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 office action for the 18/990262 application is in response to the communications filed April 28, 2026.
Claims 1, 8, 10, 13 and 17 were amended April 28, 2026.
Claims 2, 6, 7, 14 and 18 were cancelled April 28, 2026.
Claims 1, 3-5, 8-13, 15-17, 19 and 20 are currently pending and considered below.
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, 3-5, 8-13, 15-17, 19 and 20 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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method of generating synthetic claims for medical procedures, comprising: receiving a medical technical document comprising text data that defines one or more criteria that is required for approval of a medical procedure; the one or more criteria define one or more medical diagnoses required to be present to justify the medical procedure presented in a claim; retrieving one or more related medical technical documents and one or more sample claims, wherein the one or more sample claims are related to the related medical technical documents; inputting the medical technical document, the one or more related medical technical documents, and the one or more sample claims to generate one or more synthetic claims that each comprise a reimbursement request for the medical procedure; and receiving the one or more synthetic claims, wherein the one or more synthetic claims are usable to test an accuracy of program code that analyzes claims for compliance with the medical technical document. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recite a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“at a transformer model executed on a computer system, wherein the transformer model is trained on healthcare contexts”, “by the computer system”, “by the computer system, to a claim generation machine learning model”, and “by the computer system from the claim generation machine learning model” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0143] of the as-filed specification describes that the hardware that implements the steps of the abstract idea is nothing more than a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“generating, by the transformer model executed on the computer system, one or more embeddings of the medical technical document, wherein the one or more embeddings of the medical technical document are dense vector representations in a continuous vector space of the medical technical document” which corresponds to selecting a particular data source or type of data to be manipulated.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the examiner as being well‐understood, routine, and conventional functions in light of the prior art, wherein the examiner has provided multiple references as evidence as required by Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see MPEP 2106.05(d)(I), such as:
“generating, by the transformer model executed on the computer system, one or more embeddings of the medical technical document, wherein the one or more embeddings of the medical technical document are dense vector representations in a continuous vector space of the medical technical document”:
Janakiraman (US 2021/0021621) at Paragraph [0042]: “Also, FIG. 1 shows that the analyzer 140 can include embeddings model(s) 144. Typically, embedding model(s) 144 are a result of NLP-driven analysis of a crafted corpus. Embeddings model(s) 144 may be implemented as a vector space model that represents (e.g., embeds) network entities in a continuous vector space. Consequently, semantically similar network entities can be mapped to nearby points (e.g., embedded nearby each other) within the network embeddings model(s) 144. Network embeddings model(s) 144 may depend on the distributional hypothesis, as alluded to above, which posits that network entities that appear in the same, or similar, contexts are related. Additionally, the network embeddings models(s) 144 may be implemented as count-based models (e.g., latent semantic analysis) and predictive models (e.g., neural probabilistic language models) that also leverage the distributional hypothesis. Count-based models may also include processes to compute the statistics of proximity, for instance how often some network entities appear next to each other within a network activity log (e.g., transactions log). Predictive models, on another hand, attempt to predict the use of a network entity, or asset, from its neighboring entities within a network activity log in terms of learned, small, dense, embeddings vectors. A determination of which of these abovementioned methods are employed can be based on various variables, which may be related to system constraints, such as how much data is available and the availability of compute and memory in the compute resources. These embeddings vectors may be implemented as parameters of the network embeddings model(s) 144.”
Zhao et al (US 2020/0382612; herein referred to as Zhao) at Paragraph [0056]: “Embedding engine 208 can be used to convert data (e.g., user log data and/or tutorial data) into vector form. Neural networks typically process data in vector form more effectively than raw data. In embodiments, the interpretable user modeling system can convert data into vector for utilizing embedding layers to project discrete variables into continuous vector space (e.g., for input into the intent neural network). For example, an embedding matrix (e.g., a look-up table) can be used to index input variables. In some embodiments, A∈ |V a |×d a can be an embedding matrix for actions. An action (e.g., at) can be represented by xt=A(at). In instances, da can represent a dimension for the action embedding, 1≤at≤|Va|, that indexes the ai-th row in matrix A. Memory slots (e.g., where each slot can indicate a tutorial sentence) can also be vectorized in matrix M. For instance, a memory look-up table can be defined for addressing memories. In particular, M∈ |V w |×d m can designate a memory look-up table for addressing memories. Further, a memory look-up table can be defined for reading memories. In particular, matrix O (e.g., O∈ |V w |×d m can designate a memory look-up table for addressing memories. In such memory look-up tables, dm can refer to the embedding dimension for the memories. In this way, a memory slot mk can be represented by the following equations that give memory embeddings as a weighted sum of word embeddings within a sequence”
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein retrieving the one or more related medical technical documents comprises performing a similarity search …of medical technical documents using the one or more embeddings of the medical technical document.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“within an embedding datastore” introduces additional elements that is insufficient to provide a practical application or significantly more:
Step 2A Prong 2: In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“within an embedding datastore” which corresponds to mere data gathering and/or output.
