DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to a response filed 17 July 2026, on an application filed 13 January 2026, which claims domestic priority to a provisional application filed 13 January 2025.
Claims 1, 4-6, 10, 13-15 and 19 have been amended.
Claims 1-20 are currently pending and have been examined.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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, 2, 4, 5, 8-11, 13, 14 and 17-20 are rejected under 35 U.S.C. 103 as being obvious over DiMaggio et al. (U.S. PG-Pub 2018/0018602 A1), hereinafter DiMaggio, further in view of Iyer et al. (U.S. Patent 12,399,924 B1), hereinafter Iyer, and Neumann (U.S. PG-Pub 2021/0004691 A1), hereinafter Neumann.
As per claims 1, 10 and 19, DiMaggio discloses a tangible, non-transitory computer-readable medium storing instructions for artificial intelligence (AI) based data ingestion of different formats, an artificial intelligence (AI) based method and system configured to ingest data of different formats, comprising (DiMaggio, see Figs. 10, 14 and 15.):
one or more processors (DiMaggio, see Figs. 10, 14 and 15.);
an AI model programmatically accessible by the one or more processors (DiMaggio, see Figs. 10, 14 and 15.);
… configured to receive structured data comprising a standardized format (System ingests structured and unstructured data, see paragraphs 67 and 85.); and
one or more memories communicatively coupled to the one or more processors storing computing instructions that, when executed by the one or more processors, cause the one or more processors to (DiMaggio, see Figs. 10, 14 and 15.):
ingest … the structured data (System ingests structured and unstructured data, see paragraphs 67 and 85.);
ingest unstructured data (System ingests structured and unstructured data, see paragraphs 67 and 85.);
generate, by prompting the AI model … a determination of the compliance deficiency in (i) the structured data or (ii) the … compliance metadata (System determines a risk score associated with data indicating non-compliance, see Fig. 14 #1406.);
associate client and/or host data with one or more corresponding regulatory tags based on a … similarity between the compliance deficiency and the one or more applicable regulatory requirements, wherein the one or more regulatory tags correspond to one or more regulations (DiMaggio discloses assigning tag data, wherein the tags are associated with policies corresponding to regulatory requirements, see paragraph 70. DiMaggio discloses associating policies with assigned data based on similarity, see paragraphs 120-128 and Fig. 8.),
consideration of (ii) regulatory logic correlating one or more characteristics of a compliance deficiency with one or more applicable regulatory requirements (DiMaggio, see Figs. 8 and 14, and paragraphs 30-33, 42, 43 and 80-82.);
trigger, based on the associated regulatory tags, one or more compliance actions (DiMaggio, Fig. 14 discloses outputting a risk score of non-compliance, which results in assessing, recommending and tracking remediation plans to address the non-compliance issue, see paragraphs 30-33, 42, 43 and 80-82.); and
display, on a graphical user interface (GUI), one or more interfaces including at least a pathway sub interface comprising the structured data, … compliance metadata,
wherein the pathway sub interface is configured to receive user selections or inputs to a digital pathway (System generates an interactive graphical model representing the risk score, see Fig. 14 #1410 and paragraphs 46 and 103-108).
DiMaggio fails to explicitly disclose:
use of an API;
extracting metadata from unstructured data;
normalize the compliance metadata into the standardized format of the structured data, wherein normalizing the compliance metadata comprises:
aggregating, using retrieval-augmented generation (RAG), a prompt for the AI model including (i) one or more relevant vector representations retrieved via a similarity search, (ii) regulatory logic correlating one or more characteristics of a compliance deficiency with one or more applicable regulatory requirements, and (iii) a metadata-specific prompt comprising facility-specific context;
displaying the compliance deficiency, and associated regulatory tags.
