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 .
Formal Matters
Applicant's response, filed 06 June 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 3-15, 17, 19, 20, and 23 are currently pending and have been examined.
Claims 1 and 17 have been amended.
Claims 1, 3-15, 17, 19, 20, and 23 have been rejected.
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 on 21 November 2022 in parent Application No. KR10-2020-0060548 and KR10-2020-0171441, filed on 20 May 2020 and 09 December 2020 respectively.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 20 May 2020, claiming benefit to KR10-2020-0060548.
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, 3-15, 17, 19, 20, and 23 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 3-15, 17, 19, 20, and 23 are drawn to a method or system, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for predicting needs of a patient for hospital resources in part performing the steps of: generating numerical data per information type by encoding natural language data and structured data in patient data recorded in language and digits, wherein the natural language data comprises current disease information of the patient, and the structured data comprises at least one of demographic information and measurement information of the patient; performing natural language processing on the natural language data; calculating a first type of natural language embedding vector by embedding text data of the current disease information obtained through the natural language processing; calculating a second type of natural language embedding vector by embedding text data of remaining information of the natural language data obtained through the natural language processing; performing natural language processing on the structured data; calculating an embedding vector of the demographic information by embedding the text data of the demographic information obtained through the natural language processing, or performing conversion into the numerical data through the natural language processing; and predicting a task corresponding to the needs of the patient for hospital resources by applying the numerical data per information type to the artificial neural network to enable automated prediction of emergency patient needs, wherein the artificial neural network comprises an embedding model functioning as an encoder for calculating an embedding matrix of the patient data based on at least a part of the numerical data, the embedding model including a recurrent neural network structure comprising a unidirectional or bidirectional gated recurrent unit (GRU)-based hidden layer that extracts features of input data and calculates a hidden state vector, and an attention layer that receives an output matrix of the hidden layer and calculates the embedding matrix of the patient data by forming an attention matrix based on an attention weight, wherein the attention layer enables the artificial neural network to focus on input words related to the task to be predicted in the patient data, and wherein the attention matric enables the artificial neural network to analyze multiple expressions from a single sentence of the patient data; and a decision model functioning as a decoder for determining the task to which the patient data belongs by receiving the embedding matrix of the patient data, or the embedding matrix of the patient data and the numerical data of the structured data, wherein the attention weight is used by the decision model to refer once again to an entire input sentence of the encoder at every timestep at which the decision model predicts the task associated with the patient data, the decision model performing multi-label classification for multiple patient need categories by using a fully connected layer comprising a first network for determining a main task and a second network for determining an auxiliary task, wherein the first network and the second network share a hidden layer having the same input, wherein an error of the auxiliary task is used to improve generalization of the artificial neural network and wherein the artificial neural network is trained using a correlation between the main task and the auxiliary task, and wherein the artificial neural network performs an operation of checking patient data, reading current disease information in detail, and interpreting measurement values to predict the needs of the patient similar to a medical staff or emergency manager reading the patient data directly, and further performs an operation of referring back to the patient data and re-analyzing the patient data while focusing on specific parts of text relevant to a corresponding event when predicting a specific event.
Independent claim 17 recites a system for predicting needs of a patient for hospital resources in part performing the steps of receiving patient data comprising natural language data describing a condition of a patient and structured data, the patient data being recorded in language and digits, the natural language data comprises current disease information of the patient, and the structured data comprises at least one of demographic information of the patient and measurement information of the patient; encoding the natural language data and the structured data in the patient data to generate numerical data per information type; performing natural language processing on the natural language data; calculating a first type of natural language embedding vector by embedding text data of the current disease information obtained through the natural language processing; calculating a second type of natural language embedding vector by embedding text data of remaining information of the natural language data obtained through the natural language processing performing natural language processing on the structured data; calculating an embedding vector of the demographic information by embedding the text data of the demographic information obtained through the natural language processing, or performing conversion into the numerical data through the natural language processing; and predicting a task corresponding to needs of the patient for hospital resources by applying the numerical data to the artificial neural network to enable automated prediction of emergency patient needs, wherein the artificial neural network comprises: an embedding model functioning as an encoder for calculating an embedding matrix of the patient data based on at least a part of the numerical data, the embedding model including a recurrent neural network structure comprising a unidirectional or bidirectional gated recurrent unit (GRU)- based hidden layer that extracts features of input data and calculates a hidden state vector, and an attention layer that receives an output matrix of the hidden layer and calculates the embedding matrix of the patient data by forming an attention matrix based on an attention weight, wherein the attention layer enables the artificial neural network to focus on input words related to the task to be predicted in the patient data, and wherein the attention matrix enables the artificial neural network to analyze multiple expressions from a single sentence of the patient data; and a decision model functioning as a decoder for determining the task to which the patient data belongs by receiving the embedding matrix of the patient data, or the embedding matrix of the patient data and the numerical data of the structured data, wherein the attention weight is used by the decision model to refer once again to an entire input sentence of the encoder at every timestep at which the decision model predicts the task associated with the patient data the decision model performing multi-label classification for multiple patient need categories by using a fully connected layer comprising a first network for determining a main task and a second network for determining an auxiliary task, wherein the first network and the second network share a hidden layer having the same input, wherein an error of the auxiliary task is used to improve the generalization of the artificial neural network, and wherein the artificial neural network is trained using a correlation between the main task and the auxiliary task, and wherein the artificial neural network performs an operation of checking patient data, reading current disease information in detail, and interpreting measurement values to predict the needs of the patient similar to a medical staff or emergency manager reading the patient data directly, and further performs an operation of referring back to the patient data and re-analyzing the patient data while focusing on specific parts of text relevant to a corresponding event when predicting a specific event.