Step 2B: As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“within an embedding datastore” which corresponds to storing and retrieving information in memory.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein retrieving the one or more related medical technical documents comprises: determining a similarity score for each of a plurality of medical technical documents, wherein the similarity score for a respective medical technical document of the plurality of medical technical documents is based on a comparison between the one or more embeddings of the medical technical document and one or more embeddings of the respective medical technical document; and selecting the one or more related medical technical documents from the plurality of medical technical documents based on a threshold similarity score.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the transformer model is trained using contrastive learning on pairings between similar and dissimilar medical procedures and diagnoses identified from historical medical technical documents.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the one or more synthetic claims comprise a first claim that justifies the medical procedure with the first medical diagnosis.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 depends from claim 8 and inherits all the limitations of the claim from which it depends. Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising: retrieving, using the one or more embeddings, a procedure code for the medical procedure and a diagnostic code for the medical diagnosis; and providing the procedure code and the diagnostic code to the claim generation” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“machine learning model.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 10 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the one or more synthetic claims comprise a first claim that justifies the medical procedure with a second medical diagnosis that is not included in the one or more criteria.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 11 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 11 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein: the one or more synthetic claims comprise a first set of synthetic claims that satisfy the context and a second set of synthetic claims that do not satisfy the context; and each of the one or more synthetic claims is generated with a label indicating whether it satisfies the context or not.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 12,
Claim 12 depends from claim 11 and inherits all the limitations of the claim from which it depends. Claim 12 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising: … produce determinations, for each respective synthetic claim of the one or more synthetic claims, indicative of whether the respective synthetic claim complies with the medical technical document; and determining, based on a comparison between the results generated … and the label generated with each of the one or more synthetic claims, the accuracy” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“executing the program code on the one or more synthetic claims to”, “by the program code” and “of the program code” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 13,
Claim 13 is substantially similar to claim 1. Accordingly, claim 13 is rejected for the same reasons as claim 1.
“A retrieval augmented synthetic test claim generation system, comprising: one or more processors; and a computer-readable storage media storing computer-executable instructions that, when executed by the one or more processors, cause the retrieval augmented synthetic test claim generation system to:” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 15,
Claim 15 is substantially similar to claim 4. Accordingly, claim 15 is rejected for the same reasons as claim 4.
As per claim 16,
Claim 16 is substantially similar to claim 11. Accordingly, claim 16 is rejected for the same reasons as claim 11.
As per claim 17,
Claim 17 is substantially similar to claim 1. Accordingly, claim 17 is rejected for the same reasons as claim 1.
“One or more non-transitory computer-readable storage media storing one or more instructions which, when executed by one or more processors of a retrieval augmented synthetic test claim generation system, cause the one or more processors to:” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 19,
Claim 19 is substantially similar to claim 3. Accordingly, claim 19 is rejected for the same reasons as claim 3.
As per claim 20,
Claim 20 is substantially similar to claim 5. Accordingly, claim 20 is rejected for the same reasons as claim 5.
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 (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 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)(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.
Claims 1, 3-5, 8-13, 15-17, 19 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Narayan et al. (US 11,315,196).
