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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/08/2026 has been entered.
Notice to Applicant
This communication is in response to the amendment filed 07/08/2026. Claims 1, 11, 20, 22 have been amended. Claim 27 has been canceled. Claims 28-30 have been added. Claims 1-5, 8, 10-15, 18, 20, 22-25, 28-30 are presented for examination.
Subject Matter Free of Prior Art
Claim(s) 1-5, 8, 10-15, 18, 20, 22-25, 28-30 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “identifying, by the mapping engine, a plurality of unmapped standard codes for mapping to the target unmapped proprietary code, wherein the plurality of unmapped standard codes are stored as structured records in the data repository; generating, by the vector generator, a second plurality of vector embeddings corresponding to the plurality of unmapped standard codes, wherein generating the second plurality of vector embeddings comprises: generating a second vector embedding for a first unmapped standard code of the plurality of unmapped standard codes, wherein generating the second vector embedding comprises: applying a third vector embedding function, implemented as a third trained machine learning model, to a dataset of the first unmapped standard code; computing a similarity measure for the target vector embedding and each of the second plurality of vector embeddings corresponding to the plurality of unmapped standard codes to generate a second plurality of similarity values, the second plurality of similarity values comprise: a second similarity measure for the target vector embedding and the second vector embedding for the first unmapped standard code; based on the first plurality of similarity values and the second plurality of similarity values: selecting, by a standard code selector of the mapping engine, a standard code, from a candidate set of standard codes comprising the plurality of mapped standard codes and the plurality of unmapped standard codes, as a semantic match for the target unmapped proprietary code.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 11, 20 claims 1, 11, 20 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-5, 8, 10, 12-15, 18, 22-25, 28-30 incorporate the allowable features of originally numbered independent claims 1, 11, 20, through dependency, respectively.
However, the claims are still rejected under 101.
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 1-5, 8, 10-15, 18, 20, 22-25, 28-30 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to one or more non-transitory computer readable media which is within the four statutory categories (i.e., manufacture). Claim 11 is drawn to a method which is within the four statutory categories (i.e., method). Claim 20 is drawn to a system which is within the four statutory categories (i.e., machine).
Independent claim 1 (which is representative of independent claims 11, 20) recites…identifying…a target unmapped proprietary code from a plurality of unmapped proprietary codes stored as structured records…; identifying…a plurality of mapped standard codes mapped to one or more proprietary codes; identifying a plurality of unmapped standard codes for mapping to the target unmapped proprietary code; based on the first plurality of similarity values and the second plurality of similarity values: selecting…a standard code, from a candidate set of standard codes comprising the plurality of mapped standard codes and the plurality of unmapped standard codes, as a semantic match for the target unmapped proprietary code; mapping…the target unmapped proprietary code to the standard code based on selecting the standard code as the semantic match for the target unmapped proprietary code, wherein mapping the target unmapped proprietary code to the standard code comprises… wherein the machine-readable association stored in the structured mapping table enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the machine-readable association stored in the structured mapping table; receiving…a record associated with the target unmapped proprietary code; accessing, by the mapping engine, the machine-readable association between the target unmapped proprietary code and the standard code stored in the structured mapping table; and associating…the record with the standard code based on (a) the record being associated with the target unmapped proprietary code and (b) the target unmapped proprietary code being mapped to the standard code.
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to compare, identify, and associate similar codes and records between different parties (i.e., healthcare organizations) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps, as indicated supra. That is, other than reciting generic computer components discussed infra, the claimed invention amounts to managing personal behavior or relationships or interactions between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, 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 claims recite an abstract idea.
