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 .
DETAILED ACTION
Claims 1-12 are pending in this application.
Specification, Drawings and Claims 1-12 have been preliminary amended [5/26/2022].
Drawings
The drawings received on 5/26/2022 are accepted for examination purposes.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 6/2/2022, 6/3/2022, 7/10/2022, 7/12/2022, 12/10/2022, 5/29/2023, 6/28/2023, 10/3/2023, 5/11/2024 and 3/20/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities: Claim 1, lines 3 and 4 are missing “,” (comma punctuation marks) after the following units “a control and calculation unit, and an output unit,”. Appropriate correction is required.
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: “control and calculation unit” in claims 1-10.
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.
Control and calculation unit corresponds to a processing unit having one or more processors for performing logical operations and a memory unit [PG Publication: Fig 1 (12), par 0103-0105]
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.
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.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because Claim 12 recites ‘A computer program product comprising a computer program which can be loaded into a memory of a computer system’ Although a memory is claimed, paragraph 0040, 0059 of Applicant’s PG Publication only describes a computer program which can be loaded into a memory. The memory is not defined in the specification and the claim does not say the program is loaded into a memory, just that it can be loaded into a memory. A claim drawn to such a storage medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments by adding the limitation “non-transitory” to the claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saeed et al. (US-2011/029248) in view of Choi Jun-Ho et al. “EmbraceNet: A robust deep learning architecture for multimodal classification” [hereinafter Choi].
As to Claim 1, Saeed teaches ‘A computer system comprising an input unit, a control and calculation unit, and an output unit [Fig 1, par 0019, 0037, 0039-0040 – a system for monitoring and predicting patient respiratory stability including an input, microprocessor, vital signs display monitor], wherein the control and calculation unit is configured to prompt the input unit to receive patient data relating to an intensive care patient [Fig 1, par 0035, 0037 – acquiring ventilated patient data to a monitoring system and further to the patient monitoring and prediction apparatus], wherein the patient data comprises at least the following patient data: a plurality of radiological images of a thorax of the intensive care patient, wherein the plurality of radiological images show the thorax at different times [par 0055-0056, 0058 – variables are based on radiological imagery for volume estimation at different times], and a plurality of vital data relating to vital parameters of the intensive care patient, wherein the vital data specify values relating to the vital parameters at different times [par 0057-0058 – the measure of ventilated patient stability may be based on two or more sets of parameter values which are acquired at different times], wherein the control and calculation unit is configured to supply the received patient data [par 0052, 0057 – machine learning techniques identifying which parameters are associated with onset of separatory instability and rules based on the parameters],… has been trained using reference data to calculate an ARDS indicator value based on the patient data and to output the ARDS indicator value [par 0051, 0059 – providing predictive alerts for gas exchange compromise patients, e.g., ARDS by developing two reference data sets, a set of unstable patients and a set of stable patients and annotate retrospective data], wherein the control and calculation unit is configured to receive the ARDS indicator value, wherein the control and calculation unit is configured to compare the ARDS indicator value with a threshold value, and wherein the control and calculation unit is configured to prompt the output unit to output a notification if the ARDS indicator value deviates from the threshold value in a defined manner [par 0040, 0051-0052, 0059 – thresholds may be set for parameters and may require the parameter value(s) to be at or below the threshold for the rule to be satisfied while others may require the parameter value to be at or above the threshold for the rule to be met, and if parameter value(s) are at or below the threshold, indicating the patient classified is unstable to develop alerts for subsets of problems including ARDS]’.
Saeed does not disclose expressly ‘an artificial neural network, wherein the artificial neural network comprises at least three subnetworks, a first subnetwork, a second subnetwork and a third subnetwork, wherein the first subnetwork comprises a first input layer, wherein the second subnetwork comprises a second input layer, wherein the third subnetwork comprises an output layer, and wherein the first subnetwork and the second subnetwork are merged in the third subnetwork, wherein the plurality of radiological images are supplied to the first input layer and the plurality of vital data are supplied to the second input layer, wherein the first subnetwork is configured to generate a time-dependent image descriptor for each of the plurality of radiological images, wherein the second subnetwork is configured to generate time-dependent vital data descriptors from the vital data, wherein the time-dependent image descriptors and the time-dependent vital data descriptors are supplied to layers in the artificial neural network that comprise feedback neurons’.
Choi in the proposed combination of Saeed teaches ‘an artificial neural network, wherein the artificial neural network comprises at least three subnetworks, a first subnetwork, a second subnetwork and a third subnetwork, wherein the first subnetwork comprises a first input layer, wherein the second subnetwork comprises a second input layer, wherein the third subnetwork comprises an output layer, and wherein the first subnetwork and the second subnetwork are merged in the third subnetwork, wherein the plurality of radiological images are supplied to the first input layer and the plurality of vital data are supplied to the second input layer, wherein the first subnetwork is configured to generate a time-dependent image descriptor for each of the plurality of radiological images, wherein the second subnetwork is configured to generate time-dependent vital data descriptors from the vital data, wherein the time-dependent image descriptors and the time-dependent vital data descriptors are supplied to layers in the artificial neural network that comprise feedback neurons [Choi Jun-Ho et al. “EmbraceNet: A robust deep learning architecture for multimodal classification”: Abstract, Sec. 1. Introduction, 2. Related work (time-series data), 5. Optimizing the EmbraceNet architecture, 5.1. Adjusting parameters during training, 6.3-6.3.6. Network models, page 0259-0260 – an architecture for multimodal classification which uses a plurality of neural networks (i.e. integration models) in order to first convert the information of each modality to a representation suitable for combination, i.e. a descriptor, and then combines (i.e. EmbraceNet) these descriptors from convolutional and max pooling layers in an additional neural network, where the networks include convolutional neural network and recurrent (i.e. feedback) neural network]’.