As per claim 1,
Narayan discloses a method of generating synthetic claims for medical procedures:
(Column 1 Lines 22-32 and 62-67 and Column 2 Lines 1-13 of Narayan. The teaching describes systems and methods for training and using multiple classification engines that operate in parallel to classify the same input data. In some example embodiments described herein, the input data comprises insurance claims and the multiple classification engines are configured to classify individual insurance claims as “acceptable for payment” (i.e., correct or valid) or “unacceptable for payment” (i.e., likely to be denied payment or incorrect/invalid). However, the systems and methods disclosed herein are equally applicable to other types of input data beyond insurance claims. Some embodiments include using a well-defined data set to generate synthetic training data for use with training one or more machine learning classification systems. As mentioned above, in some embodiments, the one or more machine learning classification systems are configured to identify input data items as either “valid” or “invalid.” One challenge with such classification engines is having sufficient high-quality training data to train the classification engine before putting the classification engine into operation. Embodiments disclosed herein are configured to analyze a well-defined data set to identify specific features of individual data items in the well-defined set that can be altered to generate synthetic data items that are very similar to individual data items but just different enough to make the synthetic data items “invalid.” Intelligently generating synthetic “invalid” input data for training the classification engine helps the classification engine learn to discern fine distinctions between “valid” and “invalid” input data, thereby improving the performance of the classification engine.)
Narayan further discloses receiving a medical technical document comprising text data that defines one or more criteria that is required for approval of a medical procedure at a transformer model executed on a computer system, wherein the transformer model is trained on healthcare contexts:
(Column 6 Lines 49-67 of Narayan. The teaching describes how the system 100 receives insurance claims from providers 108 via a data ingestion process 101 [receiving a medical technical document], and then stores the received insurance claims into data storage 102. In some embodiments, the data ingestion process 101 occurs via an EDI-837 interface from the provider 108. The computing system 100 sends individual insurance claims from the data storage 102 to both the Rules Engine 103 and the Artificial Intelligence/Machine Learning (AI/ML) Engine 105 for classification. For each individual insurance claim, the system provides the classification result [one or more criteria] returned by the Rules Engine 103 and the classification result returned by the AI/ML Engine 105 to the Decision Module 105.)
(Column 19 Lines 10-15 of Narayan. The teaching describes confirming the edits/modifications that occurred at block 702 by approving the insurance claim. The approval at block 702 may be performed either by the computing system or by an authorized person.)
Narayan further discloses the one or more criteria define one or more medical diagnoses required to be present in order to justify a medical procedure presented in a claim:
(Column 9 Lines 4-10 of Narayan. The teaching describes insurance claims include Diagnosis Codes and Procedure Codes. Diagnosis Codes are standardized medical codes that describe medical diagnoses (e.g., diseases, ailments, conditions, and so on), and Procedure Codes are standardized medical codes that describe medical procedures (e.g., treatments, medications, surgical procedures, other medical procedures, and so on).)
Narayan further discloses generating, by the transformer model executed on the computer system, one or more embeddings of the medical technical document, wherein: the one or more embeddings of the medical technical document are dense vector representations in a continuous vector space of the medical technical document:
(Column 4 Lines 52-67 of Narayan. The teaching describes the use of two or more Machine Learning models employing complementary characteristics. For example, co-occurrence matrix based embedding and Neural Network based embedding using different initialization techniques during training can result in different behavior characteristics of the AI/ML Engine. In operation, the training dataset is very likely to be deficient in comparison to all possible combinations of medical codes that might be generally required to train an AI model because of the paucity of available healthcare related datasets. To address this critical issue, the disclosure presents a method which takes advantage of the neural embedding, which has a better accuracy, combining with a co-occurrence matrix embedding of predictable initialization for unknown situations.)
(Column 14 Lines 58-67, Column 15 Lines 1-11 of Narayan. The teaching describes At blocks 502 and 503, the potential anomalies identified at block 501 are further examined. Block 502 includes analyzing the potential anomalies of the insurance claim based on a Feature Sensitivity Database. In some embodiments, the Feature Sensitivity Database is pre-calculated and pre-configured. This Feature Sensitivity Database includes the sensitivity of each Medical Code vs. gender and/or age based on the HCUP dataset. In general, multiple features like Procedure Codes are more ‘Sensitive’ to a certain Diagnosis Code when they are all used to treat that Diagnosis Code; on the contrary, Procedure Codes used to treat multiple Diagnosis Codes are less ‘Sensitive’ to the ‘disappearing’ of any one of those Diagnosis Codes. The actual ‘Sensitivity’ value due to the ‘disappearing’ of a Diagnosis Code for a claim is calculated using the co-occurrence matrix of the entire training dataset. For example, when the co-occurrence matrix entry for Procedure Code 1 (PC1) and Diagnosis Code 1 (DC1) is 3 while the entry for PC1 and Diagnosis Code (DC2) is 7, the ‘Sensitivity’ value of the disappearing of DC2 for a claim of PC1, DC1, and DC2 only is 7/(3+7)=0.7.)
