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 .
Status of the Claims
The status of the claims as of the response filed 11/12/2025, is as follows:
Claims 1, 5-10, 14-19, 21-26 are pending and consider below.
Claims 2, 3, 4, 11, 12, 13, and 20 are canceled.
Claims 21, 22, 23, 24, 25, and 26 are new.
Claim Objections
Claim 24 and 26 objected to because of the following informalities: are identical in scope. Appropriate correction is required.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 09/30/2025 are in accordance with the provisions of 37 CFR 1.97 and are considered by the Examiner.
Response to Arguments
35 U.S.C 101 Subject Matter
Applicant’s arguments, see page 11-14 filed date 11/12/2025 , with respect to Claims 1, 5-10, 14-19, 21-26 have been fully considered and are not persuasive. The 35 U.S.C 101 Subject Matter
rejections is sustained.
Prong One:
The applicant argues that "performance of the recited steps requires execution by hardware processors" and that "claim 1 is not directed to a mental process" because it uses "trained vector-embedding functions" that cannot be done with "pen and paper."
Examiner respectfully disagrees because "the fact that a claim is limited to a computer" does not save it from being an abstract idea under MPEP § 2106.04(a)(2). The claim recites "instructions... executed by one or more hardware processors," but the "substance" of the claim is a "mental process" involving "evaluations, judgments, or opinions." While the computer performs these steps, the core "mental process" of comparing two drug descriptions to see if they are the same remains an abstract "human activity" of clinical curation and applying mathematical concept abstract ideas.
Applicant asserts that "a human mind cannot mentally perform such high-dimensional linear-algebraic calculations across thousands of code embeddings" and thus cannot be carried out with "pen and paper."
The examiner respectfully disagrees with the argument that mathematical complexity removes the claim from the judicial exception categories. While a human might not calculate a 200-dimensional cosine similarity score for 10,000 items in seconds, MPEP § 2106.04(a)(1) identifies "Mathematical Concepts" (mathematical relationships, formulas, and calculations) as a distinct category of abstract ideas. The complexity of the math actually confirms it is a "Mathematical Concept" rather than proving it is not an abstract idea. The recitation of "vector-embedding functions" and "similarity-metric computations" specifically "describes a mathematical concept" regardless of whether the calculation is done by a human or a processor. Furthermore, an improvement in "the accuracy of a mathematically calculated statistical prediction" is considered an "improvement to the abstract idea" itself rather than a technical improvement to the computer's functionality.
Applicant argues the claim is directed to "concrete digital artifacts" like "vector embeddings" and "similarity matrices" that "exist only within computer memory" and are "not abstract thought."
The examiner respectfully disagrees that the creation of data structures in memory renders the claim non-abstract. Under MPEP § 2106.04, "claims directed to a computer readable medium storing instructions... must be evaluated for eligibility." The "vector embeddings" and "similarity measures" recited are data representations of medication information.
Prong Two
The applicant argues that the claim integrates the exception because it "transforms heterogeneous, unstructured medication text into normalized, deduplicated, and interoperable data structures" and produces a "tangible improvement in the operation of healthcare data systems" by facilitating "clinical decision-support" and "treatment-recommendation engines."
Examiner respectfully disagrees that the creation of interoperable data structures constitutes a "technical improvement." Under MPEP § 2106.05(a), the claim must demonstrate a "specific technical solution to a technical problem." The applicant’s argument focuses on the "accuracy" and "normalization" of drug records, which is viewed as an improvement to the abstract idea of "deduplication" itself.
Applicant asserts that the deduplication "further improve the functioning of the computer system itself by, reducing redundant storage and network transmission" and "improving the accuracy and efficiency of data queries."
Examiner respectfully disagrees with the contention that reduced storage requirements constitute a "technological improvement." Under MPEP § 2106.05(a), "the mere fact that a computer is used to perform the steps... does not establish an improvement in computer-related technology." The "reduction in redundant storage" is a natural byproduct of the abstract idea of "removing duplicates"; it is not a "specific technical solution" to a computer hardware problem. Following the principles in Digitech, these "normalized data structures" are simply "collections of data" that occupy less space. Because the claim does not recite a "specific technical improvement to data transmission protocols" or "new memory management architecture," it fails to integrate the abstract idea into a practical application.
Step 2B:
The applicant argues that the "ordered combination of claim elements recites significantly more" because it is "non-conventional and non-routine in both structure and function." They cite the use of "embedding-space generation" and "multi-tier similarity computation" as a departure from "conventional mapping systems that rely on static lookup tables." Applicant references Berkheimer v. HP Inc. to assert that these are "new machine-generated data structures" that improve computer operation.
Examiner respectfully disagrees that the use of "vector-embedding functions" in place of "lookup tables" provides an inventive concept. Under MPEP § 2106.05(d), simply implementing a mathematical principle on a physical machine, namely a computer, is not a patentable application of that principle. While the applicant cites Berkheimer, that case requires a "specific technical improvement"; here, the "improved semantic resolution" is an improvement in the accuracy of the data, not the functioning of the computer. Improving the accuracy of a mathematically calculated statistical prediction is an improvement to the abstract idea itself and does not constitute "significantly more."
35 U.S.C. 103 - Obviousness: Claims 1, 5-10, 14-19, 21-26
Applicant’s arguments, see page 14-16 filed date 11/12/2025 , with respect to Claims 1, 5-10, 14-19, 21-26 have been fully considered and are not persuasive. The previous rejection based on Burns has been withdrawn; however, the new rejection based on Hane (US 10891352) and Agresta (PTO-892 U) was submitted and maintains the statutory ground of unpatentability, for new rejection, refer to 35 U.S.C. 103 below for further details.
Response to Amendments
Non-Statutory Double Patenting
Applicant’s arguments, see page 16 filed date 11/12/2025 , with respect to Claims 1, 5-10, 14-19, 21-26 have been fully considered and is persuasive.
The Examiner reviewed your explanation regarding the differences between this application and your other pending application (No. 18/410,219). Based on the new information provided, the Examiner agrees that the two inventions are technically different. Therefore, the Double Patenting rejection is withdrawn.
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.
Subject Matter eligibility Rejection 35 U.S.C 101
Claims 1, 5-10, 14-19, 21-26 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without reciting additional elements that integrate the exception into a practical application, and without reciting an inventive concept amounting to significantly more than the exception itself.
Step 1: Statutory Categories Analysis
The claims are directed to statutory subject matter, encompassing the following statutory categories:
Process (Claims 10, 14-18): The language reciting "A method comprising: accessing a plurality of standard medication codes... generating a plurality of vector embeddings... and removing the second medication" defines a series of acts or steps for deduplicating medication events, aligning with the definition of a process in MPEP § 2106.03.
Machine (Claim 19): The language reciting "A system comprising: at least one device including a hardware processor; the system being configured to perform operations" describes a concrete thing consisting of functional parts and hardware, aligning with the definition of a machine in MPEP § 2106.03.
