DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 12/26/2025.
Claims 1-20 are currently pending and have been examined.
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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Velez (US 11,488, 713 B2) in view of Wendell (US 2023/0039937 A1).
Claim 1:
Velez discloses one or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors (See column 7, line 56 to column 8, line 6 processor storage media in column 15, lines 17-26.), cause performance of operations comprising:
accessing a first comparison domain entity, from a comparison domain, that describes a first health concept using a first plurality of attributes (See Abstract, column 6, lines 5-33 Disease domain Vmaps reflecting expert clinical reasoning and relationships in a specific domain such as diagnoses (ICD), laboratory tests (LOINC), and medications (RXNORM).);
generating a first comparison domain vector embedding for the first comparison domain entity using the first plurality of attributes; accessing a first model domain entity, from a model domain that describes a second health concept using a second plurality of attributes(See Fig. 9 where ‘Target feature text classifier, with historical data performance’ 918 serves as a target set of vector embeddings mentioned in [column 13, line 64 to column 14, line 50] The vectorized training data combined with automatically generated ontologically-derived labels is further used in a supervised Machine Learning setting to generate a document classification predictive model using semantic characterizations of text features derived from the disease specific ontology. Also, see column 10, lines 35-48.), wherein at least a first attribute in the first plurality of attributes differs from at least a second attribute in the second plurality of attributes;
generating a first model domain vector embedding corresponding to the first model domain entity using the second plurality of attributes (See column 4, line 54 to column 5, line 5 and column 5, lines 49-63 where the Unified Medical Language System (UMLS) brings together health and biomedical vocabularies and standards including ICD, SNOMED, LOINC, RXNORM, and CPT with custom mapping. Also, see Fig. 1A Mapping Ontologies 126 mentioned in column 6, lines 19-33 and [column 13, lines 1-19] import large samples of aggregated raw structured and unstructured EMR patient and employs a data processing “pipeline” to normalize/harmonize raw EMR data to a standard (e.g. map a medication name or number used in a proprietary EMR to a concept in the RXNORM medication ontology).);
receiving user input indicating that the first health concept of the first model domain entity and the second health concept of the first comparison domain entity are a match (See Fig. 2, column 9, lines 11-32 where “Body Temperature” and “Cardiac Bypass Surgery” serve as first and second concepts. Also, the supervised machine learning is trained to recognize patterns (column 1, line 59 to column 2, line 13), see exemplary similarity identifying “antibiotic” as “prophylactic antibiotic” in column 7, lines 15-41, and exemplary time tagged temperature, pulse readings or respittory rates in column 12, lines 8-21 that allows comparison/similarity matching.);
responsive to receiving the user input, updating the model domain to reflect the match between the first health concept of the first model domain entity and the second health concept of the first comparison domain entity (See modifying and adding in column 10, lines 49-67, column 11, lines 1-15. Also, see Fig. 2, column 9, lines 11-32 where elevation of body temperature within a 24 hour window serves as a update when considering disease specific concepts based on existing medical ontologies for domains such as diseases, medications, labs, physiological findings, treatments, history, etc. in column 11, lines 1-16.); and
exchanging health code data between a first healthcare system and a second healthcare system based on the updated model domain (See Fig. 1A, Fig. 2, column 7, lines 15-28 column 9, lines 11-32 exemplary proprietary code.).
Although Velez discloses accessing a comparison domain with a vector embedding for unmapped proprietary code by applying a vector embedding function when matching health concepts mentioned above, Velez does not explicitly teach computing a similarity measure for the target vector embedding and mapping a candidate proprietary code for mapping based on a similarity measure. Wendell teaches:
computing a first similarity metric for the first comparison domain vector embedding and the first model domain vector embedding; based at least on the first similarity metric, presenting the first model domain entity as a candidate model domain entity for mapping to the first comparison domain entity (See Token Vector Similarity equation show in P0058-P0059, P0062. See P0058-P0060 where ontology synonym vector B serves as the target vector. With the candidate unmapped proprietary code as recommendations of candidate standard codes, see string match screen in P0059-P0061 and exemplary general rule preplacement of term “tumor” with “neoplasm”.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include computing a similarity measure for the target vector embedding and mapping a candidate proprietary code for mapping based on a similarity measure as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claim 2, Velez discloses the non-transitory computer readable media of Claim 1, wherein the model domain is associated with an electronic health record (EHR) provider and the comparison domain is associated with a client of the EHR provider (See column 5, lines 38-48, [column 13, line 64 to column 14, line 13] FIG. 9) that uses the domain ontology to 1) create training data for machine learning consisting of raw unstructured EMR data mapped to vectors and 2) to encode the domain ontology as NLP rules that can be used for information extraction/document classification.).
