DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a final rejection
Claims 1-4, 6, 8-13 are pending
Claims 5, 7 were cancelled
Claims 1, 6, 9, 10 were amended
Claims 11, 12, 13 were added
Claims 1-4, 6, 8-13 are rejected under 35 USC § 101
Claims 1-4, 6, 8-13 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a foreign priority date of 1-12-2023
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10-30-2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 6 is objected to because of the following informalities: The Applicant has listed claim 6 to be dependent on claim 5 that was cancelled. Appropriate correction is required. For purposes of this office action the Examiner has corrected the situation by listing claim 6 to be dependent on claim 1.
Claim 13 is objected to because of the following informalities: The claim repeats the following limitation two times, one after the other: “whether the person is in the agitated state” Appropriate correction is required. For purposes of this office action the Examiner has corrected the situation by deleting the duplicate limitation.
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, 6, 8-13 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-4, 6, 8-13, the claims recite an abstract idea of “information processing”.
Independent Claims 1, 9 & 10 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1, 9 and 10 recite a system, method and non-transitory computer-readable medium respectively for processing information.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
acquire biological data that is time-series data measured from a person whose state of agitation is to be determined by a determiner, including a determination time of the state by the determiner with respect to the person and a level of the state determined by the determiner; and
set, from the time-series data, a time section of the biological data on a basis of the determination time and select learning data to generate a model from the biological data on the time section on a basis of a skill value and the level of the state included in the determination result, the skill value representing ability of determining the state and being set for the determiner wherein the model is configured to make a decision with high accuracy whether the person is in an agitated state or not.
belong to the grouping of mental processes under concepts performed in the human mind as it recites “information processing”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “information processing”. (refer to MPP 2106.04(a)(2)). Accordingly, this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 1 recites:
at least one memory configured to store instructions;
at least one processor configured to execute instructions;
performinq machine learning;
Claim 10 recites:
non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing;
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0041-0048]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 1 recites:
at least one memory configured to store instructions;
at least one processor configured to execute instructions;
performinq machine learning;
Claim 10 recites:
non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0041-0048]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recite additional limitations that further define the abstract idea of “information processing”. These claim limitations include:
Claim 2: select the learning data from the biological data by means of a selection method previously set according to the skill value of the determiner;
Claim 3: select a larger amount of learning data from the biological data as the skill value of the determiner is higher.
Claim 4:
select the learning data by duplicating the biological data as the skill value of the determiner is higher
Claim 6: set a length of the time section to be longer depending on and the determined agitation level;
Claim 8: determine the skill value of the determiner on a basis of the determination result by the determiner with respect to the person and correct data of the state for the person;
Claim 12: and wherein acquiring the biological data that is the time-series data measured from the person whose state of agitation is to be determined comprises ... acquiring the biological data by measuring at least one of a motion of the person, a heartbeat interval of the person, and a skin temperature of the person, wherein a timing ... acquiring the biological data is synchronized to an imaging of the person, the imaging of the person showing the state of agitation;
Claim 13: wherein the level of the state indicates one of whether the person may be in the agitated state and whether the person is in the agitated state.
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 2: at least one processor is configured to execute the instructions;
Claim 3: at least one processor is configured to execute the instructions;
Claim 4: at least one processor is configured to execute the instructions;
Claim 5: at least one processor is configured to execute the instructions;
Claim 6: at least one processor is configured to execute the instructions;
Claim 7: at least one processor is configured to execute the instructions;
Claim 8: at least one processor is configured to execute the instructions;
Claim 11: wherein the at least one processor is further configured to execute the instructions to control the model to make the decision
Claim 12: a sensor.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 2: at least one processor is configured to execute the instructions;
Claim 3: at least one processor is configured to execute the instructions;
Claim 4: at least one processor is configured to execute the instructions;
Claim 5: at least one processor is configured to execute the instructions;
Claim 6: at least one processor is configured to execute the instructions;
Claim 7: at least one processor is configured to execute the instructions;
Claim 8: at least one processor is configured to execute the instructions;
Claim 11: wherein the at least one processor is further configured to execute the instructions to control the model to make the decision
Claim 12: a sensor
Claim Rejections - 35 USC § 103
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
non-obviousness.
