symptoms of the object person in advance (Paragraphs [0295] and [0695] discuss disease identifier (diagnosis marker) capable of predicting whether a subject is healthy or has a disease, the brain activity measuring devices (fMRI devices) measure data of brain activities obtained at a plurality of measuring sites, and generation of biomarkers and estimation (prediction) of diagnosis labels by the biomarkers are realized based on the brain activity data by distributed processing.).
Kashiwagi does not explicitly disclose:
configuring a wearable sensor to measure data related to an activity state of an object person;
a sleep time of the object person measured during a second period; and
estimating, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount, by using the estimation model prepared for each of the plurality of clusters,
predict psychological stress of the object person using the estimation model after the second period, and to predict recurrence and worsening of depression symptoms of the object person in advance.
Rau teaches:
configuring a wearable sensor to measure data related to an activity state of an object person(Paragraph [0011] discusses provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns.)
a sleep time of the object person measured during a second period (Paragraphs [0011] discuss provide wearable multi-sensor integrated devices with data fusion analytics designed to monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns over a specific time period.); and
estimating, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount, by using the estimation model prepared for each of the plurality of clusters (Paragraphs [0069], [0107], [0131], and [0499]-[0500] discuss an algorithm that, assuming that a plurality of objects to be clustered arise in accordance with a certain probability distribution in each cluster, performs clustering to estimate the “probability distribution” depending on the features; the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels, providing real-time information with higher specificity.),
predict psychological stress of the object person using the estimation model after the second period, and to predict recurrence and worsening of depression symptoms of the object person in advance (Paragraphs [0069], [0107], [0131], and [0499]-[0500] discuss an algorithm that, assuming that a plurality of objects to be clustered arise in accordance with a certain probability distribution in each cluster, performs clustering to estimate the “probability distribution” depending on the features; the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels, providing real-time information with higher specificity.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, a sleep time of the object person measured during a second period and an estimation step of estimating, for each of the plurality of clusters, a magnitude of psychological stress of the object person after the second period based on object person information including attribute information of the object person, a cluster into which the object person is classified, and the first feature amount, as taught by Rau, in order to provide for improved methods and apparatus for treatment and monitoring of mental health patients. (Rau Paragraph [0003].).
Regarding claim 8, Kashiwagi does not explicitly disclose wherein the first feature amount includes weekly aggregation and lag.
Rau teaches:
wherein the first feature amount includes weekly aggregation and lag (Examiner notes that the prior art does not explicitly state “weekly”, however, it includes “continuous”.) (Paragraphs [0022], [0123], and [0149] discuss the software applies traditional aggregation and cluster differentiation statistical techniques to present the data to healthcare decision makers with graphical and intuitive comparisons, real-time continuous monitoring and aggregates this information for various mental illnesses thereby creating a valuable database for future evidence-based clinical practices.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, wherein the first feature amount includes weekly aggregation and lag, as taught by Rau, in order to provide for improved methods and apparatus for treatment and monitoring of mental health patients. (Rau Paragraph [0003].).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kashiwagi in view of Rau and in further view of Nevo (U.S. Pub. No. 2015/0313529 A1).
Regarding claims 3, Kashiwagi does not explicitly disclose wherein the second feature amount includes a number of days during which a length of time of each behavior of the object person falls outside an upper limit or a lower limit of a determined confidence interval within the partial period.
Rau teaches:
wherein the second feature amount includes a number of days during which a length of time of each behavior of the object person falls outside an upper limit or a lower limit of a determined threshold within the partial period (Paragraphs [0127], [0131], [0148], [0161] discuss monitor and record patients' vital parameters, sleep patterns and patient reported changes in their daily life patterns over a specific time period, monitor continuously some or all the parameters selected and configured by the physician and alert the patients when certain predetermined thresholds for these monitored parameters are exceeded and the risk classification includes the measurement and classification of the stress severity level of the subject, differences from a patient's baseline (resting) information are computed, and those differences showing significance (statistical) and/or above the thresholds developed from each characteristic illness group averages are summarized into three features: duration, frequency, and intensity levels. Clinicians administering and interpreting these tests use these categories, compare with anticipated or expected responses for standardized clinical tests based on a patient's illness and combine the information of significant biometric changes and thresholds provided by this system through the real-time data analytics.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, wherein the second feature amount includes a number of days during which a length of time of each behavior of the object person falls outside an upper limit or a lower limit of a predetermined confidence interval within the partial period, as taught by Rau, in order to provide for improved methods and apparatus for treatment and monitoring of mental health patients. (Rau Paragraph [0003].).
Nevo teaches:
a determined confidence interval within the partial period (Paragraph [0081] discusses classification of information and a confidence score that measures a confidence in the represented classification.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kashiwagi to include, wherein the second feature amount includes a number of days during which a length of time of each behavior of the object person falls outside an upper limit or a lower limit of a predetermined confidence interval within the partial period, as taught by Nevo, in order to provide at least one behavioral pattern associated with the subject, comparing the behavioral pattern with a reference behavioral pattern, and estimating the likelihood that the subject is experiencing or is expected to experience an abnormal condition based on the comparison. (Nevo Paragraph [0007].).
Response to Arguments
Applicant’s arguments filed 6/11/2026 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. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Here, the improvement is to the abstract idea. Applicant’s claims are managing personal behavior or relationships or interactions between people because the claims are directed to predicting recurrence and worsening of depression symptoms in advance. The additional elements are recited at the apply it level and are merely used as tools to implement the abstract idea. Predicting recurrence and worsening of depression symptoms in advance, 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, “the claimed invention can achieve improvements to the medical information processing technical field for depression symptoms prediction.” (Remarks, page 12). Examiner respectfully disagrees. The Application, predicting recurrence and worsening of depression symptoms, is part of the abstract idea and the abstract idea cannot be used to integrate itself into a practical application and therefore, is not an additional element. Here, the improvement is to the abstract idea and not the technology. Here, the additional elements, including an information processing system, a wearable sensor, a clustering unit, a first feature amount generation unit, an estimation unit, a computer, and a second feature amount generation unit, do not result in a practical application or technical improvement, as they are recited at an apply it level, as stated above.
Applicant further states, “The claimed invention recites, in part, a wearable sensor configured to measure data related to an activity state of an object person. Applicants respectfully submit that the claimed invention applies the judicial exception (if any) with, or by use of, a particular machine. MPEP 2106.05(b)… is directed to transforming the data into clusters or the measurements into time-series feature amount. Applicants respectfully submit that the claimed invention can effect a transformation of a particular article to a different state or thing… features of claim 1 can also enable a specific technical application where a medical practitioner can take concrete actions to initiate early treatment.” (Remarks, pages 12-13). Examiner respectfully disagrees. Here, Applicant’s claimed invention does not provide for an improvement to the functioning of an information processing system, a wearable sensor, a clustering unit, a first feature amount generation unit, an estimation unit, a computer, a second feature amount generation unit, or any other technical field.
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. The claims recite features that are "well-understood, routine, conventional activities”. There is no technological improvement to any additional element. For the reasons stated above, claim 7 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’s arguments with respect to claims 1 and 7 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim 1 and 7 amendments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM.
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/DAWN T. HAYNES/
Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights