DETAILED ACTION
The following NON-FINAL Office Action is in response to Applicant’s Remarks filed on 03/24/2026.
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 . 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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/24/2026 has been entered.
Status of Claims
Claims 1-20 were previously pending and subject to a final Office Action mailed 11/24/2025. Claims 1 and 18 were amended. Claims 1-20 are currently pending and are subject to the non-final Office Action below.
Priority
Examiner has noted that the Applicant has claimed priority from the provisional application 63/279616 filed on 11/15/2021.
Response to Arguments
35 USC § 101
Applicant’s arguments, see pages 6-14 of Applicant’s Remarks, filed 03/24/2026, with respect to the 35 U.S.C. 101 rejections of Claims 1-20 have been fully considered and are not persuasive.
Regarding Claims 1-17
Applicant argues on pages 7-8 of Applicant’s Remarks that the claims are not directed to the abstract idea of organizing human activity. Examiner respectfully disagrees.
Applicant argues that the claimed method is a technical process for objectively measuring socioeconomic status using computation analysis of real property data. Examiner respectfully argues that such a process is reciting an abstract idea because analyzing publicly available property data to generate a standardized score of socioeconomic status for a specific individual at the behest of a user (who may be a researcher or healthcare provider as Applicant previously noted) is directed to business relations between a user who provides a request order seeking a HOUSES index score for an individual, a system which receives the user request, generates normalized address data, retrieves real property data, and generates the HOUSES index score based on the real property data, and the individual whose data is being requested, analyzed, and scored.
Applicant further argues that the claimed invention provides a technical solution to the well-recognized problem in health disparities research of obtaining accurate individual-level socioeconomic measurements when conventional measures are unavailable in administrative datasets while also preserving the data privacy of the individual.
Examiner respectfully argues that Applicant’s claimed invention provides a business solution to a business challenge as Applicant’s claimed invention provides a service to health disparities researchers. For example, when conventional measures are unavailable to a researcher, they may rely on Applicant’s service to generate socioeconomic measurements for individuals they wish to research.
Applicant argues that the focus should be on what the claim is directed to and not on how the results might be used downstream. Examiner agrees as the claim explicitly recites a client providing a request order that includes address data for an individual, the server generates normalized address data to retrieve real property data and then generates HOUSES index scores based on the real property data. Thus, enabling the client to retrieve the HOUSES index scores for the individual (seen in Claim 5 as well).
Applicant argues on page 9 of Applicant’s Remarks that the claims recite improvements to computer technology and the technical field of socioeconomic measurement. Examiner respectfully disagrees.
Regarding the improvement in “objective individual-level SES measurement” and “privacy preserving data processing”, MPEP2106.05(a)(II) states “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.”
Generating more accurate measurements and privacy-preserving computations are improvements in the abstract idea itself as the individual’s score is more accurate and their privacy is preserved when the system analyzes their data to generate a score and further, provides the user (who requested the score) with the individual’s score.
Regarding the improvement in “scalable cloud-based processing”, such a feature (i.e. large-scale real property data) is not recited in the claim. Further, it is unclear what the “reproducible, agile, and scalable manner” is and how processing the data in a reproducible, agile, and scalable manner would improve data processing capabilities.
Regarding the “secure API architecture with privacy-preserving capability”, Examiner notes that such limitations are merely indicating a field of use or technological environment in which to apply the judicial exception – specifically limiting execution of the abstract idea to an API service that has a privacy preserving capability and a server and client which communicates through encrypted protocols. See rejection below for more detailed explanation.
Regarding Applicant’s argument that the claims provide a technical solution to the problem of accurately measuring individual socioeconomic status, Examiner has addressed this above as the “problem” is a business problem and the solution is directed to the abstract idea as the system uses specific real property data to measure individual socioeconomic status, implements geocoding, generates standardized scores in order to provide a “service” to the requesting user. The server and database querying are additional elements which amount to mere instructions to “apply it” and extra-solution activity.
Applicant argues on page 11 of Applicant’s Remarks that the claims are not merely “apply it” instructions. Examiner respectfully disagrees.