Claims 1, 2, 6, 9-11 are rejected under 35 U.S.C. 103 as being un-patentable by Park et.al (US 20200168340 A1) hereinafter “Park”, in view of Kotake et.al (JP 2019160013 A) hereinafter “Kotake”
Regarding claims 1, 9 & 10 Park teaches:
at least one memory (storing unit 140 ) configured to store instructions (store the instruction of the delirium risk predicting device 100); (See at least [0113] via: “…The storing unit 140 may be configured to store the various clinical data 300 for the subject 200 received by the receiver 110 and store the instruction of the delirium risk predicting device 100 set by the input unit 120 ..”) and
at least one processor (processor) configured to execute instructions to: (See at least [0040] via: “…provide a delirium risk predicting device implemented by a processor.. wherein the processor is configured to predict a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk..”)
acquire biological data (bio signal data 330, blood data 340) [that is time-series data] measured from a person (acquired for an subject ) whose state of agitation is to be determined (delirium is predicted) by a determiner, a determination result [including a determination time] of the state by the determiner (caregivers or medical practitioners may easily recognize a high risk group of delirium) with respect to the person [and a level of the state determined by the determiner]; (See at least [0103] via: “…Referring to FIG. 1A, a delirium risk prediction system 1000 according to an embodiment of the present disclosure is configured by a delirium risk prediction device 100, clinical data 300 including medical treatment data 310, medication data 320, bio signal data 330, blood data 340, severity evaluation data 350, and mental state evaluation data 360, acquired for an subject 200, a medical treatment 400, and a medical practitioner device 500..”; in addition see at least [0106] via: “…the medical treatment 400 may be a bio signal measurement device which provides at least one bio signal data 330 from the group consisting of a body temperature, a pulse rate, an oxygen saturation, a systolic blood pressure, a diastolic blood pressure, a mean blood pressure, and a respiratory rate of the subject 200…”; in addition see at least [0021] via: “…when the delirium is predicted from the subject by the prediction model, an alarm is provided to notify the delirium risk so that caregivers or medical practitioners may easily recognize a high risk group of delirium for subjects which are required to be consistently monitored, such as critically ill patients..”; in addition see at least [0050] via: “… when a delirium inducing drug is included in the received medication data, a feedback for providing the information is provided so that medical practitioners may take a quick action for the delirium such as discontinuation of administration of delirium inducing drugs..”) The Examiner interprets agitation to be a common symptom of delirium, which is a sudden change in mental function marked by confusion, restlessness, and a state of heightened arousal. While agitation is a symptom, delirium is the underlying syndrome.
[set, from the time-series data, a time section of the biological data on a basis of the determination time] and select learning data (learning data) for performinq machine learning (neural network) to generate a model from the biological data (blood data, bio signal data) on the time section on a basis of a skill value and the level of the state included in the determination result, the skill value representing ability of determining the state (caregivers or medical practitioners may easily recognize a high risk group of delirium) and being set for the determiner wherein the model is configured to make a decision with high accuracy whether the person is in an agitated state or not. (See at least [0037] via: “…the delirium risk prediction model is a model trained by receiving learning data configured by at least one of blood data, severity evaluation data, mental state evaluation data, and bio signal data, medication data, and medical treatment data for a delirium sample subject and a normal sample subject; and predicting to be delirium or normal based on the learning data, and the normal sample subject is a subject who clinically does not have delirium and is evaluated not to be delirium..”; in addition see at least [0038] via: “…the delirium risk predicting method further comprising: evaluating the learning data after receiving the learning data, wherein the evaluating of the learning data includes: calculating a relevance score to the delirium for the learning data, and determining delirium related learning data within a predetermined ranking, based on the relevance score..”; in addition see at least [0100] via: “…the prediction model may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a deep convolutional neural network (DCNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a single shot detector (SSD) model or a U-net based prediction model..”; in addition see at least [0021] via: “…when the delirium is predicted from the subject by the prediction model, an alarm is provided to notify the delirium risk so that caregivers or medical practitioners may easily recognize a high risk group of delirium for subjects which are required to be consistently monitored, such as critically ill patients..”)
