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 .
Status of the Claims
Claims 1, 3-7, 9-12, and 14-21 are currently pending. Claim 8 is canceled and Claim 21 is newly added in the Claims filed on March 11, 2026.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-7, 9-12, and 14-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claims 1 and 12, Claims 1 and 12 recite “assigning the provider match prediction output as a predicted best provider match for the patient,” and subsequently recites “for each patient entry in the non-excluded subset; determining a predicted best provider match for the patient entry.” It is unclear if the first recited “a” predicted best provider match corresponding to the output refers to the same “a” predicted best provider match recited as determined for the patient in the non-excluded subset. In the interest of compact prosecution, Examiner will interpret both recitations of the “a” predicted best provider match as referring to the same metric (i.e. the provider match prediction output of the trained machine learning model).
Additionally, Examiner notes that Claims 1 and 12 also recite “transmitting the provider match to a computing device…to display the provider match on a user interface.” In the interest of compact prosecution, this “the provider match” will be interpreted as “the predicted best provider match,” in accordance with the language throughout the remainder of Claims 1 and 12.
Appropriate correction is required.
Dependent Claims 3-7, 9-11 and 14-21 are also rejected under 35 U.S.C. 112(b) due to their dependence from independent Claims 1 and 12.
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, 3-7, 9-12, and 14-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1, 3-7, 9-12, and 14-21 are within the four statutory categories. Claims 1 and 3-7, 9-11, and 21 are drawn to a method for matching patients to providers, which is within the four statutory categories (i.e. process). Claims 12 and 14-20 are drawn to a system for matching patients to providers, which is within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A method for automated entity field correction in an electronic database, the method comprising:
digitally tracking longitudinal engagement of multiple providers by patients, wherein a patient switching between multiple providers in a first period of time and then staying with another provider for a second period of time which is longer than the first period, is indicative of a good match between the provider for the second period of time and the patient;
generating historical feature vector inputs including a sequence of good provider matches and poor provider matches based on the tracked longitudinal engagement, with poor provider matches being different than the good provider match;
training a machine learning model, in circuitry using a long term short term neural network implementing a root mean squared error, with historical feature vector inputs including the sequence of good provider matches and poor provider matches for each patient to generate a provider match prediction output, wherein determining the provider match prediction output includes supplying the at least a portion of stored provider data to the machine learning model to generate the provider match prediction output, and assigning the provider match prediction output as a predicted best provider match for the patient, wherein the long term short term neural network includes inputs nodes that equal a number of the historical vector inputs and a plurality of intermediate nodes connected to the input nodes to receive respective input based, in part, on the historical vector inputs, and wherein the plurality of intermediate nodes are pruned when a weight associated with any individual intermediate node being zero;
receiving one or more target health conditions;
obtaining a set of multiple patient entries, stored patient data, stored claims data and stored prescription data, the set of multiple patient entries, store patient data, stored claims data and stored prescription data each stored in one or more databases;
for each patient entry in the set of multiple patient entries;
accessing at least a portion of the stored patient data, stored claims data and stored prescription data corresponding to the patient entry;
determining an eligibility status for the patient entry according to specified eligibility criteria and the accessed portion of the stored patient data, stored claims data, and stored prescription data, wherein the determined eligibility status is indicative of the patient entry being eligible for targeted outreach regarding the one or more target health conditions; and
in response to the patient entry having an eligible status, assigning the patient entry to an eligible subset of the set of multiple patient entries;
for each patient entry in the eligible subset;
determining an exclusion status for the patient entry according to specified exclusion criteria and the accessed portion of the stored patient data, stored claims data, and stored prescription data, wherein the exclusion status is indicative of the patient entry being excluded from targeted outreach regarding the one or more target health conditions; and
in response to the patient entry having a non-excluded status, assigning the patient entry to a non-excluded subset of the set of multiple patient entries;
accessing stored provider data; and
for each patient entry in the non-excluded subset;
determining a predicted best provider match for the patient entry by supplying at least a portion of the stored provider data and data of the patient entry to the machine learning model to generate the provider match prediction output, wherein the stored provider data supplied to the machine learning model includes different weights applied to different provider factors stored in a table or memory; and
transmitting the provider match to a computing device associated with the patient entry, to display the provider match on a user interface for selection by a user;
receiving a selection of the predicted best provider match via the user interface of the computing device; and
in response to receiving the selection of the predicted best provider match, transmitting the selection of the predicted best provider match to a server to update a database entry associated with the patient entry.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of a mathematical concept and/or a certain method of organizing human activity because they recite mathematical relationships, formulas, equations, and/or mathematical calculations (in this case, the step of training the machine learning model using the long term short term neural network implementing a root mean squared error, the limitations pertaining to the feature vector inputs and the provider match prediction output, and the limitations pertaining to the nodes of the long short term neural network and the pruning of nodes with zero weights recite mathematical calculations because these terms include optimization algorithms which compute neural network parameters using a series of mathematical calculations), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of tracking longitudinal patient engagement data, generating historical feature vectors, generating a provider match prediction output, receiving target health conditions, accessing patient, claims, and prescription data, and analyzing the accessed data to determine patient eligibility, then further analyzing eligible patients to determine non-excluded patients, accessing provider data, determining a predicted best provider match for each eligible and non-excluded patient, and receiving a selection of the predicted best provider match recite following rules or instructions to determine and/or classify a patient-provider relationship), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claim 12 is identical as the abstract ideas for Claim 1, because the only difference between Claims 1 and 12 is that Claim 1 recites a method, whereas Claim 12 recites a system and its associated structural elements.