(Column 11 Lines 65-67, Column 12 Lines 1-28 of Narayan. The teaching describes scores 321 generated from the latent space matrices 315 and 316 can at times make it challenging for any predictive model to understand the physical context. Scores generated from latent space matrices can at times make it challenging for any predictive model to understand physical context. This is because scoring functions are a further reduction of vectors representing medical codes in a claim [dense vector representations in a continuous vector space of the medical technical document], and are used to represent the association between Diagnosis and Procedure codes. It is likely, that one such low scoring pair's low value is suppressed by other higher scoring associations. By including the latent space vectors, the machine learning model is exposed to more context and information that just the scores. Using a combination of scores and vectors allows the machine learning model to be exposed to as much relevant information about the overall latent space that the codes are being transformed to. The word “latent” means “hidden.” Latent can be used in machine learning to mean the following—you observe some data which is in the space that you can observe, and you want to map it to a latent space where similar data points are closer together. In some embodiments, the latent space matrices 315 and 316 include latent space features for both Diagnosis Codes and Procedure Codes separately. The vectors stored in 317 and 318 are generated by the latent space matrices 315 and 316, respectively. Examples of implementing functions for latent space matrices 315 and 316 include Latent Semantic Allocation and Latent Dirichlet Allocation. The AI/ML Model 300 makes decisions about whether to classify an insurance claim as valid or invalid based at least in part on the scores 321 of denial possibility and the context in latent space vectors 317, 318.)
Narayan further discloses retrieving, by the computer system, using the one or more embeddings, one or more related medical technical documents and one or more sample claims, wherein the one or more sample claims are related to the related medical technical documents:
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
Narayan further discloses inputting, by the computer system, to a claim generation machine learning model, the medical technical document, the one or more related medical technical documents, and the one or more sample claims to generate one or more synthetic claims that each comprise a reimbursement request for the medical procedure:
(Column 12 Lines 29-67 and Column 13 Lines 1-26 of Narayan. The teaching describes an example operational flow 400 of a Fault Injection method to generate synthesized denial data for training the AI/ML Engine (such as any of the AI/ML Engines disclosed herein) according to some embodiments. When a fault has been injected into an insurance claim, the insurance claim is likely to be denied for payment by a payer. To train an AI/ML Engine to classify input data, the training data should align with business knowledge. For example, in the context of insurance claim processing, current estimates are that the claim denial rate is around 15%. Thus, it would be advantageous for about 85% of the insurance claims in the training data to correspond to valid insurance claims and about 15% of the insurance claims in the training dataset to correspond to invalid insurance claims. In practice, however, the available dataset for training may not reflect a similar distribution between valid and invalid claims. Therefore, some embodiments disclosed herein include a novel Fault Injection process for synthesizing a variety of invalid insurance claims for inclusion into the training dataset to reflect a realistic distribution of denials. Discrepancy Information 402 includes both Diagnosis Codes and Procedure Codes classified into different categories. There are rules that govern which Procedure Code category would produce denial when it is associated with another Diagnosis Code category. The Fault Injection process 400 uses this information to add, delete or replace a code from a known non-denial claim (i.e., a known valid claim) to produce a denial claim (i.e., a synthesized invalid claim).)