Nevertheless Park is silent the following limitations that are taught by Kotake:
biological data that is time-series data (input of time-series data) (See at least [page 4, line 12] via: “…The time-series data input unit 101 receives input of time-series data whose values change according to time..”)
a determination result including a determination time of the state (determines a time zone in which a specific event occurs) by the determiner (determiner 103) (See at least [Page 4, lines 15-16] via: “…The determiner 103 determines a time zone in which a specific event occurs with respect to the time-series data input from the time-series data input unit 101..”)
a level of the state determined by the determiner (See at least [page 4, line 12] via: “…The time-series data input unit 101 receives input of time-series data whose values change according to time..”; in addition see at least [Page 4, lines 15-16] via: “…The determiner 103 determines a time zone in which a specific event occurs with respect to the time-series data input from the time-series data input unit 101..”)
set, from the time-series data, a time section of the biological data on a basis of the determination time and select learning data (See at least [Page 3, lines 1-4] via: “…suitable learning data is generated by adjusting the determination results obtained by determining the start time and end time of the specific event according to the determination accuracy. In the following description, a device that determines the start time and end time of occurrence of a specific event is referred to as a determiner..”; in addition see at least [Page 3, lines 4-5] via: “…a device that determines the start time and end time of occurrence of a specific event is referred to as a determiner…”; in addition see at least [Page 3 lines 21-23] via: “…if the determiner that determines the start time and end time of a specific event is replaced with a determiner (person), the determination is set according to the degree of confidence that the determiner himself sets for each determination, and the determiner..”) The Examiner interprets that a range of data within the time series of data is selected and determined corresponding to the time range when specific events occur as determined by the high skilled determiner
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park with Kotake. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose selecting a time period of data within the time series of data as determined by the high skilled determiner as taught by Kotake. Combining Park and Kotake helps with selecting a range of data within the time series corresponding to times when a specific event occurred which would improve in determining with greater accuracy the state of the patient.
Regarding claim 2: Park and Kotake teach the invention as claimed and detailed above with respect to claim 1. Park also teaches:
wherein the at least one processor is configured to execute the instructions (delirium risk predicting device implemented by a processor) tolearning data configured by at least one of blood data, bio signal data,) by means of a selection method previously set according to the skill value of the determiner (caregivers .. may easily recognize a high risk group of delirium for subjects). (See at least [0037] via: “…the delirium risk prediction model is a model trained by receiving learning data configured by at least one of blood data, severity evaluation data, mental state evaluation data, and bio signal data, medication data, and medical treatment data for a delirium sample subject..”; in addition see at least [0021] via: “…caregivers or medical practitioners may easily recognize a high risk group of delirium for subjects which are required to be consistently monitored, such as critically ill patients..”; in addition see at least [0040] via: “…provide a delirium risk predicting device implemented by a processor.. wherein the processor is configured to predict a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk..”).
Regarding claim 6: Park and Kotake teach the invention as claimed and detailed above with respect to claim 1. Park teaches:
the determined agitation level (See at least [0037] via: “...the delirium risk prediction model is a model trained by receiving learning data configured by at least one of blood data, severity evaluation data, mental state evaluation data, and bio signal data, medication data, and medical treatment data for a delirium sample subject and a normal sample subject; and predicting to be delirium or normal based on the learning data, and the normal sample subject is a subject who clinically does not have delirium and is evaluated not to be delirium...”; in addition see at least [0077] via: “...the mental state evaluation data disclosed in the present specification may be Richmond agitation and sedation scale (RASS) or state-trait anxiety inventory (STAI) score. The mental state evaluation data may be relevant to occurrence of delirium...”)
Nevertheless Park is silent the following limitation that is taught by Kotake:
wherein the at least one processor is configured to execute the instructions toa length of the time section to be longer depending on and the determined agitation level] (See at least [Page 3, lines 1-4] via: “…suitable learning data is generated by adjusting the determination results obtained by determining the start time and end time of the specific event according to the determination accuracy. In the following description, a device that determines the start time and end time of occurrence of a specific event is referred to as a determiner...”; in addition see at least [Page 3, lines 21-25] via: “…... if the determiner that determines the start time and end time of a specific event is replaced with a determiner (person), the determination is set according to the degree of confidence that the determiner himself sets for each determination, and the determiner. Various measures such as the proficiency level and the time required to specify the time when the gripping work is started may be used as the accuracy information..”; in addition see at least [Page 3, lines 39-46] via: “…. the information processing apparatus adjusts the start time point and the end time point of the event occurrence time zone according to the accuracy information set in advance for the determination result. As a result, the information processing apparatus includes the entire time zone in which the event occurs (in this example, the time zone in which the gripping operation is performed) even if the accuracy of the determination result by the determiner is low. In addition, learning data can be generated. In addition, when the accuracy of the determination result is high, it is possible to suppress the unnecessary time zone from being included in the learning data by reducing the adjustment range of the time zone. As a result, the accuracy of the discriminator generated by learning of the learning data is also improved..