Manufacture (Claims 1, 5-9, 21-26): The language reciting "One or more non-transitory computer-readable media comprising instructions which, when executed... cause performance of operations" describes a tangible article of manufacture given a new form and utility through the encoding of computer-executable instructions, aligning with the definition of a manufacture in MPEP § 2106.03.
Having confirmed the claims are directed to statutory subject matter, the analysis proceeds to Step 2A, Prong One.
Step 2A, Prong One requires determining if a claim recites a judicial exception, such as an abstract idea, law of nature, or natural phenomenon. According to MPEP § 2106.04, abstract ideas are categorized into Mathematical Concepts, Certain Methods of Organizing Human Activity, and Mental Processes.
The whole invention is related to deduplicating medication events using NLP concept mapping of free text to standard medication codes and then removing duplicates from a patient’s medication list.
The invention is directed to a "medication synchronization system" [20] and method for "deduplicating medication events associated with free text using natural language processing" [1] to ensure "data remains consistent and up to date across all synchronized endpoints" [49]. The process involves generating "vector embeddings" [43] for both "standard medication codes" [14] and "medication free text" [14] to compute a "similarity metric" [65] that facilitates "identifying and removing duplicate medication entries from a patient's medication list" [70].
More specifically, Claim 1, 5-10, 14-19, 21-26 recites a system that matches a patient's "medication free text" to "standard medication codes" to identify drugs. It determines if different drug entries belong to the "same grouping" and removes "duplicative" items to create a clean "deduplicated listing." This list is then used to suggest a "treatment recommendation," such as a "substitution" or "adjustment" of medicine, therefore recite mental, human activity and mathematical concepts.
Claims Recites the following non-bold parts:
Claim 1.
One or more non-transitory computer-readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising:
accessing a plurality of standard medication codes, each standard medication code being mapped to a corresponding set of attributes, each set of attributes associated with at least one medication;
generating a plurality of vector embeddings corresponding respectively to the plurality of standard medication codes, wherein generating the plurality of vector embeddings comprises:
applying a first vector embedding function to text of a first set of attributes associated with a first standard medication code of the plurality of standard medication codes for a first medication event, to generate a first vector embedding, wherein the first medication event is associated with a first medication,
and applying the first vector embedding function to text of a second set of attributes associated with a second standard medication code of the plurality of standard medication codes for a second medication event, to generate a second vector embedding, wherein the second medication event is associated with a second medication:
accessing patient medication data of a patient from one or more sources to generate a listing of medications for the patient, wherein the patient medication data comprises a first target unmapped medication code corresponding to a first target medication event and a second target unmapped medication code corresponding to a second target medication event, the first target unmapped medication code comprises a first set of medication free text associated with the first medication and the second target unmapped medication code comprises a second set of medication free text associated with the second medication;
applying a second vector embedding function to:
(a) the first set of medication free text to generate a first target vector embedding for the first target unmapped medication code, and
(b) the second set of medication free text to generate a second target vector embedding for the second target unmapped medication code;
computing a similarity measure for each of the first and second target vector embeddings and each of the plurality of vector embeddings to generate a plurality of similarity measures, the plurality of similarity measures comprise:
(a) a first similarity measure for the first target vector embedding and the first vector embedding, and
(b) a second similarity measure for the second target vector embedding and the second vector embedding:
based at least on the first and second similarity measures:
(a)mapping the first target unmapped medication code to the first standard medication code to the first standard medication code, and
(b) mapping the second target unmapped medication code to the second standard medication code;
based at least on the mapping of the first target unmapped medication code to the first standard medication code and the mapping of the second target unmapped medication code to the second standard medication code, determining that the first medication and the second medication belong to a same grouping of medications;
Claim Abstract Classification Rational
Claims 1, 10, and 19 recite (i) mathematical concepts for deriving similarity between text representations and (ii) human-activity / mental-process steps for collecting, reconciling, and curating medication records to remove duplicates. Under MPEP § 2106.04, these fall within the abstract idea groupings of Mathematical Concepts and Mental Processes / Certain Methods of Organizing Human Activity.
Mathematical Concepts
The claim recites limitations that are mathematical in nature:
Applying a first vector embedding function… to generate a first vector embedding
Applying a second vector embedding function… to generate… target vector embedding
Computing a similarity measure… to generate… similarity measures
A vector embedding function is a mathematical mapping from text to a vector of real numbers, and a similarity measure (e.g., cosine similarity) is a mathematical relationship applied to those vectors to compute closeness. Under MPEP § 2106.04(a)(1), mathematical relationships/formulas and calculations are abstract ideas. The claim’s core operation is converting text into numeric vectors and then calculating similarity values i.e., manipulating data using mathematical computations—without reciting a physical transformation of an article or a change in hardware operation.
Certain Methods of Organizing Human Activity (Clinical record management) includes “accessing” limitations
The claim recites limitations that align with organizing and managing healthcare records and coding information:
accessing a plurality of standard medication codes… mapped to… attributes
accessing patient medication data… from one or more sources to generate a listing of medications for the patient
These “accessing” steps correspond, under their broadest reasonable meaning, to retrieving and assembling medication identifiers and related descriptive fields from healthcare repositories and sources for the purpose of medication reconciliation. In ordinary clinical practice, humans perform this same activity: they gather medication histories from different sources (EHR, pharmacy lists, discharge summaries), consult standard vocabularies or code sets, and compile a consolidated medication list. That is fundamentally administrative/clinical record management method of organizing information about patient medications rather than a technical improvement to data retrieval mechanisms.
Mental Processes (Reconciliation, equivalence, and deduplication decisions)
The claim recites steps that represent mental reconciliation of information:
mapping the first target unmapped medication code to the first standard medication code
mapping the second target unmapped medication code to the second standard medication code
determining that the first medication and the second medication belong to a same grouping of medications
removing the second medication… as duplicative… to generate a deduplicated listing
These limitations describe decision logic for equivalence and list cleanup. A human can perform the same sequence mentally or with pen and paper: compare two medication descriptions, decide whether they correspond to the same standardized drug concept, group them as duplicates, and remove one entry to maintain a clean list. The claim thus recites a mental process for resolving duplicative records, merely expressed in computerized form.
Manual Replication Scenario (Human Equivalence)
To illustrate the mental-process nature of Claims 1, 10, and 17, imagine a clinician who first retrieves standard medication information (e.g., from an RxNorm reference) and then reviews patient medication free text such as Tylenol 500mg. The clinician mentally converts the text into its meaning, compares it to the standard entry, and decides whether the two refer to the same medication (a similarity judgment and mapping decision). After determining that Tylenol and Acetaminophen belong to the same medication grouping, the clinician removes one entry from the patient’s list as duplicative. The end result is the same record-reconciliation outcome the claim recites performed through ordinary human comparison, classification, and list maintenance.
Dependent Claims Analysis
The dependent claims 5-9 and 21-26 are also directed to an abstract idea, as they further specify the mathematical models or administrative record-keeping steps without moving the claim away from the identified judicial exceptions.
Claims 5, 14 and 25: These claims recite under BRI identifying if a medication is a "generic medication" or a "name brand medication" and "consolidating prescription quantities", which is a Certain Method of Organizing Human Activity (specifically, clinical record management and categorization).
Claim 6 and 15: This claim recites the use of a "weighted cosine similarity measure", which is a Mathematical Concept representing a specific formula for calculating the relationship between two vectors.
Claims 7, 16 and 8 and 17: These claims recite identifying "n highest similarity measures" or those that "meet a threshold" to present "candidate standard medication codes", which is a Mental Process of ranking and selecting items based on comparative criteria.
Claim 9 and 18: This claim recites using specific models like "BioWordVec fastText" or "SAPBERT", which are Mathematical Concepts or specialized data processing models for text representation.
Claims 21 and 23: These claims recite "transmitting the deduplicated listing" to a "clinical decision support engine" or an "external electronic health record system", which is a Certain Method of Organizing Human Activity (managing healthcare workflows and data synchronization).
Claim 22: This claim recites generating a "treatment recommendation" such as an "adjustment, substitution, or discontinuation", which is a Mental Process of clinical decision-making and evaluation.
Claims 24 and 26: These claims recite "fine-tuning the first vector embedding function" based on "similarity feedback", which is a Mathematical Concept related to iterative model optimization.
Because these additional limitations merely narrow the scope of the abstract ideas by specifying the types of math or the specific clinical environment used, they do not shift the focus of the claims away from the judicial exceptions. Having determined that the claims are directed to an abstract idea under Step 2A, Prong One, the analysis proceeds to Prong Two to determine if the additional elements integrate the abstract idea into a practical application.
Step 2A, Prong Two:
Integration into a Practical Application Step 2A Prong Two asks whether the claim recites additional elements that integrate the judicial exception into a practical application, considering the claim as a whole. Here, the additional elements mainly provide a computing setting for executing embeddings/similarity calculations and acting on the resulting “match” to clean a medication list; that is a data-reconciliation application of the abstract idea rather than a claimed technological integration (e.g., a claimed improvement to computer/NLP technology itself). Because the improvement is to the abstract idea itself rather than the technology.
Evaluation of Representative Claims 1, 10, and 19 Additional Elements
Processor and Storage (Media + Hardware Processor)
Recited element (claim language): one or more non-transitory computer-readable media and one or more hardware processors.
The claim recites generic execution context (storage + processor) for performing the embedding and similarity computations and then updating a list; it does not recite a claimed improvement to computer operation or NLP computation itself. Step 2A Prong Two requires looking for integration, and “mere physical or tangible implementation” is not the driver of eligibility. The additional elements function as instructions to implement an abstract idea on a computer (compute embeddings/similarity, then map/group/remove) rather than claiming a specific technological mechanism that changes how computers perform the task. This tracks the “mere instructions to implement… on a computer / using a computer as a tool” consideration.
Data Source Environment
The recitation of accessing data from "one or more sources" or "healthcare databases” is insufficient for integration under MPEP 2106.05(h). These elements merely "limit the use of the abstract idea to a particular technological environment," which the courts have consistently held does not transform an abstract idea into a patent-eligible practical application.
The additional elements in claims 1, 10 and 19 do not meaningfully constrain or transform the exception-driven analysis into a claim-limited technical application; instead, they present the exception as a tool applied to healthcare records. That matches the Prong Two pattern where claims fail integration because they use a computer as a tool, and/or (ii) generally link the exception to a field of use or technological environment.
Dependent Claims Analysis
The dependent claims do not overcome Prong Two.
As a group and individually, these claims either (i) further specify how the similarity/embedding math is performed (e.g., weighted cosine similarity, selecting specific embedding models, fine-tuning based on feedback), which merely narrows the mathematical concept already identified, or (ii) add clinical record-management and workflow outputs (e.g., generic vs brand categorization, consolidating refills/quantities, presenting candidate codes, transmitting to CDS/EHR, generating treatment recommendations), which are information reconciliation/organization and clinical decision activity using the abstract results.
Taken together, the dependent claims add no claim-limited technical mechanism that improves computer functionality or another technical field; they apply the same abstract matching/reconciliation logic to healthcare data and use the results in downstream workflows. Therefore, the dependent claims do not integrate the abstract idea into a practical application, and the analysis proceeds to Step 2B.
Step 2B:
Step 2B is a search for an "inventive concept" to determine if the additional elements, considered both individually and as an ordered combination, amount to "significantly more" than the abstract idea itself. In this analysis, the additional elements identified in Prong Two fail to overcome the Step 2B inquiry because they do not provide a technological improvement to computer functionality or a technical field, but rather use generic computer components as tools to perform the abstract tasks of data reconciliation and clinical record management.
Evaluation of Independent Claims 1, 10, and 19 Additional Elements
Processor and Storage (Media + Hardware Processor): The claim recites "one or more non-transitory computer-readable media" and "one or more hardware processors" to execute the instructions. These elements represent a "mere physical or tangible implementation" of the abstract idea on a computer, which MPEP § 2106.05(f) clarifies does not provide an inventive concept because they are "recited at a high level of generality" and do not improve the computer's internal operations. Per the specification, the "hardware processor 604" is a "general purpose microprocessor, par. 129" used to perform the calculations, confirming that these elements are simply "instructions to implement… by a processor, par. 130" rather than a specialized hardware configuration.
Data Source Environment (Healthcare Databases/External Sources): The limitations "accessing a plurality of standard medication codes" and "accessing patient medication data... from one or more sources" serve to limit the "use of the abstract idea to a particular technological environment" (healthcare records), which MPEP § 2106.05(h) identifies as insufficient to provide an inventive concept. These elements merely identify "data sources for the exception" or a "field of use," and the specification at paragraph [76] describes these as "external sources 108, par. 21" such as "health information exchange (HIE) 154 and healthcare databases 156," which are generic components of the medical data landscape.
As a whole, the combination of additional elements is not enough because it merely describes the automation of the mental process of medication reconciliation using generic computer tools within a clinical field of use. The combination does not yield a "technical solution to a technical problem" but rather uses a computer to perform administrative and mathematical tasks more efficiently.
Dependent Claims Analysis
The dependents claims further specify the mathematical technique/model used to implement the abstract similarity/mapping concept, but they remain directed to improving or selecting an algorithm at a high level, without claiming a specific technical implementation that improves computer operation (e.g., a concrete training pipeline, hardware acceleration mechanism, memory/computation architecture, or other claim-limited computer-functionality improvement). They therefore do not add “significantly more” beyond the judicial exception.
As a whole, the combination of dependent claims and additional elements is not enough because they only refine the mathematical steps or provide additional administrative outputs without changing the technical nature of the computer or the NLP process.
The claims are directed to an abstract idea (mathematical concepts, mental processes, and methods of organizing human activity) and lack an inventive concept because the additional elements, individually and in combination, consist of generic computer implementation, insignificant pre- and post-solution activities, and limitations that merely narrow the field of use to healthcare records. Therefore, Claims 1, 5-10, 14-19, 21-26 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 6-10, 15-19 and 21-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hane - US10891352 and further in view of Agresta – PTO-892 U
Hane teaches Claim 1.
One or more non-transitory computer-readable media comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising: (Hane, Col. 5, ll. 25-45)
accessing a plurality of standard medication codes, each standard medication code being mapped to a corresponding set of attributes, each set of attributes associated with at least one medication; (Hane, Col.1, ll.63 – Col. 2., ll. 25, Col. 12, ll. 9 -28)
Hane et al. expressly describes accessing many coded medical entries including drug codes, and storing a code-to-data mapping read on each standard medication code being mapped to a corresponding set of attributes because the multi-dimensional vector is a defined set of values linked to the code in the dictionary.
generating a plurality of vector embeddings corresponding respectively to the plurality of standard medication codes, wherein generating the plurality of vector embeddings comprises:
applying a first vector embedding function to text of a first set of attributes associated with a first standard medication code of the plurality of standard medication codes for a first medication event, to generate a first vector embedding, wherein the first medication event is associated with a first medication, (Hane, abstract “Hane, See at least, The computing entity generates an embedding vector dictionary comprising a plurality of multi-dimensional vectors based on a medical embedding model trained using machine learning and the one or more medical sentences. Each multi-dimensional vector corresponds to a medical code.” , Col. 2, ll1-25, Col. 12, ll. 9 -28)
Above limitation require, transforming alphanumeric identifiers into numerical vectors by executing a mathematical process on descriptive data points that characterize a clinical encounter involving a drug. Reference Applicant Language, See at least, Standard medication codes, as referred to herein, are alphanumeric identifiers that represent medication events, par. 14. Initially, the system generates vector embeddings for the standard medication codes by applying a vector embedding function to a set of attributes associated with the standard medication codes. Applying a vector embedding function to the set of attributes includes applying the vector embedding function to text of the set of attributes, par. 15
Hane demonstrates the creation of numerical vectors where each vector specifically maps to a medical code, such as a drug or prescription code. The mathematical transformation is performed by a medical embedding model acting as the required vector embedding function which processes medical sentences to output multi-dimensional vectors. Under the Broadest Reasonable Interpretation (BRI), Hane’s medical sentences, which are disclosed as strings of alphanumeric medical codes, constitute the text of a first set of attributes characterizing a medication event. Hane explicitly links these codes to specific events such as a particular patient visit, a prescription, or a claim.
and applying the first vector embedding function to text of a second set of attributes associated with a second standard medication code of the plurality of standard medication codes for a second medication event, to generate a second vector embedding, wherein the second medication event is associated with a second medication; (Hane, abstract, Col.12, ll. 1-27)
Hane et al. teaches generating a multi-dimensional vector per medical code using the same embedding model across many codes, and expressly identifies at least two different medication-related codes tied to different medications
accessing patient medication data of a patient from one or more sources to generate a listing of medications for the patient, wherein the patient medication data comprises ; (Hane, abstract, Col. 12, ll. 50 – 67, abstract
Hane describes a processing architecture where a computing entity performs automated retrieval of medical events to construct structured data sets for downstream modeling.
computing a similarity measure for each of the first and second target vector embeddings and each of the plurality of vector embeddings to generate a plurality of similarity measures, the plurality of similarity measures comprise:
a first similarity measure for the first target vector embedding and the first vector embedding,(Hane, Col. 2, ll. 1 – 25, Col. 4, ll. 1-20)
Hane et al. utilizes mathematical distance as a metric to evaluate the relationship between different subject records.
and (b) a second similarity measure for the second target vector embedding and the second vector embedding; (Hane, Col. 4, ll. 9-16, Col. 16, 50-67)
Hane describes a computer performing distance and angle calculations between aggregate vectors to identify similarity between subjects like patients or providers.
based at least on the first and second similarity measures:
(a)mapping the first target unmapped medication code to the first standard medication code to the first standard medication code, and (b) mapping the second target unmapped medication code to the second standard medication code; (Hane, Col.2, ll. 15 -50)
Hane describes identifying whether two vectors are similar to find related patients or to link a resubmitted insurance claim to an original claim. This process evaluates the relationship between existing data objects.
based at least on the mapping of the first target unmapped medication code to the first standard medication code and the mapping of the second target unmapped medication code to the second standard medication code, determining that the first medication and the second medication belong to a same grouping of medications; (Hane, Col.2, ll. 15 -50)
Hane describes identifying that two insurance claims are related, such as an original claim and a resubmitted claim. While Hane identifies these items as being similar or different and uses this to link them together
and removing the second medication associated with the second target unmapped medication code from the listing of medications for the patient as duplicative of the first medication associated with the first target unmapped medication code to generate a deduplicated listing of medications for the patient.(Hane abstract, Col. 2, ll. 15-50, Col. 14, ll. 25-41, )
Hane describes identifying when an insurance claim is similar to another, specifically when one claim is resubmitted as a second, different claim. Hane uses this similarity to create a connection or link between the two claims. Hane also disclosed creating a single, consolidated output of a person's drug history by identifying and removing redundant entries to ensure each unique treatment is listed only once. Hane demonstrates the express generation of a consolidated, non-redundant record by filtering a patient's medical history to remove repeated data points. Specifically, the system processes medical sentences—which consist of strings of alphanumeric codes—and applies a filtering operation to remove duplicate drug or prescription identifiers, leaving only the first instance of a medication. Under the Broadest Reasonable Interpretation (BRI), this filtered set of first-instance drug codes constitutes a "deduplicated listing of medications for the patient," as it provides the identical technical result of a non-redundant drug history for a specific patient identifier.
35 U.S.C Obviousness Rational:
Hane disclosed explained limitations above, however does not disclosed:
a first target unmapped medication code corresponding to a first target medication event and a second target unmapped medication code corresponding to a second target medication event, the first target unmapped medication code comprises a first set of medication free text associated with the first medication and the second target unmapped medication code comprises a second set of medication free text associated with the second medication
Agresta teaches missing elements above that requires the recognition and ingestion of multiple non-standardized, unstructured text strings from clinical notes or pharmacy records that have not yet been linked to a formal terminology system. Agresta recognizes that in medical reconciliation, medication lists often arrive as free-form entries for multiple medications (e.g., prescriptions, OTC meds, and supplements) that fail to match standard identifiers like RxNorm, necessitating a system capable of handling a plurality of these unmapped textual inputs. . (Agresta, See at least, Section II.B; "prescriptions may be recorded with more free-form names that do not match any of the provided RxNorm term type formats... reliance on free-text entry of medications"; See also Fig. 2 page 8 and Fig. 4 page 9 showing multiple medications such as Gabapentin and Warfarin retrieved across different sources with varying degrees of completeness).
The combination of Hane + Agresta applications make obvious the full limitation above because a POSITA would implement the ingestion of multiple instances of free-text data as a routine configuration to make Agresta's reconciliation technique operate within Hane's vector-embedding workflow, because Hane seeks to aggregate "all" instances of medical information for comprehensive modeling (Reference Hane, See at least, Abstract; "Aggregate vectors corresponding to non-textual information / data are provided... A computing entity access a plurality of instances of medical information comprising medical codes... generates one or more medical sentences from the plurality of instances of medical information.") and Agresta teaches that "unmapped free text" for multiple medications is a prevalent and necessary data source that predictably achieves the goal of a complete longitudinal medication history. (Agresta, See at least, Section III.B, pag. 5; "Medication reconciliation requires the ability to... obtain potentially incomplete or erroneous patient medication lists from multiple secure sources; extract the exact medications listed in a potentially free-form format...").