The server, client, and real property database (which is queried) are recited at a high-level of generality (generic computer/functions) such that when viewed as a whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components.
The new limitations regarding privacy/security (i.e. encrypted communication, privacy-preserving, etc.) may be considered as field of use limitations.
“Via a privacy-preserving capability”, “wherein no protected health information is persisted by the server”, “in a de-identified manner that preserves data privacy for the individual”, and “without personal identifiable information of the individual” are limiting the abstract idea of “business relations between a user who provides a request order seeking a HOUSES index score for an individual, a system which receives the user request, generates normalized address data, retrieves real property data, and generates the HOUSES index score based on the real property data, and the individual whose data is being requested, analyzed, and scored” to data which is privacy-preserving/de-identified/without PII or PHI because limiting application of the abstract idea to HIPAA compliant data which preserves the privacy of the individuals is simply an attempt to limit the use of the abstract idea to a particular technological environment. Such conclusions are further supported by paragraph 15, 51, and 74 of Applicant’s specification “formulating and managing HOUSES index data that is compliant with relevant data privacy regulations (HIPAA)…thereby preserving data privacy”, “HIPAA compliance can be realized by encrypting all data at rest”, and “implemented…as a set of secured APIS running on the server in a HIPAA compliant manner”.
Applicant’s arguments would be more convincing if the claimed invention were to recite more than data collection (“receiving”, “generating”, “retrieving”), data analysis (“generating”), and data output/storage (“storing”).
Regarding Claims 18-20
Applicant argues on pages 11-14 of Applicant’s Remarks that the new limitation of “adjusting” transforms the claims from mere analysis into a practical application that improves system fairness. Examiner respectfully disagrees.
First, Examiner respectfully notes that Applicant’s specification does not provide support for the limitation of “adjusting”. See 35 U.S.C. 112(a) rejection below.
Applicant compares Applicant’s claims to Desjardins. However, Examiner notes that the Desjardin feature specifically adjusts the first values of the plurality of parameters to optimize performance of the ML model on the second ML task while protecting performance of the ML model on the first ML task.
In contrast, Applicant’s claims merely recite “adjusting an AI model based on the quantified bias to reduce differential performance across SES groups”. Given broadest reasonable interpretation, “adjusting” (based on the quantified bias) could involve collecting more data, re-training the AI model, or any number of functions which are conventional to AI models and would not be considered as an improvement to how the machine learning model itself operates.
See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, similar to here, “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
Applicant further argues that “adjusting” requires actual technical modification of an AI model’s parameters or training data to improve its performance across different socioeconomic groups. Examiner respectfully disagrees as the feature is not recited in Applicant’s claims nor is it supported by Applicant’s specification.
Applicant cites paragraph 61 and 28 of Applicant’s specification. Examiner notes that the cited paragraphs broadly describe using HOUSES index data to mitigate AI model bias and how biased AI models provide disadvantages. However, Applicant’s specification does not describe how the AI model is adjusted and how the adjustment is based on quantified bias.
The limitation of “Adjusting” amounts to merely indicating a field of use or technological environment (i.e. AI models) in which to apply a judicial exception – limiting the use of the abstract idea of “computing a fairness metric and quantifying AI model bias” to AI models which did no more than describe how the abstract idea of “computing a fairness metric and quantifying AI model bias” could be used to reduce differential performance across SES groups.
Accordingly, the 35 U.S.C. 101 rejection of Claims 1-20 have been maintained.
35 USC § 103
Applicant’s arguments, see pages 14-17 of Applicant’s Remarks, filed 03/24/2026, with respect to the 35 U.S.C. 103 rejections of Claims 1-17 have been fully considered and are persuasive.
Specifically, Applicant’s arguments regarding “wherein no protected health information is persisted by the server” were convincing as Examiner’s base reference Pankoke stores and processes personal identifiable information and links the information across databases to maintain connections between individuals and their data. Further, Examiner agrees that Stevens teaches de-identification of healthcare data after processing and not a system where no protected health information is persisted.
According the 35 U.S.C. 103 rejections of Claims 1-17 have been withdrawn. See “Closest Prior Art” section below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 18-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 18 recites the limitation of “adjusting the AI model based on the quantified bias to reduce differential performance across SES groups”.
Applicant on page 6 of Applicant’s Remarks note that the limitation is supported at least by paragraphs 28 and 62 of the specification.
The closest description of “adjusting” is in paragraphs 28 and 61-62. Paragraph 28 states “Thus, the HOUSES index (and individual-level SES) can be used for assessing, monitoring, and mitigating AI model bias by SES when AI models are applied to clinical care”.
Paragraph 61 states “the HOUSES index data may be used to assess and mitigate AI model bias driven by a patient's SES…Advantageously, the HOUSES index data generated by the systems and methods described in the present disclosure can be used to assess and mitigate AI model bias by individual-level SES”.
Paragraph 62 states “To address this challenge in the equitable implementation of health care AI, the HOUSES index data can be used as a measure of SES with important features (e.g., validity, precision, objectivity (instead of self-report), and scalability) that can be integrated with AI model development. As a non-limiting example, differential data availability and quality of EHR data among study subjects according to SES as measured by HOUSES indices can be assessed, and HOUSES index data can be applied to quantify bias in commonly used metrics of model performance by SES”.
Thus, the specification supports the HOUSES index used for “mitigating AI model bias by SES” and where, broadly, features can be integrated with AI model development.
However, the specification does not support adjusting the AI model and further does not support adjusting the AI model based on/using the quantified bias. Thus, “adjusting” was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Dependent Claims 19-20 inherit the rejection as they do not cure the deficiencies of Claim 18.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 17 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 17 recites “wherein the request order is received from the client using an encrypted communication protocol”.
Claim 1 recites “receiving a request order at a server by a client using an application programming interface (API) service that matches end-user addresses via a privacy-preserving capability , wherein the request order comprises address data for an individual including a housing unit address for the individual, wherein the request order is received using an encrypted communication protocol”.
Thus, Claim 17 fails to further limit the subject matter of Claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-17 and Claims 18-20 are directed to a method (i.e., a process). Therefore, the claims all fall within the one of the four statutory categories of invention.
Regarding Claims 1-17
Step 2A - Prong 1:
Independent Claim 1 recites:
receiving a request order… wherein the request order comprises address data for an individual including a housing unit address for the individual;
generating normalized address data… by geocoding the housing unit address to link the address data to real property data;
retrieving real property data… and the normalized address data, wherein the real property data comprise at least number of bedrooms, number of bathrooms, square footage of the unit, and estimated building value of the unit;
(d) generating HOUSES index scores based on the real property data.
Certain Methods of Organizing Human Activity
The limitations stated above are processes that under broadest reasonable interpretation covers “certain methods of organizing human activity” (commercial interactions or managing personal behavior or relationships or interactions between people). Specifically, business relations between a user who provides a request order seeking a HOUSES index score for an individual, a system which receives the user request, generates normalized address data, retrieves real property data, and generates the HOUSES index score based on the real property data, and the individual whose data is being requested, analyzed, and scored in light of Applicant’s specification background and summary. Accordingly, the claims recite an abstract idea.
Step 2A - Prong 2:
This judicial exception is not integrated into a practical application. The independent claim recites the additional elements of a server, a client, and a real property database (which is queried using the address data) which are recited at a high-level of generality (generic computer/functions) such that when viewed as a whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components. See MPEP 2106.05(f).
The claim recites the limitation of “(e) storing the HOUSES index scores on the server in a de-identified manner that preserves data privacy for the individual and enables end-user retrieval of the HOUSES index scores without personal identifiable information of the individual”. The limitation “(e)” and the real property database are performing the extra-solution activity of data storage and retrieval. See MPEP 2106.05(g).
The claim recites the limitation of “using an application programming interface (API) service that matches end-user addresses via a privacy-preserving capability” which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception – specifically limits the execution of the abstract idea of “matches end-user addresses” to an API service that that has a privacy-preserving capability. See MPEP 2106.05(h).