Iyer teaches that it was old and well known in the art of data communications before the effective filing date of the claimed invention to provide:
use of an API (Iyer, see C34L10-37);
extracting metadata from unstructured data and normalizing it into structured data and displaying that data (Iyer, see C9L34-C10L26 and Fig. 4 #414.), wherein normalizing the compliance metadata comprises:
aggregating, using retrieval-augmented generation (RAG), a prompt for the AI model including (i) one or more relevant vector representations retrieved via a similarity search … (Iyer discloses creating an AI prompt using RAG, see C13L46-C14L2, C18L4-7 and C23L10-23 and C34L31-48., that includes vector representations of a similarity search, and C20L53-60.); and
compliance deficiency indicators, and displaying those indicators (Iyer, Fig. 4 #410 and 414 and corresponding text);
in order to provide “a system and method for muti-domain processing for large language models (LLMS)” (Iyer, C1L20-21.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method of determining risk level and maturity of compliance activities of DiMaggio to include use of an API, extracting metadata from unstructured data and normalizing it into structured data using RAG techniques, and compliance deficiency indicators, and displaying those indicators, as taught by Iyer, in order to arrive at a method of determining risk level and maturity of compliance activities that can provide “a system and method for muti-domain processing for large language models (LLMS)” (Iyer, C1L20-21.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Neither DiMaggio nor Iyer discloses consideration of semantic similarity nor creating a prompt concerning a facility-specific context.
Neumann teaches that it was old and well known in the art of data communications before the effective filing date of the claimed invention to provide consideration of semantic similarity and creating a prompt concerning a facility-specific context (Neumann, paragraphs 51 and 67.) in order to provide “behavioral pattern matching and language generation” (Neumann, paragraph 1.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method of determining risk level and maturity of compliance activities of DiMaggio/Iyer to include consideration of semantic similarity nor creating a prompt concerning a facility-specific context, as taught by Neumann, in order to arrive at a method of determining risk level and maturity of compliance activities that can provide “behavioral pattern matching and language generation” (Neumann, paragraph 1.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
DiMaggio, Iyer and Neumann are all directed to the electronic processing of electronic data and specifically to the determination of compliance deficiencies.
As per claims 2, 4, 8, 20, 13 and 20, DiMaggio/Iyer/Neumann discloses claims 1, 10 and 19, discussed above. DiMaggio further discloses:
2,11,20. determine based on (i) the user selections or inputs, (ii) structured data, and (iii) normalized compliance metadata, a risk score for at least one of a resident or a facility (DiMaggio, Fig. 14 #1406 and corresponding text. Iyer discloses normalized data, as shown above.);
determine, based on the risk score and the user selections or inputs, a survey readiness metric (DiMaggio discloses a maturity level corresponding to measurement of a specific and generic goal or state of achievement of a compliance policy, etc., based on risk criteria, see paragraphs 32, 36, 41-65 and 87.);
generate, by prompting the AI model with the structured data and normalized compliance metadata, a plan of correction (POC) (DiMaggio is operative to generate compliance plans, see paragraphs 38, 64, 69-74, 80-82, 91 and 116. This is generated pursuant to use of the AI model to analyze data, see Fig. 14.); and
generate, based on the risk score and the compliance deficiency, a risk alert, wherein the one or more interfaces includes a dashboard sub interface comprising the survey readiness metric, the POC, and the risk alert (DiMaggio presents graphic presenting risk score or maturity level, see Fig. 14 #1410 and corresponding text.);
4,13. wherein the one or more compliance actions include at least one of (i) an audit, (ii) a risk score update, (iii) a quality improvement recommendation, or (iv) a mock survey (System generates customized compliance plans based on policy tags associated with utilized data, see DiMaggio paragraph 70.); and
wherein the computing instructions further case the one or more processors to: generate an audit assessment tool based on the compliance deficiency or the user selections or inputs (DiMaggio considers effects of audits based on maturity level/risk scores, see paragraphs 56 and 94.); and
8,17. detect triggers within compliance issues; and escalate, based on a regulatory deadline, the compliance issues containing triggers (DiMaggio is operative to generate compliance plans based on compliance requirements, see paragraphs 38, 64, 69-74, 80-82, 91 and 116.).
As per claims 5, 9, 14 and 18, DiMaggio/Iyer/Neumann discloses claims 1 and 10, discussed above. DiMaggio further discloses:
9,18. wherein (i) the structured data or (ii) unstructured data includes medical data, and wherein the unstructured data is received from a user in real time (DiMaggio receives medical data, including PHI or EPHI, see DiMaggio paragraphs 76 and 97. It is the Office’s position that data input into a system is received in real time.).
