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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/7/2026 has been entered.
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 1-22 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-11, 12-22 are directed to a method, system of using a hierarchical vectoriser for representation of healthcare data. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g. mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 12 these claims recite
receiving the healthcare data;
mapping each of the different code types to a taxonomy using a measure of closeness of the health-related concept represented by the code type to points in a mapping, and generating node embeddings using relationships in the taxonomy for each code type with a graph embedding model, wherein a vector distance between two respective node embeddings corresponds to a measure of similarity between corresponding codes and their respective healthcare-related concepts;
generating an event embedding for each event comprising aggregating vectors using a non-linear mapping to the node embeddings, each vector associated with the parameter vector of the respective healthcare-related code type;
generating a patient embedding for each patient by encoding the event embeddings related to said patient; and
outputting the patient embedding for each patient for use by a multi-task machine learning model to predict a future healthcare-related aspect of the patient using the patient embedding as input.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level and disclosed as a human user performing these functions, simply using a computer as a tool as disclosed at specification [0042]-[0047] etc. Fig. 1, Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 12 these claims
This limitation recites using one or more neural networks as a tool to perform an abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
MPEP § 2106.05(f): Mere Instructions to Apply an Exception. Do the additional element(s) amount to merely the words “apply it” (or an equivalent)
or are mere instructions to implement an abstract idea or other exception on a computer? (Yes) which is not indicative of integration into a practical application.
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element or combination of elements sufficient to ensure that the claim amounts to significantly more than the judicial exception (Step 2B). In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons given above in the Step 2A, Prong 2 analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1 and 12: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites mapping each of the different code types to a taxonomy using a measure of closeness of the health-related concept represented by the code type to points in a mapping, and generating node embeddings using relationships in the taxonomy for each code type with a graph embedding model, wherein a vector distance between two respective node embeddings corresponds to a measure of similarity between corresponding codes and their respective healthcare-related concepts; generating an event embedding for each event comprising aggregating vectors using a non-linear mapping to the node embeddings, each vector associated with the parameter vector of the respective healthcare-related code type;
generating a patient embedding for each patient by encoding the event embeddings related to said patient; and outputting the patient embedding for each patient for use by a multi-task machine learning model to predict a future healthcare-related aspect of the patient using the patient embedding as input. These elements “mapping”, “generating”, “generating” and “outputting” are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 13
Claim 2, 13 merely recite other additional elements that define the node which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3-4, 14-15
Claim 3-4, 14-15 merely recite other additional elements that define aggregating the vectors which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 16
Claim 5, 16 merely recite other additional elements that define mapping which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 17
Claim 6, 17 merely recite other additional elements that define patient embedding which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7-8, 18-19
Claim 7-8, 18-19 merely recite other additional elements that define ML encoder which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 9, 20
Claim 9, 20 merely recite other additional elements that define predicting future healthcare aspects which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 10-11, 21-22
Claim 10-11, 21-22 merely recite other additional elements that define multi-task learning which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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.
Claims 1-10, 12-21 are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (Choi) “GRAM: Graph-based Attention Model for Healthcare Representation Learning” KDD’17, KDD 2017 Research Paper, August 13–17, 2017, Halifax, NS, Canada, DOI: http://dx.doi.org/10.1145/3097983.3098126 ( the same as provided NPL) in view of Barker et al. (Barker) US 2017/0193185
In regard to claim 1, Choi disclose A computer-implemented method for using a hierarchical vectoriser for representation of healthcare data, the healthcare data comprising a plurality of different healthcare-related code types, the healthcare data further comprising healthcare- related events and healthcare-related patients, the events having event parameters for the respective health-related code type associated therewith, the method comprising: (abstract, 1. Introduction, 2.1 Basic Notation, 2.3 End-to-End Training with a Predictive Model:, Graph-based Attention Model with health records with hierarchical information related to patients, code and clinical events, the medical codes from the HER contain ICD-9 code, SNOMED-CT, CCS etc. for example, ICD-9, each code can represent a specific diseases related to each patient of each visit)