A skilled Artisan in the art who read Hane application, would combine Agresta with Hane, because both references are directed to the same subject matter of Health Information Exchange (HIE) and medical data aggregation (Hane, abstract + Agresta, Introduction, page 1), where Hane provides the high-level infrastructure for processing medical events into structured "sentences" but lacks a robust mechanism for handling unstructured clinical inputs, which is the specific problem resolved by the free-text processing techniques disclosed in Agresta. (Reference Agresta, See at least, Section I, page 2, "Substantial difficulty remains in compiling a patient’s medication list from numerous disparate sources, often containing duplicate, missing, or inaccurate information."). The integration of Agresta's method for identifying multiple medications from free-form clinical notes into Hane’s system for generating medical listings would benefit the skilled artisan by ensuring that "unmapped" medications which are often critical safety risks—are not omitted from the final patient medication list, thereby improving the clinical accuracy and safety of the resulting medical model. (Reference Agresta, See at least, Section III.B, page 5, "Medication reconciliation requires the ability to... extract the exact medications listed in a potentially free-form format..."). The combination would have a Reasonable Expectation of Success because Agresta explicitly demonstrates that existing medication normalization tools (such as RxNorm and RxTerms) are technically compatible with the RESTful and FHIR-based architectures utilized in Hane, allowing for the predictable transformation of multiple free-text strings into the specific medical identifiers required for Hane's embedding and vector-generation workflows. (Reference Agresta, See at least, Section II.B, page 3, "RxTerms improves drug search capabilities by further normalizing the full drug names... allowing easier automatic matching to EHR medication lists, where prescriptions may be recorded with more free-form names.").
applying a second vector embedding function to:
(a) the first set of medication free text to generate a first target vector embedding for the first target unmapped medication code, and
(b) the second set of medication free text to generate a second target vector embedding for the second target unmapped medication code;
Agresta teaches processing a first and second set of medication free text associated with first and second medications of the Claim, "the first set of medication free text associated with the first medication and the second set of medication free text associated with the second medication", that required identifying and isolating unstructured descriptive strings of medication names and dosages from diverse electronic health records. Agresta discloses that medication reconciliation necessitates extracting information from "free-form clinical notes" or "free-text entry" when standard codes are unavailable, ensuring multiple medications (e.g., Gabapentin and Warfarin) are captured. (Reference Agresta, See at least, Section III.B, page 5; "extract the exact medications listed in a potentially free-form format...").
The combination of Hane + Agresta applications make obvious the full limitation "applying a second vector embedding function to: (a) the first set of medication free text... and (b) the second set of medication free text to generate... vector embedding[s]" because a POSITA would implement Hane's embedding function as a routine configuration to make Agresta's free-text data operate within Hane's modeling workflow, because Hane seeks to quantify the relationships between all medical events and Agresta teaches that free text is a standard input for medical events that predictably achieves the goal of semantic normalization when processed by such an algorithm. (Reference Hane, See at least, Abstract; "Aggregate vectors corresponding to non-textual information / data are provided... analyze at least a portion of the plurality of aggregate vectors to identify two or more... that are similar.").
A skilled Artisan in the art who read Hane application, would combine Agresta with Hane, because both references are in the same field of clinical data informatics and Hane explicitly suggests that its embedding model is "configurable" and trained on "medical sentences" to determine similarity, while Agresta provides the specific "free text" inputs that constitute those sentences when standard codes are missing. (Reference Hane, See at least, Col. 15, ll. 1-5; "The dimensionality of the multi-dimensional space may be predetermined, predefined, and/or configurable."). The integration of Agresta's free-text strings into Hane’s embedding engine resolves the problem of analyzing "unmapped" medical data that lacks formal coding, providing the benefit of a unified numerical representation for both coded and non-coded medication events. (Reference Agresta, See at least, Section II.B, page 3; "The increased granularity of medication names allows easier automatic matching to EHR medication lists, where prescriptions may be recorded with more free-form names."). They would have a Reasonable Expectation of Success because vector embedding is a known technique in natural language processing (as cited in Hane's mention of Word2Vec and FastText) and applying these functions to clinical free text to create "medical sentences" is the specific objective described across both prior art frameworks.
Hane in combination with Agresta teaches Claim 6.
The one or more non-transitory computer-readable media of Claim 1, wherein the first similarity measure comprises a weighted cosine similarity measure for the first target vector embedding and the first vector embedding. Hane, See at least, analyze at least a portion of the plurality of aggregate vectors to identify two or more aggregate vectors, claim 12… that are similar or different based on a distance or angle between the two or more aggregate vectors in the multi-dimensional space, Col. 1-15. the distance within the multi-dimensional space may be Euclidean distance, cosine distance, and/or other distance measure. In an example embodiment the distance between the multi-dimensional space is an angle or value indicative of an angle (e.g., cosine distance), Col.16, ll. 41-60.)
Hane in combination with Agresta teaches Claim 7.
The one or more non-transitory computer-readable media of Claim 1, wherein the operations further comprise:
identifying n highest similarity measures of the plurality of similarity measures;
Hane explicitly teaches identifying a "configurable number of one or more closest aggregate vectors, Col. 18, ll. 19-35" based on the "smallest distances". One of ordinary skill in the art would understand that in a multi-dimensional vector space, similarity and distance are inversely related; thus, identifying a configurable number of vectors with the "smallest distance" is mathematically identical to identifying "n highest similarity measures"
and presenting standard medication codes, mapped to embedding vectors that correspond to the n highest similarity measures, as candidate standard medication codes for mapping to the first target unmapped medication code.
Hane describes providing an "output identifying the investigation subject... [and] one or more identified similar or different subjects, Col. 18, ll. 44-56" to determine which subjects are "most similar". Under the Broadest Reasonable Interpretation (BRI), providing a list of similar identified subjects for the purpose of investigation is functionally identical to presenting candidates for mapping to an unmapped entry.
Hane in combination with Agresta teaches Claim 8.
The one or more non-transitory computer-readable media of Claim 1, wherein the operations further comprise: identifying a subset of similarity measures, of the plurality of similarity measures, that meet a threshold similarity measure;
Hane explicitly teaches a filtering step where the computing entity identifies "all of the aggregate vectors that satisfy a threshold distance requirement with respect to an investigation aggregate vector, Col. 18, ll. 19-44". This identifies a specific "subset" of vectors from the broader population that qualify based on a defined mathematical boundary.
and presenting standard medication codes, mapped to embedding vectors that correspond to the subset of similarity measures, as candidate standard medication codes for mapping to the first target unmapped medication code. (Hane, Col. 18, 19-53, Col. 2, ll. 43-55, Col. 1. Ll.30-45)
Hane’s disclosure of outputting aggregate vectors to identify similar subjects is functionally identical to the claimed presentation of candidate standard medication codes, as both processes select and display specific data points that satisfy a similarity threshold. Because Hane explicitly teaches that these vectors correspond to medical codes such as "prescription or drug codes," the output of vector-based subjects meeting the threshold constitutes the same functional disclosure as presenting "standard medication codes mapped to embedding vectors" for an unmapped entry.