The claim also recites “wherein the request order is received using an encrypted communication protocol”. Such limitations, when viewed as a whole, may be considered as generally linking the use of a judicial exception to a particular technological environment or field of use. The limitation merely requires that the abstract idea of business relations between the system and the user is limited to execution by a client and server that receives/sends information by encrypted communication protocols and thus, limits the use of the abstract idea to particular computer environments.
Further Examiner notes that limitations such as “via a privacy-preserving capability”, “wherein no protected health information is persisted by the server”, “in a de-identified manner that preserves data privacy for the individual”, and “without personal identifiable information of the individual” are limiting the abstract idea of “business relations between a user who provides a request order seeking a HOUSES index score for an individual, a system which receives the user request, generates normalized address data, retrieves real property data, and generates the HOUSES index score based on the real property data, and the individual whose data is being requested, analyzed, and scored” to data which is privacy-preserving/de-identified/without PII or PHI because limiting application of the abstract idea to HIPAA compliant data which preserves the privacy of the individuals is simply an attempt to limit the use of the abstract idea to a particular technological environment. Such conclusions are further supported by paragraph 15, 51, and 74 of Applicant’s specification.
Thus, the claim as a whole, looking at additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components, extra-solution activity, and/or field of use which does not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a server, a client, and a real property database to perform the steps/functions recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
The limitation “(e)” and the real property database are performing the extra-solution activity of data storage which is similar to “storing and retrieving information in memory” which the courts have recognized as a well-understood, routine, and conventional computer function. See MPEP2106.05(d)(II).
Again, the limitation of “using an application programming interface (API) service that matches end-user addresses via a privacy-preserving capability” which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception – specifically limits the execution of the abstract idea of “matches end-user addresses” to an API service that that has a privacy-preserving capability.
The limitations of “wherein the request order is received using an encrypted communication protocol” amounts to generally linking the use of a judicial exception to a particular technological environment or field of use – such as merely requiring that the abstract idea of business relations between the system and the user is limited to execution by a client and server that receives and sends information by encrypted communication protocols and thus, limits the use of the abstract idea to particular computer environments.
Limitations such as “via a privacy-preserving capability”, “wherein no protected health information is persisted by the server”, “in a de-identified manner that preserves data privacy for the individual”, and “without personal identifiable information of the individual” are limiting the abstract idea of “business relations between a user who provides a request order seeking a HOUSES index score for an individual, a system which receives the user request, generates normalized address data, retrieves real property data, and generates the HOUSES index score based on the real property data, and the individual whose data is being requested, analyzed, and scored” to data which is privacy-preserving/de-identified/without PII or PHI because limiting application of the abstract idea to HIPAA compliant data which preserves the privacy of the individuals is simply an attempt to limit the use of the abstract idea to a particular technological environment.
None of the steps of Claim 1 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to the abstract idea, amount to no more than mere instructions to apply the exception using a generic computer, extra-solution activity, and/or field of use, thus, the analysis does not change when considered as an ordered combination. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 1 is ineligible.
Dependent Claim 2-3 specify further recite where the HOUSES index scores are stored (in a database or in a memory of the server) which, when viewed as an ordered combination, is extra-solution activity of data storage similar to “storing and retrieving information in memory” which the courts have recognized as a well-understood, routine, and conventional computer function. See MPEP2106.05(d)(II).
Dependent Claim 4-5 specify further presenting the scores to the user. Such limitations are further directed to organizing human activity as the relations/interactions between the user and system are further defined.
Dependent Claim 6 and 17 specify further that data is transmitted/received through an encrypted communication protocol. Such limitations, when viewed as a whole, may be considered as generally linking the use of a judicial exception to a particular technological environment or field of use as the limitation merely requires that the abstract idea of business relations between the system and the user is limited to execution by a client and server that receives and sends information by encrypted communication protocols and thus, limits the use of the abstract idea to particular computer environments.
Claim 7-8 specify further what the request order is and how it is generated. Claim 8 specifies that the request order is an API initiated request order generated by the client. Such limitations, when viewed as a whole, may be considered as generally linking the use of a judicial exception to a particular technological environment or field of use as the limitation merely requires that the abstract idea of business relations between the system and the user is limited to be performed using a computer that receives and sends information by APIs generated by the computer and thus, limits the use of the abstract idea to computer environments.
