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 in reply to the application filed on 10/31/2024.
Claims 1-17 are currently pending and have been examined.
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 “module” (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) 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: Claims 17-19 recite “weighting module,” “feature-level module,” “region scoring module,” and “domain scoring module”. 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.
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.
Claim Rejections – 35 § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, recites in part, “prediction module configured to input the learning data group into one or more machine learning models, respectively, to learn the machine learning models to predict mortality of the corresponding patient”. It is unclear how inputting the learning data group into one or more machine learning models will cause the machine learning model to predict mortality of the corresponding patient. Is there an algorithm or formula that is being used by the machine learning models to predict mortality of the corresponding patient? Also, it appears that the phrasing of the limitation is unclear, as the limitation states “… to input the learning data groups into one or more machine learning models … to learn the machine learning models to predict …”. Claims 9 and 17 recite similar limitations. Claims 1, 9 and 17 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-8 and 10-16 are rejected for at least the same reason.
Claim 6, recites in part, “input the learning data group for deceased patients and the learning data group for survived patients into a plurality of machine learning models, respectively, to perform performance evaluation for the plurality of machine learning models”. It is unclear how performance evaluation is performed for the plurality of machine learning models. Is there an algorithm or formula that is being used to determine whether one machine learning model is better than another? Claim 14 recites similar limitations. Claims 6 and 14 are therefore found to be indefinite, because the resulting claims does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 7 and 15 are rejected for at least the same reason.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-17: Step 1
Claims 1-8 are drawn to a mortality prediction device of trauma patients equipped with one or more processors and a memory storing one or more programs executed by the one or more processors, which is within the four statutory categories (i.e. machine). Claims 9-16 are drawn to a mortality prediction method of trauma patients, which is within the four statutory categories (i.e. process). Claim 17 is drawn to a computer program stored in a non-transitory computer readable storage medium, the computer program comprising one or more instructions executed by a computing device, which is within the four statutory categories (i.e. machine).
Claims 1-17: Step 2A Prong One
Claim 1 recites collecting patient-related data of patients visiting an emergency department for a certain period of time, generating a learning data group for one patient by extracting a plurality of data preset from the patient-related data, and inputting the learning data group into one or more machine learning models. Claims 9 and 17 recite similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user collecting patient-related data of patients visiting an emergency department for a certain period of time, generating a learning data group for one patient by extracting a plurality of data preset from the patient-related data, and inputting the learning data group. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claims 9 and 17 recite similar limitations.
Claims 2-8 and 10-16 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claims 2 and 10 further define excluding patient-related data corresponding to exclusion conditions. Claims 3 and 11 further define determining whether the corresponding patient corresponds to the preset exclusion conditions. Claims 4 and 12 further define dividing patient-related data of patients that do not correspond to exclusion conditions to deceased group and survived group and inputting these to predict mortality. Claims 5 and 13 further define generating learning data group. Claims 6 and 14 further define inputting the learning data group. Claims 7 and 15 further define calculating performance evaluation score. Claims 8 and 16 further define calculating importance of each variable. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-17: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 recites the additional elements of a mortality prediction device of trauma patients equipped with one or more processors and a memory storing one or more programs executed by the one or more processors. Claim 9 recites additional elements of a method performed in a computing device equipped with one or more processors and a memory storing one or more programs executed by the one or more processors. Claim 17 recites additional elements of a computer program stored in a non-transitory computer readable storage medium, the computer program comprising one or more instructions executed by a computing device having one or more processors.
Claims 1-17, directly or indirectly, recite the following generic computer components: “a processor,” “computing device equipped with one or more processors and a memory,” and “non-transitory computer readable storage medium, the computer program comprising one or more instructions executed by a computing device having one or more processors” which are similar to adding the words “apply it” to the abstract idea. The written description discloses that the recited computer components encompass generic components including “The computing device (12) comprises at least one processor (14), a computer readable storage medium (16) and a communication bus (18). The processor (14) may make the computing device (12) to operate according to the afore-mentioned exemplary examples. For example, the processor (14) may execute one or more programs stored in the computer readable storage medium” (see at least Paragraph [0078]) and “The computer readable storage medium (16) is configured to store computer executable instructions or program codes, program data and/or other suitable forms of information. The programs (20) stored in the computer readable storage medium (16) comprise a set of instructions executable by the processor (14). In one example, the computer readable storage medium (16) may be a memory (volatile memory such as a random-access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, other forms of storage media that are accessed by the computing device (12) and store desired information, or a suitable combination thereof “ (see at least Paragraph [0079]). Although the additional element “machine learning model” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning), and thus fails to add an inventive concept to the claims. See MPEP 2106.05 (h). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-17: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements (for example, machine learning) are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components and machine learning are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 4-6, 5-10, 12-14, 16 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (CN Patent Application Publication CN 103038772 A).