receiving the healthcare data; (1. Introduction infuses “health care data from electronic health data records (HER)”)
mapping each of the different code types to a taxonomy, (2.1, Basic Notation: SNOMED_CT” links medical concepts to parent-child relationships. “a given medical ontology G typically expresses the hierarchy of various medical concepts in the form of a parent-child relationship, where the medical codes C form the leaf nodes. Ontology G is represented as a directed acyclic graph (DAG) whose nodes form a set D = C + C’” and “3.3. Qualitative evaluation of interpretable representations” “Other pneumothorax (ICD9 512.89) in Figure 4a is rarely observed in the data and has only five siblings. In this case, most information is derived from the highest ancestor. Temporomandibular joint disorders & articular disc disorder (ICD9 524.63) in Figure 4b is rarely observed but has 139 siblings.” Each ICD-9 code links to a disease on the ontology, etc.) using a measure of closeness of the health-related concept represented by the code type to points in a mapping, (page 2, 1. Introduction, and page 10, 3.3 “Qualitative evaluation of interpretable representations” Fig. 3, This can be alleviated by exploiting medical ontologies that encodes hierarchical clinical constructs and relationships among medical concepts. … Nodes (i.e. medical concepts) close to one another in medical ontologies are likely to be associated with similar patients,” “where GRAM provides more intuitive representations by grouping similar medical concepts close to one another” grouping similar medical concepts close to one another with groups of points on the ontology which inherently disclose using a measure of the closeness of the medical concepts to group similar medical concepts) and
generating node embeddings using relationships in the taxonomy for each code type with a graph embedding model; (2.1, Basic Notation: “medical cods… can be represented as a binary vector… medical concepts the form of parent-child relationship”, 2.2. Knowledge DAG and Attention Mechanism: “a basic embedding of medical codes”);
generating an event embedding for each event comprising aggregating vectors using a non-linear mapping to the node embeddings, each vector associated with the parameter vector of the respective healthcare-related code type; (1. Introduction “convolutional neural network (CNN”, 2.3. End-to-End Training with a Predictive Model:” convert visit… to a visit representation… indicating the clinical events in visit”;
2.1, Basic Notation: “medical cods… can be represented as a binary vector… medical concepts the form of parent-child relationship”, 2.2. Knowledge DAG and Attention Mechanism: “a basic embedding of medical codes” “We formulate a leaf node’s final representation as a convex combination of the basic embeddings of itself and its ancestors:”
2.4: Initializing Basic Embedding: “medical events (i.e. visits)” for each patient and for each visit with the specific ICD-9 code (HER), and generate the event embedding based on the non-linear algorithm linking to the node embedding (hierarchy ontology) “Leaf nodes (solid circles) represents a medical concept in the HER”);
generating a patient embedding for each patient by encoding the event embeddings related to said patient; (1. Introduction, “embed patient visit vector… to a visit representation”; 2.1, Basic Notation: “The clinical record of each patient can be viewed as a sequence of visits”) and
outputting the patient embedding for each patient (1. Introduction, “embed patient visit vector… to a visit representation”; 2.1, Basic Notation: “The clinical record of each patient can be viewed as a sequence of visits”, 2.4: Initializing Basic Embedding: “medical events (i.e. visits) the embeddings for the patients are generated) for use by a multi-task machine learning model to predict a future healthcare-related aspect of the patient using the patient embedding as input. (page 4, 2.3 “End-to-End Training with a Predictive Model” predict the diagnoses prediction using the embedding as input with a predictive model. Note: please use functional language to describe the invention instead of intended use language since the intended use language has not much patent weight)
But Choi fail to explicitly disclose “wherein a vector distance between two respective node embeddings corresponds to a measure of similarity between corresponding codes and their respective healthcare-related concepts;”
Barker disclose wherein a vector distance between two respective node embeddings corresponds to a measure of similarity between corresponding codes and their respective healthcare-related concepts. ([0017]-[0030] [0039]-[0045] vector distance between the two entity semantics corresponding to a measure of similarity between their medical concepts represented by the codes. Note: please further define how the similarity is determined based on vector distance, and based on what criteria, etc. to help move forward the prosecution)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Barker’s medical concept clustering into Choi’s invention as they are related to the same field endeavor of health information analyzation. The motivation to combine these arts, as proposed above, at least because Barker’s medical concept clustering based on similarity measurement between the concepts would help to provide more information clustering into Choi’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing information clustering by measuring similarity between the concepts would improve diagnosis accuracy and therefore improve user experience using the system.
In regard to claim 2, Choi and Barker disclose The method of claim 1, the rejection is incorporated herein.
Choi disclose wherein each of the node embeddings are aggregated into a respective vector. (2.2 Knowledge DAG and the Attention Mechanism, the each node embedding is assigned a basic embedding and “We formulate a leaf node’s final representation as a convex combination of the basic embeddings of itself and its ancestors”)
In regard to claim 3, Choi and Barker disclose The method of claim 2, the rejection is incorporated herein.
