DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/03/2026 has been entered.
Status of Application
The following is a Final Office Action. In response to Examiner's communication on 10/02/2025, Applicant on 04/03/2026, filed a Request for Continued Examination (RCE) without amending, removing, or adding claims. Claims 1-4, 7-13, 16-19 are now pending in this application and have been rejected below.
Response to Amendment
Applicants’ amendments are insufficient to overcome the 35 USC 101 rejections set forth in the previous action. The rejections are maintained below.
Applicants’ amendments are insufficient to overcome the 35 USC 103 rejections set forth in the previous action. Therefore, these rejections have been updated to address the amendments and are maintained below.
Response to Arguments – 35 USC § 101
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive.
Applicant firstly argues that the claims do not recite abstract ideas, owing to the integral nature of the ML module and predictive modelling. Examiner respectfully disagrees. Firstly, Examiner notes that the operations of “receive…request comprising user profile data”, “parse…to derive a plurality of features”, “generate at least one feature vector” are expressly performable in the human mind; this corresponds to mentally observing data and performing judgments to generate an encoded representation of salient data. “continuously monitor the user profile…generate an updated care provider recommendation” also correspond to mentally observing and performing judgments as well as a means of managing the personal behavior of patient. Examiner notes that Applicant’s characterization of the invention as performing: “GraphRAG architecture that constructs knowledge graphs with thousands of interconnected nodes…performs multi-hop reasoning across the thousands of nodes simultaneously, analyzing...hundreds of potential connection paths” that would render the operations unable to be practically performed in the human mind is not present in the language of the claims; there is no assertion as to a magnitude of data or complexity of judgment that a human could not facilitate. Further, even if the data were vast, accelerating a process of analyzing data "when the increased speed comes solely from the capabilities of a general-purpose computer" is not an improvement in computers. MPEP 2106.05(a). “'Claiming the improved speed or efficiency inherent with applying the abstract idea on a computer' does not integrate a judicial exception into a practical application or provide an inventive concept." MPEP 2106.05(f). The claimed limitations regarding observing data and generating a vector representation amount to relatively mental assessments and therefore mental processes.
Regarding Applicant’s assertion that the deployment of machine
learning model(s) does not recite a mental process, Examiner completely agrees. However, it is essential to note that the performance of recited abstract ideas is not implicitly tied to the operation of machine learning models. The operations of receiving patient data, generating a vector representation recite abstract ideas, and arriving at recommendations in tandem with monitoring for changes in user data recite abstract ideas pursuant to the logic as outlined above; these operations are performable without aid by particular technology. Without such an implicit connection between the recited abstract ideas and the machine learning model(s), it is appropriate to analyze said models as additional elements under Step 2A Prong II and Step 2B of the Alice/Mayo Subject Matter Eligibility Test, and Examiner respectfully points to the discussion of such below.
Finally, even if Applicant’s allegation that the aforementioned
processes were unable to be performed in the human mind were true,
analyzing patient data to generate a care provider recommendation, are expressly Certain Methods of Organizing Human Activity, by virtue of Managing Personal Relationships Behavior. Applicant’s arguments with respect to this point, see Page 3 of Remarks, address the claimed machine learning module (ML) and its lack of organizing human activity; this is somewhat contradictory to the claims, the purpose of the model is expressly to generate a care provider recommendation which is intended to guide a patient ‘s decision making.
Further, as invoking the machine learning model amounts to an assertion of eligibility under Step 2A Prong II or Step 2B by the discussion of the relationship between recited abstract ideas and additional elements outlined above, Examiner respectfully notes the discussion below.