Claim 1:
Wang discloses the following limitations as shown below:
a data collection module configured to collect patient-related data of patients visiting an emergency department for a certain period of time (see at least Paragraph 10, additionally based on the collected patient demographic information and patient history information to calculate the survival probability. The method may also further include: selecting time of between 4 and 24 hours or a time limit of between 4 and 72 hours; Paragraph 89, may be used to train another parameter of the artificial neural network is patient characteristics. patient features include such as patient age, gender and medical history information; Paragraph 204, the user can input patient 401 of other parameters, such as age, other agents, Grassmann coma score, respiratory rate, blood pressure Sp02 and heart. about the patient 401, the parameter is used to compute a risk score to predict patient 401 of viability. in calculating the risk score, worthy of appreciation, the analysis block 406 has been training of the artificial neural network is as in FIG. 1 to FIG. 3 in the above. output of the analysis block 406 will be a risk score that includes death, each of ICU hospital entering the sickroom and the result in which the patient is classified as "high", "medium" or "low" risk; Paragraph 280, patients is selected from the Department of Emergency Medicine (DEM));
a learning data generation module configured to generate a learning data group for one patient by extracting a plurality of data preset from the patient-related data (see at least Paragraph 148, after training of the ANN 300 may be used to help related to show some symptoms of the patient will be survival or death of clinical decision, namely after training of the ANN 300 can help the liveness of the prediction to the patient; Paragraph 204, the user can input patient 401 of other parameters, such as age, other agents, Grassmann coma score, respiratory rate, blood pressure Sp02 and heart. about the patient 401, the parameter is used to compute a risk score to predict patient 401 of viability. in calculating the risk score, worthy of appreciation, the analysis block 406 has been training of the artificial neural network is as in FIG. 1 to FIG. 3 in the above; Paragraph 221, Generally, steps 702 to 726 is to detect the position of the QRS composite body, which allows us to RR interval is calculated. Position, amplitude, and shape of the QRS composite body and adjacent abnormal beat duration between composite body allowing the analysis excluded from the HRV and other sinus rhythm. In this manner, is able to be extracted from the ECG signal from the patient out of reliable heart rate variability data); and
a prediction module configured to input the learning data group into one or more machine learning models, respectively, to learn the machine learning models to predict mortality of the corresponding patient (see at least Paragraph 148, after training of the ANN 300 may be used to help related to show some symptoms of the patient will be survival or death of clinical decision, namely after training of the ANN 300 can help the liveness of the prediction to the patient; Paragraph 204, the user can input patient 401 of other parameters, such as age, other agents, Grassmann coma score, respiratory rate, blood pressure Sp02 and heart. about the patient 401, the parameter is used to compute a risk score to predict patient 401 of viability. in calculating the risk score, worthy of appreciation, the analysis block 406 has been training of the artificial neural network is as in FIG. 1 to FIG. 3 in the above; Paragraph 288, Because one purpose of artificial neural network is to predict mortality, the artificial neural network is implemented to solve two class classification problem (patient result is death or survival)).
Claims 9 and 17 recite substantially similar method and computer program product limitations to those of apparatus claim 1 and, as such, are rejected for similar reasons as given above.
Claim 2:
Wang discloses the limitations as shown in the rejections above. Wang further discloses the following limitations:
wherein the mortality prediction device further comprises a pretreatment module that excludes the patient-related data of the patient corresponding to exclusion conditions preset from the collected patient-related data (see at least Paragraph 221, Generally, steps 702 to 726 is to detect the position of the QRS composite body, which allows us to RR interval is calculated. Position, amplitude, and shape of the QRS composite body and adjacent abnormal beat duration between composite body allowing the analysis excluded from the HRV and other sinus rhythm. In this manner, is able to be extracted from the ECG signal from the patient out of reliable heart rate variability data).
Claim 10 recites substantially similar method limitations to those of apparatus claim 2 and, as such, is rejected for similar reasons as given above.