DiMaggio fails to explicitly disclose:
5,14. wherein normalizing the compliance metadata into the standardized format further comprises:
chunking the compliance metadata into segments;
embedding, using an embedding model, the segments into vector representations;
9,18. wherein the standardized format comprises at least one of JavaScript Object Notation (JSON) format, eXtensible Markup Language (XML) format, Comma Separated Value (CSV) format, or Health Level Seven (HL7) format.
Iyer teaches that it was old and well known in the art of data communications before the effective filing date of the claimed invention to provide:
5,14. wherein normalizing the compliance metadata into the standardized format further comprises (Iyer, see C9L34-C10L26):
chunking the compliance metadata into segments (Iyer discloses segmentation, see C32L1-12.);
embedding, using an embedding model, the segments into vector representations (Iyer discloses embedding into vectors, see C32L15-C33L31.); and
9,18. wherein the standardized format comprises at least one of JavaScript Object Notation (JSON) format, eXtensible Markup Language (XML) format, Comma Separated Value (CSV) format, or Health Level Seven (HL7) format (Iyer discloses documents in JOSN and XML formats, see C11L37 and L55 and C15L26.);
in order to provide “a system and method for muti-domain processing for large language models (LLMS)” (Iyer, C1L20-21.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method of determining risk level and maturity of compliance activities of DiMaggio/Iyer/Neumann to include normalizing data and various standard formats, as taught by Iyer, in order to arrive at a method of determining risk level and maturity of compliance activities that can provide “a system and method for muti-domain processing for large language models (LLMS)” (Iyer, C1L20-21.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being obvious over DiMaggio/Iyer/Neumann further in view of Heinze et al. (U.S. PG-Pub 2007/0226211 A1), hereinafter Heinze.
As per claims 3 and 12, DiMaggio/Iyer/Neumann discloses claims 1, 10 and 19, discussed above. DiMaggio further discloses:
prioritize, based on the … risk score, the digital pathway (DiMaggio discloses prioritized recommendations based on risk score/maturity level, see paragraph 80.);
update the risk score (DiMaggio, Fig. 14.); and
update, based on at least one of (i) the user selections or inputs, (ii) audit results, (iii) the compliance deficiency, or (iv) survey outcome data corresponding to a previous compliance deficiency determination, at least one of the risk score, the survey readiness metric, or a vector database (Risk score and/or maturity level [survey readiness metric] are updated based on modification to compliance program data, see DiMaggio Fig. 14 #1408.).
DiMaggio fails to explicitly disclose:
correlate diagnostic codes with historical citation patterns; and
weight, based on the correlated diagnostic codes and historical citation patterns, a parameter.
However, Bua teaches that it was old and well known in the art of data communications before the effective filing date of the claimed invention to disclose correlate diagnostic codes with historical citation patterns; and weight, based on the correlated diagnostic codes and historical citation patterns, a parameter (Heinze, see paragraph 54.) in order to provide “a scoring process … for auditing individual coded documents” (Heinze, paragraph 54.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method of determining risk level and maturity of compliance activities of DiMaggio/Iyer/Neumann to include correlate diagnostic codes with historical citation patterns; and weight, based on the correlated diagnostic codes and historical citation patterns, a parameter, as taught by Heinze, in order to arrive at a method of determining risk level and maturity of compliance activities that can “a scoring process … for auditing individual coded documents” (Heinze, paragraph 54.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Both DiMaggio and Heinze are directed to the electronic processing of electronic data.
Claims 6, 7, 15 and 16 are rejected under 35 U.S.C. 103 as being obvious over DiMaggio/Iyer/Neumann further in view of Bua et al. (U.S. PG-Pub 2003/0167187 A1), hereinafter Bua.
As per claims 6, 7, 15 and 16, DiMaggio/Iyer/Neumann discloses claims 1, 2, 10 and 11, discussed above. DiMaggio further discloses:
6,15. wherein the pathway sub interface is configured (DiMaggio generates an interactive graphical model representing the risk score, see Fig. 14 #1410 and paragraphs 46 and 103-108); and
7,16. wherein the POC includes systemic changes, monitoring plans, responsible roles, due dates, and evidence placeholders (DiMaggio is operative to generate compliance plans, see paragraphs 38, 64, 69-74, 80-82, 91 and 116.);
DiMaggio fails to explicitly disclose (i) enforce completion of a required field before the user selections or inputs and (ii) associate the user selections or inputs with the corresponding regulatory tags, and wherein the regulatory tags include at least one of an F-tag, a K-tag, or an E-tag.