The steps of acquiring patient data and predicting tasks related to the hospital resources patient needs amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
The steps of applying the patient data to an artificial natural network with a specific model structure and the decision model 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)(A) citing the abstract idea grouping for mathematical concepts for mathematical relationships).
Examiner notes that, in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence and Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), the claims recite both a method of organizing human activity and a mathematical concept and is not subject matter eligible. The use of a computer to apply a neural network with corresponding limitations describing the mathematical structure of the network amount to applying data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2.
Dependent claim 3 recites, in part, wherein the predicting of the task corresponding to the needs of the patient for the hospital resources comprises calculating, by the embedding model, an embedding matrix of the patient data from the first type of natural language embedding vector and a contextual embedding vector wherein the contextual embedding vector is based on the second type of natural language embedding vector and the embedding vector of the demographic information.
Dependent claim 4 recites, in part, wherein an initial hidden state of the embedding model is specified as the contextual embedding vector, wherein the predicting of the task corresponding to the needs of the patient for the hospital resources comprises inputting the first type of natural language embedding vector into the initial hidden layer of the embedding model.
Dependent claim 5 recites, in part, when a plurality of first types of natural language embedding vectors are input to the embedding model, sequentially inputting the plurality of first types of natural language embedding vectors to the hidden layer.
Dependent claim 6 recites, in part, wherein the predicting of the task corresponding to the needs of the patient for the hospital resources comprises inputting a combined vector obtained by combining the first type of natural language embedding vector with the contextual embedding vector into the initial hidden layer of the embedding model.
Dependent claim 7 recites, in part, wherein the predicting of the task corresponding to the needs of the patient for the hospital resources comprises forming a hidden matrix H consisting of final hidden state vectors, and wherein the attention layer calculates the embedding matrix M of the patient data based on the hidden matrix H and the attention matrix A based on an attention weight.
Dependent claim 8 recites, in part, wherein the predicting of the task corresponding to the needs of the patient for the hospital resources includes receiving, by the decision model, at least the embedding matrix of the patient data among the embedding matrix of the patient data, a final hidden state vector, and the numerical data of the measurement information, and wherein the decision model is a fully connected layer composed of two or more layers.
Dependent claim 9 recites, in part, wherein the artificial neural network is pre-trained such that the decision model determines at least one task among multiple tasks using a training data set for a plurality of training patients, and wherein the training data set consists of training samples for each training patient, and each training sample comprises at least an embedding matrix of patient data among the embedding matrix of the patient data, a final hidden state vector, and numerical data of measurement information for a corresponding training patient.
Dependent claim 10 recites, in part, wherein the decision model is trained to perform multiple binary classification for determining a task class to which the patient data belongs among a plurality of task classes included in a corresponding task to determine at least one task among multiple tasks.
Dependent claim 11 recites, in part, wherein the fully connected layer comprises one or more of a first network for determining a main task, a second network for determining a first auxiliary task, and a third network for determining a second auxiliary task, wherein the first network or the second network is configured to receive the embedding matrix of the patient data and the final hidden state vector, and wherein the third network is configured to receive the embedding matrix of the patient data, the final hidden state vector, and the numerical data of the measurement information.
Dependent claim 12 recites, wherein a loss function of the artificial neural network comprises: a term indicating a weighted sum of a cross entropy loss function between networks per task of the fully connected layer, and another term obtained by applying the Frobenius norm to an attention matrix, a transform matrix of the attention matrix, and an identity matrix.
Dependent claim 13 recites, in part, wherein the natural language data further includes one or more of main symptom-related information, injury-related information, and history-related information, and wherein the demographic information comprises one or more information among gender and age.
Dependent claim 14 recites, in part, wherein the measurement information includes measurement values for one or more measurement items among a pupil state, a systolic blood pressure (SBP), a diastolic blood pressure (DBP), a pulse, a respiration rate, a body temperature, a consciousness level, and an initial O2 saturation.