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park with Kotake. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose determining the period of time of data if necessary so as to examine the specific part with greater confidence based on the competence of the examiner as taught by Kotake. Combining Park with Kotake helps with selecting an optimal range of data that could be most thoroughly analyzed by a practitioner with a higher skill set.
Regarding claim 11: Park and Kotake teach the invention as claimed and detailed above with respect to claim 1. Park also teaches:
wherein the at least one processor is further configured to execute the instructions to control the model to make the decision. (See at least [0040] via: “…provide a delirium risk predicting device implemented by a processor.. wherein the processor is configured to predict a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk..”)
Claims 3, 4 are rejected under 35 U.S.C. 103 as being un-patentable by Park, in view of Kotake and in further view of Moll et.al (US 20080176210 A1) hereinafter “Moll”
Regarding claim 3: Park and Kotake teach the invention as claimed and detailed above with respect to claim 1. Park and Kotake are silent the following claim that is taught by Moll:
wherein the at least one processor is configured to execute the instructions to. (See at least [0020] via: “…Reasonably, one should tend to put a larger number of data sets and thus functional processes to the disposal of an experienced user than to an average-skilled nurse. The latter kind of users will be given a restricted range of options. In this manner, very specific and optimized treatment processes can be used without entailing the risk of misguided operation by normal heath-care personnel…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park and Kotake with Moll. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose providing a larger number of data sets and thus functional processes to the disposal of an experienced user as taught by Moll. Combining Park and Moll helps with “very specific and optimized treatment processes can be used without entailing the risk of misguided operation by normal heath-care personnel” (Moll 0020)
Regarding claim 4: Park and Kotake teach the invention as claimed and detailed above with respect to claim 1. Park is silent the following claim that is taught by Moll:
wherein the at least one processor is configured to execute the instructions to. (See at least [0020] via: “…Reasonably, one should tend to put a larger number of data sets and thus functional processes to the disposal of an experienced user than to an average-skilled nurse. The latter kind of users will be given a restricted range of options. In this manner, very specific and optimized treatment processes can be used without entailing the risk of misguided operation by normal heath-care personnel…”) The Examiner interprets duplicating the biological data as synonymous with expanding, increasing, augmenting the amount of biological data
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park and Kotake with Moll. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose providing a larger number of data sets and thus functional processes to the disposal of an experienced user as taught by Moll. Combining Park and Moll helps with “very specific and optimized treatment processes can be used without entailing the risk of misguided operation by normal heath-care personnel” (Moll 0020)
Claims 8 is rejected under 35 U.S.C. 103 as being un-patentable by Park in view of Bernardez et.al (WO 2013076327 A1) hereinafter “Bernardez”
Regarding claim 8: Park and Kotake teach the invention as claimed and detailed above with respect to claim 1. Park is silent the following claim that is taught by Bernardez:
wherein the at least one processor is configured to execute the instructions toby the determiner with respect to the person and correct data of the state for the person. (See at least [page 2, lines 7-9] via: “…measure of these methods is always subjective and depends on the ability of the … specialist, to get the most accurate data…”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park and Kotake with Bernardez. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose getting the most accurate data based on the ability of the specialist as taught by Bernardez. Combining Park and Bernardez is helpful in that a specialist that has a higher level of skills would generally provide a more accurate evaluation of the state of a patient.
Claims 12, 13 are rejected under 35 U.S.C. 103 as being un-patentable by Park in view of Kotake in further view of Trim et.al (US 20200110461 A1) hereinafter “Trim”
Regarding claim 12: Park and Kotake teach the invention as claimed and detailed above with respect to claims 1 & 11. Park also teaches:
further comprising a sensor, and [wherein acquiring the biological data that is the time-series data measured from the person] whose state of agitation is to be determined comprises the sensor acquiring the biological data by measuring at least one of a motion of the person, a heartbeat interval of the person, and a skin temperature of the person, [wherein a timing of the sensor acquiring the biological data is synchronized to an imaging of the person, the imaging of the person showing the state of agitation]. (See at least [0108] via: “…referring to FIG. 1B, the delirium risk predicting device 100 includes a receiver 110, an input unit 120, an output unit 130, a storage unit 140, and a processor 150...”; in addition see at least [0024] via: “…provide a delirium risk predicting device, including a receiver configured to receive at least one of blood data, severity evaluation data, mental state evaluation data, and bio signal data, medication data, and medical treatment data, for an subject and a processor configured to be connected to communicate with the receiver and predict a delirium risk of the subject based on a prediction model...”; in addition see at least [0030] via: “…the at least one data includes the bio signal data and the bio signal data includes at least one of a pulse rate, a respiration rate, a body temperature, a systolic blood pressure (SBP), and a diastolic blood pressure (DBP)...”)