Moving on to analysis under Step 2A Prong II and Step 2B of the Alice/Mayo test, Applicant argues that the additional elements integrate any alleged abstract ideas into a practical application and amounts to significantly more by virtue of presenting an improvement to technology. Examiner respectfully disagrees. Applicant rebuts the characterization of “ML technology as a ready-to-go tool that can be used on any general-purpose computer” on Page 6 of Remarks. However, examining the relevant limitations concretely in Claim 1, “provide the at least one feature to the ML module for generating a predictive model…generate an updated care provider recommendation…produced by the predictive model”, the ML module is effectively taken as a “ready-to-go” tool. There is no mention of how it’s trained, deployed, or any architecture that might be relevant to implementation. In the claim, the module functions as a black box where patient information goes in, and recommendations come out. This is the crux of why it is permissible to take “only a portion of the claims” as Applicant asserts in the beginning of the 35 USC 101 rejection, as the process claimed works equivalently with any similarly black box model or means of processing information and providing recommendations; the “ML module” is not tied to an abstract idea but instead represents a generic additional element. While the vector provides some level of guidance in its implementation, it is at a very high level that still fails to impose significant boundaries on the ML module itself; knowing that we want to encode input information as a vector does not invoke a specific machine learning model or the particulars of its implementation. Applicant asserts on Page 7 that “the claims recite significant and very specific algorithmic limitations performed by a physical network-based system employing AI implemented on the server”. From a technological perspective, this is patently false. With generic hardware components and a black-box “ML module”, it cannot be said that there is a specific technical system being claimed here. The various examples Applicant cites are distinct from the language of Applicant’s claims for this reason, there is no analogous improvement to actual technology, or specific boundaries on the technology deployed, but rather additional elements that serve as generic computing components.
Accordingly, Applicant’s arguments as to the rejections under 35 USC 101 have not been found to be persuasive, and they have been updated to address the amendments below.
Response to Arguments – 35 USC § 103
Applicant’s argument center around the limitation “responsive to the at least one medical record having been added or removed from the user profile data, generate an updated feature vector based on the user profile data and generate an updated care provider recommendation to be sent to the at least one user device based on the at least one care provider selection parameter produced by the predictive model in response to the updated feature vector”, which Applicant asserts is not taught by Abedini nor Valdizan. Examiner respectfully disagrees in view of updated citations to Abedini combined with Valdizan, and notes Examiner notes that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. The test for obviousness is not that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed.Cir. 1986). At a conceptual level, the dynamic update monitoring and responsive retraining of Abedini is combined with the dynamic monitoring of Valdizan, teaching the disputed limitations. See pasted excerpt from the rejection regarding this combination.
Abedini teaches:
continuously monitor the user profile data stored on a database of the RS node
(In [0062], “One or more hardware processors 304 may periodically communicating with one or more servers 310 to receive updated data associated with one or more of the user and the health care providers, and continue training the machine learning model further based on the updated data”.)
to determine if at least one medical record has been added or removed from the user profile data.
(In [0037], “The patient and doctor treatment philosophical profiles may be reconstructed based on feedback, for example, from patients and doctors. The reconstruction may occur, for example after each treatment activity, or periodically after a number of treatment activities. For instance, feedback is collected from both patients and doctors to modify the matching algorithm (228, 230) as well as the treatment philosophical profile of each other (232, 234). Treatment philosophical profile for both patients and doctors may also be reconstructed on regular interval to cater for temporal changes”.)
responsive to the at least one medical record having been added or removed from the user profile data, generate an updated feature vector based on the user profile data…in response to the updated feature vector
(Note the implicit modelling of user profiles, which are updated per [0037] above, as vectors in [0043-0054], “The machine learning in one embodiment of the present disclosure may include modelling the preference of a patient for a doctor in treating specific ailment and/or health concern, e.g., the treatment philosophical profile shown with reference to 202 and 212 in FIG. 2. The modelling may include collecting a numeric indicator of the strength of each of the concepts, for example, the features defining the treatment philosophy (e.g., disease controlling or curing, short term or long term solution, surgical or medical treatment, informed or uninformed consent, individual vs. shared decision, doctor superiority vs. patient empowerment, benefiting or straightforward harshly, and others), but not limited to only those features, from patients (and doctors) denoting the importance of that concept when a disease is being treated… Conceptually, P.sub.m.sup.d=[P.sub.1m.sup.d,P.sub.2m.sup.d, . . . ,P.sub.Nm.sup.d]…”)
produced by the care provider model in response to the updated feature vector
(In [0008], “One or more of the hardware processors may be further operable to build a machine learning model comprising user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition and store the machine learning model on one or more of the memory devices…One or more of the hardware processors may be further operable to modify the machine learning model based on the feedback to retrain the machine learning model for the predicting.” Note the vector representation of user profiles as outlined in [0043]-[0054] above.)