Iyer teaches that it was old and well known in the art of data communications before the effective filing date of the claimed invention to provide (i) enforce completion of a required field before the user selections or inputs and (ii) associate the user selections or inputs with the corresponding tags (Iyer, discloses a system that requires feedback before associates the feedback with the data indicator, see C7L14-37.) in order to provide “a system and method for muti-domain processing for large language models (LLMS)” (Iyer, C1L20-21.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method of determining risk level and maturity of compliance activities of DiMaggio/Iyer/Neumann to include (i) enforce completion of a required field before the user selections or inputs and (ii) associate the user selections or inputs with the corresponding tags, as taught by Iyer, in order to arrive at a method of determining risk level and maturity of compliance activities that can provide “a system and method for muti-domain processing for large language models (LLMS)” (Iyer, C1L20-21.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Neither DiMaggio nor Iyer disclose wherein the tags include at least one of an F-tag, a K-tag, or an E-tag.
However, Bua teaches that it was old and well known in the art of data communications before the effective filing date of the claimed invention to disclose wherein the tags include at least one of an F-tag, a K-tag, or an E-tag (Bua, see paragraphs 52-63.) in order “to provide information related to nursing home care and conduct in a manner and in a format that permits consumers to compare performance and care of nursing homes and to make informed decisions, for instance, in connection with selection of an appropriate nursing home” (Bua, paragraph 9.)
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method of determining risk level and maturity of compliance activities of DiMaggio/Iyer/Neumann to include wherein the tags include at least one of an F-tag, a K-tag, or an E-tag, as taught by Bua, in order to arrive at a method of determining risk level and maturity of compliance activities that can “provide information related to nursing home care and conduct in a manner and in a format that permits consumers to compare performance and care of nursing homes and to make informed decisions, for instance, in connection with selection of an appropriate nursing home” (Bua, paragraph 9.). Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Both DiMaggio and Bua are directed to the electronic processing of electronic data and specifically to the determination of compliance deficiencies.
Response to Arguments
Applicant’s arguments filed 17 July 2026 concerning the rejection of all claims under 35 U.S.C. 101 have been fully considered and they are persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 13-18 that the claims comprise statutory material.
The Office agrees. Applicant’s claimed invention is patent-eligible because of comparison to elements of Examples 37, 39 and 42 from the 2019 PEG. A process of generating AI prompts using RAG with vector representation of data to solve regulatory issues creates a system that reduces computational overload and memory utilization to enable efficient AI model inference would be a practical application of an abstract idea that otherwise would be defined as an abstract idea overall. Alternatively, the limitations amount to “significantly more’ than the abstract idea. For at least these reasons, the claims are patent eligible under 35 U.S.C. §101.
Accordingly, the statutory rejection has been removed.
Applicant’s arguments filed 17 July 2026 concerning the rejection of all claims under 35 U.S.C. 103(a) have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 103, Applicant argues on pages 9-13 that the claims as amended overcome the citation of DiMaggio in view of Iyer.
Applicant's arguments have been fully considered but are moot in view of the new ground(s) of rejection, specifically with reference to the new reference necessitated by amendment, Neumann, as detailed above, or because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In conclusion, all of the limitations which Applicant disputes as missing in the applied references, including the features newly added by amendment, have been fully addressed by the Office as either being fully disclosed or obvious in view of the collective teachings of DiMaggio, Iyer, Neumann, Heinze and Bua, based on the logic and sound scientific reasoning of one ordinarily skilled in the art at the time of the invention, as detailed in the remarks and explanations given in the preceding sections of the present Office Action and in the prior Office Action (13 March 2026), and incorporated herein.
Conclusion
Unused but cited relevant prior art includes:
Olejar et al. (U.S. Patent 12,301,756 B1) discloses a system and method for emergency response digital assistant that can generate emergency response insights..
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 of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
14 August 2026