Hane in combination with Agresta teaches Claim 9.
The one or more non-transitory computer-readable media of Claim 1, applying the second vector embedding function to the first target medication event comprises using one or more of BioWordVec fastText or Self-Alignment Pretraining for Biomedical Entity Representations (SAPBERT). (Hane, See at least, the medical embedding model may be a modified fastText, word2vec, GloVe, or other algorithm, Col. 14, ll. 53-65)
Hane in combination with Agresta teaches, Claim 21.
The one or more non-transitory computer-readable media of claim 1, wherein the operations further comprise: transmitting the deduplicated listing of medications for the patient to a clinical decision support engine for generating medication alerts or treatment recommendations. (Hane, See at least, the computing entity 200 may provide the output as input to another application and/or program operating on the computing entity 200, Col. 18, ll. 44 - 67. the predictive model may be used to predict the occurrence of a clinical event for an investigation subject based on the corresponding investigation aggregate vector. determining the medical event prediction, Col. 19 ll. 61 – Col. 20, ll. 17)
Hane demonstrates the electronic transfer of analyzed medical data, which includes filtered medication lists—to integrated software components designed to evaluate patient outcomes . The system expressly describes providing output data as input to other programs for determining a medical event prediction, such as probabilities related to specific medical codes. Hane’s disclosure of inputting patient-specific vector data into predictive models to identify clinical risks constitutes transmitting medication data to a clinical decision support engine for generating alerts and recommendations, as the technical purpose is to provide automated insight for medical intervention.
Hane in combination with Agresta teaches, Claim 22.
The one or more non-transitory computer-readable media of claim 1, wherein the operations further comprise: generating, based on the determined grouping of medications and the patient medication data,
Hane discloses forming an "embedding vector dictionary" that links medical codes (including "prescription or drug codes") to multi-dimensional vectors, effectively grouping them in a mathematical space.
Hane utilizes "medical sentences" consisting of "one or more medical codes" extracted from a patient identifier's history as the primary data input.
Hane teaches identifying "similar" or "different" clinical events based on the distance between vectors, which serves as the technical foundation for identifying discrepancies .
However does not disclosed “a treatment recommendation comprising a suggested (a) adjustment, (b) substitution, or (c) discontinuation of at least one medication in the listing of medications for the patient.”
Agresta teaches a suggested (a) adjustment, (b) substitution, or (c) discontinuation of the Claim 5, that required a specialized decision-making layer to reconcile medication list conflicts. Agresta provides the missing clinical species by teaching a process to "Make clinical decisions based on the comparison, page 2" and utilizing the "CancelRx, page 2" standard to electronically terminate therapy. Specifically, Agresta’s teaching of a clinical decision to resolve a dosing error is the functional equivalent of the claimed suggested adjustment.
A skilled artisan in the art who read the Hane application would combine Agresta with Hane because both references are directed to the same field of endeavor—medical informatics and clinical data processing—and address the shared problem of resolving discrepancies within fragmented medical records to ensure patient safety.
The combination of Hane + Agresta makes obvious the full limitation "generating... a treatment recommendation comprising a suggested (a) adjustment, (b) substitution, or (c) discontinuation" because a POSITA would combine Hane’s automated vector-similarity detection with Agresta’s clinical rationalization framework using KSR Rationale (Combining prior art elements according to known methods to yield predictable results). A POSITA would implement Agresta’s clinical decision outputs as a routine configuration to make Hane’s "similar/different" alerts actionable within a healthcare workflow, because Hane seeks to identify similar medical practices and Agresta teaches an algorithm that predictably achieves that goal by resolving the discrepancies Hane identifies (Hane, See at least, “identify providers that have similar practices, Col. 4, ll. Ll. 19-26”; Agresta, See at least, “algorithm for medication reconciliation... to identify potential conflicts, page 1”).
The integration of Agresta’s specific clinical reconciliation outputs (such as discontinuation, rationalization, and adding missing medications) into Hane’s machine learning grouping engine resolves the technical ambiguity of "similarity" by providing a specific functional purpose for the distance calculations: the automated detection and correction of medication errors. A PHOSITA would be motivated to integrate these teachings because Hane provides the "how" (the computational embedding method) while Agresta provides the "what" (the clinical reconciliation logic), and the combination yields the predictable result of a high-speed, automated system capable of not just grouping data, but performing the specific clinical task of "Medication Reconciliation" with higher accuracy than manual review. This combination represents the application of a known technique (Agresta's clinical reconciliation categories) to a known device/method (Hane's vector embedding engine) to improve it in the same way (by making the data analysis actionable for patient safety).
A skilled artisan would have a Reasonable Expectation of Success because both systems are built upon the FHIR (Fast Healthcare Interoperability Resources) standard and RxNorm terminology. These protocols are specifically designed to enable interoperability between disparate clinical data sources, allowing for the predictable automation of drug "substitution" or "adjustment" by linking branded names to their generic equivalents (SCD/SBD).
Hane in combination with Agresta teaches ,Claim 23.
The one or more non-transitory computer-readable media of claim 1, wherein the operations further comprise: transmitting the deduplicated listing of medications to an external electronic health record system in . (Hane, Col. 18, ll. 43-67)
Hane teaches transmitting the deduplicated listing of medications of the Claim, transmitting the deduplicated listing of medications to an external electronic health record system in a standardized format for longitudinal patient record synchronization, that required the electronic movement of medication data from a processing engine to a remote destination. Hane describes a computing entity that provides its output (identified similar/deduplicated medical codes) via a network interface to other applications or programs using wired transmission protocols. (Hane, See at least, “the computing entity 200 may provide (e.g., transmit) the output via a network interface 220,... and/or provide the output as input to another application and/or program... Col. 18, 43-67 Such communication may be executed using a wired data transmission protocol, such as... Ethernet” [Col. 8, lines 51-67]). However, Hane does not describe an external electronic health record system, the standardized format (as a clinical data structure), or the specific goal of longitudinal patient record synchronization. Hane’s disclosure of "Ethernet" relates to the hardware transport layer rather than the clinical data formatting required by the claim.
Agresta teaches transmitting the... medications to an external electronic health record system in a standardized format for longitudinal patient record synchronization of Claim 23, that required the use of an interoperable data exchange protocol to ensure a patient’s medical history remains consistent across multiple provider organizations over time. Agresta teaches a FHIR-based extensible software solution for medication reconciliation which can seamlessly include new medication sources to improve the longitudinal sharing of this information across the various health IT platforms (Agresta, Abstract; Section I, page 2.). Agresta specifically utilizes the Fast Healthcare Interoperability Resources (FHIR) standard (a standardized format) to promote secure sharing of healthcare data among multiple health information technology (HIT) systems (including external EHRs) to avoid medication errors such as... duplications.