Independent claim 1 further recites… generating…a target vector embedding for the target unmapped proprietary code, wherein generating the target vector embedding comprises: applying a first vector embedding function…to a dataset of the target unmapped proprietary code to generate a target set of one or more vector embeddings; generating…a first plurality of vector embeddings corresponding to the plurality of mapped standard codes, wherein generating the first plurality of vector embeddings comprises: generating a first vector embedding for a first mapped standard code of the plurality of mapped standard codes, wherein generating the first vector embedding comprises: applying a second vector embedding function…to a dataset of one or more proprietary codes mapped to the first mapped standard code; computing…a similarity measure for the target vector embedding and each of the first plurality of vector embeddings corresponding to the plurality of mapped standard codes to generate a first plurality of similarity values, the first plurality of similarity values comprise: a first similarity measure for the target vector embedding and the first vector embedding of the first mapped standard code; generating…a second plurality of vector embeddings corresponding to the plurality of unmapped standard codes, wherein generating the second plurality of vector embeddings comprises: generating a second vector embedding for a first unmapped standard code of the plurality of unmapped standard codes, wherein generating the second vector embedding comprises: applying a third vector embedding function…to a dataset of the first unmapped standard code; computing…a similarity measure for the target vector embedding and each of the second plurality of vector embeddings corresponding to the plurality of unmapped standard codes to generate a second plurality of similarity values, the second plurality of similarity values comprise: a second similarity measure for the target vector embedding and the second vector embedding for the first unmapped standard code.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers mathematical relationships, but for the recitation of generic computer components. For example, with regards to generating vector embeddings, the specification mentions: “Vector embedding functions are mathematical functions that map objects, such as words, sentences, or other data points, into vector representations in a multi-dimensional space” [36]. In light of the disclosure, the claims recite generating vector embeddings and computing similarity measures, which encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
Claim 1 recites additional elements (i.e., one or more non-transitory computer readable media comprising instructions; one or more hardware processors; a mapping engine; a data repository; a vector generator of the mapping engine; a first trained machine learning model; wherein the plurality of mapped standard codes and the one or more proprietary codes are stored as structured records in the data repository; a second trained machine learning model; a similarity score calculator of the mapping engine; wherein the plurality of unmapped standard codes are stored as structured records in the data repository; a third trained machine learning model; a standard code selector of the mapping engine; storing, in a structured mapping table in the data repository, a machine-readable association between the target unmapped proprietary code and the standard code that is accessible by the mapping engine during automated data ingestion or processing). Claim 11 recites additional elements (i.e., a mapping engine; a data repository; a vector generator of the mapping engine; a first trained machine learning model; wherein the plurality of mapped standard codes and the one or more proprietary codes are stored as structured records in the data repository; a second trained machine learning model; a similarity score calculator of the mapping engine; wherein the plurality of unmapped standard codes are stored as structured records in the data repository; a third trained machine learning model; a standard code selector of the mapping engine; storing, in a structured mapping table in the data repository, a machine-readable association between the target unmapped proprietary code and the standard code that is accessible by the mapping engine during automated data ingestion or processing). Claim 20 recites additional elements (i.e., a system comprising: at least one device including a hardware processor; a mapping engine; a data repository; a vector generator of the mapping engine; a first trained machine learning model; wherein the plurality of mapped standard codes and the one or more proprietary codes are stored as structured records in the data repository; a second trained machine learning model; a similarity score calculator of the mapping engine; wherein the plurality of unmapped standard codes are stored as structured records in the data repository; a third trained machine learning model; a standard code selector of the mapping engine; storing, in a structured mapping table in the data repository, a machine-readable association between the target unmapped proprietary code and the standard code that is accessible by the mapping engine during automated data ingestion or processing). Looking to the specifications, a computing device having one or more non-transitory computer readable media comprising instructions; one or more hardware processors; an engine having functions (i.e., a vector generator, a similarity score calculator, a standard code selector) is described at a high level of generality ([32]; [36]; [40]; [43]; [85]-[93]), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “a data repository,” “wherein the plurality of mapped standard codes and the one or more proprietary codes are stored as structured records in the data repository,” “wherein the plurality of unmapped standard codes are stored as structured records in the data repository,” “storing, in a structured mapping table in the data repository, a machine-readable association between the target unmapped proprietary code and the standard code that is accessible by the mapping engine during automated data ingestion or processing” only invokes the data repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Also, “a