Regarding claim 3, Velez discloses the non-transitory computer readable media of Claim 1, wherein the operations further comprise: determining that the first similarity metric meets or exceeds a threshold value; and responsive to determining that the first similarity metric meets or exceeds the threshold value: presenting data in a user interface indicating that the first model domain entity and the first comparison domain entity are a likely match for a particular health concept (See [column 10, line 49 to column 11, line 16] An expert's or experts' evidence-based knowledge, experience, heuristics and/or intuition may be used as an expert system to achieve an optimal balance of rule engine sensitivity/specificity and/or properly interpret EMR data through the “eyes” of expert teams of clinicians to achieve “clinically reasonable” diagnostic rule engine assessments and accurate ML-learnt probabilistic models/thresholds for alerting.).
Regarding claim 4, although Velez teaches the non-transitory computer readable media of Claim 1 mentioned above, Velez does not explicitly teach computing concept-based similarity metrics, comparing and presenting domain vector embedded, candidate entity for adding to the model domain. Wendell teaches:
wherein the operations further comprise: accessing a second model domain entity, from the model domain, that describes a third healthcare concept using a third plurality of attributes; generating a second model domain vector embedding corresponding to the second model domain entity (See Token Vector Similarity equation show in P0058-P0059, P0062.);
computing a second similarity metric for the first comparison domain vector embedding and the second model domain vector embedding; based at least on the second similarity metric (See Fig. 3A Document Vectorization and Entity Mention Vectorization when screening and generating candidates in the automated ontology mapping process in P0071-P0072.),
refraining from presenting the second model domain entity as any candidate model domain entity for mapping to the first comparison domain entity (See P0057 where stopword and phrases removed from a list serve as refraining from presenting.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include computing concept-based similarity metrics, comparing and refrain from presenting domain vector embedded, candidate entity for adding to the model domain as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claims 5 and 19, although Velez teaches the non-transitory computer readable media of Claim 1 and the system Claim of 15 mentioned above, Velez does not explicitly teach computing concept-based similarity metrics, comparing and presenting domain vector embedded, candidate entity for adding to the model domain. Wendell teaches:
accessing a second comparison domain entity, from the comparison domain, that describes a third healthcare concept using a third plurality of attributes; generating a second comparison domain vector embedding corresponding to the second comparison domain entity (See Token Vector Similarity equation show in P0058-P0059, P0062.);
computing a second similarity metric for the second comparison domain vector embedding and the first model domain vector embedding; based at least on the second similarity metric for the second comparison domain vector embedding and the first model domain vector embeddings (See Fig. 3A Document Vectorization and Entity Mention Vectorization when screening and generating candidates in the automated ontology mapping process in P0071-P0072.);
presenting the second comparison domain entity as a candidate entity for adding to the model domain (See P0058-P0060 where ontology synonym vector B serves as the candidate entity.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include computing concept-based similarity metrics, comparing and presenting domain vector embedded, candidate entity for adding to the model domain as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claims 6 and 20, although Velez discloses the one or more non-transitory computer readable media of Claim 1 and the system of Claim 15 mentioned above, Velez does not explicitly teach mapping vector embeddings that correspond to the predetermined number of highest similarity values, as candidate model domain entities for mapping to the first comparison domain entity. Wendell teaches wherein the operations further comprise:
identifying a predetermined number of highest similarity values of a plurality of similarity metrics; and presenting model domain entities, mapped to vector embeddings that correspond to the predetermined number of highest similarity values, as candidate model domain entities for mapping to the first comparison domain entity (See Token Vector Similarity equation when determining ontology matches in show in P0058-P0059, P0062.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include mapping vector embeddings that correspond to the predetermined number of highest similarity values, as candidate model domain entities for mapping to the first comparison domain entity as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claim 7, although Velez discloses the one or more non-transitory computer readable media of Claim 1 mentioned above, Velez does not explicitly teach accessing a healthcare concept using attributes, generating domain vector embedding corresponding when computing a second similarity metric. Wendell teaches wherein the operations further comprise:
accessing a second comparison domain entity, from the comparison domain, that describes a third healthcare concept using a third plurality of attributes; generating a second comparison domain vector embedding corresponding to the second comparison domain entity; computing a second similarity metric for the second comparison domain vector embedding and the first model domain vector embedding (See Token Vector Similarity equation show in P0058-P0059, P0062. See Fig. 3A Document Vectorization and Entity Mention Vectorization when screening and generating candidates in the automated ontology mapping process in P0071-P0072. See P0058-P0060 where ontology synonym vector B serves as the candidate entity.);
and classifying the first comparison domain entity and the second comparison domain entity into separate categories based at least on the respective first and second similarity scores (See [P0183] There can be one model for each class of entity pairs (e.g., gene-disorder, gene-phenotype). Each model can evaluate a plurality of classes. For example, each model can evaluate up to 4 classes: Positive relation score, Neutral relation score, Negative relation score, Causal relation score. Two entities can have high positive relation score if they are shown to be correlated or causal. For example, “Increased Gene 1 expression was found in patients with XYZ disease.” Two entities can have a high neutral score if there is no measured association between them.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include accessing a healthcare concept using attributes, generating domain vector embedding corresponding when computing a second similarity metric as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Claim 8:
Velez discloses A method comprising:
accessing a first comparison domain entity, from a comparison domain, that describes a first health concept using a first plurality of attributes (See Abstract, column 6, lines 5-33 Disease domain Vmaps reflecting expert clinical reasoning and relationships in a specific domain such as diagnoses (ICD), laboratory tests (LOINC), and medications (RXNORM).);
generating a first comparison domain vector embedding for the first comparison domain entity using the first plurality of attributes; accessing a first model domain entity, from a model domain that describes a second health concept using a second plurality of attributes (See Fig. 9 where ‘Target feature text classifier, with historical data performance’ 918 serves as a target set of vector embeddings mentioned in [column 13, line 64 to column 14, line 50] The vectorized training data combined with automatically generated ontologically-derived labels is further used in a supervised Machine Learning setting to generate a document classification predictive model using semantic characterizations of text features derived from the disease specific ontology. Also, see column 10, lines 35-48.), wherein at least a first attribute in the first plurality of attributes differs from at least a second attribute in the second plurality of attributes;
generating a first model domain vector embedding corresponding to the first model domain entity using the second plurality of attributes (See column 4, line 54 to column 5, line 5 and column 5, lines 49-63 where the Unified Medical Language System (UMLS) brings together health and biomedical vocabularies and standards including ICD, SNOMED, LOINC, RXNORM, and CPT with custom mapping. Also, see Fig. 1A Mapping Ontologies 126 mentioned in column 6, lines 19-33 and [column 13, lines 1-19] import large samples of aggregated raw structured and unstructured EMR patient and employs a data processing “pipeline” to normalize/harmonize raw EMR data to a standard (e.g. map a medication name or number used in a proprietary EMR to a concept in the RXNORM medication ontology).);
receiving user input indicating that the first health concept of the first model domain entity and the second health concept of the first comparison domain entity are a match (See Fig. 2, column 9, lines 11-32 where “Body Temperature” and “Cardiac Bypass Surgery” serve as first and second concepts. Also, the supervised machine learning is trained to recognize patterns (column 1, line 59 to column 2, line 13), see exemplary similarity identifying “antibiotic” as “prophylactic antibiotic” in column 7, lines 15-41, and exemplary time tagged temperature, pulse readings or respittory rates in column 12, lines 8-21 that allows comparison/similarity matching.);
responsive to receiving the user input, updating the model domain to reflect the match between the first health concept of the first model domain entity and the second health concept of the first comparison domain entity (See Fig. 2, column 9, lines 11-32 where elevation of body temperature within a 24 hour window serves as a update when considering disease specific concepts based on existing medical ontologies for domains such as diseases, medications, labs, physiological findings, treatments, history, etc. in column 11, lines 1-16.); and
exchanging health code data between a first healthcare system and a second healthcare system based on the updated model domain, wherein the method is performed by at least one device including a hardware processor (See Fig. 1A, Fig. 2, column 7, lines 15-28 column 9, lines 11-32 exemplary proprietary code. Also, see Fig. 3 processor 302 (column 9, line 50 to column 10, line 14.).