The combination of Hane + Agresta makes obvious the full limitation obvious, because it represents the combination of prior art elements according to known methods to yield predictable results (MPEP 2143, Rationale A). Hane provides the "what" (highly accurate identification of duplicates via vector similarity metrics) and Agresta provides the "how/where" (the standardized FHIR communication layer for external EHR transmission). A POSITA would integrate Hane’s deduplication output into Agresta’s reconciliation workflow to resolve the "Data Silo" problem explicitly mentioned in both arts, where disparate clinical and pharmacy information systems... often contain duplicate, missing, or inaccurate information (Agresta, Section I page 2; See also Hane, Col 1, ll 1-30). Using Hane’s vector-based "similarity" to identify duplicates for Agresta’s "longitudinal sharing" predictably results in the claimed synchronized, deduplicated listing.
A skilled Artisan in the art who read Hane’s application would combine Agresta with Hane because Hane expressly suggests providing its output as input to "another application and/or program" (Hane, Col 18, ll 47-67) but identifies that its medical codes are non-interpretable, alpha-numeric strings (Hane, Col 1, ll 1-40). The artisan would be motivated to integrate Agresta’s standardized mapping (using RxNorm and FHIR) because Hane’s raw vector data is clinically unusable without being formatted into the standardized format taught by Agresta, which is designed exactly for this type of integration (MPEP 2143, VI). By combining these, the artisan achieves a longitudinal patient record synchronization that is human-readable and clinically actionable within an external EHR. The artisan would have a Reasonable Expectation of Success because Agresta confirms that FHIR is web-based and free for use, and allows... interoperability features (Agresta, Section II.C, page 4), making the transmission of processed data from Hane to an EHR a matter of routine system integration using established APIs.
Hane in combination with Agresta teaches, Claim 24.
The one or more non-transitory computer-readable media of claim 1, wherein generating the plurality of vector embeddings further comprises: fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings.
Hane teaches generating the plurality of vector embeddings of Claim 24, that required converting medical codes into high-dimensional numerical representations using a machine learning model. Hane describes a computing entity that generates an embedding vector dictionary comprising a plurality of multi-dimensional vectors based on a medical embedding model trained using machine learning (Hane, abstract, Col. 2, ll. 8-12). Hane further demonstrates using these vectors to identify two or more aggregate vectors that are similar to resolve discrepancies such as resubmitted insurance claims (Hane, abstract, Col. 4, ll. 15-18). However, Hane does not describe fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings.
Agresta teaches confirmed mappings of Claim 24, that required verified associations between disparate medication records to serve as a source of truth. Agresta teaches a system where medication reconciliation is the process of comparing a patient's medication orders and making clinical decisions based on the comparison. Agresta specifically notes that the final authority as to which medications are duplicates rests with the user and that the system allows for a shared reconciliation of medications where clinicians validate the accuracy of the records (Agresta, The medications are displayed reconciled in the app, allowing the user to confirm that the reconciled medications are correct (Page 8)). This validation by a clinician constitutes the confirmed mappings (verified data points) missing from Hane’s automated vector analysis.
The combination of Hane + Agresta makes obvious the full limitation [fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings] because it involves combining prior art elements according to known methods to yield predictable results (MPEP 2143, Rationale A). Hane teaches a model trained using machine learning (Hane, Col. 12, ll. 1-15) and Agresta in page 5 teaches the generation of a best possible medication list through user-validated clinical decisions. A POSITA would be motivated to use the confirmed mappings from Agresta’s interface as similarity feedback to fine-tune Hane’s embedding function because Hane acknowledges that human medical knowledge is generally required for determining the actual similarity of a set of medical codes (Hane, Col 1, ll. 23-25). Using the "human authority" results from Agresta to refine the mathematical weights of Hane’s model is a common-sense application of "Active Learning" to improve model accuracy over time.
A skilled Artisan in the art who read Hane’s application would combine Agresta with Hane because both references seek to solve the problem of "non-interpretable" and "duplicate" medical data (Hane, Col 1, ll 12-25; Agresta, Section I, page 2). The artisan would recognize that while Hane’s vectors provide a mathematical estimate of similarity, the results can be further trained to reduce errors (Hane, Col 13, 60-67, Col.20, ll. 46-67). Agresta provides the corrective data (the confirmed mappings) necessary to perform this refinement. Integrating this feedback loop would resolve the "communication gaps" mentioned in Agresta by ensuring the processing engine (Hane) becomes more aligned with actual clinical reality through a recursive learning process. The artisan would have a Reasonable Expectation of Success because fine-tuning a model using verified labels (supervised learning) is a foundational technique in the art of machine learning.
Hane in combination with Agresta teaches, Claim 25.
The one or more non-transitory computer-readable media of claim 1, wherein removing the second medication from the listing of medications for the patient comprises: consolidating prescription quantities and refill data associated with the first and second medications into a unified medication record. (Hane, Col. 14, ll. 15-35)
Hane teaches removing the second medication of the Claim 25, removing the second medication from the listing of medications for the patient comprises: consolidating prescription quantities and refill data associated with the first and second medications into a unified medication record, that required the functional exclusion of a redundant medical entry from a patient list to maintain record integrity. Hane identifies similar drug vectors and is configured to exclude subsequent occurrences of those codes to ensure the data set is clean for downstream model application. (Hane, Col. 14, ll. 15-35)
However Hane, does not describe consolidating prescription quantities and refill data associated with the first and second medications into a unified medication record.
Agresta teaches consolidating prescription quantities and refill data of the Claim 25, missing in Hane that required the clinical aggregation of numerical counts and refill instances into a single accurate health profile. Under MPEP 2111, this requires capturing cumulative properties of a drug record because failing to merge these attributes misrepresents a patient’s total medication supply. Agresta teaches a system designed to retrieve and reconcile medication lists by merging information specifically to avoid mistakes in clinical treatment. (Agresta, See at least, reconciliation is done to avoid medication errors such as omissions, duplications, dosing errors, or drug interactions and combine and reconcile medication into a medication list. Section I, page 2 and Abstract, page 1.)
The examiner applies MPEP 2143 Rationale A involving the combination of prior art elements according to known methods to yield predictable results. This rationale is utilized because the consolidation of numeric attributes is a predictable variation of the reconciliation logic taught by Agresta to ensure no clinical data is lost during the deduplication trigger identified by Hane.
The combination of Hane + Agresta applications make obvious claim 25 because a POSITA would recognize that deleting a record in Hane results in the loss of critical fulfillment data that Agresta expressly preserves to prevent patient harm. A POSITA would implement consolidating as a routine configuration to make the reconciliation technique of Agresta operate within the deduplication engine of Hane, because Hane seeks to identify similar medical codes and Agresta teaches a medication reconciliation technique that predictably achieves the goal of an accurate medication list to avoid clinical discrepancies on the same patient input.
Hane, See at least, identify two or more aggregate vectors that are similar abstract, Col. 2, lines 17-20. Agresta, See at least, reconciliation is done to avoid medication errors such as... dosing errors and accurate in order to maximize therapeutic impact. Section I, page 2 and Abstract.