Claim 4:
Wang discloses the limitations as shown in the rejections above. Wang further discloses the following limitations:
wherein the learning data generation module is configured to divide patient-related data of patients who do not correspond to the preset exclusion conditions into patient-related data of deceased patients and patient-related data of survived patients, and extracts a plurality of data preset from the patient-related data of deceased patients and patient-related data of survived patients, respectively, to generate a learning data group for one patient (see at least Paragraph 114, the viability of predicting patient is death of the patient or patient survival; Paragraph 148, system may be used to carry out the medical treatment to the patient is selected, such as in battlefield situations, mass casualty situations many vehicles such as a motor vehicle accident or terrorist event. After training of the ANN 300 may be used to help related to show some symptoms of the patient; Paragraph 279, to analyze, comprising 40 cases of death condition and 60 cases of survival. vital signs and patient results obtained from hospital records, such as a patient demographic (age, race, gender) and priority information of the level code; Paragraph 391, a patient survivability prediction system 1500 further includes a processor 1508 for executing stored instructions to the memory module 1506 based on the first parameter set and the second parameter set execution function of the artificial neural network and output a prediction of survival for the patient), and
the prediction module is configured to input the learning data group of deceased patients and the learning data group of survived patients into one or more machine learning models, respectively, to learn the machine learning models to predict mortality of the corresponding patient (see at least Paragraph 114, the viability of predicting patient is death of the patient or patient survival; Paragraph 148, system may be used to carry out the medical treatment to the patient is selected, such as in battlefield situations, mass casualty situations many vehicles such as a motor vehicle accident or terrorist event. After training of the ANN 300 may be used to help related to show some symptoms of the patient will be survival or death of clinical decision, namely after training of the ANN 300 can help the liveness of the prediction to the patient).
Claim 12 recites substantially similar method limitations to those of apparatus claim 4 and, as such, is rejected for similar reasons as given above.
Claim 5:
Wang discloses the limitations as shown in the rejections above. Wang further discloses the following limitations:
wherein the learning data generation module is configured to generate a learning data group for the corresponding decreased patient and a learning data group for the corresponding survived patient, by extracting the patient’s age, emergency patient classification level, intentionality information, injury mechanism information, presence or absence of emergency symptoms, AVPU (Alert Verbal Pain Unresponsive) scale, gender, preset vital signs, and ICD-10 code, from the patient-related data of deceased patients and the patient-related data of survived patients (see at least Paragraph 88, data relating to patient survivability typically associated with respective set law variability data and vital sign data about the centre of the same patient; Paragraph 106, Each data set has at least relating to heart rate variability data and the parameter relating to vital sign data of the parameter, each data set further having a parameter relating to patient survivability. The method includes processing the first parameter set and the second parameter set to generate suitable input to processing data in the artificial neural network).
Claim 13 recites substantially similar method limitations to those of apparatus claim 5 and, as such, is rejected for similar reasons as given above.
Claim 6:
Wang discloses the limitations as shown in the rejections above. Wang further discloses the following limitations:
wherein the prediction module is configured to input the learning data group for deceased patients and the learning data group for survived patients into a plurality of machine learning models, respectively, to perform performance evaluation for the plurality of machine learning models, and according to the performance results, selects one or more machine learning models among the plurality of machine learning models (see at least Paragraph 279, is selected to analyze, comprising 40 cases of death condition and 60 cases of survival. vital signs and patient results obtained from hospital records, such as a patient demographic (age, race, gender) and priority information of the level code).
Claim 14 recites substantially similar method limitations to those of apparatus claim 6 and, as such, is rejected for similar reasons as given above.
Claim 8:
Wang discloses the limitations as shown in the rejections above. Wang further discloses the following limitations:
wherein the prediction module is configured to calculate importance of each variable included in the learning data group, and weights the corresponding variable depending on the importance of the variables included in the learning data group (see at least Paragraph 5, artificial neural network provides a network of nodes including interconnects, nodes comprising a plurality of artificial neurons, each artificial neuron comprises at least one input with an associated weight, through using an electronic database of a plurality of data sets for training the artificial neural network to adjust associated weights. Each data set has at least relating to heart rate variability data and the parameter relating to vital sign data of the parameter, each data set further having a parameter relating to patient survivability; processing the first parameter set and the second parameter set to generate suitable for input into the artificial neural network processing data in the processing data is provided as an input to the artificial neural network, and obtaining the output from the artificial neural network, the output providing a prediction of ACP events and survivability of a patient).
Claim 16 recites substantially similar method limitations to those of apparatus claim 8 and, as such, is rejected for similar reasons as given above.
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 of this title, if the differences between the claimed invention and the prior art axe such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over CN Patent Application Publication CN 103038772 A to Wang et al. in view of US Patent US 11,854,701 B2 to Constantine et al.