Saeed and Choi are analogous art because they are from the same field of endeavor, namely machine learning techniques. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include the use of a plurality of neural networks to combine heterogeneous data sources, as taught by Choi. The motivation for doing so would have been to providing an alternative system for calculating an ARDS indicator with a plurality of neural networks. Therefore, it would have been obvious to combine Choi with Saeed to obtain the invention as specified in claim 1.
Further, in regards to claim 11, the computer system of claim 1 performs the method of claim 11.
Further, in regards to claim 12, the method of claim 11 performs the steps for the computer program of claim 12.
As to Claim 2, Saeed teaches ‘wherein the plurality of radiological images comprise at least three X-rays images of the thorax of the intensive care patient, wherein at least one X-ray image has been generated within an immediately preceding twelve hours period, preferably within an immediately preceding three hours period [par 0051 – applying machine learning algorithms of prior patient data in which respiratory instability was subsequently observed (within the prediction time window, e.g., 2, 12, or 24 hours)]’.
As to Claim 3, Saeed teaches ‘wherein the vital parameters are selected from a group comprising: heart rate, respiratory rate, blood pressure, body temperature, blood oxygen saturation, partial pressure of oxygen, fraction of inspired oxygen, oxygenation index and/or blood pH of the intensive care patient [par 0057 – airway pressure, plateau pressure, inspiration pressure, dynamic lung/chest compliance, respiratory compliance, respiratory rate, saturation of peripheral oxygen, and heart rate]’.
As to Claim 4, Saeed in view of Choi teaches ‘wherein the control and calculation unit is configured to prompt the input unit to receive further patient data relating to the intensive care patient, wherein the further patient data are selected from a group comprising: age, sex, body weight, height, existing disease(s) and/or previous disease(s) of the intensive care patient, wherein the control and calculation unit is configured to supply the further patient data to the third input layer [Saed: par 0056 – the rules may vary by patient type, e.g., age, sex, medical condition, or other factors; Choi: Figs 1, 3, 4, Sec. 3.1. Docking layers, 3.2. Embracement layer – taking output vectors of independent network models of different modalities as inputs, obtaining m vectors from docking layers by combining these vectors to a single vector]’.
Saeed and Choi are analogous art because they are from the same field of endeavor, namely machine learning techniques. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include a robust deep learning architecture for multimodal classification, as taught by Choi. The motivation for doing so would have been to providing an alternative system for calculating an ARDS indicator with a plurality of neural networks. Therefore, it would have been obvious to combine Choi with Saeed to obtain the invention as specified in claim 4.
As to Claim 5, Saeed teaches ‘wherein the notification comprises recommended actions to be taken by a physician or hospital staff in order to prevent deterioration of a state of health of the intensive care patient [par 0049 – providing an alert to medical personnel so that appropriate treatment can be provided when an instability measurement is indicated]’.
As to Claim 6, Saeed teaches ‘wherein the computer system is further configured to monitor a state of health of the intensive care patient in an intensive care unit of a hospital based on the vital parameters [par 0035, 0051 – in a typical hospital environment measuring values every four hours for patients on mechanical ventilation]’.
As to Claim 7, Saeed teaches ‘wherein the computer system can access at least one database of a hospital in which some of the patient data are stored [par 0037 – all acquired patient data is stored in a hospital-wide or other medical system-wide patient data repository]’.
As to Claim 8, Saeed teaches ‘wherein the computer system is configured to calculate a new ARDS indicator value whenever new defined patient data are available [par 0050 – repeating measuring and predicting patients’ respiratory stability for the same patient at intervals, such as at least each time new patient data is input]’.
As to Claim 9, Choi teaches ‘wherein the first subnetwork is a CNN or comprises a CNN and/or wherein the third subnetwork is an RNN or comprises an RNN [Figs 3, 4, Sec. 1. Introduction, Sec. 8. Conclusion – convolutional neural networks (CNNs), recurrent neural networks]’.
Saeed and Choi are analogous art because they are from the same field of endeavor, namely machine learning techniques. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include a robust deep learning architecture for multimodal classification, as taught by Choi. The motivation for doing so would have been to providing an alternative system for calculating an ARDS indicator with a plurality of neural networks. Therefore, it would have been obvious to combine Choi with Saeed to obtain the invention as specified in claim 9.
As to Claim 10, Choi teaches ‘wherein the second subnetwork is an RNN followed by an autoencoder [Figs 3, 4, Sec. 1. Introduction, 6.3. Network models, 6.3.5. Multimodal autoencoder, 8. Conclusion – convolutional neural networks (CNNs) and/or recurrent neural networks with multimodal autoencoder]’.
Saeed and Choi are analogous art because they are from the same field of endeavor, namely machine learning techniques. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to include a robust deep learning architecture for multimodal classification, as taught by Choi. The motivation for doing so would have been to providing an alternative system for calculating an ARDS indicator with a plurality of neural networks. Therefore, it would have been obvious to combine Choi with Saeed to obtain the invention as specified in claim 10.
Conclusion
The prior art made of record
a. US Publication No. 2011/0029248
b. Choi Jun-Ho et al. “EmbraceNet: A robust deep learning architecture for multimodal classification”
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/MIYA J CATO/Primary Examiner, Art Unit 2681