Although Velez discloses accessing a comparison domain with a vector embedding for unmapped proprietary code by applying a vector embedding function when matching health concepts mentioned above, Velez does not explicitly teach computing a similarity measure for the target vector embedding and mapping a candidate proprietary code for mapping based on a similarity measure. Wendell teaches:
computing a first similarity metric for the first comparison domain vector embedding and the first model domain vector embedding; based at least on the first similarity metric, presenting the first model domain entity as a candidate model domain entity for mapping to the first comparison domain entity (See Token Vector Similarity equation show in P0058-P0059, P0062. See P0058-P0060 where ontology synonym vector B serves as the target vector. With the candidate unmapped proprietary code as recommendations of candidate standard codes, see string match screen in P0059-P0061 and exemplary general rule preplacement of term “tumor” with “neoplasm”.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include computing a similarity measure for the target vector embedding and mapping a candidate proprietary code for mapping based on a similarity measure as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claims 9 and 16, Velez discloses the method of Claim 8 and the system of Claim 15, wherein the model domain is associated with an electronic health record (EHR) provider and the comparison domain is associated with a client of the EHR provider (See column 5, lines 38-48, [column 13, line 64 to column 14, line 13] FIG. 9) that uses the domain ontology to 1) create training data for machine learning consisting of raw unstructured EMR data mapped to vectors and 2) to encode the domain ontology as NLP rules that can be used for information extraction/document classification.).
Regarding claims 10 and 17, Velez discloses the method of Claim 8 and the system of Claim 15, determining that the first similarity metric meets or exceeds a threshold value; and responsive to determining that the first similarity metric meets or exceeds the threshold value: presenting data in a user interface indicating that the first model domain entity and the first comparison domain entity are a likely match for a particular health concept (See [column 10, line 49 to column 11, line 16] An expert's or experts' evidence-based knowledge, experience, heuristics and/or intuition may be used as an expert system to achieve an optimal balance of rule engine sensitivity/specificity and/or properly interpret EMR data through the “eyes” of expert teams of clinicians to achieve “clinically reasonable” diagnostic rule engine assessments and accurate ML-learnt probabilistic models/thresholds for alerting.).
Regarding claims 11 and 19, Velez discloses the method of Claim 8 and the system of Claim 15, accessing a second model domain entity, from the model domain, that describes a third healthcare concept using a third plurality of attributes; generating a second model domain vector embedding corresponding to the second model domain entity; computing a second similarity metric for the first comparison domain vector embedding and the second model domain vector embedding; based at least on the second similarity metric, refraining from presenting the second model domain entity as any candidate model domain entity for mapping to the first comparison domain entity (See [column 7, lines 42-55] the Comprehensive Disease-specific Ontology (OWL+embedded rules) of FIG. 1a illustrates that ontologies can be created and customized to express concepts, relationships and rules in a highly specific domain and be used in conjunction with generic UMLS medical ontologies 122 (such as SNOMED and others). Also, see column 11, lines 17-36.).