Nevertheless Park is silent the following limitation taught by Kotake:
wherein acquiring the biological data that is the time-series data measured from the person (See at least [page 4, line 12] via: “…The time-series data input unit 101 receives input of time-series data whose values change according to time..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park with Kotake. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose determining the period of time of data if necessary so as to examine the specific part with greater confidence based on the competence of the examiner as taught by Kotake. Combining Park with Kotake helps with selecting an optimal range of data that could be most thoroughly analyzed by a practitioner with a higher skill set.
However Park and Kotake are silent the following limitation taught by Trim:
wherein a timing of the sensor acquiring the biological data is synchronized to an imaging of the person, the imaging of the person showing the state of agitation (See at least [claim 3] via: “…wherein the determining that the monitored person is currently in the threshold state of excitement comprises matching gestures within image data of the biometric data to excited gesture profile gestures..”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park and Kotake with Trim. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose obtaining image and biometric data of a person in a state of excitement as taught by Trim. Combining Park and Kotake with Trim helps in obtaining a fuller picture of an agitated patient by simultaneously obtaining biometric, and image data.
Regarding claim 13: Park and Kotake teach the invention as claimed and detailed above with respect to claims 1, 11. Park, Kotake and Trim teach the invention as claimed and detailed above with respect to claim 12. Park also teaches:
wherein the level of the state indicates one of whether the person may be in the agitated state. (See at least [0013] via: “…clinical data such as bio signal data, blood data, mental state evaluation and severity evaluation data as well as medication data and medical treatment data could be used to predict the delirium risk for patients, specifically, critically ill patients...”; in addition see at least [0037] via: “…the delirium risk prediction model is a model trained by receiving learning data configured by at least one of blood data, severity evaluation data, mental state evaluation data, and bio signal data, medication data, and medical treatment data for a delirium sample subject and a normal sample subject; and predicting to be delirium or normal based on the learning data, and the normal sample subject is a subject who clinically does not have delirium and is evaluated not to be delirium..”; in addition see at least [0027] via: “…the at least one data includes the mental state evaluation data and the mental state evaluation data is a Richmond agitation and sedation scale (RASS) or state-trait anxiety inventory (STAI) score..”)
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
MOON, Kyoung Ja (WO 2021235849 A1) – DELIRIUM INTERVENTION MOBILE DEVICE AND DELIRIUM INTERVENTION SYSTEM FOR NURSING HOME PERSONNEL - teaches: a mobile device running an application for nursing home personnel for delirium intervention targeting nursing home subjects is used to provide a predicted delirium risk according to risk factors, a delirium assessment result, and a checklist for delirium intervention, and thus, the nursing home personnel can easily and conveniently monitor delirium and intervene so as to prevent delirium using the application, thereby enabling nursing home personnel to preemptively exercise caution, or preemptive prevention of an onset of delirium, and effectively preventing the risk of a sudden onset of delirium.
KAMOUSI (US 20230225665 A1) - SYSTEMS AND METHODS FOR DETECTION OF DELIRIUM AND OTHER NEUROLOGICAL CONDITIONS - teaches: systems and methods for the detection and monitoring of delirium in a subject. Other neurological conditions may also be detected and monitored. The systems may include a data module configured to obtain a plurality of electroencephalography (EEG) signals collected from a subject. The systems may also include a processing module in communication with the data module. The processing module may be configured to process the data to detect and monitor delirium and/or one or more other neurological conditions that the subject is experiencing or likely to experience. The processing module may also generate indications or assessments for delirium and/or for each neurological condition at an individual level, or optionally, between two or more related neurological conditions.
Response to Arguments
Applicant's arguments filed 7-16-2025, have been fully considered but not found
persuasive.
Applicant amended independent claims 1, 9, 10, dependent claim 6, canceled claims 5, 7 and added claims 11-13 as posted in the above analysis with additions underlined and deletions as ..
