DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 06/30/2026.
Claims 1, 3, 5, 8, 10-11, 14, 16, 18, 20, 23-24 and 27 have been amended.
Claims 12, 25 and 28 have been cancelled.
Claims 29-30 have been newly added.
Claims 1-11, 13-24, 26-27 and 29-30 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 § 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 1-11, 13-24, 26-27 and 29-30 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-11, 13 and 29-30 are directed to non-transitory computer readable medium (i.e., a manufacture) and claims 14-24 and 26 are directed to a method (i.e., a process) and claim 27 is directed to a system (i.e., a machine). Accordingly, claims 1-11, 13-24, 26-27 and 29-30 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 1 includes limitations that recite an abstract idea. Note that independent claim 1 is the computer readable medium claim, while claim 14 covers a method claim and claim 27 covers the systems claim.
Specifically, independent claim 1 recites:
A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, cause performance of operations comprising:
identifying, in one or more sets of proprietary codes, a plurality of unmapped proprietary codes for mapping to standard codes, each proprietary code in the one or more sets of proprietary codes comprising a code-specific dataset that does not include any patient- specific datasets;
generating a plurality of vector embeddings corresponding to the plurality of unmapped proprietary codes, wherein generating the plurality of vector embeddings comprises:
generating a first vector embedding for a first unmapped proprietary code of the plurality of unmapped proprietary codes, wherein generating the first vector embedding for the first unmapped proprietary code comprises:
applying a vector embedding function to a first code-specific dataset corresponding to the first unmapped proprietary code; and
generating a second vector embedding for a second unmapped proprietary code of the plurality of unmapped proprietary codes, wherein generating the second vector embedding for the second unmapped proprietary code comprises:
applying the vector embedding function to a second code-specific dataset corresponding to the second unmapped proprietary code;
storing, in a data repository, the plurality of vector embeddings corresponding to the plurality of unmapped proprietary codes;
generating a first target vector embedding for a target standard code, wherein generating the first target vector embedding comprises:
applying the vector embedding function to a first dataset of code-specific datasets corresponding to mapped proprietary codes corresponding to the target standard code to generate the first target vector embedding;
computing a similarity measure for the first target vector embedding and each of the plurality of vector embeddings to generate a first plurality of similarity values, the first plurality of similarity values comprises:
a first similarity measure for the first target vector embedding and the first vector embedding;
based at least on the first similarity measure, mapping the first unmapped proprietary code to the target standard code as a semantic match for the first unmapped proprietary code;
storing, in the data repository, an association between the first unmapped proprietary code and the target standard code based on the mapping of the first unmapped proprietary code to the target standard code;
generating a second dataset of code-specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code by updating the first dataset of code- specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code to include the first code-specific dataset corresponding to the first unmapped proprietary code;
generating a second target vector embedding for the target standard code, wherein generating the second target vector embedding comprises:
applying the vector embedding function to the second dataset of code-specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code;
accessing, from the data repository, the plurality of vector embeddings corresponding to the plurality of unmapped proprietary codes, wherein the plurality of vector embeddings includes the second vector embedding for the second unmapped proprietary code of the plurality of unmapped proprietary codes;
computing a second similarity measure for the second target vector embedding and the second vector embedding;
based at least on the second similarity measure, mapping the second unmapped proprietary code to the target standard code as a semantic match for the second unmapped proprietary code;
storing, in the data repository, an association between the second unmapped proprietary code and the target standard code based on the mapping of the second unmapped proprietary code to the target standard code;
receiving a patient record comprising patient data for a patient; and
responsive to determining (a) that the patient data is associated with the second unmapped proprietary code; and (b) the second unmapped proprietary code is mapped to the target standard code: associating the target standard code with the patient record.