Claims 9-11 specify further what the request order includes. Such limitations are further directed to organizing human activity as the relations/interactions between the user and system are further defined.
Examiner additionally noting that the additional elements of Claim 9 (authentication request that is processed by the server to authenticate the client), Claim 10 (authentication request includes a bearer token), and Claim 11 (bearer token comprises a JavaScript object notation web token) when considered as a whole, may be also be considered as generally linking the use of a judicial exception to a particular technological environment or field of use as the limitation merely confines the use of the abstract idea to a particular technological environment (authentication request tokens) and thus fails to add an inventive concept to the claims.
Dependent Claims 12-14 specify further what generating normalized address data comprises and how the address match is performed which is further directed to organizing human activity as the system seeks to match the individual’s address in order to retrieve property data.
Dependent Claim 15 specifies further how the server processes the request order and generates the scores without persisting any PII of the individual which is further directed to organizing human activity as the system seeks to protect the individual’s identifying information.
Dependent Claim 16 specifies further what data the real property database is storing which is extra-solution activity of data storage similar to “storing and retrieving information in memory” which the courts have recognized as a well-understood, routine, and conventional computer function. See MPEP2106.05(d)(II).
Thus, when viewed as an ordered combination, nothing in dependent claims 2-17 adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1-17 are ineligible.
Regarding Claims 18-20
Step 2A - Prong 1:
Independent Claim 18 recites:
computing a fairness metric based on the HOUSES index scores and
quantifying AI model bias by SES in the study cohort based on the fairness metric.
Mathematical Concepts
Given specification paragraph 63-64, “Computing” amounts to calculating a balanced error rate metric on the HOUSES index scores where the balanced error rate is a ratio comparing two groups of individuals. Given specification paragraph 65-67 and tables 1-4, “quantifying” amounts to measuring or summarizing model bias by different categories based on the computed balanced error rate. Thus, both limitations fall within the “mathematical concepts” grouping. Accordingly, the claim recites an abstract idea.
Mental Processes
The broadest reasonable interpretation of “computing” and “quantifying” encompasses mathematical calculations which may be performed in the human mind. The limitations also fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgement, and opinion. See MPEP 2106.04(a)(2), subsection III. Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2:
This judicial exception is not integrated into a practical application. The independent claim recites the additional elements of a computer system which is recited at a high-level of generality (generic computer/functions) such that when viewed as a whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components. See MPEP 2106.05(f).
The claim additionally recites the limitation of “accessing with a computer system housing-based socioeconomic status (HOUSES) index scores for individuals in a study cohort, wherein the HOUSES index scores are generated based on real property data comprising at least number of bedrooms, number of bathrooms, square footage of a housing unit for each individual, and estimated building value of each housing unit” which is performing the insignificant extra-solution activity of pre-solution data retrieval. See MPEP 2106.05(g).
The claim also recites the limitation of “adjusting the AI model based on the quantified bias to reduce differential performance across SES groups” which amounts to merely indicating a field of use or technological environment in which to apply a judicial exception – limiting the use of the abstract idea of computing a fairness metric and quantifying AI model bias to AI models.
Limiting the use did no more than describe how the abstract idea of “computing a fairness metric and quantifying AI model bias” could be used to reduce differential performance across SES groups. See MPEP 2106.05(h).
Thus, the claim as a whole, looking at additional elements individually and in combination, does not integrate the judicial exception into a practical application as the additional elements are mere instructions to apply the judicial exception using generic computer components, extra-solution activity, and/or field of use which does not impose meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer system to perform the steps/functions recited above amounts to no more than mere instructions to apply the exception using a generic computer. Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
The limitation of “accessing” is insignificant extra-solution activity of data retrieval which is similar to “storing and retrieving information in memory” which the courts have recognized as a well-understood, routine, and conventional computer function. See MPEP2106.05(d)(II).
Again, the limitation of “adjusting the AI model based on the quantified bias to reduce differential performance across SES groups” amounts to merely indicating a field of use or technological environment in which to apply a judicial exception – limiting the use of the abstract idea of computing a fairness metric and quantifying AI model bias to AI models.