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1, 9 and 10 since the claims recite a system, method and non-transitory computer-readable medium respectively for processing information.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as a mental process as it recites “information processing”. Alternatively, the selected abstract idea belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “information processing”
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea. The current invention does not represent an inventive concept since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
Step 2A Prong ONE
The Applicant argues that the invention does not belong to the grouping of mental processes under concepts performed in the human mind as it recites “information processing”. Neither does it belong to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “information processing”. The Applicant argues that this interpretation is inconsistent with the specification. The Applicant argues that the invention cannot be practically performed by the human mind. Furthermore the Applicant argues that there does not seem to be any “human activity” regarding organizing human activity under managing personal behavior or relationships or interactions between people.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Examiner explains the method used to select the abstract idea, which is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claims 1, 9 and 10:
Grouping of claims 1, 9, 10:
at least one memory configured to store instructions; and
at least one processor configured to execute instructions to:
A non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing to:
acquire biological data that is time-series data measured from a person whose state of agitation is to be determined by a determiner,
set, from the time-series data, a time section of the biological data on a basis of the determination time and select learning data for performinq machine learning to generate a model from the biological data on the time section on a basis of a skill value and the level of the state included in the determination result, the skill value representing ability of determining the state and being set for the determiner wherein the model is configured to make a decision with high accuracy whether the person is in an agitated state or not.
The selected abstract idea (boldened limitations) of claims 1, 9 and 10 can be implemented by pencil and paper and thus belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “information processing”. Alternatively, the selected abstract idea belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “information processing”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
Step 2A Prong TWO
The Applicant argues that even if the invention belongs to an abstract idea the claimed subject matter is directed to a practical application based on the amendments. Specifically the Applicant argues that the claims represent an improvement as the specification describes a problem in a technokogy or technical field which would have been understood by one of ordinary skill in the art as an improvement over that problem. Accordingly the Applicant requests withdrawal of the 101 rejection.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Applicant refers to a colloquial interpretation of a practical application. What is required instead is a demonstration of improvement to the functioning of a computer, or to any other technology or technical field that the invention has not recited. All of the added amendments include physical components that are generic whose function amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea.
The Examiner restates that claims 1, 9 and 10 do not integrate the abstract idea into a practical application. Neither claims 1, 9 and 10 recite additional elements that impose a meaningful limit on the abstract idea:
Claim 1 recites the following additional elements:
at least one memory configured to store instructions;
at least one processor configured to execute instructions;
performinq machine learning;
Claim 10 recites the following additional elements:
non-transitory computer-readable medium storing thereon a program comprising instructions for causing a computer to execute processing
The additional elements as recited above for claims 1, 10 amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0042-0049). (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions. As a result, the Examiner restates the rejection of the invention under 35 USC §101.
Step 2B
Similar to the analysis under Step 2A Prong Two, the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0042-0049). (refer to MPEP 2106.05(f)). The use of generic computer components, in combination, do not perform functions that are not merely generic, and non-conventional even if the generic computer operations on a generic computing device is used to implement the abstract idea. Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 103.
The Applicant argues that the following limitation regarding claim 1 are not taught by Park in view of Kotake:
Claim 1
acquire biological data that is time-series data measured from a person whose state of agitation is to be determined;
set, from the time-series data, a time section of the biological data on a basis of the determination time and select learning data for performinq machine learning to generate a model from the biological data on the time section;
The Examiner disagrees since the Applicant’s arguments are not persuasive.
Regarding the first cited limitation, Park teaches: “acquire biological data measured from a person whose state of agitation is to be determined“. See paragraphs [0103], [0106] where it is recited that bio signal data 330 and blood data 340 are acquired for an subject . Park nevertheless fails to teach “biological data that is time-series data” that is taught by Kotake [page 4, line 12] via: “...The time-series data input unit 101 receives input of time-series data whose values change according to time..”. The motivation for the combination is: It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Park with Kotake. Park teaches a delirium risk predicting method which includes receiving at least one of blood data, and bio signal data, for a subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, and providing the delirium risk predicted for the subject. However, Park fails to disclose selecting a time period of data within the time series of data as determined by the high skilled determiner as taught by Kotake. Combining Park and Kotake helps with selecting a range of data within the time series corresponding to times when a specific event occurred which would improve in determining with greater accuracy the state of the patient.
Regarding the second cited limitation, Park teaches “select learning data for performinq machine learning to generate a model from the biological data on the time section” See paragraphs [0014], [0037], [0038], [0100] [0014]. Park nevertheless fails to teach “set, from the time-series data, a time section of the biological data on a basis of the determination time” that is taught by Kotake [Page 3, lines 1-4], [Page 3, lines 4-5] [Page 3 lines 21-23] where the Examiner interprets that a range of data within the time series of data is selected and determined corresponding to the time range when specific events occur as determined by the high skilled determiner. The motivatio