The Examiner submits that the foregoing underlined limitations constitute: (a) “mathematic concepts” because generating vector embeddings, applying a vector embedding function to a dataset and computing a similarity measure for a target vector embedding, generating a second dataset of code-specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code by updating the first dataset of code- specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code to include the first code-specific dataset corresponding to the first unmapped proprietary code, generating a second target vector embedding for the target standard code, wherein generating the second target vector embedding comprises: applying the vector embedding function to the second dataset of code-specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code, where the vector embeddings includes the second vector embedding for the second unmapped proprietary code of the plurality of unmapped proprietary codes, computing a second similarity measure for the second target vector embedding and the second vector embedding, based the second similarity measure, mapping the second unmapped proprietary code to the target standard code as a semantic match for the second unmapped proprietary code are all mathematical relationships. Furthermore, these limitations constitute (b) “certain methods of organizing human activity” because codes used by organizations or vendors that have not been mapped to a standard code of a concept that might not otherwise have an industry standardized code to belong to and associating the target standard code with the record based on the mapping of the first unmapped proprietary code to the target standard code are ways of recommending candidate code, mapping patient records across different healthcare databases, which are managing human behavior/interactions between people. The foregoing underlined limitations also relate to claim 1 (similarly to claims 14 and 27).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2-11, 13, 15-24, 26 and 29-30, these claims merely recite determining steps such as: claims 2 & 15 - generating the first vector embedding comprises: applying the vector embedding function to the dataset of the first unmapped proprietary code to generate a first set of vector embeddings; and generating the first vector embedding based on the first set of vector embeddings, claims 3 & 16 - applying the vector embedding function to the dataset of mapped proprietary codes mapped to the target standard code to generate a target set of vector embeddings; and generating the target vector embedding based on the first target set of vector embeddings, claims 4 & 17 - training the machine learning model based on training datasets to compute vector embeddings from mapped proprietary codes, wherein particular training data, of the training datasets, comprises: one or more historical mapped proprietary codes; a vector embedding corresponding to the historical mapped proprietary codes; wherein applying the vector embedding function to the dataset of the first unmapped proprietary code comprises applying the machine learning model to the dataset of the first unmapped proprietary code; receiving feedback based on an accuracy of results generated by applying the vector embedding function; and retraining the machine learning model based on the feedback, claims 5 & 18 – generating a second vector embedding for a second unmapped proprietary code of the plurality of unmapped proprietary codes at least by: applying the vector embedding function to a dataset of the second unmapped proprietary code; wherein the plurality of similarity values further comprises: a second similarity measure for the target vector embedding and the second vector embedding; wherein the operations further comprise: based at least on the second similarity measure, refraining from presenting the second unmapped proprietary code as any candidate unmapped proprietary code for mapping to the target standard code, claims 6 & 19 - aggregating the dataset of the first unmapped proprietary code into an aggregated data record; identifying a plurality of tokens based on the aggregated data record; and applying the machine learning model to each token of the plurality of tokens to generate the first set of vector embeddings, claims 7 & 20 - applying the vector embedding function to the dataset of the first unmapped proprietary code comprises applying the vector embedding function to an aggregated set of text that is generated by aggregating text corresponding to each dataset of the first unmapped proprietary code, claims 8 & 21 - the first similarity measure comprises a weighted cosine similarity measure for the target vector embedding and the first vector embedding, claims 9 & 22 - prior to applying the vector embedding function to the first unmapped proprietary code, pre-processing the dataset of the first unmapped proprietary code at least by: (a) converting text data into lowercase, (b) retaining numeric tokens, (c) handling special characters, (d) removing unwanted text from event set hierarchy, and (e) custom reprocessing for synonyms, abbreviations, and short hands, claims 10 & 23 - identifying “N” highest similarity values of the plurality of similarity values; and presenting unmapped proprietary codes, mapped to vector embeddings that correspond to the “N” highest similarity values, as candidate unmapped proprietary codes for mapping to the target standard code, claims 11 & 24 - identifying a subset of similarity values, of the plurality of similarity values, that meet a threshold similarity values; and presenting unmapped proprietary codes, mapped to vector embeddings that correspond to the subset of similarity values, as candidate unmapped proprietary codes for mapping to the target standard code, claims 13 & 26 - selecting the first unmapped proprietary code for mapping to the target standard code, claim 29 - the first unmapped proprietary code of the plurality of unmapped proprietary codes is associated with a first healthcare entity and the second unmapped proprietary code of the plurality of unmapped proprietary codes is associated with a second healthcare entity, wherein the mapped proprietary codes corresponding to the target standard code are associated with at least a third healthcare entity; wherein the patient record is received from an electronic health record (EHR) system associated with the second healthcare entity and claim 30 -the patient record is received from the EHR system associated with the second healthcare entity during integration of patient records from the EHR system associated with the second healthcare entity into another EHR system.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 14 and 27, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations as mathematical for mapping a candidate but for the recitation of generic computer components. That is, other than reciting a non-transitory computer readable medium comprising instructions executed by one or more hardware processors and an electronic health record (EHR) system to perform the limitations, nothing in the claim elements precludes the steps from practically being performed mathematically. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a biomedical environment, done mathematically for mapping a candidate but for the recitation of generic computer components, then it falls within the “mathematic concepts” and “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the non-transitory computer readable medium comprising instructions executed by one or more hardware processors are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “a plurality of unmapped proprietary codes for mapping to standard codes”, “a plurality of vector embeddings”, “a first vector embedding”, “target standard code”, “ and “a machine learning model” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “dataset” and “training datasets ……” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 1, regarding the additional limitations of the non-transitory computer readable medium comprising instructions executed by one or more hardware processors and an electronic health record (EHR) system, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 1 and analogous independent claims 14 and 27 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-11, 13-24, 26-27 and 29-30 are ineligible under 35 USC §101.