Choi disclose wherein aggregating the vectors comprises an addition of summations over each event for each of the node embeddings multiplied by a weight. (2.2 Knowledge DAG and the Attention Mechanism, Fig. 1 weighted sum of G “The fnal representations form the embedding matrix G for all leaf concepts. After that, we use G to embed patient visit vector xt to a visit representation vt”)
In regard to claim 4, Choi and Barker disclose The method of claim 2, the rejection is incorporated herein.
Choi disclose wherein aggregating the vectors comprises self-attention layers to determine feature importance. (2.2 Knowledge DAG and the Attention Mechanism, Fig. 1 “The fnal representation gi of the leaf concept ci is computed by combining the basic embeddings ei of ci and eg, ec and ea of its ancestors cg,cc and ca via an attention mechanism “ with attention layers to determine feature importance using weight)
In regard to claim 5, Choi and Barker disclose The method of claim 1, the rejection is incorporated herein.
Choi disclose wherein the non-linear mapping comprises using a trained machine learning model, the machine learning model taking as input a set of node embeddings previously labelled with event and patient information. (2.2 Knowledge DAG and the Attention Mechanism, Fig. 1, 2.3. End-to-End Training with a Predictive Model:” with a predictive model, “By concatenating final representation g1, g2, . . . , g|C | of all medical codes, we have the embedding matrix G ∈ Rm× |C | where gi is the i-th column of G. As shown in the right side of Figure 1, we can convert a visit Vt to a visit representation vt by multiplying the embedding matrix G with a multi-hot (i.e. multi-label binary) vector xt indicating the clinical events in the visit Vt , followed by a nonlinear activation via tanh. Finally the visit representation vt will be used as an input to the neural network model for predicting the target label yt .”)
In regard to claim 6, Choi and Barker disclose The method of claim 1, the rejection is incorporated herein.
Choi disclose wherein the patient embedding is determined using a trained machine learning encoder. (1. Introduction, “The demand for high volume can be reduced by exploiting medical ontologies to encode hierarchical clinical constructs and relationships among medical concept” “convolutional neural networks (CNN) [30] or stacked denoising autoencoders (SDA)”)
In regard to claim 7, Choi and Barker disclose The method of claim 6, the rejection is incorporated herein.
Choi disclose wherein the trained machine learning encoder comprises a long short-term memory artificial recurrent neural network. (1. Introduction, “novel applications are emerging that use deep learning methods such as word embedding [11, 13], recurrent neural network (RNN) 3.4 Qualitative evaluation of interpretable representations. Fig. 3 with RNNs) and RNN has long short-term memory)
In regard to claim 8, Choi and Barker disclose The method of claim 6, the rejection is incorporated herein.
Choi disclose wherein the trained machine learning encoder comprises a transformer model comprising self-attention layers. (2.2 Knowledge DAG and the Attention Mechanism, Fig. 1 “The fnal representation gi of the leaf concept ci is computed by combining the basic embeddings ei of ci and eg, ec and ea of its ancestors cg,cc and ca via an attention mechanism “ with attention layers and 3.1 Experiment Setup, transformer with attention layers, GRAM, RNN, etc.)
In regard to claim 9, Choi and Barker disclose The method of claim 1, the rejection is incorporated herein.
Choi disclose further comprising predicting future healthcare aspects associated with the patient using multi-task learning, the multi-task learning trained using a set of labels for each patient embedding according to recorded true outcomes. (2.3. End-to-End Training with a Predictive Model:” predicting “Finally the visit representation vt will be used as an input to the neural network model for predicting the target label yt . In this work, we use RNN as the choice of the NN model to perform sequential diagnoses prediction [9, 10]. That is, we are interested in predicting the disease codes of the next visit Vt+1 given the visit records up to the current timestepV1,V2, . . . ,Vt”, 3.4 Qualitative evaluation of interpretable representations “Figures 3c and 3f confirm that interpretable representations cannot simply be learned only by co-occurrence or supervised prediction without medical knowledge. GRAM+ and GRAM learn interpretable disease representations that are significantly more consistent with the given knowledge DAG G. Based on the prediction performance shown by Table 2, and the fact that the representations gi ’s are the final product of GRAM, we can infer that such medically meaningful representations are necessary for predictive models to cope with data insufficiency and make more accurate predictions.”)
In regard to claim 10, Choi and Barker disclose The method of claim 9, the rejection is incorporated herein.