Regarding claim 12, although Velez teaches the method of Claim 8 mentioned above, Velez does not explicitly teach computing concept-based similarity metrics, comparing and presenting domain vector embedded, candidate entity for adding to the model domain. Wendell teaches:
further comprising, accessing a second comparison domain entity, from the comparison domain, that describes a third healthcare concept using a third plurality of attributes; generating a second comparison domain vector embedding corresponding to the second comparison domain entity (See Token Vector Similarity equation show in P0058-P0059, P0062.);
computing a second similarity metric for the second comparison domain vector embedding and the first model domain vector embedding; based at least on the second similarity metric for the second comparison domain vector embedding and the first model domain vector embeddings (See Fig. 3A Document Vectorization and Entity Mention Vectorization when screening and generating candidates in the automated ontology mapping process in P0071-P0072.),
presenting the second comparison domain entity as a candidate entity for adding to the model domain (See P0058-P0060 where ontology synonym vector B serves as the candidate entity.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include computing concept-based similarity metrics, comparing and presenting domain vector embedded, candidate entity for adding to the model domain as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claim 13, although Velez discloses the method of Claim 8 mentioned above, Velez does not explicitly teach mapping vector embeddings that correspond to the predetermined number of highest similarity values, as candidate model domain entities for mapping to the first comparison domain entity. Wendell teaches further comprising, identifying a predetermined number of highest similarity values of a plurality of similarity metrics; and presenting model domain entities, mapped to vector embeddings that correspond to the predetermined number of highest similarity values, as candidate model domain entities for mapping to the first comparison domain entity (See Token Vector Similarity equation when determining ontology matches in show in P0058-P0059, P0062.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include mapping vector embeddings that correspond to the predetermined number of highest similarity values, as candidate model domain entities for mapping to the first comparison domain entity as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claim 14, although Velez discloses the method of Claim 8 mentioned above, Velez does not explicitly teach accessing a healthcare concept using attributes, generating domain vector embedding corresponding when computing a second similarity metric. Wendell teaches wherein the operations further comprise:
accessing a second comparison domain entity, from the comparison domain, that describes a third healthcare concept using a third plurality of attributes; generating a second comparison domain vector embedding corresponding to the second comparison domain entity; computing a second similarity metric for the second comparison domain vector embedding and the first model domain vector embedding (See Token Vector Similarity equation show in P0058-P0059, P0062. See Fig. 3A Document Vectorization and Entity Mention Vectorization when screening and generating candidates in the automated ontology mapping process in P0071-P0072. See P0058-P0060 where ontology synonym vector B serves as the candidate entity.);and classifying the first comparison domain entity and the second comparison domain entity into separate categories based at least on the respective first and second similarity scores (See [P0183] There can be one model for each class of entity pairs (e.g., gene-disorder, gene-phenotype). Each model can evaluate a plurality of classes. For example, each model can evaluate up to 4 classes: Positive relation score, Neutral relation score, Negative relation score, Causal relation score. Two entities can have high positive relation score if they are shown to be correlated or causal. For example, “Increased Gene 1 expression was found in patients with XYZ disease.” Two entities can have a high neutral score if there is no measured association between them.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include accessing a healthcare concept using attributes, generating domain vector embedding corresponding when computing a second similarity metric as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Claim 15:
Velez discloses A system comprising: at least one device including a hardware processor (See Fig. 3 processor 302 (column 9, line 50 to column 10, line 14) the system being configured to perform operations comprising:
accessing a first comparison domain entity, from a comparison domain, that describes a first health concept using a first plurality of attributes (See Abstract, column 6, lines 5-33 Disease domain Vmaps reflecting expert clinical reasoning and relationships in a specific domain such as diagnoses (ICD), laboratory tests (LOINC), and medications (RXNORM).);
generating a first comparison domain vector embedding for the first comparison domain entity using the first plurality of attributes; accessing a first model domain entity, from a model domain that describes a second health concept using a second plurality of attributes (See Fig. 9 where ‘Target feature text classifier, with historical data performance’ 918 serves as a target set of vector embeddings mentioned in [column 13, line 64 to column 14, line 50] The vectorized training data combined with automatically generated ontologically-derived labels is further used in a supervised Machine Learning setting to generate a document classification predictive model using semantic characterizations of text features derived from the disease specific ontology. Also, see column 10, lines 35-48.), wherein at least a first attribute in the first plurality of attributes differs from at least a second attribute in the second plurality of attributes;