Narayan further discloses receiving, by the computer system from the claim generation machine learning model, the one or more synthetic claims, wherein the one or more synthetic claims are usable to test an accuracy of program code that analyzes claims for compliance with the medical technical document:
(Column 13 Lines 35-51 of Narayan. The teaching describes a modified SMOTE (Synthetic Minority Over-sampling Technique) creates synthetic samples from the minority class which may include both synthesized invalid claims and the original valid claims instead of simply creating copies. Synthetic Minority Oversampling Technique (SMOTE) is a very popular oversampling method to improve random oversampling. The algorithm associated with SMOTE selects two or more similar claims/instances (using a distance measure) and perturbs a claim/instance one at a time by a random amount with a difference from the neighboring instances according to the Feature and Fault Association Weight in the Fault Injection module. Models focused on achieving just high accuracy, incur the same cost for both False Positives and False Negatives. On the other hand, Penalized/Cost-Sensitive Training penalizes mistakes on the minority class by an amount proportional to how under-represented it is in the dataset. This SMOTE process is construed as an accuracy test of the trained machine learning model that generates the synthetic test claims.)
As per claim 3,
Narayan discloses the limitations of claim 1.
Narayan further discloses wherein retrieving the one or more related medical technical documents comprises performing a similarity search within an embedding datastore of medical technical documents using the one or more embeddings of the medical technical document:
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
As per claim 4,
Narayan discloses the limitations of claim 1.
Narayan further discloses wherein retrieving the one or more related medical technical documents comprises :determining a similarity score for each of a plurality of medical technical documents, wherein the similarity score for a respective medical technical document of the plurality of medical technical documents is based on a comparison between the one or more embeddings of the medical technical document and one or more embeddings of the respective medical technical document; and selecting the one or more related medical technical documents from the plurality of medical technical documents based on a threshold similarity score:
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
As per claim 5,
Narayan discloses the limitations of claim 1.
Narayan further discloses wherein the transformer model is trained using contrastive learning on pairings between similar and dissimilar medical procedures and diagnoses identified from historical medical technical documents:
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other [similar or dissimilar; contrastive]. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
As per claim 8,
Narayan discloses the limitations of claim 1.
Narayan further discloses wherein the one or more synthetic claims comprise a first claim that justifies the medical procedure with the first medical diagnosis:
(Column 9 Lines 4-10 of Narayan. The teaching describes insurance claims include Diagnosis Codes and Procedure Codes. Diagnosis Codes are standardized medical codes that describe medical diagnoses (e.g., diseases, ailments, conditions, and so on), and Procedure Codes are standardized medical codes that describe medical procedures (e.g., treatments, medications, surgical procedures, other medical procedures, and so on).)
(Column 12 Lines 29-67 and Column 13 Lines 1-26 of Narayan. The teaching describes an example operational flow 400 of a Fault Injection method to generate synthesized denial data for training the AI/ML Engine (such as any of the AI/ML Engines disclosed herein) according to some embodiments. When a fault has been injected into an insurance claim, the insurance claim is likely to be denied for payment by a payer. To train an AI/ML Engine to classify input data, the training data should align with business knowledge. For example, in the context of insurance claim processing, current estimates are that the claim denial rate is around 15%. Thus, it would be advantageous for about 85% of the insurance claims in the training data to correspond to valid insurance claims and about 15% of the insurance claims in the training dataset to correspond to invalid insurance claims. In practice, however, the available dataset for training may not reflect a similar distribution between valid and invalid claims. Therefore, some embodiments disclosed herein include a novel Fault Injection process for synthesizing a variety of invalid insurance claims for inclusion into the training dataset to reflect a realistic distribution of denials. Discrepancy Information 402 includes both Diagnosis Codes and Procedure Codes classified into different categories. There are rules that govern which Procedure Code category would produce denial when it is associated with another Diagnosis Code category. The Fault Injection process 400 uses this information to add, delete or replace a code from a known non-denial claim (i.e., a known valid claim) to produce a denial claim (i.e., a synthesized invalid claim).)
As per claim 9,
Narayan discloses the limitations of claim 8.
Narayan further discloses further comprising: retrieving, using the one or more embeddings, a procedure code for the medical procedure and a diagnostic code for the medical diagnosis; and providing the procedure code and the diagnostic code to the claim generation machine learning model:
(Column 9 Lines 4-10 of Narayan. The teaching describes insurance claims include Diagnosis Codes and Procedure Codes. Diagnosis Codes are standardized medical codes that describe medical diagnoses (e.g., diseases, ailments, conditions, and so on), and Procedure Codes are standardized medical codes that describe medical procedures (e.g., treatments, medications, surgical procedures, other medical procedures, and so on).)