Choi disclose wherein the multi-task learning comprises determining loss aggregation by defining a loss function for each of the predictions and optimizing the loss functions jointly. (2.3. End-to-End Training with a Predictive Model: Fig. 1, “Calculate prediction loss L using Eq .(5)” “Algorithm 1 GRAM Optimization” “until convergence” to optimizing the loss functions together)
In regard to claims 12-21, claims 12-21 are system claims corresponding to the method claims 1-10 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-10.
Claims 11, 22 are rejected under 35 U.S.C. 103 as being unpatentable over Choi et al. (Choi) “GRAM: Graph-based Attention Model for Healthcare Representation Learning” KDD’17, KDD 2017 Research Paper, August 13–17, 2017, Halifax, NS, Canada, DOI: http://dx.doi.org/10.1145/3097983.3098126 ( the same as provided NPL) and Barker et al. (Barker) US 2017/0193185 as applied to claim 1, further in view of Billings “Generation and Utilization of Latent Spaces for Prediction and Interpretation” , University of Cambridge, Department of Computer Science and Technology August 30, 2018, researchgate.net” and Wu et al. (Wu) US 2018/0322391
In regard to claim 11, Choi and Baker disclose The method of claim 10, the rejection is incorporated herein.
But Choi and Baker fail to explicitly disclose “wherein the multi-task learning comprises reweighing the loss functions according to an uncertainty for each prediction, the reweighing comprising learning a noise parameter integrated in each of the loss functions.”
Billings disclose wherein the multi-task learning comprises reweighing the loss functions according to an uncertainty for each prediction, the reweighing comprising learning a noise parameter integrated in each of the loss functions. (5.7 pathology weighted loss adjustment, “The Pathology Weighted Loss Adjustment (PWLA) weighting strategy is a conceptual generalization of the idea that certain, more difficult to predict, points can be weighted in such a way as to improve the performance of a predictor. It attempts to solve the two issues of WLS by enforcing a consistent methodology for determining loss weights and by being classifier model independent.” 6.1.1. “Noise and Out-of-Sample Robustness” “Making models robust against label noise is explored in [17] and [18]. In [17], the authors show that a modification of the stoachastic gradient descent optimizer to incorporate an estimator of µ combined with their proposed loss factorization can be used to increase the performance of a linear model compared to a baseline as the labels of the data are gradually made unobservable”)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Billings’’s prediction model into Baker and Choi’s invention as they are related to the same field endeavor of prediction model. The motivation to combine these arts, as proposed above, at least because Billings’’s prediction model using adjusted weight loss function would help to provide more prediction calculating method into Baker and Choi’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing adjusted weight loss function to help prediction would improve prediction accuracy and therefore improve user experience using the system.
But Choi, Baker and Billings fail to explicitly disclose “to bring all the losses to the same scale.”
Wu disclose to bring all the losses to the same scale. ([0040]-[0045][0061] [0073] [0082]-[0088]-[00101] [011]-[0114] and scaling the loss value by S and adjust weights for each iteration)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Wu’s scaling for the DNN training into Billings, Baker and Choi’s invention as they are related to the same field endeavor of prediction model. The motivation to combine these arts, as proposed above, at least because Wu’s scaling for the DNN training would help to provide more prediction calculating method into Billings, Baker and Choi’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing adjusted weight loss function to bring the losses to the same scaling would improve prediction accuracy and therefore improve user experience using the system.
Response to Arguments
Applicant’s arguments with respect to claims 1-22 filed on 1/7/2026 have been considered but are moot because the arguments do not apply to the current rejection.
With respect to 35 USC § 101 rejection, please see the above rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20160179945 A1 2016-06-23 LASTRA DIAZ et al.
SYSTEM AND METHOD FOR THE INDEXING AND RETRIEVAL OF SEMANTICALLY ANNOTATED DATA USING AN ONTOLOGY-BASED INFORMATION RETRIEVAL MODEL
DIAZ et al. disclose System and method for the indexing and retrieval of semantically annotated information units from a collection of semantically annotated indexed information units in response to a query using an ontology-based IR model. The retrieval method comprises: receiving a semantically annotated query with semantic annotations to individuals or classes within a determined populated base ontology; embedding, as a set of weighted-mentions to individuals or classes within the populated base ontology, the semantically annotated query in a semantic representation space of an ontology-based metric space IR model; obtaining the representation in the semantic representation space for every indexed information unit; computing the Hausdorff distance between the space representation of the query and the space representation of all the indexed information units of the collection; retrieving and ranking, the relevant information units based on the computed Hausdorff distance… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143