first trained machine learning model,” “a second trained machine learning model,” and “a third trained machine learning model” is only used to generally apply the abstract idea without placing any limits on how the machine learning model functions (i.e., no details about how the function is accomplished) and only recite the outcome of the abstract idea, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computing device having one or more non-transitory computer readable media comprising instructions; one or more hardware processors; an engine having functions) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, “a data repository,” “wherein the plurality of mapped standard codes and the one or more proprietary codes are stored as structured records in the data repository,” “wherein the plurality of unmapped standard codes are stored as structured records in the data repository,” “storing, in a structured mapping table in the data repository, a machine-readable association between the target unmapped proprietary code and the standard code that is accessible by the mapping engine during automated data ingestion or processing” only invokes the data repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent). Furthermore, receiving or transmitting data over a network, electronic recordkeeping, and storing and retrieving information in memory has been recognized by the courts as well-understood, routine, and conventional elements/functions. See: MPEP § 2106.05(d)(II). Also, “a first trained machine learning model,” “a second trained machine learning model,” and “a third trained machine learning model” is only used to generally apply the abstract idea without placing any limits on how the machine learning model functions (i.e., no details about how the function is accomplished) and only recite the outcome of the abstract idea, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-5, 8, 10, 12-15, 18, 22-25, 28-30 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-5, 10, 12-15, 21-25, 28, 30 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claims 8, 18 further recites the additional elements of “Facebook Al Similarity Search (FAISS) combined with Hierarchical Navigable Small World (HNSW) as an indexing approach”; and claim 10 further recites the additional elements of “Self-Alignment Pretraining for Biomedical Entity Representations (SAPBERT) word embedding technique,” which is described at a high level of generality (i.e., no description of the mechanism for accomplishing the result), such that using Facebook Al Similarity Search (FAISS) combined with Hierarchical Navigable Small World (HNSW) and Self-Alignment Pretraining for Biomedical Entity Representations (SAPBERT) word embedding technique amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., computer technology), which does not impose meaningful limits on the scope of the claim. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 29 further recites the additional elements of “wherein the record associated with the target unmapped proprietary code is an electronic health record (EHR), and is received from an EHR database,” which only invokes the database merely as a tool in its ordinary capacity to perform an existing process (i.e., storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only generally links the claimed invention to a particular technological environment or field of use, which does not impose meaningful limits on the scope of the claim. Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Response to Arguments
Applicant's arguments filed 07/08/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 07/08/2026.
In the remarks, Applicant argues in substance that:
Regarding the 101 rejections,
“Claim 1 does not recite a certain method of organizing human activity The operations of representative claim 1 are performed entirely by a mapping engine, vector generator, similarity score calculator, and standard code selector operating on structured records in a data repository. Representative claim 1 recites an automated, computer-implemented semantic mapping architecture that generates vector embeddings, computes similarity values, identifies a semantic match, stores a machine-readable association in a structured mapping table accessible by the mapping engine during automated data ingestion or processing, and subsequently associates incoming records with a standard code based on the stored mapping. Claim 1 does not recite a mathematical concept… These mathematical operations are not claimed in isolation. They are constrained to specific data structures (structured records in a data repository and a structured mapping table), specific model roles, and specific downstream system behavior. In particular, representative claim 1 recites that the machine-readable association stored in the structured mapping table enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association, and further recites the mapping engine receiving a record, accessing the stored association from the table, and associating the record with the standard code based on that stored mapping. Accordingly, while the claim involves mathematical computations, those computations are embedded within and limited by a concrete, computer-implemented semantic mapping framework”;
“Representative claim 1 reflects improvements to the functioning of computerized terminology mapping and data ingestion systems Specifically, claim 1 recites: (a) generating embeddings for different code classes using trained machine learning models; (b) computing separate similarity distributions for mapped and unmapped standard code sets; (c) selecting a standard code from a combined candidate set based on both similarity groupings; (d) persisting the selected mapping as a machine-readable association in a structured mapping table accessible by the mapping engine during automated data ingestion or processing, wherein that stored association enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association; and (e) subsequently receiving a record, accessing the stored association from the structured mapping table, and associating the record with the standard code based on that stored mapping. These limitations improve how the computer system performs semantic normalization and downstream data processing. Rather than requiring the mapping engine to re-invoke any