(Column 12 Lines 29-67 and Column 13 Lines 1-26 of Narayan. The teaching describes an example operational flow 400 of a Fault Injection method to generate synthesized denial data for training the AI/ML Engine (such as any of the AI/ML Engines disclosed herein) according to some embodiments. When a fault has been injected into an insurance claim, the insurance claim is likely to be denied for payment by a payer. To train an AI/ML Engine to classify input data, the training data should align with business knowledge. For example, in the context of insurance claim processing, current estimates are that the claim denial rate is around 15%. Thus, it would be advantageous for about 85% of the insurance claims in the training data to correspond to valid insurance claims and about 15% of the insurance claims in the training dataset to correspond to invalid insurance claims. In practice, however, the available dataset for training may not reflect a similar distribution between valid and invalid claims. Therefore, some embodiments disclosed herein include a novel Fault Injection process for synthesizing a variety of invalid insurance claims for inclusion into the training dataset to reflect a realistic distribution of denials. Discrepancy Information 402 includes both Diagnosis Codes and Procedure Codes classified into different categories. There are rules that govern which Procedure Code category would produce denial when it is associated with another Diagnosis Code category. The Fault Injection process 400 uses this information to add, delete or replace a code from a known non-denial claim (i.e., a known valid claim) to produce a denial claim (i.e., a synthesized invalid claim).)
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
As per claim 10,
Narayan discloses the limitations of claim 1.
Narayan further discloses wherein the one or more synthetic claims comprise a first claim that justifies the medical procedure with a second medical diagnosis that is not included in the one or more criteria:
(Column 9 Lines 4-10 of Narayan. The teaching describes insurance claims include Diagnosis Codes and Procedure Codes. Diagnosis Codes are standardized medical codes that describe medical diagnoses (e.g., diseases, ailments, conditions, and so on), and Procedure Codes are standardized medical codes that describe medical procedures (e.g., treatments, medications, surgical procedures, other medical procedures, and so on).)
(Column 12 Lines 29-67 and Column 13 Lines 1-26 of Narayan. The teaching describes an example operational flow 400 of a Fault Injection method to generate synthesized denial data for training the AI/ML Engine (such as any of the AI/ML Engines disclosed herein) according to some embodiments. When a fault has been injected into an insurance claim, the insurance claim is likely to be denied for payment by a payer. To train an AI/ML Engine to classify input data, the training data should align with business knowledge. For example, in the context of insurance claim processing, current estimates are that the claim denial rate is around 15%. Thus, it would be advantageous for about 85% of the insurance claims in the training data to correspond to valid insurance claims and about 15% of the insurance claims in the training dataset to correspond to invalid insurance claims. In practice, however, the available dataset for training may not reflect a similar distribution between valid and invalid claims. Therefore, some embodiments disclosed herein include a novel Fault Injection process for synthesizing a variety of invalid insurance claims for inclusion into the training dataset to reflect a realistic distribution of denials. Discrepancy Information 402 includes both Diagnosis Codes and Procedure Codes classified into different categories. There are rules that govern which Procedure Code category would produce denial when it is associated with another Diagnosis Code category. The Fault Injection process 400 uses this information to add, delete or replace a code from a known non-denial claim (i.e., a known valid claim) to produce a denial claim (i.e., a synthesized invalid claim).)
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
As per claim 11,
Narayan discloses the limitations of claim 1.
Narayan further discloses wherein: the one or more synthetic claims comprise a first set of synthetic claims that satisfy the context and a second set of synthetic claims that do not satisfy the context; and each of the one or more synthetic claims is generated with a label indicating whether it satisfies the context or not:
(Column 9 Lines 4-10 of Narayan. The teaching describes insurance claims include Diagnosis Codes and Procedure Codes. Diagnosis Codes are standardized medical codes that describe medical diagnoses (e.g., diseases, ailments, conditions, and so on), and Procedure Codes are standardized medical codes that describe medical procedures (e.g., treatments, medications, surgical procedures, other medical procedures, and so on).)