Dependent claim 15 recites, in part, wherein the main task comprises one or more of hospital admission, endotracheal intubation, mechanical ventilation, vasopressor infusion, cardiac catheterization, surgery, intensive care unit (ICU) admission, and cardiac arrest as a task class, wherein the first auxiliary task comprises an emergency room diagnosis disease name code as a task class, and wherein the second auxiliary task comprises one or more of discharge, ward admission, intensive care unit (ICU) admission, transfer, and death as a task class.
Dependent claim 19 recites, in part, calculate, by the embedding model, an embedding matrix of the patient data from the first type of natural language embedding vector and a contextual embedding vector, wherein the contextual embedding vector is based on the second type of natural language embedding vector and the embedding vector of the demographic information.
Dependent claim 20 recites, in part, input the first type of natural language embedding vector to the initial hidden layer of the embedding model when an initial hidden state of the embedding model is specified as the contextual embedding vector; or input a combined vector obtained by combining the first type of natural language embedding vector with the contextual embedding vector into the initial hidden layer of the embedding model; or input, to the decision model, at least the embedding matrix of the patient data among the embedding matrix of the patient data, a final hidden state vector, and the numerical data of the measurement information.
Dependent claim 23 recites, in part, train the artificial neural network such that the decision model determines at least one task among multiple tasks using an intermediate data set for training patients, wherein the training data set consists of training samples for each training patient, and each training sample comprises at least an embedding matrix of patient data.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 17 accordingly, and hence are nonetheless directed towards fundamentally the same abstract ideas as the independent claim and utilize the additional elements analyzed below in the expected manner.
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)].
Claim 1 recites a system comprising a processor and a memory for storage of instructions, an encoding module and a prediction module, the prediction module comprising a neural network, wherein when the instructions are executed by the processor. Claim 17 recites a processor, memory, an encoding module, and a prediction module comprising a neural network. The instant specification provides that the hardware requirement for the processor and memory are any processors capable of performing the claimed functions (see the instant specification on p. 4 lines 24- p. 5 line 3 and p. 24 line28- p. 25 line 10), the is the neural network and decision model are both program code/algorithms executed by the computer hardware but require no specific hardware configurations.
The use of a processor, and memory, in this case to performing the method for predicting needs of a patient for hospital resources, only recites the processor and memory as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
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.
Claim 1 recites a system comprising a processor and a memory for storage of instructions, an encoding module and a prediction module, the prediction module comprising a neural network, wherein when the instructions are executed by the processor. Claim 17 recites a processor, memory, an encoding module, and a prediction module comprising a neural network.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
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.
Claims 1, 3-15, 17, 19, 20, and 23 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 06/08/2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that the claimed encoder/decoder combination creates a new technical implementation that is “not simply arranging a generic computer or system”. Examiner is unpersuaded. The bidirectional GRU autoencoder is performing in the manner expected and is not an unconventional technical solution to a technical problem. While Applicant has amended the claim to include additional detail regarding the mathematical realization of the bidirectional GRU, the inclusion of additional detail does not meaningfully change the analysis that the instant claims are applying a known technique to a unique abstract idea similar to Example 47 claim 2 wherein the recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Furthermore, the recitation of “ (i) "wherein an error of the auxiliary task is used to improve generalization of the artificial neural network," and (ii) "wherein the artificial neural network is trained using a correlation between the main task and the auxiliary task”” are not objective and verifiable training mechanisms that support patentable implementation of the claimed architecture as stated by Applicant but instead recite only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished – that is, there is no detail in the claim that reflects the solution but instead the intended outcome of the training and re-training processes.
Under Step 2A Prong 2, Applicant asserts that the analysis of 42000 pieces of patient data accurately by the neural network is evidence of a technical improvement. Examiner is not persuaded. The accuracy of the mathematical model is not a technical problem – that is, the specification clearly outlines that the problem the instant application is attempting to solve is with predicting the needs of a patient for hospital resources. an improvement to the abstract idea of patient resource prediction does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”).
Finally, Applicant asserts that the claim as a whole should be considered under Step 2B. Examiner is not persuaded. The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ruan et al, Representation learning for clinical time series prediction tasks in electronic health records, 19 BMC Medical Informatics and Decision Making (2019) teaching on a recurrent neural network based denoising autoencoder for encoding patient records in the § Methods on p. 3-4 and § Patient representation learning on p. 4-5;
Brown et al., (US Patent App No 2020/0411170) teaching on the use of a trained neural network to determine healthcare resource needs from patient structured and unstructured medical data in the Detailed Description in ¶ 0107, ¶ 0080, and ¶ 0044
Meaker et al. (US Patent App No 2019/0303,758) teaching on predicting hospital resources via a neural network artificial intelligence model in the Detailed Description in ¶ 0061-62, and in the Figures at fig. 3 ref chars 42 and 44
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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