of the trained machine learning models, re-generate any vector embeddings, or re-compute any similarity values for each subsequent record that arrives, the system accesses the stored machine- readable association directly from the structured mapping table. This eliminates repeated model invocation for subsequent records, reducing computational overhead, improving system throughput, and enhancing consistency of downstream data processing. Managing large-scale code normalization across proprietary and standard terminologies is a core technical challenge in healthcare data systems. Conventional approaches rely on rule- based mappings, manual curation, or repeated semantic analysis at ingestion time. Representative claim 1 introduces a persistent, embedding-driven mapping architecture in which, once a semantic match is determined, the result is stored as a machine-readable association in a structured mapping table and reused directly during subsequent automated processing. This improves system throughput, reduces computational overhead, and enhances consistency of downstream EHR updates. As in Desjardins, the claimed improvement is not to a mathematical formula in isolation, but to how the machine learning-based mapping system operates within a larger computing environment”;
“The claim sets forth an unconventional combination of: - multiple distinct trained embedding models; - dual similarity computations generating separate pluralities of similarity values across mapped and unmapped standard code sets; - selection from a unified candidate set informed by both similarity pluralities; - storage of the mapping as a machine-readable association in a structured mapping table, wherein that association enables the mapping engine to associate, during automated data ingestion, subsequent records with the standard code without re- invoking any of the three trained machine learning models; and - automated association of subsequent records with the standard code by accessing the stored mapping directly from the structured mapping table. This ordered combination reflects more than generic data processing. It defines a particular semantic normalization architecture that persistently links proprietary and standard codes in a manner that fundamentally alters how subsequent records are processed during automated ingestion. Under Bascom, even if individual elements are known, a non-conventional and non-generic arrangement of those elements can provide an inventive concept. Here, the specific multi-model embedding structure, dual similarity evaluation, persistent mapping table, and automated downstream association based on the stored mapping represent a particular technical solution to large-scale semantic interoperability in healthcare data systems. The Office confirms that the claims are allowable over all prior art of record, providing strong evidence that this combination is not routine or conventional.”
Regarding the 112(a) rejections, the amendments overcome the rejections.
It is respectfully submitted that Examiner has considered Applicant’s arguments and does not find them persuasive. Examiner has attempted to address all of the arguments presented by Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
In response to Applicant’s argument that (b) regarding the 101 rejections,
“Claim 1 does not recite a certain method of organizing human activity The operations of representative claim 1 are performed entirely by a mapping engine, vector generator, similarity score calculator, and standard code selector operating on structured records in a data repository. Representative claim 1 recites an automated, computer-implemented semantic mapping architecture that generates vector embeddings, computes similarity values, identifies a semantic match, stores a machine-readable association in a structured mapping table accessible by the mapping engine during automated data ingestion or processing, and subsequently associates incoming records with a standard code based on the stored mapping. Claim 1 does not recite a mathematical concept… These mathematical operations are not claimed in isolation. They are constrained to specific data structures (structured records in a data repository and a structured mapping table), specific model roles, and specific downstream system behavior. In particular, representative claim 1 recites that the machine-readable association stored in the structured mapping table enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association, and further recites the mapping engine receiving a record, accessing the stored association from the table, and associating the record with the standard code based on that stored mapping. Accordingly, while the claim involves mathematical computations, those computations are embedded within and limited by a concrete, computer-implemented semantic mapping framework”:
It is respectfully submitted that Applicant argues “The operations of representative claim 1 are performed entirely by a mapping engine, vector generator, similarity score calculator, and standard code selector operating on structured records in a data repository.” However, the claim limitations to which Applicant seem to refer are not interpreted as part of the abstract idea, but as additional elements, which are described at a high level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Applicant argues “Representative claim 1 recites an automated, computer-implemented semantic mapping architecture that generates vector embeddings, computes similarity values, identifies a semantic match, stores a machine-readable association in a structured mapping table accessible by the mapping engine during automated data ingestion or processing, and subsequently associates incoming records with a standard code based on the stored mapping.” However, the courts have indicated that “Mere automation of manual processes” may not be sufficient to show an improvement in computer functionality. See: MPEP § 2106.05(a)(I). Furthermore, the claim limitations to which Applicant seem to refer as “generates vector embeddings, computes similarity values, identifies a semantic match” encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas. Furthermore, the claim limitations to which Applicant seem to refer as “associates incoming records with a standard code based on the stored mapping” encompasses