Response to Arguments
Applicant argues that claim 1 addresses at least two technical problems in automated healthcare code normalization: (a) static representation of target standard codes, and (b) redundant re- computation for subsequent mapping request. see pg. 16-17 and 20-22 of Remarks – Examiner disagrees.
identifying records to be matched using data vectors is merely data manipulation and not facilitating any technological implementation for the process. As such, it does not present any technical improve or solving a technical problem. Furthermore, computing a similarity measure for a target standard codes in the claims are well-understood, routine, and conventional. That is, use of vector embedding corresponding to unmapped and mapped healthcare data is well-known in the art, as evidenced by at least McCallie et al. (US 11,526,508 B1) column 2, line 60 to column 3, line 8, Fig. 5A-Fig. 6, Hane et al. (US 10,891,352 B1) particularly linked to medical code in column 1, lines 33-62, column 14, line 65 to column 15, line 25 Figs. 4A-4B; and Godbole et al. (US 2024/0221949 A1) P0064-P0065, Fig. 8-Fig.9. Further, Velez’s Fig. 2, Fig. 7 use of raw EMR data to semantically identify characterize and interpret clinical features is taught in column 3, lines 36-52, column 4, line 57 to column 5, line 5) and [column 16, line 58 to column 17, line 9]. Accordingly, the processes to perform analysis in the invention do not provide “significantly more” than the abstract idea.
Applicant argues that claim 1 does not cover mathematical concepts, organizing human activity grouping. see pg. 17-18 and 20 of Remarks – Examiner disagrees.
The instant case claims mapping a first unmapped proprietary code to a target standard code is done by applying a vector embedding function. An embedding vector is a mathematical representation of an object (like a word, image or sound) as a numerical array of numbers, called a vector, with a multi-dimensional space similarly described in paragraph 34 of Applicant’s own specification. Also, presenting unmapped proprietary code as a candidate unmapped proprietary code for mapping to the target standard code are ways of recommending candidate code, which is done in real world situations among individuals within organizations, regions and global populations when the individuals are accessing, exchanging and integrating healthcare data and cooperatively, described in paragraph 3 of Applicant’s own specification.
Applicant argues that claim 1 reflects a technical solution to a technical problem, not performing basic function. see pgs. 21-23 of Remarks – Examiner disagrees.
The instant case has to do with mapping patient records across different health care databases, but this is a problem that has already been solved and no technological improvements to mapping unmapped proprietary code to standard code and the functioning of the computer itself have been genuinely set forth and are nonetheless directed towards improving the abstract idea and not the computer itself — that is, the recited invention may improve facilitating entry (i.e. the abstract idea), but there is no evidence to show that it improves the structural or functional properties of the computer itself, outside of improving the computer specifically for implementing the abstract idea. In fact, identifying generating a target vector, computing a similarity measure, applying a vector embedding function, mapping unmapped proprietary code and associating target standard code are operations any generic computer is merely using the computer as a tool to implement the abstract idea (saying “apply it”) and is merely using the computer in the manner in which it was designed to be used, i.e., performing generic computer functions.
Regarding the prior art rejection, Applicant’s amendments and arguments, see pages 24-26, filed 06/30/2026, with respect to the 103 rejection have been fully considered and are persuasive. The 103 rejection of claims 1-11, 13-24, 26-27 and 29-30 has been withdrawn. Velez (US 11,488,713 B2) and Wendell (US 2023/0039937 A1) do not teach at least the following limitations required by claim 1: “generating a second dataset of code-specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code by updating the first dataset of code-specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code to include the first code-specific dataset corresponding to the first unmapped proprietary code,” “generating a second target vector embedding for the target standard code, wherein generating the second target vector embedding comprises: applying the vector embedding function to the second dataset of code-specific datasets corresponding to the mapped proprietary codes corresponding to the target standard code,” and “computing a second similarity measure for the second target vector embedding and the second vector embedding; based at least on the second similarity measure, mapping the second unmapped proprietary code to the target standard code as a semantic match for the second unmapped proprietary code.”
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
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/T.S.W./Examiner, Art Unit 3687 09/05/2026
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