(Column 12 Lines 29-67 and Column 13 Lines 1-26 of Narayan. The teaching describes an example operational flow 400 of a Fault Injection method to generate synthesized denial data for training the AI/ML Engine (such as any of the AI/ML Engines disclosed herein) according to some embodiments. When a fault has been injected into an insurance claim, the insurance claim is likely to be denied for payment by a payer. To train an AI/ML Engine to classify input data, the training data should align with business knowledge. For example, in the context of insurance claim processing, current estimates are that the claim denial rate is around 15%. Thus, it would be advantageous for about 85% of the insurance claims in the training data to correspond to valid insurance claims and about 15% of the insurance claims in the training dataset to correspond to invalid insurance claims. In practice, however, the available dataset for training may not reflect a similar distribution between valid and invalid claims. Therefore, some embodiments disclosed herein include a novel Fault Injection process for synthesizing a variety of invalid insurance claims for inclusion into the training dataset to reflect a realistic distribution of denials. Discrepancy Information 402 includes both Diagnosis Codes and Procedure Codes classified into different categories. There are rules that govern which Procedure Code category would produce denial when it is associated with another Diagnosis Code category. The Fault Injection process 400 uses this information to add, delete or replace a code from a known non-denial claim (i.e., a known valid claim) to produce a denial claim (i.e., a synthesized invalid claim).)
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
As per claim 12,
Narayan discloses the limitations of claim 11.
Narayan further discloses further comprising: executing the program code on the one or more synthetic claims to produce determinations, for each respective synthetic claim of the one or more synthetic claims, indicative of whether the respective synthetic claim complies with the medical technical document; and determining, based on a comparison between the results generated by the program code and the label generated with each of the one or more synthetic claims, the accuracy of the program code:
(Column 9 Lines 4-10 of Narayan. The teaching describes insurance claims include Diagnosis Codes and Procedure Codes. Diagnosis Codes are standardized medical codes that describe medical diagnoses (e.g., diseases, ailments, conditions, and so on), and Procedure Codes are standardized medical codes that describe medical procedures (e.g., treatments, medications, surgical procedures, other medical procedures, and so on).)
(Column 12 Lines 29-67 and Column 13 Lines 1-26 of Narayan. The teaching describes an example operational flow 400 of a Fault Injection method to generate synthesized denial data for training the AI/ML Engine (such as any of the AI/ML Engines disclosed herein) according to some embodiments. When a fault has been injected into an insurance claim, the insurance claim is likely to be denied for payment by a payer. To train an AI/ML Engine to classify input data, the training data should align with business knowledge. For example, in the context of insurance claim processing, current estimates are that the claim denial rate is around 15%. Thus, it would be advantageous for about 85% of the insurance claims in the training data to correspond to valid insurance claims and about 15% of the insurance claims in the training dataset to correspond to invalid insurance claims. In practice, however, the available dataset for training may not reflect a similar distribution between valid and invalid claims. Therefore, some embodiments disclosed herein include a novel Fault Injection process for synthesizing a variety of invalid insurance claims for inclusion into the training dataset to reflect a realistic distribution of denials. Discrepancy Information 402 includes both Diagnosis Codes and Procedure Codes classified into different categories. There are rules that govern which Procedure Code category would produce denial when it is associated with another Diagnosis Code category. The Fault Injection process 400 uses this information to add, delete or replace a code from a known non-denial claim (i.e., a known valid claim) to produce a denial claim (i.e., a synthesized invalid claim).)
(Column 11 Lines 38-64 of Narayan. The teaching describes that similarity of two objects with multiple components or features are calculated by treating each object as a “vector.” The metric distance between two vectors represents their similarity to each other. This is under an assumption that features and components of the objects are orthogonal or independent to each other. However, this assumption does not always hold with Medical Codes. Thus, the similarity of two insurance claims takes associations among the Medical Codes into consideration in this feature extraction process associated with the autocorrelation matrices 312. The compatibility of an insurance claim in comparison with a set of multiple known paid insurance claims in the training dataset should also take the Medical Codes' collective associations into consideration. Thus, the association based on the “binned” (or bucketed or data binning) categories is considered; for example, some codes are associated with ‘digestive’ and some are associated with ‘circulatory.’ Different scores can be obtained when comparing the similarity between the two objects using different weights for each component and their associations which take the form of a matrix or tensor. Likewise, different scores can be obtained when comparing the compatibility of an object with a collection of multiple known objects using not only the values of the specific component but also their associations with a particular set of weights.)
