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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C.
119 (a)-(d). The certified copy has been filed in parent Application JP2021-049214, filed on 09/22/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/22/2023, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 limitation(s) is/are:
An input unit (Claims 1 and 8)
A processing unit (Claims 1, 4, 7-13)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
The Specification recites: “The information processing device 10 can use a computer such as a personal computer (PC) and a workstation. The information processing device 10 may be disposed, for example, in a medical institution such as a hospital, an information processing facility that aggregates information from a plurality of medical institutions, or the like. As illustrated in FIG. 1, the information processing device 10 includes an input unit 11, a processing unit 12, an output unit 13, and a storage unit 14. The input unit 11 is a portion where the information processing device 10 receives an input of time-series data” (Para. 0038-0039) and “The processing unit 12 executes various arithmetic processing. The processing unit 12 includes one or more processors and memories. The "processor" can be, for example, a general-purpose processor, a dedicated processor specialized for a specific process, or the like, but is not limited to a general-purpose processor or a dedicated processor specialized for a specific process” (Para. 0050).
The specification provides that for each of these generic placeholders are implemented by a computer or a computer component. Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Therefore, the Specification provides sufficient structure for the input unit and processing unit.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-20 are drawn to a device, a method, and a non-transitory computer readable medium, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system for calculate an acquisition rate and an acquisition frequency of each of the plurality of first parameters included in the plurality of sets of time-series data, and to select a second parameter to be used for training data from the plurality of first parameters by using at least one of the calculated acquisition rate and the calculated acquisition frequency.
Independent claim 16 recites a method for calculating an acquisition rate and an acquisition frequency of each of the first parameters included in the plurality of sets of time-series data; and selecting a second parameter to be used for training data from the plurality of first parameters using at least one of the calculated acquisition rate and the calculated acquisition frequency.
Independent claim 20 recites an article of manufacture for calculating an acquisition rate and an acquisition frequency of each of the first parameters included in the plurality of sets of time-series data; and selecting a second parameter to be used for training data from the plurality of first parameters using at least one of the calculated acquisition rate and the calculated acquisition frequency.
These steps amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(B) citing the abstract idea grouping for mental processes with or without physical aid).
Examiner also notes that calculate an acquisition rate and an acquisition frequency can be analyzed as mathematical concepts. These steps amount to a
mathematical concept which includes mathematical relationships, mathematical
formulas or equations, and mathematical calculations. The mathematical concept need
not be expressed in mathematical symbols but not merely limitations that are based on
or involve a mathematical concept (MPEP § 2106.04(a)(2)(I) citing the abstract idea
grouping for mathematical concepts in general).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
The claims recite the additional elements of an information processing device, an input unit, a processing unit, and a non-transitory computer readable storage medium.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the abstract idea by use of general-purpose computer which does not integrate the abstract idea into a practical application.
Claim 1 recites receive an input of a plurality of sets of time-series data corresponding to a plurality of patients, the plurality of sets of time-series data including a plurality of first parameters related to at least one of a condition and a treatment of each of the plurality of patients. Claim 16 and 20 recite acquiring a plurality of sets of time-series data corresponding to a plurality of patients, the plurality of sets of time-series data including a plurality of first parameters related to at least one of a condition and a treatment of each of the patients. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
As discussed above with respect to integration of the abstract idea into a
practical application, the claims recite the additional elements of an information processing device, an input unit, a processing unit, and a non-transitory computer readable storage medium.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claim 1 recites receive an input of a plurality of sets of time-series data corresponding to a plurality of patients, the plurality of sets of time-series data including a plurality of first parameters related to at least one of a condition and a treatment of each of the plurality of patients. Claim 16 and 20 recite acquiring a plurality of sets of time-series data corresponding to a plurality of patients, the plurality of sets of time-series data including a plurality of first parameters related to at least one of a condition and a treatment of each of the patients. These limitations amount to well-understood, routine, and convention computer functions because they are claimed at a high level of generality and include receiving or transmitting data, which have been found by the court to be well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality)(MPEP § 2106.05(d)(II)).
For the reasons stated, these claims fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim 2 and 17 recite wherein the acquisition rate indicates a ratio at which the first parameter is included in the plurality of sets of time-series data.
Dependent claim 3 and 18 recite wherein the acquisition frequency indicates a frequency at which the first parameter is included in the plurality of sets of time-series data within a predetermined period of the plurality of sets of time-series data.
Dependent claim 4 and 19 recite wherein in a case where the at least one of the acquisition rate and the acquisition frequency of the first parameter exceeds a predetermined threshold, the processing unit is configured to select the first parameter as the second parameter to be used for the training data.
Dependent claim 5 recites wherein a threshold of the acquisition frequency is different among the plurality of first parameters.
Dependent claim 6 recites wherein the threshold of the acquisition frequency is determined on a basis of a number of sets of the plurality of sets of time-series data that include the first parameter and exceed the threshold.
Dependent claim 7 recites wherein the plurality of sets of time-series data includes a first time-series data group and a second time-series data group, and the processing unit is configured to execute processing of selecting the second parameter from data of the first time-series data group and the second time-series data group combined into one, and processing of individually selecting the second parameter from the first time-series data group and the second time-series data group.
Dependent claim 8 recites the processing unit is configured to group the time-series data into a plurality of groups on a basis of the input of additional information and to select the second parameter for each group among the plurality of groups.
Dependent claim 9 recites wherein the processing unit is configured to increase the predetermined period for calculating the acquisition frequency as time elapses.
Dependent claim 10 recites wherein the processing unit is configured to generate training data by using the selected second parameter.
Dependent claim 11 recites wherein the processing unit is configured to generate the training data in a data format based on the acquisition frequency of the selected second parameter.
Dependent claim 12 recites wherein the processing unit is configured to generate a learned model for predicting prognosis of a patient using the training data.
Dependent claim 13 recites wherein for each of a plurality of provisional thresholds, in a case where the at least one of the acquisition rate and the acquisition frequency of the first parameter exceeds the provisional threshold, the processing unit is configured to select the first parameter as a provisional parameter to be used for the training data, to generate the training data and test data using the provisional parameter, to generate a learned model for predicting prognosis of a patient using the training data, performs processing of determining accuracy of the learned model using the test data, and to select the provisional parameter with the determined highest accuracy as the second parameter.
Dependent claim 14 recites wherein the plurality of sets of time-series data includes at least any one of administration information of a medicine, a vital value, examination information, finding information, water intake information, water loss information, and treatment information.
Dependent claim 15 recites wherein the administration information of the medicine includes at least one of information of a type, an administration route, a dose, and an administration rate of an administration medicine; the vital value includes at least one of information of a body temperature, a blood pressure, a heart rate, a respiratory rate, a pulse rate, oxygen saturation, a weight value, a central venous pressure, and an oxygen concentration during inhalation; the examination information includes at least one of information of blood examination data, blood gas data, a urine examination, an electrocardiogram, and a diagnostic imaging result; the finding information includes at least one of information of congestion, cyanosis, and a level of consciousness; the water intake information includes at least one of information of a water intake amount and an infusion amount; the water loss information includes at least one of information of a urine amount and a blood loss amount; and the treatment information includes at least one of information of introduction of a dialysis device, disengagement of the dialysis device, setting of the dialysis device, introduction of a ventilator, disengagement of the ventilator, and setting of the ventilator.
Each of these steps of the preceding dependent claims 2-11 and 13-15 only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Dependent claim 8 recites the input unit is configured to receive an input of additional information including at least one of an initial symptom, an individual attribute, or a disease for each patient among the plurality of patients. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
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.
Claim(s) 1, 4, 10-14, 16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ng (US 20190341153 A1) in view of Jung (US 20190221294 A1) in view of Jain (US 12248384 B1).
REGARDING CLAIM 1
Ng teaches an information processing device used in a system that predicts prognosis of a patient by machine learning, the information processing device comprising:
to select a second parameter to be used for training data from the plurality of first parameters by using at least one of the calculated parameter values. ([Para. 0024] The Extraction Component 135 also generates a Training Dataset 240 and a Scoring Dataset 245 using the extracted variables (or using the variables determined to be salient))
Ng does not explicitly teach, however Jung teaches
an input unit configured to receive an input of a plurality of sets of time-series data corresponding to a plurality of patients, the plurality of sets of time-series data including a plurality of first parameters related to at least one of a condition and a treatment of each of the plurality of patients; ([Para. 0037] The EMR collection device 110 may collect an electronic medical record (EMR) indicating user's health conditions generated by diagnosis, treatment, or medication prescription at a medical institution. [Para. 0038] The EMR collection device 110 may collect EMRs from a medical institution, such as a public institution or hospital. The EMR is generated each time a user visits a medical institution, and may be grouped and managed in a time series for each user in the EMR database 115. [Para. 0040] The PHR collection device 120 may collect PHRs from the PHR database 125 established by a user or a management company or institution designated by the user. The PHR may be generated each time a user uses a personal health sensor and may be grouped and managed in a time series in the PHR database 125. [Para. 0041] Because EMR is generated by specialized medical institutions using precise medical equipment, it may be highly accurate in diagnosing, evaluating, and predicting personal health conditions.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng and incorporate multi-dimensional time series data processing device as taught by Jung, with the motivation of improve the prediction accuracy of future health conditions (Jung Para. 0004).
Ng/ Jung do not explicitly teach, however Jain teaches
and a processing unit configured to calculate an acquisition rate and an acquisition frequency of each of the plurality of first parameters included in the plurality of sets of time-series data, ([Col. 4, Lines 32-38] Determining a score for each of multiple different measurable items, and each score is based on a level of relevance or correlation determined between the use of the measurable item in previous monitoring programs or published research; and selecting, as the one or more candidate items, a subset of the measurable items determined based on the scores. [Col. 27, Lines 61-66] The system 110 determines whether the significance of the observation or commonality identified meets a threshold level. This can include determining whether the magnitude, frequency, number of examples, and so on for an observed condition meets the minimum level.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, and incorporate collecting patient monitoring data as taught by Jain, with the motivation of increasing the accuracy and completeness of data gathered for monitoring patients (Col. 1 Lines 25-26).
REGARDING CLAIM 4
Ng/ Jung/ Jain teach the information processing device according to claim 1, Ng further teaches the processing unit is configured to select the first parameter as the second parameter to be used for the training data. ([Para. 0024] The Extraction Component 135 also generates a Training Dataset 240 and a Scoring Dataset 245 using the extracted variables (or using the variables determined to be salient))
Ng does not explicitly teach, however Jain teaches
wherein in a case where the at least one of the acquisition rate and the acquisition frequency of the first parameter exceeds a predetermined threshold, ([Col. 4, Lines 32-38] Determining a score for each of multiple different measurable items, and each score is based on a level of relevance or correlation determined between the use of the measurable item in previous monitoring programs or published research; and selecting, as the one or more candidate items, a subset of the measurable items determined based on the scores. [Col. 27, Lines 61-66] The system 110 determines whether the significance of the observation or commonality identified meets a threshold level. This can include determining whether the magnitude, frequency, number of examples, and so on for an observed condition meets the minimum level.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, and incorporate collecting patient monitoring data as taught by Jain, with the motivation of increasing the accuracy and completeness of data gathered for monitoring patients (Col. 1 Lines 25-26).
REGARDING CLAIM 10
Ng/ Jung/ Jain teach the information processing device according to claim 1, Ng further teaches wherein the processing unit is configured to generate training data by using the selected second parameter. ([Para. 0038] The Training Application 130 extracts a plurality of attributes from a plurality of electronic health records, wherein each electronic health record is associated with a patient in a plurality of patients. The method 700 then proceeds to block 710, where the Training Application 130 generates a training data set and a scoring data set based on the plurality of attributes.)
REGARDING CLAIM 11
Ng/ Jung/ Jain teach the information processing device according to claim 10, Jain further teaches wherein the processing unit is configured to generate the training data in a data format based on the acquisition frequency of the selected second parameter. ([Col. 34, Lines 62-67] The input to the model, for training and later for inference, can include factors such as characteristics of a primary study and characteristics of a potential sub-study (e.g., topic, data to be collected, disease condition to address, objectives, health outcomes that prompted the sub-study, etc.). [Col. 35, Lines 11-18] In many cases the system can use a confidence score or probability value generated by the model for scoring opportunities for sub-studies. For example, the system 110 can determine, for each identified sub-study opportunity, a confidence score indicating how likely the trained model considers the input data set to fit the criteria for the classification of a high-value, viable sub-study (e.g., those give a training target of “1”).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, and incorporate collecting patient monitoring data as taught by Jain, with the motivation of increasing the accuracy and completeness of data gathered for monitoring patients (Col. 1 Lines 25-26).
REGARDING CLAIM 12
Ng/ Jung/ Jain teach the information processing device according to claim 10, Jung further teaches wherein the processing unit is configured to generate a learned model for predicting prognosis of a patient using the training data. ([Para. 0017] The health predictor may generate the prediction data corresponding to the electronic medical record of the future time point, based on a prediction model for analyzing a change trend of the first time series data with respect to time and a change trend of the second time series data with respect to time in parallel. [Para. 0021] Receiving first time series data generated to have a first type at past time points, through a network interface; embedding the first time series data to generate input data; inputting the input data to a generation model to generate second time series data corresponding to past time points having a reference time interval and having a second type; and generating prediction data of a future time point based on the first time series data and the second time series data. [Para. 0059] The embedder 241a may convert the learning EMR EMRa and the learning PHR PHRa to generate learning data TDa which is time series data. The embedder 241a converts the learning EMR EMRa and the learning PHR PHRa to have the same type and outputs them as time series data arranged over time. The learning data TDa is inputted to the generator 242a. [Para. 0051] The generator 242a may be a neural network model constructed through learning, but not limited thereto, and various learning models capable of performing machine learning may be applied to the generator)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng and incorporate multi-dimensional time series data processing device as taught by Jung, with the motivation of improve the prediction accuracy of future health conditions (Jung Para. 0004).
REGARDING CLAIM 13
Ng/ Jung/ Jain teach the information processing device according to claim 1, Jung teaches the processing unit is configured to select the first parameter as a provisional parameter to be used for the training data, to generate the training data and test data using the provisional parameter, to generate a learned model for predicting prognosis of a patient using the training data, performs processing of determining accuracy of the learned model using the test data, and to select the provisional parameter with the determined highest accuracy as the second parameter. ([Para. 0017] The health predictor may generate the prediction data corresponding to the electronic medical record of the future time point, based on a prediction model for analyzing a change trend of the first time series data with respect to time and a change trend of the second time series data with respect to time in parallel. [Para. 0021] Receiving first time series data generated to have a first type at past time points, through a network interface; embedding the first time series data to generate input data; inputting the input data to a generation model to generate second time series data corresponding to past time points having a reference time interval and having a second type; and generating prediction data of a future time point based on the first time series data and the second time series data. [Para. 0059] The embedder 241a may convert the learning EMR EMRa and the learning PHR PHRa to generate learning data TDa which is time series data. The embedder 241a converts the learning EMR EMRa and the learning PHR PHRa to have the same type and outputs them as time series data arranged over time. The learning data TDa is inputted to the generator 242a. [Para. 0051] The generator 242a may be a neural network model constructed through learning, but not limited thereto, and various learning models capable of performing machine learning may be applied to the generator)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng and incorporate multi-dimensional time series data processing device as taught by Jung, with the motivation of improve the prediction accuracy of future health conditions (Jung Para. 0004).
Jung does not explicitly teach, however Jain teaches
wherein for each of a plurality of provisional thresholds, in a case where the at least one of the acquisition rate and the acquisition frequency of the first parameter exceeds the provisional threshold, ([Col. 4, Lines 32-38] Determining a score for each of multiple different measurable items, and each score is based on a level of relevance or correlation determined between the use of the measurable item in previous monitoring programs or published research; and selecting, as the one or more candidate items, a subset of the measurable items determined based on the scores. [Col. 27, Lines 61-66] The system 110 determines whether the significance of the observation or commonality identified meets a threshold level. This can include determining whether the magnitude, frequency, number of examples, and so on for an observed condition meets the minimum level.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, and incorporate collecting patient monitoring data as taught by Jain, with the motivation of increasing the accuracy and completeness of data gathered for monitoring patients (Col. 1 Lines 25-26).
REGARDING CLAIM 14
Ng/ Jung/ Jain teach the information processing device according to claim 1, Jung further teaches wherein the plurality of sets of time-series data includes at least any one of administration information of a medicine, a vital value, examination information, finding information, water intake information, water loss information, and treatment information. ([Para. 0039] The PHR may be generated from medical data measured from individual health sensors that are individually provided, such as a home body scanner, and may include feature data generated based on features measured by the personal health sensor. The defined PHR will be understood as time series medical data measured directly by the user using a personal health sensor (i.e. vital value) [Para. 0040] The PHR may be generated each time a user uses a personal health sensor (i.e. plurality of sets of time-series data) and may be grouped and managed in a time series in the PHR database 125.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng and incorporate multi-dimensional time series data processing device as taught by Jung, with the motivation of improve the prediction accuracy of future health conditions (Jung Para. 0004).