Abedini does not expressly disclose the remaining limitations.
However, Valdizan teaches:
and generate an updated care provider recommendation to be sent to the at least one user device based on the at least one care provider selection parameter produced … in response to the updated ...
(Note the automated providing of care provider notes in [0043-0044], “FIG. 2 is a flowchart illustrating a process 200 for an automated alert system, according to an embodiment of the present invention. The process begins with authorized hospital staff accessing and updating hospital alert records at 210 to record a change in the condition of the patient. An EMR message monitor application then detects the condition change at 220 and sends a message at 230 to a server-side automated alert application running on an application server that includes information about the patient's change in condition. For instance, a PHI-sensitive payload may be sent to the application server that provides an optional message to the authorized individual's device. The server-side automated alert application receives the message, processes it, and sends an alert to a user device at 240”. Further, this interfaces with automated scanning logic for change detection, in [0047], “The automated alert system may also have the capabilities to continuously scan the EMR system until a change in location or a critical medical condition is detected to alert the emergency contact(s) listed in the patient record”.)
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 1-4, 7-13, 16-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims are directed to a method and apparatus. Therefore, the claim is directed to at least one of the four statutory categories.
101 Analysis – Step 2A
Regarding Prong 1 of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether they recite subject matter that is directed to a judicial expectation, namely a law of nature, a natural phenomenon, or one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea and will henceforth be used as a representative claim for the 101 rejection until otherwise noted. Claim 1 recites:
A system, comprising: a processor of a recommendation server (RS) node connected to at least one user device over a network and configured to host a machine learning (ML) module; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a care provider request comprising user profile data from the at least one user device; parse the care provider request to derive a plurality of features from the user profile data; query an aggregated care providers' database to retrieve historical care providers' matching data based on the user profile data; generate at least one feature vector based on the plurality of features and the historical care providers' matching data; provide the at least one feature vector to the ML module for generating a predictive model configured to output at least one care provider selection parameter for generating a care provider recommendation responsive to the care provider request, continuously monitor the user profile data stored on a database of the RS node to determine if at least one medical record has been added or removed from the user profile data: and responsive to the at least one medical record having been added or removed from the user profile data, generate an updated feature vector based on the user profile data and generate an updated care provider recommendation to be sent to the at least one user device based on the at least one care provider selection parameter produced by the predictive model in response to the updated feature vector.
The examiner submits that the foregoing bolded limitations constitute an abstract idea because under its broadest reasonable interpretation, the claim covers a certain method of organizing human activity, namely that of managing personal behavior or relationships or interactions between people, by virtue of matching care providers to patients. “Receiving a care provider request…”, “parse the care provider request to derive a plurality of features…”, “generate at least one feature vector…”, “provide the at least one feature vector…”, “continuously monitor…”, “generate an updated feature vector…”, are abstract ideas – namely, these are mental processes that could be performed by a human with a pen and paper, per the MPEP, merely adapting them into the context of a technological environment with computing parts does not remove them from being abstract.
Accordingly, the claim recites at least one abstract idea.
Independent Claims 10 and 16 recite abstract ideas by virtue of presenting substantially similar limitations.