generating a first model domain vector embedding corresponding to the first model domain entity using the second plurality of attributes (See column 4, line 54 to column 5, line 5 and column 5, lines 49-63 where the Unified Medical Language System (UMLS) brings together health and biomedical vocabularies and standards including ICD, SNOMED, LOINC, RXNORM, and CPT with custom mapping. Also, see Fig. 1A Mapping Ontologies 126 mentioned in column 6, lines 19-33 and [column 13, lines 1-19] import large samples of aggregated raw structured and unstructured EMR patient and employs a data processing “pipeline” to normalize/harmonize raw EMR data to a standard (e.g. map a medication name or number used in a proprietary EMR to a concept in the RXNORM medication ontology).);
receiving user input indicating that the first health concept of the first model domain entity and the second health concept of the first comparison domain entity are a match (See Fig. 2, column 9, lines 11-32 where “Body Temperature” and “Cardiac Bypass Surgery” serve as first and second concepts. Also, the supervised machine learning is trained to recognize patterns (column 1, line 59 to column 2, line 13), see exemplary similarity identifying “antibiotic” as “prophylactic antibiotic” in column 7, lines 15-41, and exemplary time tagged temperature, pulse readings or respittory rates in column 12, lines 8-21 that allows comparison/similarity matching.);
responsive to receiving the user input, updating the model domain to reflect the match between the first health concept of the first model domain entity and the second health concept of the first comparison domain entity (See Fig. 2, column 9, lines 11-32 where elevation of body temperature within a 24 hour window serves as a update when considering disease specific concepts based on existing medical ontologies for domains such as diseases, medications, labs, physiological findings, treatments, history, etc. in column 11, lines 1-16.); and
exchanging health code data between a first healthcare system and a second healthcare system based on the updated model domain (See Fig. 1A, Fig. 2, column 7, lines 15-28 column 9, lines 11-32 exemplary proprietary code.).
Although Velez discloses accessing a comparison domain with a vector embedding for unmapped proprietary code by applying a vector embedding function when matching health concepts mentioned above, Velez does not explicitly teach computing a similarity measure for the target vector embedding and mapping a candidate proprietary code for mapping based on a similarity measure. Wendell teaches:
computing a first similarity metric for the first comparison domain vector embedding and the first model domain vector embedding; based at least on the first similarity metric, presenting the first model domain entity as a candidate model domain entity for mapping to the first comparison domain entity (See Token Vector Similarity equation show in P0058-P0059, P0062. See P0058-P0060 where ontology synonym vector B serves as the target vector. With the candidate unmapped proprietary code as recommendations of candidate standard codes, see string match screen in P0059-P0061 and exemplary general rule preplacement of term “tumor” with “neoplasm”.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include computing a similarity measure for the target vector embedding and mapping a candidate proprietary code for mapping based on a similarity measure as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Regarding claim 18, although Velez teaches the system of Claim 15 mentioned above, Velez does not explicitly teach computing concept-based similarity metrics, comparing and refrain from presenting domain vector embedded, candidate entity for adding to the model domain. Wendell teaches:
wherein the operations further comprise: accessing a second model domain entity, from the model domain, that describes a third healthcare concept using a third plurality of attributes; generating a second model domain vector embedding corresponding to the second model domain entity (See Token Vector Similarity equation show in P0058-P0059, P0062.);
computing a second similarity metric for the first comparison domain vector embedding and the second model domain vector embedding; based at least on the second similarity metric (See Fig. 3A Document Vectorization and Entity Mention Vectorization when screening and generating candidates in the automated ontology mapping process in P0071-P0072.),
refraining from presenting the second model domain entity as any candidate model domain entity for mapping to the first comparison domain entity (See P0057 where stopword and phrases removed from a list serve as refraining from presenting.).
Therefore, it would have been obvious to one of ordinary skill in the technology of ontology concepts arts before the effective filing date of the claimed invention to modify the software and method of Velez to include computing concept-based similarity metrics, comparing and refrain from presenting domain vector embedded, candidate entity for adding to the model domain as taught by Wendell for curating and indexing large scales of documentation infeasible for humans mentioned in Wendel’s P0003.
Response to Arguments
Applicant argues on the basis that the Velez reference does not teach "receiving user input that indicates the two concepts are a match". Recognizing similarity by concept matching patterns is taught in Velez’ column 1, line 59 to column 2, line 13, column 7, lines 15-41 exemplary as similarity identifying “antibiotic” as “prophylactic antibiotic”. Also, see exemplary time tagged temperature, pulse readings or respittory rates in column 12, lines 8-21 that allows comparison/similarity matching.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (See Vlaskin (US 2025/0086500 A1) & Hane (US 10,891,352 B1)).
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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/T.S.W./Examiner, Art Unit 3687 04/04/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687