REGARDING CLAIM 16
Ng teaches an information processing method executed by an information processing device used in a system that predicts prognosis of a patient by machine learning, the information processing method comprising:
and selecting a second parameter to be used for training data from the plurality of first parameters using at least one of the calculated parameter values. (([Para. 0023] The Extraction Component 135 selects the most salient variables from the guideline variables and data variables (e.g., patient attributes) extracted from the Electronic Health Records 165. For example, in some embodiments, the Extraction Component 135 may identify hundreds or thousands of variables in the Treatment Guidelines 160 and Electronic Health Records 165. In an embodiment, the Extraction Component 135 uses one or more feature selection techniques, algorithms, or models to identify which variables are the most salient, and which are duplicative or irrelevant. [Para. 0024] The Extraction Component 135 also generates a Training Dataset 240 and a Scoring Dataset 245 using the extracted variables (or using the variables determined to be salient))
Ng does not explicitly teach, however Jung teaches
acquiring a plurality of sets of time-series data corresponding to a plurality of patients, the plurality of sets of time-series data including a plurality of first parameters related to at least one of a condition and a treatment of each of the patients; ([Para. 0037] The EMR collection device 110 may collect an electronic medical record (EMR) indicating user's health conditions generated by diagnosis, treatment, or medication prescription at a medical institution. [Para. 0038] The EMR collection device 110 may collect EMRs from a medical institution, such as a public institution or hospital. The EMR is generated each time a user visits a medical institution, and may be grouped and managed in a time series for each user in the EMR database 115. [Para. 0040] The PHR collection device 120 may collect PHRs from the PHR database 125 established by a user or a management company or institution designated by the user. The PHR may be generated each time a user uses a personal health sensor and may be grouped and managed in a time series in the PHR database 125. [Para. 0041] Because EMR is generated by specialized medical institutions using precise medical equipment, it may be highly accurate in diagnosing, evaluating, and predicting personal health conditions.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng and incorporate multi-dimensional time series data processing device as taught by Jung, with the motivation of improve the prediction accuracy of future health conditions (Jung Para. 0004).
Ng/ Jung do not explicitly teach, however Jain teaches
calculating an acquisition rate and an acquisition frequency of each of the first parameters included in the plurality of sets of time-series data; ([Col. 4, Lines 32-38] Determining a score for each of multiple different measurable items, and each score is based on a level of relevance or correlation determined between the use of the measurable item in previous monitoring programs or published research; and selecting, as the one or more candidate items, a subset of the measurable items determined based on the scores. [Col. 27, Lines 61-66] The system 110 determines whether the significance of the observation or commonality identified meets a threshold level. This can include determining whether the magnitude, frequency, number of examples, and so on for an observed condition meets the minimum level.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, and incorporate as taught by Jain, with the motivation of increasing the accuracy and completeness of data gathered for monitoring patients (Col. 1 Lines 25-26).
REGARDING CLAIM 19
Claim(s) 19 is/are analogous to Claim(s) 4, thus Claim(s) 19 is/are similarly analyzed
and rejected in a manner consistent with the rejection of Claim(s) 4.
REGARDING CLAIM 20
Ng teaches a non-transitory computer-readable medium storing a computer program for causing an information processing device to execute information processing executed by the information processing device used in a system that predicts prognosis of a patient by machine learning, the information processing comprising:
and selecting a second parameter to be used for training data from the plurality of first parameters using at least one of the calculated parameter values. (([Para. 0023] The Extraction Component 135 selects the most salient variables from the guideline variables and data variables (e.g., patient attributes) extracted from the Electronic Health Records 165. For example, in some embodiments, the Extraction Component 135 may identify hundreds or thousands of variables in the Treatment Guidelines 160 and Electronic Health Records 165. In an embodiment, the Extraction Component 135 uses one or more feature selection techniques, algorithms, or models to identify which variables are the most salient, and which are duplicative or irrelevant.[Para. 0024] The Extraction Component 135 also generates a Training Dataset 240 and a Scoring Dataset 245 using the extracted variables (or using the variables determined to be salient).)
Ng does not explicitly teach, however Jung teaches
acquiring a plurality of sets of time-series data corresponding to a plurality of patients, the plurality of sets of time-series data including a plurality of first parameters related to at least one of a condition and a treatment of each of the patients; ([Para. 0037] The EMR collection device 110 may collect an electronic medical record (EMR) indicating user's health conditions generated by diagnosis, treatment, or medication prescription at a medical institution. [Para. 0038] The EMR collection device 110 may collect EMRs from a medical institution, such as a public institution or hospital. The EMR is generated each time a user visits a medical institution, and may be grouped and managed in a time series for each user in the EMR database 115. [Para. 0040] The PHR collection device 120 may collect PHRs from the PHR database 125 established by a user or a management company or institution designated by the user. The PHR may be generated each time a user uses a personal health sensor and may be grouped and managed in a time series in the PHR database 125. [Para. 0041] Because EMR is generated by specialized medical institutions using precise medical equipment, it may be highly accurate in diagnosing, evaluating, and predicting personal health conditions.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng and incorporate multi-dimensional time series data processing device as taught by Jung, with the motivation of improve the prediction accuracy of future health conditions (Jung Para. 0004).