Dependent Claims 2-4, 7-9, 11-13, 17-19 recite abstract ideas by virtue of their dependency on independent Claims.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into practical application. As noted in the MPEP, it must be determined whether any additional elements in the claim beyond the judicial exception integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements, such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A system, comprising: a processor of a recommendation server (RS) node connected to at least one user device over a network and configured to host a machine learning (ML) module; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a care provider request comprising user profile data from the at least one user device; parse the care provider request to derive a plurality of features from the user profile data; query an aggregated care providers' database to retrieve historical care providers' matching data based on the user profile data; generate at least one feature vector based on the plurality of features and the historical care providers' matching data; and provide the at least one feature vector to the ML module for generating a predictive model configured to output at least one care provider selection parameter for generating a care provider recommendation responsive to the care provider request, continuously monitor the user profile data stored on a database of the RS node to determine if at least one medical record has been added or removed from the user profile data: and responsive to the at least one medical record having been added or removed from the user profile data, generate an updated feature vector based on the user profile data and generate an updated care provider recommendation to be sent to the at least one user device based on the at least one care provider selection parameter produced by the predictive model in response to the updated feature vector.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
These additional limitations, including “a processor of a recommendation server (RS) node connected to at least one user device over a network and configured to host a machine learning (ML) module”, “a memory on which are stored machine-readable instructions” : “user device”, “database”, “the ML module”, “the predictive model”, are merely generic computing components that are merely used as a tool to perform the recited abstract idea and do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use under Step 2A Prong Two.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more thana drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing an abstract idea.
Claims 10 does not integrate the abstract ideas into a practical application by analogous reasoning.
Claims 2, 11, 17 recites “at least one 3d-party database…service locations within a pre-set distance range”.
Claim 7 recites “a blockchain ledger”.
Claim 9 recites “a smart contract”.
Claim 16 recites “a non-transitory computer readable medium…”
These limitations do not integrate the abstract ideas into a practical application by analogous reasoning as above.
Claims 3-4, 8, 12-13,18-19 do not present additional elements beyond those found in Claims upon which they are dependent, and therefore do not integrate the abstract ideas into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to adding generic computing parts to integrate the mental process into a general technological environment and are well-understood and routine activities.
Claims 10 does not integrate the abstract ideas into a practical application or amount to significantly more by analogous reasoning as above.
Claims 2, 11, 17 recites “at least one 3d-party database…service locations within a pre-set distance range”.
Claim 7 recites “a blockchain ledger”.
Claim 9 recites “a smart contract”.
Claim 16 recites “a non-transitory computer readable medium…”
These limitations do not integrate the abstract ideas into a practical application or amount to significantly more by analogous reasoning as above.
Claims 3-4, 8, 12-13,18-19 do not present additional elements beyond those found in Claims upon which they are dependent, and therefore do not integrate the abstract ideas into a practical application or amount to significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 10, 13, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Abedini(US 20170177814 A1) in view of Valdizan(US20180322944).
Claim 1
Abedini teaches:
A system, comprising: a processor of a recommendation server (RS) node connected to at least one user device over a network and configured to host a machine learning (ML) module; a memory on which are stored machine-readable instructions that when executed by the processor,
(In [0008] of Abedini, "A system of training a machine to provide specialized health care apparatus, in one aspect, may include one or more hardware processors and one or more memory devices coupled to one or more of the hardware processors...One or more of the hardware processors may be further operable to build a machine learning model comprising user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition and store the machine learning model on one or more of the memory devices." In [0009] "A computer readable storage medium storing a program of instructions executable by a machine to perform one or more methods described herein also may be provided.")
cause the processor to: receive a care provider request comprising user profile data from the at least one user device;
(At 102, a user, for example, a patient may enter the patient's medical complaint and/or issues, for example, in layperson's expression or text, not necessarily including technical medical terms.)
parse the care provider request to derive a plurality of features from the user profile data;
(In [0025], "At 104, the entered description is converted into corresponding medical terms. For example, description including the term “depression” may be converted to “encephalopathy”. For example, computer-implemented natural language processing (NLP) module may parse the description and convert the terms in the description into one or more medical terminologies. As another example, an automated machine learning algorithm may be utilized that can process large volume of text data, connect the meaning of the words and in the process describe an entered description by corresponding medical terms. Such advanced machine learning technique can retrieve additional “concepts” for a given one.")
query an aggregated care providers' database to retrieve historical care providers' matching data based on the user profile data;
(In [0026], "At 106, medical terms of the patient's complaints are transmitted to a computer-implemented searching module. At 108, the searching module searches doctors and generates a list of doctors with appropriate specialties matched with the patient's complaints, for example, shown at 110.")