a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to compare, identify, and associate similar codes and records between different parties (i.e., healthcare organizations) in the manner described in the identified abstract idea, supra, which covers managing personal behavior or relationships or interactions between people within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Furthermore, the claim limitations to which Applicant seem to refer as “stores a machine-readable association in a structured mapping table accessible by the mapping engine during automated data ingestion or processing” are not interpreted as part of the abstract idea, but as additional elements, which only invokes the data repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “Claim 1 does not claim vector mathematics or similarity calculations in the abstract. Rather, claim 1 recites a specific, structured, multi-model embedding architecture.” However, the claim limitations to which Applicant refer as “generate a target vector embedding for an unmapped proprietary code,” “generate embeddings for mapped standard codes based on datasets of proprietary codes previously mapped to those standard codes, “generate embeddings for unmapped standard codes,” “computing two distinct pluralities of similarity values” encompasses the creation of mathematical interrelationships between data in the manner described in the identified abstract idea, supra, which covers mathematical relationships within the “Mathematical Concepts” grouping of abstract ideas. The “first trained machine learning model,” “second trained machine learning model,” and “third trained machine learning model” are not interpreted as part of the abstract idea, but as additional elements, which is only used to generally apply the abstract idea without placing any limits on how the machine learning model functions (i.e., no details about how the function is accomplished) and only recite the outcome of the abstract idea, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). The claim limitations to which Applicant refer as “selecting a standard code from a unified candidate set based on both similarity groupings” encompasses a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to compare, identify, and associate similar codes and records between different parties (i.e., healthcare organizations) in the manner described in the identified abstract idea, supra, which covers managing personal behavior or relationships or interactions between people within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “Claim 1 does not recite a mathematical concept… These mathematical operations are not claimed in isolation. They are constrained to specific data structures (structured records in a data repository and a structured mapping table), specific model roles, and specific downstream system behavior. In particular, representative claim 1 recites that the machine-readable association stored in the structured mapping table enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association, and further recites the mapping engine receiving a record, accessing the stored association from the table, and associating the record with the standard code based on that stored mapping. Accordingly, while the claim involves mathematical computations, those computations are embedded within and limited by a concrete, computer-implemented semantic mapping framework.” However, the claim limitations to which Applicant seem to refer as “the machine-readable association stored in the structured mapping table enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association, and further recites the mapping engine receiving a record, accessing the stored association from the table, and associating the record with the standard code based on that stored mapping” encompasses a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to compare, identify, and associate similar codes and records between different parties (i.e., healthcare organizations) in the manner described in the identified abstract idea, supra, which covers managing personal behavior or relationships or interactions between people within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and not the “Mathematical Concepts” grouping of abstract ideas, which Applicant now seems to argue. Furthermore, the computer-implemented framework is described at a high level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Thus, the claims are directed to an abstract idea.
“Representative claim 1 reflects improvements to the functioning of computerized terminology mapping and data ingestion systems Specifically, claim 1 recites: (a) generating embeddings for different code classes using trained machine learning models; (b) computing separate similarity distributions for mapped and unmapped standard code sets; (c) selecting a standard code from a combined candidate set based on both similarity groupings; (d) persisting the selected mapping as a machine-readable association in a structured mapping table accessible by the mapping engine during automated data ingestion or processing, wherein that stored association enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association; and (e) subsequently receiving a record, accessing the stored association from the structured mapping table, and associating the record with the standard code based on that stored mapping. These limitations improve how the computer system performs semantic normalization and downstream data processing. Rather than requiring the mapping engine to re-invoke any of the trained machine learning models, re-generate any vector embeddings, or re-compute any similarity values for each subsequent record that arrives, the system accesses the stored machine- readable association directly from the structured mapping table. This eliminates repeated model invocation for subsequent records, reducing computational overhead, improving system throughput, and enhancing consistency of downstream data processing. Managing large-scale code normalization across proprietary and standard terminologies is a core technical challenge in healthcare data systems. Conventional approaches rely on rule- based mappings, manual curation, or repeated semantic analysis at ingestion time. Representative claim 1 introduces a persistent, embedding-driven mapping architecture in which, once a semantic match is determined, the result is stored as a machine-readable association in a structured mapping table and reused directly during subsequent automated processing. This improves system throughput, reduces computational overhead, and enhances consistency of downstream EHR updates. As in Desjardins, the claimed improvement is not to a mathematical formula in isolation, but to how the machine learning-based mapping system operates within a larger computing environment”:
Applicant argues “claim 1 recites: (a) generating embeddings for different code classes using trained machine learning models; (b) computing separate similarity distributions for mapped and unmapped standard code sets; (c) selecting a standard code from a combined candidate set based on both similarity groupings; (d) persisting the selected mapping as a machine-readable association in a structured mapping table accessible by the mapping engine during automated data ingestion or processing, wherein that stored association enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association; and (e) subsequently receiving a record, accessing the stored association from the structured mapping table, and associating the record with the standard code based on that stored mapping.” However, the claim limitations to which Applicant refer as “generating embeddings for different code classes,” “computing separate similarity distributions for mapped and unmapped standard code sets” are interpreted as part of the abstract idea of mathematical relationships, and not as additional elements to be interpreted in Step 2A, Prong Two. The “distinct trained machine learning models” to which Applicant refer is only used to generally apply the abstract idea without placing any limits on how the machine learning model functions (i.e., no details about how the function is accomplished) and only recite the outcome of the abstract idea, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). The claim limitations to which Applicant refer as “selecting a standard code from a combined candidate set based on both similarity groupings,” “wherein that stored association enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association,” “subsequently receiving a record, accessing the stored association from the structured mapping table, and associating the record with the standard code based on that stored mapping” are interpreted as part of the abstract idea of managing personal behavior or relationships or interactions between people, and not as additional elements to be interpreted in Step 2A, Prong Two. The claim limitations to which Applicant seem to refer as “persisting the selected mapping as a machine-readable association in a structured mapping table in a second data repository” only invokes the data repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “These limitations improve how the computer system performs semantic normalization and downstream data processing. Rather than requiring full datasets for each subsequent record, the system leverages the stored machine-readable association to automatically associate records that arrive without the original dataset. This reduces repeated embedding generation and similarity computation for subsequent records, thereby improving computational efficiency, reducing processing latency, and minimizing redundant model invocation. Managing large-scale code normalization across proprietary and standard terminologies is a core technical challenge in healthcare data systems. Conventional approaches often rely on rule-based mappings, manual curation, or repeated semantic analysis at ingestion time. Claim 1 introduces a persistent, embedding-driven mapping architecture in which once a semantic match is determined, the semantic match is stored in a structured mapping table and reused during automated processing. This improves system throughput, reduces computational overhead, and enhances consistency of downstream EHR updates and workflow initiation. As in Desjardins, the claimed improvement is not to a mathematical formula in isolation, but to how the machine learning-based mapping system operates within a larger computing environment.” However, the claim limitations to which Applicant seem to refer as providing the aforementioned improvements (i.e., “storing, in a structured mapping table in the data repository, a machine-readable association between the target unmapped proprietary code and the standard code that is accessible by the mapping engine during automated data ingestion or processing”) only invokes the data repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Furthermore, the claim limitations to which Applicant refer as providing the aforementioned improvements (i.e., “wherein that stored association enables the mapping engine to associate, during automated data ingestion, a subsequent record associated with the target unmapped proprietary code with the standard code based on the stored machine-readable association,” “subsequently receiving a record, accessing the stored association from the structured mapping table, and associating the record with the standard code based on that stored mapping”) are interpreted as part of the abstract idea of managing personal behavior or relationships or interactions between people, and not as additional elements to be interpreted in Step 2A, Prong Two. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
As stated previously in Office Action dated 03/23/2026 and above, Examiner cannot find any problem caused by the technological environment to which the claims are confined, which per broadest reasonable interpretation of the claim in light of the specification, is a well-known, general purpose computer. The computing system did not cause the argued problem and thus it is not a technical problem caused by the technological environment to which the claims are confined. While the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing any technical improvement or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a).
Furthermore, the courts have indicated that “Mere automation of manual processes” may not be sufficient to show an improvement in computer functionality. See: MPEP § 2106.05(a)(I).
Thus, the claim as a whole does not integrate the recited judicial exception into a practical application.