Ng/ Jung do not explicitly teach, however Jain teaches
calculating an acquisition rate and an acquisition frequency of each of the first parameters included in the plurality of sets of time-series data; ([Col. 4, Lines 32-38] Determining a score for each of multiple different measurable items, and each score is based on a level of relevance or correlation determined between the use of the measurable item in previous monitoring programs or published research; and selecting, as the one or more candidate items, a subset of the measurable items determined based on the scores. [Col. 27, Lines 61-66] The system 110 determines whether the significance of the observation or commonality identified meets a threshold level. This can include determining whether the magnitude, frequency, number of examples, and so on for an observed condition meets the minimum level.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, and incorporate collecting patient data as taught by Jain, with the motivation of increasing the accuracy and completeness of data gathered for monitoring patients (Col. 1 Lines 25-26).
Claim(s) 3, 5, 8, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ng (US 20190341153 A1) in view of Jung (US 20190221294 A1) in view of Jain (US 12248384 B1) in view of Roy (US 20210382472 A1).
REGARDING CLAIM 3
Ng/ Jung/ Jain teach the information processing device according to claim 1,however Roy teaches wherein the acquisition frequency indicates a frequency at which the first parameter is included in the plurality of sets of time-series data within a predetermined period of the plurality of sets of time-series data. ([Abstract] A physical system receives and records measurements from a plurality of sensors for the physical system over a period of time. [Para. 0026] The sensors of a monitoring system (i.e. plurality of sets of time-series data) can generate data at different frequencies. For example, ECG patches generate data every few milliseconds while blood pressure and weight might be recorded only a few times a day. The data generated by a sensor is essentially time-series data. [Para. 0027] Multiple sensors and downstream (external) devices produce streaming data that can be considered as time-series data. Thus, a physical system can be defined by the characteristics of multiple time-series data.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient data as taught by Jain, and incorporate receives and records measurements from a plurality of sensors for the physical system over a period of time as taught by Roy, with the motivation of monitoring a physical system and predicting an event relating to the physical system (Roy Para. 0002).
REGARDING CLAIM 5
Ng/ Jung/ Jain teach the information processing device according to claim 4, however Roy teaches wherein a threshold of the acquisition frequency is different among the plurality of first parameters. ([Para. 0026] The sensors of a monitoring system can generate data at different frequencies. For example, ECG patches generate data every few milliseconds while blood pressure and weight might be recorded only a few times a day. The data generated by a sensor is essentially time-series data. Examiner interprets that the number of collection periods would be indicative of a threshold for acquisition frequency.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient data as taught by Jain, and incorporate receives and records measurements from a plurality of sensors for the physical system over a period of time as taught by Roy, with the motivation of monitoring a physical system and predicting an event relating to the physical system (Roy Para. 0002).
REGARDING CLAIM 8
Ng/ Jung/ Jain teach the information processing device according to claim 1, Ng further teaches wherein the input unit is configured to receive an input of additional information including at least one of an initial symptom, an individual attribute, or a disease for each patient among the plurality of patients; ([Para. 0024] The Training Dataset 240 includes a row for each identified clinical decision point. Each row may include multiple data variables determined to be salient (such as a patient's demographic details, laboratory measurements, comorbidities, medications, and additional characteristics such as smoking status, alcohol use, as well as other health behaviors and social determinants of health). The Training Dataset 240 may also contain an outcome data element, denoted as the “label” for each row.)
Ng does not explicitly teach, however Roy teaches
and the processing unit is configured to group the time-series data into a plurality of groups on a basis of the input of additional information and to select the second parameter for each group among the plurality of groups. ([Para. 0029] logic selects a set of time-series data to use for modeling a particular physical system. The set may include one or both of original sensor/device measurements (e.g. the weight, respiration rate, and blood pressure of a patient) and derived measurements (e.g., QRS complex and atrial premature complexes from an electrocardiography (ECG)). [Para. 0066] the method creates clusters to characterize the distribution of the data during normal operations of the physical system. In one embodiment, Kohonen's Self Organizing Map (SOM) were used for clustering (i.e. grouping). [Para. 0067] One can get feature rankings by a variety of means. The table in FIG. 8 shows the four highest-ranking (i.e. selection of second parameter) features (out of 42 features) for three different heart failure patients. Here the features correspond to the time-series.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient data as taught by Jain, and incorporate receives and records measurements from a plurality of sensors for the physical system over a period of time as taught by Roy, with the motivation of monitoring a physical system and predicting an event relating to the physical system (Roy Para. 0002).