(Column 13 Lines 35-51 of Narayan. The teaching describes a modified SMOTE (Synthetic Minority Over-sampling Technique) creates synthetic samples from the minority class which may include both synthesized invalid claims and the original valid claims instead of simply creating copies. Synthetic Minority Oversampling Technique (SMOTE) is a very popular oversampling method to improve random oversampling. The algorithm associated with SMOTE selects two or more similar claims/instances (using a distance measure) and perturbs a claim/instance one at a time by a random amount with a difference from the neighboring instances according to the Feature and Fault Association Weight in the Fault Injection module. Models focused on achieving just high accuracy, incur the same cost for both False Positives and False Negatives. On the other hand, Penalized/Cost-Sensitive Training penalizes mistakes on the minority class by an amount proportional to how under-represented it is in the dataset. This SMOTE process is construed as an accuracy test of the trained machine learning model that generates the synthetic test claims.)
As per claim 13,
Claim 13 is substantially similar to claim 1. Accordingly, claim 13 is rejected for the same reasons as claim 1.
Narayan further discloses a retrieval augmented synthetic test claim generation system, comprising: one or more processors; and a computer-readable storage media storing computer-executable instructions that, when executed by the one or more processors, cause the retrieval augmented synthetic test claim generation system:
(Column 3 Lines 38-60 of Narayan. The teaching describes the modules are implemented in a single computing system comprising one or more processors and tangible, non-transitory computer-readable media having instructions encoded therein, wherein the instructions, when executed by the one or more processors, cause the computing system to perform the functions of one or more (or all) of the five modules.)
As per claim 15,
Claim 15 is substantially similar to claim 4. Accordingly, claim 15 is rejected for the same reasons as claim 4.
As per claim 16,
Claim 16 is substantially similar to claim 11. Accordingly, claim 16 is rejected for the same reasons as claim 11.
As per claim 17,
Claim 17 is substantially similar to claim 1. Accordingly, claim 17 is rejected for the same reasons as claim 1.
Narayan further discloses one or more non-transitory computer-readable storage media storing one or more instructions which, when executed by one or more processors of a retrieval augmented synthetic test claim generation system:
(Column 3 Lines 38-60 of Narayan. The teaching describes the modules are implemented in a single computing system comprising one or more processors and tangible, non-transitory computer-readable media having instructions encoded therein, wherein the instructions, when executed by the one or more processors, cause the computing system to perform the functions of one or more (or all) of the five modules.)
As per claim 19,
Claim 19 is substantially similar to claim 3. Accordingly, claim 19 is rejected for the same reasons as claim 3.
As per claim 20,
Claim 20 is substantially similar to claim 5. Accordingly, claim 20 is rejected for the same reasons as claim 5.
Response to Arguments
Applicant's arguments filed April 28, 2026 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that there exists a technical problem of generative AI models suffering from a “closed universe”. Here, for the models to remain accurate, the models must be constantly updated to retain accuracy. This constant updating of models puts unnecessary stress on the computer system the model is implemented on. By incorporating dense vector state embeddings, computer performance is improved by minimizing the need for constant retraining.
The Examiner respectfully disagrees. The problem identified does not appear to be a technological problem, but rather a business problem. If the business space in which this invention exists did not have so many updates, there would have been no need for the model itself to update as often. This removes the problem from the technical sphere as its source is found in how this business area uses technology. However, regardless of whether this is principally a technical problem or not, the Applicant’s solution is to provide nothing more than well-known, routine and conventional methods of embedding to solve the alleged technical problem. This does not constitute an improvement to technology because of the conventionality of using embeddings as dense vector representations of data in a continuous vector space. Such is well-known, routine and conventional as evidenced above.
Applicant’s argument pertaining to rejections made under 35 U.S.C. 102 are not persuasive.
The Applicant argues that Narayan lacks the specific technical mechanism of using context aware transformer embeddings of a medical technical document to drive retrieval of relevant training artifacts.
The Examiner respectfully disagrees. After further reviewing the specification of Narayan, the Examiner does not find difference in what is claimed and what is taught by Narayan. Specifically, the newly cited portions of Narayan provide adequate basis to conclude that Narayan discloses the limitations of claim 1. Please see the above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681
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