generate at least one feature vector based on the plurality of features and the historical care providers' matching data;
([0032] and [0043]-[0054] and elements 202 and 212 in fig 2, "The machine learning in one embodiment of the present disclosure may include modelling the preference of a patient for a doctor in treating specific ailment and/or health concern, e.g., the treatment philosophical profile shown with reference to 202 and 212 in FIG. 2. The modelling may include collecting a numeric indicator of the strength of each of the concepts...Conceptually,
P.sub.m.sup.d=[P.sub.1m.sup.d,P.sub.2m.sup.d, . . . ,P.sub.Nm.sup.d]
where
P.sub.m.sup.d=The preferred strength vector of concepts of m-th patient for the treatment of disease “d”;)
and provide the at least one feature vector to the ML module
([0043]-[0057], "Given the vectors, [P.sub.1.sup.d,P.sub.2.sup.d, . . . , P.sub.M.sup.d, D.sub.1.sup.d, D.sub.2.sup.d, . . . , D.sub.D.sup.d], machine learning algorithm of the present disclosure in one embodiment builds a model to estimate [R.sub.m,n.sup.d], where M=number of patients D=number of doctors)" and "R.sub.m,n.sup.d=Ranking given by m-th patient to n-th doctor in treating the disease “d”.)
for generating a predictive model configured to output at least one care provider selection parameter for generating a care provider recommendation responsive to the care provider request,
In [0026], "At 106, medical terms of the patient's complaints are transmitted to a computer-implemented searching module. At 108, the searching module searches doctors and generates a list of doctors with appropriate specialties matched with the patient's complaints". In [0029], "At 118, the doctors are recommended to the patient in a ranked order based on the best personality matching score". Finally, in [0036], "The matching may output a recommendation list of doctors/physicians".
continuously monitor the user profile data stored on a database of the RS node
(In [0062], “One or more hardware processors 304 may periodically communicating with one or more servers 310 to receive updated data associated with one or more of the user and the health care providers, and continue training the machine learning model further based on the updated data”.)
to determine if at least one medical record has been added or removed from the user profile data.
(In [0037], “The patient and doctor treatment philosophical profiles may be reconstructed based on feedback, for example, from patients and doctors. The reconstruction may occur, for example after each treatment activity, or periodically after a number of treatment activities. For instance, feedback is collected from both patients and doctors to modify the matching algorithm (228, 230) as well as the treatment philosophical profile of each other (232, 234). Treatment philosophical profile for both patients and doctors may also be reconstructed on regular interval to cater for temporal changes”.)
responsive to the at least one medical record having been added or removed from the user profile data, generate an updated feature vector based on the user profile data…in response to the updated feature vector
(Note the implicit modelling of user profiles, which are updated per [0037] above, as vectors in [0043-0054], “The machine learning in one embodiment of the present disclosure may include modelling the preference of a patient for a doctor in treating specific ailment and/or health concern, e.g., the treatment philosophical profile shown with reference to 202 and 212 in FIG. 2. The modelling may include collecting a numeric indicator of the strength of each of the concepts, for example, the features defining the treatment philosophy (e.g., disease controlling or curing, short term or long term solution, surgical or medical treatment, informed or uninformed consent, individual vs. shared decision, doctor superiority vs. patient empowerment, benefiting or straightforward harshly, and others), but not limited to only those features, from patients (and doctors) denoting the importance of that concept when a disease is being treated… Conceptually, P.sub.m.sup.d=[P.sub.1m.sup.d,P.sub.2m.sup.d, . . . ,P.sub.Nm.sup.d]…”)
produced by the care provider model in response to the updated feature vector
(In [0008], “One or more of the hardware processors may be further operable to build a machine learning model comprising user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition and store the machine learning model on one or more of the memory devices…One or more of the hardware processors may be further operable to modify the machine learning model based on the feedback to retrain the machine learning model for the predicting.” Note the vector representation of user profiles as outlined in [0043]-[0054] above.)
Abedini does not expressly disclose the remaining limitations.
However, Valdizan teaches:
and generate an updated care provider recommendation to be sent to the at least one user device based on the at least one care provider selection parameter produced … in response to the updated ...