REGARDING CLAIM 18
Claim(s) 18 is/are analogous to Claim(s) 3, thus Claim(s) 18 is/are similarly analyzed
and rejected in a manner consistent with the rejection of Claim(s) 3.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ng (US 20190341153 A1) in view of Jung (US 20190221294 A1) in view of Jain (US 12248384 B1) in view of Chari (US 20130097103 A1).
REGARDING CLAIM 6
Ng/ Jung/ Jain teach the information processing device according to claim 4, however Chari teaches wherein the threshold of the acquisition frequency is determined on a basis of a number of sets of the plurality of sets of time-series data that include the first parameter and exceed the threshold. ([Para. 0041] any different methods can be used to determine the threshold. The threshold of a cluster size is set to one tenth of the unlabeled data size (i.e., a cluster cannot contain more than 10% of the entire data set).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient data as taught by Jain, and incorporate determining a threshold as taught by Chari, with the motivation of improved techniques for generating training samples for predictive modeling.
Claim(s) 7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ng (US 20190341153 A1) in view of Jung (US 20190221294 A1) in view of Jain (US 12248384 B1) in view of Meyer (US 20190378619 A1).
REGARDING CLAIM 7
Ng/ Jung/ Jain teach the information processing device according to claim 1, however Meyer teaches wherein the plurality of sets of time-series data includes a first time-series data group and a second time-series data group, and the processing unit is configured to execute processing of selecting the second parameter from data of the first time-series data group and the second time-series data group combined into one, and processing of individually selecting the second parameter from the first time-series data group and the second time-series data group. ([Para. 0004] Obtain a first data set comprising one or more features of health data associated with one or more patients to train a machine learning model to predict health conditions of patients, perform a first epoch of training using the first data set to train the machine learning model, upon completing the performance of the first epoch, generate a second data set by applying a bias value to values of a first feature of the first data set, and perform a second epoch of training using the second data set to train the machine learning model. [Para. 0019] The bias value may be selected such that the values of the feature remain within a clinically acceptable range of values for the feature after the bias value is applied. In another example, the augmentation technique may involve removing one or more data points from the data set. For example, a particular value of a feature may be removed, a whole feature may be removed, values corresponding to a particular time interval may be removed, etc. In another example, the augmentation technique may involve time warping, or modifying (e.g., increasing, decreasing, etc.) a length of time interval of the time series data set.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient data as taught by Jain, and incorporate modifying the time intervals for patient health data collection as taught by Meyer, with the motivation of predicting health conditions of patients using artificial intelligence methods (Meyer Para. 0001).
REGARDING CLAIM 9
Ng/ Jung/ Jain teach the information processing device according to claim 3, however Meyer teaches wherein the processing unit is configured to increase the predetermined period for calculating the acquisition frequency as time elapses. ([Para. 0044] The augmentation technique for generating the fourth data set may involve modifying a length of a time interval comprising the plurality of time values. In some example, the length of the time interval may be preferred to be increased. In some example, the length of the time interval may be preferred to be decreased. For example, data collected for a feature that were collected from different systems may have been measured or collected at different intervals. A first system may contain data that is measured or available at, for example, a 30 minute interval, while another system may contain data that is measured or available for every one minute interval. Using an augmentation technique to modify time intervals, in one scenario, the frequency of data may be decreased. That is, for both systems, data may be used for each 30 minute interval. In that case, the measurements available at every minute intervals may be skipped to match with the 30 minute intervals of the first system. Alternatively, frequency of data may be increased, where data from the every minute interval is used.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient data as taught by Jain, and incorporate modifying the time intervals for patient health data collection as taught by Meyer, with the motivation of predicting health conditions of patients using artificial intelligence methods (Meyer Para. 0001).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ng (US 20190341153 A1) in view of Jung (US 20190221294 A1) in view of Jain (US 12248384 B1) in view of Medina (US 20220347359 A1) in view of Wu (US 20170086698 A1).