A skilled Artisan in the art who read Hane application, would combine Agresta with Hane, because Hane admits that medical codes are non-interpretable, alpha-numeric strings and provides an explicit invitation for clinical improvement by stating that human medical knowledge is generally required for determining the actual similar of a set of medical codes. This admission creates a technical gap regarding clinical interpretation that Agresta’s standardized reconciliation framework is specifically designed to fill. (Hane, See at least, medical codes are non-interpretable, alpha-numeric strings and human medical knowledge is generally required. Col. 1, lines 1-30)
The integration of consolidating prescription quantities resolves the problem of information loss in Hane where discarding records creates a dosing error by misrepresenting the total medication volume available to the patient. Because the total medication volume available to the patient is an attribute of the medication list, failing to consolidate these quantities misrepresents the record. Integrating Agresta’s reconciliation ensures that the unified medication record preserves the refill data identified by Hane as similar, thereby preventing the life-threatening patient safety events that occur when a clinical history is incomplete or misrepresented. (Agresta, See at least, it is extremely important that medication lists are accurate in order to... prevent potentially life-threatening patient safety events and avoid medication errors such as... dosing errors. Abstract and Section I, page 2.)
A skilled artisan would have a Reasonable Expectation of Success because merging clinical attributes via FHIR RESTful APIs and RxNorm mapping tables is a standardized and routine interoperability function.
Hane in combination with Agresta teaches, Claim 26.
The one or more non-transitory computer-readable media of claim 1, wherein generating the plurality of vector embeddings further comprises: fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings.
Hane teaches generating the plurality of vector embeddings of the Claim 26, generating the plurality of vector embeddings further comprises: fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings, that required the conversion of medical information into high-dimensional numerical vectors using a machine learning engine. Hane describes a computing entity that generates an embedding vector dictionary by training a model on medical code sequences to identify similarity. ( Hane, See at least, computing entity generates an embedding vector dictionary comprising a plurality of multi-dimensional vectors based on a medical embedding model trained using machine learning. Col. 2, lines 8-12.)
However Hane, does not describe fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings.
Agresta teaches confirmed mappings of the Claim 24, fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings missing in Hane that required a clinical validation framework where verified matches serve as a source of truth. Fine-tuning is the mathematical application of Agresta’s validation results back into Hane’s model, ensuring the processing engine is updated rather than merely displaying a static list. ( Agresta, See at least, clinicians... validate and update the right information on a patient’s medication list and final authority as to which medications are duplicates rests with the user. Section I, page 2 and Section IV.B, page 3.)
The combination of Hane + Agresta applications make obvious the full limitation under fine-tuning the first vector embedding function based on similarity feedback from previously confirmed mappings because a POSITA would recognize that Hane’s automated vectors are non-interpretable and require clinical alignment to be actionable. A POSITA would implement fine-tuning as a routine configuration to make the validated clinical decisions of Agresta operate as a feedback mechanism for the embedding engine of Hane, because Hane seeks to identify two or more aggregate vectors that are similar and Agresta teaches the human-led reconciliation necessary to confirm those mappings. (Hane, See at least, human medical knowledge is generally required for determining the actual similar of a set of medical codes. Col. 1, lines 1-35. Agresta, See at least, clinicians... validate and update the right information. Section I, page 3.)
A skilled Artisan in the art who read Hane application, would combine Agresta with Hane, because Hane admits that medical codes are non-interpretable, alpha-numeric strings and that human knowledge is required for accuracy. This admission creates a technical gap that Agresta’s standardized reconciliation framework is specifically designed to fill by providing the human-verified labels needed to refine the engine. (Hane, See at least, medical codes are non-interpretrace, alpha-numeric strings and human medical knowledge is generally required. Col. 1, lines 1-35.)
The integration of fine-tuning based on similarity feedback resolves the safety risks inherent in Hane’s automated output. A model that is not fine-tuned has a higher risk of the potentially life-threatening patient safety events mentioned in Agresta, such as dosing errors, making the combination of Agresta’s validation and Hane’s model a clinical necessity to ensure the total medication volume available to the patient is accurately represented. (Agresta, See at least, it is extremely important that medication lists are accurate in order to maximize therapeutic impact and prevent potentially life-threatening patient safety events. Abstract. )
A skilled artisan would have a Reasonable Expectation of Success because the process of fine-tuning a model using verified labels is a routine and foundational technique in machine learning and medical informatics.
Note: Claims 15-19 and 26 are rejected with the above analysis for being very similar to claims 1, 6-9, and 24.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hane - US10891352 and further in view of Agresta – PTO-892-U and further view of US11556579- Bhatia.
Hane in combination with Agresta teaches, Claim 5.
The one or more non-transitory computer-readable media of Claim 1, wherein the first medication comprises a
Hane teaches first and second medications of the Claim 5, "wherein the first medication comprises a generic medication and the second medication comprise a name brand medication," that required a digital storage medium to process distinct medication identifiers within a multi-dimensional space to identify similar clinical entities. Hane provides the functional infrastructure for this limitation by processing "drug codes" as key attributes in generating vector embeddings for medical events (Hane, See at least, Col. 4, ll. 27 -41“Medical information is often encoded using medical codes such as... prescription or drug codes.”). However, Hane does not describe the first medication comprises a generic medication and the second medication comprise a name brand medication. Hane processes medical codes as generic alphanumeric data attributes without expressly categorizing the drug hierarchy into branded vs. generic types.
Bhatia teaches a generic medication and a name brand medication of the Claim 5 that required a specialized classification system to link unstructured drug text to standardized medical categories. Bhatia provides the specific "species" classification missing from Hane’s "genus" of drug codes by using an ontology that expressly distinguishes between branded and generic drug identities (Bhatia, See at least, “the standardized ontology is a standardized medical ontology for generic and branded medication names.” [Claim 18], Col. 3, ll. 21-40).
The combination of Hane + Bhatia applications make obvious the full limitation "wherein the first medication comprises a generic medication and the second medication comprise a name brand medication" because a POSITA would combine Hane’s vector-based similarity analysis with Bhatia’s ontology-based drug labeling using KSR Rationale 1 (Combining prior art elements according to known methods to yield predictable results). If Hane seeks to “identify providers that have similar practices, Col. 4, ll. 20-30” and Bhatia teaches a “standardized medical ontology, Col. 3, ll. 21-40” for drug types, a POSITA would implement Bhatia's categorization as a routine data-labeling configuration to make Hane’s similarity engine operate with higher clinical specificity. Specifically, tagging medications as generic or brand-name before vectorization predictably ensures that the model recognizes different identifiers for the same active ingredient (Hane, See at least, “identify two or more aggregate vectors that are similar, abstract”; Bhatia, See at least, “standardized medication name for medication text, claim 17”).
Note: Claim 14 is rejected with the same analysis above for being very similar to claim 5.
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 JOSHUA DAMIAN RUIZ whose telephone number is (571)272-0409. The examiner can normally be reached 0800-1800.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684