Dependent Claims 3-7, 9-11 and 14-21 include other limitations, for example Claims 3-4 and 14-15 recite searching for multiple provider data sources, Claims 5-7, 10, 16-18, and 20 recite various types of eligibility criteria, Claim 8 recites receiving a selection of a provider and updating the patient data based on the provider selection, Claims 9 and 19 recite displaying provider subsets based on patient preferences, Claim 11 recites a type of exclusion criteria, and Claim 21 recites receiving prescription data, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 3-7, 9-11, and 14-21 are nonetheless directed towards fundamentally the same abstract ideas as independent Claims 1 and 12.
Prong 2 of Step 2A
Claims 1 and 12 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the one or more databases, the computing device, the memory, and the processor, and the steps of transmitting and displaying the provider match) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the database, the computing device, the memory, and the processor, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0100], [0201], and [0216]-[0217] of the present Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of patient, claims, prescription, and provider data, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, the recitation of transmitting and displaying provider match data and the selection of the predicted best provider match, which amounts to an insignificant application, see MPEP 2106.05(g).
Additionally, dependent Claims 3-7, 9-11 and 14-21 include other limitations, but these limitations also amount to no more than mere instructions to apply an exception (e.g. the APIs recited in dependent Claims 3-4 and 14-15, the dispensing operation recited in dependent Claim 21), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 5-7, 10, 16-18, and 20), and/or do not include any additional elements beyond those already recited in independent Claims 1 and 12, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-7, 9-12, and 14-21 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1 and 12 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the one or more databases, the computing device, the memory, and the processor, and the steps of transmitting and displaying the provider match), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0100], [0201], and [0216]-[0217] of the Specification discloses that the additional elements (i.e. the recitation of the database, the computing device, the memory, and the processor) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives health condition and patient data, performs and analysis to determine provider match data, and transmits the provider match data and the selection of the predicted best provider match over a network, e.g. see [0047]-[0048] of the present Specification;
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the current invention merely recites the storing of patient and provider data on a database and/or electronic memory;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing patient and provider data in a database and/or electronic memory, and retrieving the patient and provider data from storage in order to determine the provider match data;
Dependent Claims 3-7, 9-11 and 14-21 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the APIs recited in dependent Claims 3-4 and 14-15, the dispensing operation recited in dependent Claim 21), generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 5-7, 10, 16-18, and 20)and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claims 1 and 12, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1, 3-7, 9-12, and 14-21 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1, 3-7, 9-12, and 14-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free From Prior Art
Claims 1, 3-7, 9-12, and 14-21 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 101 and 112. The following represents Examiner’s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103, and is reproduced from the Final Rejection mailed on June 4, 2025:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Olsen (US 2017/0278209) teaches a system for matching patients with care providers, wherein the matching utilizes a plurality of rules, for example a patient being diagnosed with a particular disease or malady, that ultimately determine a ranked list of care providers for the patient. However, Olsen does not teach training a machine learning model utilizing historical feature vector comprising good and poor provider matches, and utilizing the trained machine learning model to generate a provider match prediction output that is assigned as the provider match. Furthermore, Olsen does not teach tracking longitudinal engagement data, wherein the longitudinal engagement data is indicative of a good match between a patient and provider when it shows a patient switching between multiple providers for a first period of time and then staying with another provider for a second period of time that is longer than the first period of time.
Karampourniotis (US 2021/0265063) teaches a machine learning module that utilizes historical data, for example electronic health records and historical treatment plans, as training data in order to determine an appropriate healthcare provider (i.e. a provider match prediction output) for a patient. However, Karampourniotis does not teach that the historical data includes longitudinal engagement data that is indicative of a good match between a patient and provider when it shows a patient switching between multiple providers for a first period of time and then staying with another provider for a second period of time that is longer than the first period of time.