“The claim sets forth an unconventional combination of: - multiple distinct trained embedding models; - dual similarity computations generating separate pluralities of similarity values across mapped and unmapped standard code sets; - selection from a unified candidate set informed by both similarity pluralities; - storage of the mapping as a machine-readable association in a structured mapping table, wherein that association enables the mapping engine to associate, during automated data ingestion, subsequent records with the standard code without re- invoking any of the three trained machine learning models; and - automated association of subsequent records with the standard code by accessing the stored mapping directly from the structured mapping table. This ordered combination reflects more than generic data processing. It defines a particular semantic normalization architecture that persistently links proprietary and standard codes in a manner that fundamentally alters how subsequent records are processed during automated ingestion. Under Bascom, even if individual elements are known, a non-conventional and non-generic arrangement of those elements can provide an inventive concept. Here, the specific multi-model embedding structure, dual similarity evaluation, persistent mapping table, and automated downstream association based on the stored mapping represent a particular technical solution to large-scale semantic interoperability in healthcare data systems. The Office confirms that the claims are allowable over all prior art of record, providing strong evidence that this combination is not routine or conventional”:
Applicant argues “an unconventional combination of: - multiple distinct trained embedding models; - dual similarity computations generating separate pluralities of similarity values across mapped and unmapped standard code sets; - selection from a unified candidate set informed by both similarity pluralities; - storage of the mapping as a machine-readable association in a structured mapping table, wherein that association enables the mapping engine to associate, during automated data ingestion, subsequent records with the standard code without re- invoking any of the three trained machine learning models; and - automated association of subsequent records with the standard code by accessing the stored mapping directly from the structured mapping table.” However, per MPEP § 2106.05(I)(A), evaluating whether a claim limitation is “well-understood, routine, conventional activity” is not a standalone test for determining eligibility, but only one consideration “For Evaluating Whether Additional Elements Amount To An Inventive Concept. Furthermore, the claim limitations to which Applicant refer as “multiple distinct trained embedding models applied to different code classes,” “dual similarity computations across mapped and unmapped standard code sets” are interpreted as part of the abstract idea of mathematical relationships, and not as additional elements to be interpreted in Step 2B. The “trained [machine learning] embedding models” to which Applicant refer is only used to generally apply the abstract idea without placing any limits on how the machine learning model functions (i.e., no details about how the function is accomplished) and only recite the outcome of the abstract idea, and thus, provide nothing more than mere instructions to implement an abstract idea on a generic computer, and merely indicates a field of use or technological environment (i.e., machine learning) in which the judicial exception is performed. See MPEP § 2106.05(f). The claim limitations to which Applicant refer as “selection from a unified candidate set informed by both similarity pluralities,” “wherein that association enables the mapping engine to associate, during automated data ingestion, subsequent records with the standard code without re- invoking any of the three trained machine learning models; and - automated association of subsequent records with the standard code by accessing the stored mapping directly from the structured mapping table” are interpreted as part of the abstract idea of managing personal behavior or relationships or interactions between people, and not as additional elements to be interpreted in Step 2B. The claim limitations to which Applicant seem to refer as “storage of the mapping as a machine-readable association in a structured mapping table” only invokes the data repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually.
Applicant argues “the specific multi-model embedding structure, dual similarity evaluation, persistent mapping table, and automated downstream association based on the stored mapping represent a particular technical solution to large-scale semantic interoperability in healthcare data systems.” However, while the specification need not explicitly set forth the improvement, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing “a particular technical solution to large-scale semantic interoperability in healthcare data systems,” any technical improvement, or any physical improvement to the computer. See MPEP § 2106.04(d)(1) and 2106.05(a).
Applicant argues “The Office confirms that the claims are allowable over all prior art of record, providing strong evidence that this combination is not routine or conventional.” However, per MPEP § 2106.05(I): “the search for an inventive concept should not be confused with a novelty or non-obviousness determination…As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter…a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty…Because [novelty and obviousness] are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101.”
Thus, the claim as a whole does not amount to significantly more than the judicial exception.
Thus, Examiner maintains the 101 rejections of claims 1-5, 8, 10-15, 18, 20, 22-25, 28-30, which have been updated to address Applicant’s remarks and to comply with the 2019 Revised Patent Subject Matter Eligibility Guidance and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence in the above Office Action.
In response to Applicant’s argument that (b) regarding the 112(a) rejections, the amendments overcome the rejections:
It is respectfully submitted that Examiner withdraws the aforementioned 112(a) rejections of Office Action dated 03/23/2026 because the amendments have rendered the rejections moot.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683
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