Limiting the use did no more than describe how the abstract idea of “computing a fairness metric and quantifying AI model bias” could be used to reduce differential performance across SES groups.
None of the steps of Claim 18 when evaluated individually or as an ordered combination amount to significantly more than the abstract idea. The additional elements are merely used to perform the limitations directed to the abstract idea, amount to no more than mere instructions to apply the exception using a generic computer, extra-solution activity, and/or field of use, thus, the analysis does not change when considered as an ordered combination. Thus, the additional elements do not meaningfully limit the claim. Accordingly, Claim 18 is ineligible.
Dependent Claims 19 and 20 specify further that the fairness metric comprises a balanced error rate metric which is computed as a ratio. Such limitations are further directed to mathematical concepts and mental processes as discussed above.
Thus, nothing in dependent claims 19-20 adds additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 18-20 are ineligible.
Closest Prior Art
Examiner noting that Claims 1-20 are rejected under 35 U.S.C. 101.
The following is a statement of reasons for the indication of closest prior art:
Current prior art alone or in combination fail to disclose every element of Claim 1, specifically the limitations of “matches end-user addresses via a privacy preserving capability…wherein no protected health information is persisted by the server”.
The following are the closest prior art:
Pankoke teaches a client using an API; wherein the request order is receiving using a communication protocol; and further generating normalized address data (para. 24 the client device accesses the SDOH system via an API; para. 34 one or more APIs are used to transmit an SDOH request to the SDOH system; para. 39 client supplied identifying data is in the SDOH request; para. 34 the client device may initiate the SDOH request which is transmitted to the SDOH system via a secure transfer protocol; para. 35 the demographic data fields may be rearranged, merged, split, or omitted to normalize them, truncated (9 digit zip code to 5 digit zip code))
Stevens teaches storing the data on the server in a de-identified manner that preserves data privacy for the individual and enables end-user retrieval of the data without personal identifiable information of the individual
Le Saint et al. (US2016/0241389) teaches a client and server communicating using an encrypted communication protocol.
Paris (US Patent No. 12,182,877) is not available as prior art. However, Paris teaches de-identifying patient identifies from healthcare facility encounters and retrieving information from pre or post encounter activity.
Salsbury et al. (US2010/0082362) teaches components of community healthy can be compared across communities and correlated with data on specific health outcomes across communities to identify health outcome disparities.
Khan et al. (US2014/0006039) teaches obtaining clinical and non-clinical information, determining a composite healthcare index for the consumer, and sharing it with a plurality of healthcare stakeholders.
Current prior art alone or in combination fail to disclose every element of Claim 18, specifically the limitations of “computing a fairness metric based on the HOUSES index scores using the computer system; quantifying AI model bias by SES in the study cohort based on the fairness metric.”
The following are the closest prior art:
Fischer et al. (US2022/0101062) teaches bias estimation in AI models. However, Fischer teaches quantifying AI model bias by race, gender, and age not socioeconomic status.
Miroshnikov et al. (US2021/0383268) teaches measuring the performance of a model using a balanced error rate and constructing a fair score. Further, the reference teaches detecting bias in a model relative to a protected class (age, gender, and race/ethnicity). Miroshnikov does not teach the limitation of “quantifying”.
Hacmon et al. (US2022/0076080) teaches determining fairness of an AI model by calculating a balance error rate for protected feature groups and quantifying bias scores where a protected feature is a feature that can present unwanted discrimination towards its values. Hacmon teaches quantifying by age, gender, disability, and race.
Lookabaugh et al. (US2020/0387990) teaches the real property data (number of bedrooms, number of bathrooms, square footage, estimated building value).
Gupta (US2024/0186012) is not available as prior art, but Gupta teaches determining a social determinant of health risk index for localities of interest.
Blonsky et al. (US2023/0359652) is not available as prior art, but Blonsky teaches community SDOH factors that influence health where one of the factors is housing.
Hayward (US2021/0151195) teaches collecting property data to determine impact on the individual’s health and computing a property score representing a health index of the property area.
Conclusion
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/L.M./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628