Aunger (US 2018/0211059) teaches tracking a threshold period of time during which the patient has seen an outside medical professional with a same type as an earlier trusted medical professional, indicating that the patient is seeing a new doctor instead of an old doctor. However, Aunger does not teach a plurality of threshold periods of time (i.e. first and second periods of time), or that one threshold period of time is longer than another (i.e. the second period of time being longer than the first period of time). Additionally, Aunger teaches tracking the fact that the patient is seeing a new doctor instead of an old doctor (i.e. engagement data) in order to determine whether or not the doctor should be provided access to a patient data, but does not teach inputting the engagement data into a machine learning model to train the model, and/or utilizing the trained machine learning model to generate a provider match prediction output that is assigned as the provider match.
Schneberk (“Opiod prescription patterns among patients who doctor shop; Implications for providers,” PLOS ONE, May 26, 2020) teaches tracking patient and doctor behaviors during doctor shopping periods, specifically statistics relating to number and type of prescriptions issued during the period and usage data of the drugs prescribed, and further teaches tracking the same data outside of doctor shopping periods, wherein the time outside of doctor shopping periods may be longer than the time for the doctor shopping period. However, Schneberk does not teach that the patient behaviors indicate a good or poor match between the providers and the patient. Additionally, although Schneberk defines doctor shopping as a patient receiving greater than 6 or more prescriptions from at least 6 different prescribers within 6 months of time (i.e. during the first period of time), it does not teach that the patient stays with a single doctor during the non-doctor shopping period (i.e. the second period of time). Additionally, Schneberk does not teach inputting the patient and doctor behavior data, both during and outside the doctor shopping period, into a machine learning model to train the model, and further does not teach generating the provider match prediction output using the trained machine learning model. Furthermore, Schneberk does not teach utilizing the aforementioned data to determine provider matches.
The Health Foundation (“Measurement The various measures of Continuity of Care,” Morecambe Bay Primary Care Collaborative, April 2020) teaches various metrics measuring continuity of care including Usual Provider of Care (UPC), which describes the proportion of visits to the patient’s regular care provider out of all visits, Continuity of Care Index, which describes the frequency of visits to each care provider, and St Leonard’s Index of Continuity of Care, which compares groups of patients and is intended for use within a practice. However, The Health Foundation does not teach calculating the aforementioned metrics using different time periods that vary in length (i.e. the first and second periods of time). Furthermore, The Health Foundation does not teach inputting any of the aforementioned metrics into a machine learning model to train the model, and further does not teach generating the provider match prediction output using the trained machine learning model. Furthermore, The Health Foundation does not teach utilizing the aforementioned data to determine provider matches.
The aforementioned references are understood to be the closest prior art. Various aspects of the present invention are known individually, but for the reasons disclosed above, the particular manner in which the elements are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claims 1, 3-12, and 14-20 is not considered to be a non-novel and/or obvious variant of the inventions taught by the closest prior art references.
Response to Arguments
Applicant’s arguments, see Remarks, filed March 11, 2026, with respect to the rejections of Claims 1, 3-7, 9-12, and 14-21 under 35 U.S.C. 112(b) have been fully considered and, in combination with the amendments, are persuasive. The previous rejections of Claims 1, 3-7, 9-12, and 14-21 under 35 U.S.C. 112(b) have been withdrawn. However, as shown above, Claims 1, 3-7, 9-12, and 14-21 are nonetheless currently rejected under 35 U.S.C. 112(b), due to the newly amended claim language for the reasons disclosed above.
Applicant’s arguments, see Remarks, filed March 11, 2026, with respect to the rejections of Claims 1, 3-7, 9-12, and 14-21 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant alleges that the claimed invention is patent eligible because it does not recite an abstract idea, specifically because it is properly analogized to the invention of Example 39 of the USPTO-issued examples, e.g. see pgs. 12-13 of Remarks – Examiner disagrees.
Similar to Claim 2 of the invention of Example 47 of the USPTO-issued examples, the claimed invention of the present application recites training a machine learning algorithm using a specific mathematical algorithm (i.e. a long term short term neural network implementing a root mean squared error). This is distinct from the invention of Example 39, which does not recite a particular mathematical technique for performing the training operation. Hence, the claimed invention is more properly analogized to Claim 2 of Example 47 of the USPTO-issued examples, which was directed towards an abstract idea.