REGARDING CLAIM 15
Ng/ Jung/ Jain teach the information processing device according to claim 14, however Medina teaches wherein the administration information of the medicine includes at least one of information of a type, an administration route, a dose, and an administration rate of an administration medicine; ([Para. 0013] The processor is configured to identify one or more treatment parameters for the dialysis treatment based on the data corresponding to the identity of the short-range wireless device. The processor is also configured to cause the dialysis machine to carry out the dialysis treatment based on the identified one or more treatment parameters. [Para. 0015] the treatment parameter includes one or more of a dialysate type, a dialysate fill volume, and a dialysate flow rate.)
and the treatment information includes at least one of information of introduction of a dialysis device, disengagement of the dialysis device, setting of the dialysis device, introduction of a ventilator, disengagement of the ventilator, and setting of the ventilator. ([Para. 0064] The hemodialysis machine 102 also includes a control unit 101 (e.g., a processor) configured to receive signals from and transmit signals to the touch screen 118, the control panel 120, and a communication module 107 (e.g., an NFC transceiver). The control unit 101 can control the operating parameters (i.e. setting) of the hemodialysis machine 102, for example, based at least in part on the signals received by the touch screen 118, the control panel 120, and the communication module 107. [Para. 0078] The hemodialysis system 100 can identify treatment parameters included in the dialysis treatment and identify particular values for those treatment parameters. The control unit 101 can cause the hemodialysis machine 102, including the dialyzer 110, to carry out the dialysis treatment based on the identified treatment parameters.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient monitoring data as taught by Jain, and incorporate a dialysis system as taught by Medina, with the motivation of receiving inputs and providing information to users to help with the operation of the dialysis machines (Medina Para. 0007).
Medina does not explicitly teach, however Wu teaches
the vital value includes at least one of information of a body temperature, a blood pressure, a heart rate, a respiratory rate, a pulse rate, oxygen saturation, a weight value, a central venous pressure, and an oxygen concentration during inhalation; ([Para. 0022-0024] Automatically measuring and computing life condition data, the acquired signal includes: vital signs and physiological data, including: pulse rate, non-invasive blood pressure, pulse oxygen saturation, breath-end carbon-dioxide concentration, inhaled oxygen concentration, body temperature, respiratory rate, and body weight, exercise sweat amount.)
the examination information includes at least one of information of blood examination data, blood gas data, a urine examination, an electrocardiogram, and a diagnostic imaging result; ([Para. 0374 and Table 1] Extracting the content of the image examination displaying in all of the precious medical records within a specific time period with the explanation of the imaging examination is also the content of the medical treatment information generated image during seeing the doctors, which is also the examination reference data for the health, and is also the displaying in all important diagnostic reference data when of the previous going to a doctor next time.)
the finding information includes at least one of information of congestion, cyanosis, and a level of consciousness; ([Para. 0374 and Table 1] Measuring your sensory sensitivity represents the size of your brain’s ability to eliminate the interference and indirectly reflects the degree of the sensitivity of response consciousness)
the water intake information includes at least one of information of a water intake amount and an infusion amount; ([Para. 0022 and 0025] Automatically measuring and computing life condition data, the acquired signal includes: water intake)
the water loss information includes at least one of information of a urine amount and a blood loss amount; ([Para. 0022-0024] Automatically measuring and computing life condition data, the acquired signal includes: vital signs and physiological data, including: urine volume (i.e. urine amount).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of generating personalized treatment options using extraction methods on patient data driven models as taught by Ng, a multi-dimensional time series data processing device as taught by Jung, collecting patient monitoring data as taught by Jain, a dialysis system as taught by Medina, and incorporate collecting patient physiological data and information, with the motivation of predicting and preventing the occurrence and development of diseases (Wu Abstract).
Subject Matter Free of the Prior Art
The limitation in dependent claim 2 and claim 17 (Claim 2 being representative) wherein the information processing device according to claim 1, wherein the acquisition rate indicates a ratio at which the first parameter is included in the plurality of sets of time-series data were reported free of the prior art.
The most remarkable prior arts of record are as follows:
Jung (US 20190221294 A1) teaches on personal health record is generated by using a personal health sensor may be generated regularly in time series (Para. 0042)
Langheier (US 20060173663 A1) teaches on data collection module for obtaining clinical data from a plurality of different sources for a population of individuals (Para. 0013).
While Jung teaches collecting time-series data from health sensors and Langheier teaches on collection clinical data from different sources for a population of individuals, none of the cited references disclose wherein the acquisition rate indicates a ratio at which the first parameter is included in the plurality of sets of time-series data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chang (US 20220044818 A1), which discloses quantifying the effect of missing or defective patient features on both a health risk score and the confidence interval associated with that health risk score.
De Bruin et al (US 20140279746 A1), which discloses a medical digital expert system to predict a patient's response to a variety of treatments (using pre-treatment information)
Lehman et al., "A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction," which discloses using a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological “state” of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the heart rate and blood pressure dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality. Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.
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/P.K.E./Examiner, Art Unit 3682
/EVANGELINE BARR/Primary Examiner, Art Unit 3682
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