Claim 3:
Wang discloses the limitations as shown in the rejections above. Wang further discloses the following limitations:
wherein the pretreatment module is configured to determines whether the corresponding patient corresponds to the preset exclusion code conditions (see at least Paragraph 221, Generally, steps 702 to 726 is to detect the position of the QRS composite body, which allows us to RR interval is calculated. Position, amplitude, and shape of the QRS composite body and adjacent abnormal beat duration between composite body allowing the analysis excluded from the HRV and other sinus rhythm. In this manner, is able to be extracted from the ECG signal from the patient out of reliable heart rate variability data)
Wang may not specifically disclose the following limitations, but Constantine as shown does:
exclusion code conditions based on one or more of time of death of a patient based on arrival at a hospital, whether the patient is treated after arrival at the hospital, whether the patient has trauma, whether the patient is irrecoverable, whether the patient is voluntarily discharged, the patient’s diagnosis code, and whether the patient’s identity is not confirmed (see at least (51), Patients with incomplete MOD scores for D2 to D5 (due to discharge from the ICU), or that died prior to discharge, were excluded in order to capture a complete data set for the derivation group; (127), Among the 96 excluded patients, 91 were discharged from ICU prior to day 4).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the system and method of predicting viability of a patient of Wang with the exclusion features of Constantine with the motivation of providing benefit of “… the identification of meaningful outcome-based endpoints, in addition to mortality, and the validation of methods to expeditiously stratify for patients most likely to benefit from a given intervention” (Constantine, see at least (3)).
Claim 11 recites substantially similar method limitations to those of apparatus claim 3 and, as such, is rejected for similar reasons as given above.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over CN Patent Application Publication CN 103038772 A to Wang et al. in view of US Patent Application Publication US 2024/0105336 A1 to Liao et al.
Claim 7:
Wang discloses the limitations as shown in the rejections above. Wang further discloses the following limitations:
wherein the prediction module is configured to: calculate a first performance evaluation score of each machine learning model by calculating the accuracy, sensitivity, and specificity of each machine learning model based on the prediction results of the plurality of machine learning models, and adding them up (see at least Paragraph 81, the fourth parameter can be in a training phase of algorithm development is used as a training algorithm means, and being able to use phase is used to improve precision of means to improve accuracy by recording the actual accuracy of the prediction algorithm and properly modifying data set even without a parameter relating to patient survivability. Alternatively, the set of each patient health data may include all four parameters);
Wang may not specifically disclose the following limitations, but Liao as shown does:
generate an ROC (Receiver Operating Characteristic) curve for the prediction result of each machine learning model based on the prediction result of each machine learning model, and calculates a second performance evaluation score of each machine learning model based on an AUC (Area Under the Curve) value from the ROC curve (Liao - see at least Paragraph 126, To validate the performance of the machine learning classifier model, different performance metrics may be generated. For example, an area under the receiver-operating curve (AUROC) may be used to determine the diagnostic capability of the machine learning classifier. For example, the machine learning classifier may use classification thresholds which are adjustable, such that specificity and sensitivity are tunable, and the receiver-operating curve (ROC) can be used to identify the different operating points corresponding to different values of specificity and sensitivity); and
select one or more machine learning models based on an overall evaluation score that is the sum of the first performance evaluation score and the second performance evaluation score (see at least Paragraph 118, The machine learning classifier algorithm may process the input features to generate output values comprising one or more classifications, one or more predictions, or a combination thereof. For example, such classifications or predictions may include a binary classification of a disease or a non-disease state, a classification between a group of categorical labels (e.g., ‘no disease, ‘disease apparent’, and ‘disease likely’), a likelihood (e.g., relative likelihood or probability) of developing a particular disease or disorder, a score indicative of a ‘presence of urgent symptoms’, a ‘risk factor’ for the likelihood of adverse health events (e.g., hospitalization or mortality) of the patient, a prediction of the time at which the patient is expected to have developed the disease or disorder or experienced an adverse health event, and a confidence interval for any numeric predictions. Various machine learning techniques may be cascaded such that the output of a machine learning technique may also be used as input features to subsequent layers or subsections of the machine learning classifier. Various machine learning techniques may be cascaded such that the output of a machine learning technique may also be used as input features to subsequent layers or subsections of the machine learning classifier; Paragraph 121, In some cases, datasets are annotated or labeled. Datasets may be split into subsets (e.g., discrete or overlapping), such as a training dataset, a development dataset, and a test dataset).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of the system and method of predicting viability of a patient of Wang with the features of Liao with the motivation of providing benefit of “… establishing and using a neural network for predicting, assessing, diagnosing, treating, and managing chronic conditions such as heart failure in subjects” (Liao, see at least Paragraph 3).
Claim 15 recites substantially similar method limitations to those of apparatus claim 7 and, as such, is rejected for similar reasons as given above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Friday, 9:00am-5:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JASON DUNHAM can be reached on 571.272.8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Joy Chng/
Primary Examiner, Art Unit 3686