Furthermore, regarding Desjardins, as Applicant notes, the invention of Desjardins recited a particular method of training a machine learning model that achieved the technological improvements of reduced storage requirements, reduced complexity, and prevention of catastrophic forgetting of AI systems. In contrast, Claims 1 and 12 of the present application merely recite performing the training of a machine learning model utilizing known techniques (i.e. a long term short term neural network implementing a root mean squared error). That is, the invention of Desjardins was directed towards a particular manner of training a machine learning model that achieved the aforementioned improvements, whereas the claimed invention of the current application recites utilizing known mathematical operations to train a machine learning model. Additionally, the as-filed Specification does not disclose that the claimed machine learning training achieves technological improvements such as increased flexibility, faster search times, and smaller memory requirements, but instead discloses that the claimed invention improves provider matching, e.g. see [0165] of the as-filed Specification, and/or improved patient health and well-being, e.g. see [0004] and [0122] of the as-filed Specification, both of which represent improvements to the abstract idea of a certain method of organizing human activities, and an improvement to the abstract idea itself is not an improvement in technology, e.g. see MPEP 2106.05(a)(II). Hence, the claimed invention is not properly analogized to the invention of Desjardins.
Applicants further allege that the claimed invention is patent eligible because they integrate any abstract idea into a practical application, specifically because it recites a particular configuration for implementing the trained machine learning model and provides specific technological improvements, e.g. see pgs. 13-14 of Remarks – Examiner disagrees.
Regarding preemption, Examiner notes that the absence of complete preemption does not guarantee that a claim will be eligible, and further notes that preemption is not a stand-alone test for patentability, but rather is inherent in the two-part Alice/Mayo framework, e.g. see MPEP 2106.04. That is, even assuming, arguendo, that the claimed invention recites a particular configuration for an abstract idea, a narrow abstract idea nonetheless recites an abstract idea, and the broadness/narrowness of the abstract idea is not, by itself, dispositive of the eligibility of the claim. Furthermore, as shown above, Examiner has provided evidence demonstrating that the present invention is directed towards at least one court-identified abstract idea that is not integrated into a practical application, and further that the additional elements of the present invention (i.e. any elements not identified as part of the abstract idea) do not represent significantly more than the abstract idea, and hence has addressed any concerns arising from preemption.
Additionally, even assuming, arguendo, that the claimed invention achieved the improvements of “facilitating assisting patients in getting access to behavioral health services” and/or “assisting patients with quality of life, and unaddressed behavioral conditions associated with higher medical costs,” the aforementioned improvements are not technological improvements because the aforementioned problems have existed since long before the advent of any computer technology.
Applicants further allege that the invention recited in newly-added Claim 21 is patent eligible because it satisfies the requirements of the machine or transformation test, e.g. see pgs. 14-15 of Remarks – Examiner disagrees.
As an initial matter, Examiner agrees that a medication dispenser that performs operations including the dispensing of medication as a result of the data processing steps of the method of Claim 1 (from which Claim 21 depends) would constitute a particular machine because it would recite a particular machine (i.e. the medication dispenser) implementing the steps of a method, e.g. see MPEP 2106.05(b). However, the aforementioned invention would not constitute a particular transformation because a “transformation” of an article means that the “article” has changed to a different state or thing, and changing to a different state or thing usually means more than simply using an article or changing the location of an article, and the aforementioned limitations of the claimed invention merely change the location of an article (i.e. the medication).
However, as presently claimed, the limitations of Claim 21 do not recite a particular machine to implement the steps of the method of Claim 1. For example, Claim 21 recites “receiving prescription data from a prescriber corresponding to the predicted best provider match, the prescription data identifying a prescription drug associated with the patient entry.” Hence, given the broadest reasonable interpretation, the prescriber “corresponding to” the predicted best provider match, merely denotes some sort of relationship between the prescriber and the provider match, without delineating what this relationship actually comprises. That is, Claim 21 does not even require, for example, that the prescriber be selected by the provider of the selected predicted best provider match, and/or selected by the patient of the patient entry, and/or how the prescriber is specifically related to the determinations executed by the method of Claim 1. Similarly, the prescription drug being “associated with” the patient entry merely denotes that the prescription drug has some sort of relationship to the patient, without disclosing any specifics explaining what this relationship comprises. That is, the claim language of Claim 21 reciting the receiving of the prescription does not specify how the prescriber is chosen and/or what the relationship is between the prescriber and the selected predicted best provider match, and further the control of the containers and the dispensing operations are performed for a drug whose relationship to the patient is similarly unspecified. Hence, there is an insufficient nexus between the dispenser and dispensing operations claimed in Claim 21 and the implementation of the method of Claim 1. Hence, Claim 21, as presently claimed, does not satisfy the requirements under 35 U.S.C. 101.
For the aforementioned reasons, Claims 1, 3-7, 9-12, and 14-21 are rejected under 35 U.S.C. 101.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H CHOI can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN P GO/Primary Examiner, Art Unit 3681
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