(Note the automated providing of care provider notes in [0043-0044], “FIG. 2 is a flowchart illustrating a process 200 for an automated alert system, according to an embodiment of the present invention. The process begins with authorized hospital staff accessing and updating hospital alert records at 210 to record a change in the condition of the patient. An EMR message monitor application then detects the condition change at 220 and sends a message at 230 to a server-side automated alert application running on an application server that includes information about the patient's change in condition. For instance, a PHI-sensitive payload may be sent to the application server that provides an optional message to the authorized individual's device. The server-side automated alert application receives the message, processes it, and sends an alert to a user device at 240”. Further, this interfaces with automated scanning logic for change detection, in [0047], “The automated alert system may also have the capabilities to continuously scan the EMR system until a change in location or a critical medical condition is detected to alert the emergency contact(s) listed in the patient record”.)
Abedini discloses a system for utilizing machine learning to match users to healthcare providers. Valdizan discloses a system meant to automate the delivery of alerts, interfacing with electronical medical records. Each reference discloses a system that interfaces with electronic medical records to optimize aspects of providing healthcare on the basis of them. Integrating the continuous monitoring, as taught by Valdizan, would be applicable to Abedini as the technique is applicable to a broad range of software; we are already provided that the system of Abedini is capable of querying a third-party database to source matching-relevant information.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to employ the usage of continuously monitoring medical records as taught in Valdizan and apply that to the system as taught in Abedini. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting would provide real-time updates into the condition of the patient.
Claims 10 and 16 are rejected as presenting substantially similar limitations to Claim 1.
Claim 4
Abedini teaches:
The system of claim 1, wherein the instructions further cause the processor to periodically scan the at least one user device to acquire the user profile data based on pre-set time intervals.
(In [0062], "One or more hardware processors 304 may periodically communicating with one or more servers 310 to receive updated data associated with one or more of the user and the health care providers, and continue training the machine learning model further based on the updated data". As regular time intervals are implicitly disclosed by the idea of "periodically" communicating with the server, we understand this limitation to be disclosed.)
Claims 13 and 19 are rejected as presenting substantially similar limitations to Claim 4.
Claims 2-3,11-12, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Abedini(US 20170177814 A1) in view of Valdizan(US20180322944) in further view of Zebarjadi(US 20150370975 A1).
Claims 2, 11, 17
As to claim 2, Abedini combined with Valdizan teaches all the limitations of claim 1 as discussed above.
Abedini also teaches:
The system of claim 1, wherein the instructions further cause the processor to retrieve care provider matching-related data from at least one 3d-party database based on the user profile data,
(In [0034], "For example, doctor preference or personality profile may be inferred from textual data at 214, for example, from various online media, for example, by communicating with social network or social media servers. At 218, based on the textual data from online media obtained at 204, doctors with similar preference or personality may be detected, and at 220, treatment preference of those similar doctors may be obtained, for example, based on the textual data from online media obtained at 214." In [0036], "The match between patient and doctor at 226 for a specific health problem may be carried out from metrics such as comparing patient's preference 222 and doctor's approach 224 as obtained from the respective treatment philosophical profiles".)
Abedini combined with Valdizan does not expressly disclose the remaining limitations.
However, Zebarjadi teaches:
wherein the care provider matching-related data is collected at service locations within a pre-set distance range from the at least one user device.
(In [0005], it's prescribed that we can store patient data, as "location information describing the geographical position of the patient may be collected, such as by entering a street address on the part of the patient or through use of location services, such as a GPS, operating on a computing device associated with the patient". In [0006], it's disclosed that this can also be doctor data, as "a doctor's location may be provided either by the doctor herself or himself or through the use of location services, such as a GPS device, operating on a computing device associated with the doctor". Finally, in [0007], "a doctor may be matched with a patient request based upon physical proximity to the patient", with it being provided that this can by "selecting a doctor with a base location within a predetermined distance from the patient location" in Claim 3 of Zebarjadi. In [0029] of Abedini, it's given that "feedback may be collected from both the patient and the doctor regarding the personality matching experience recommended by the methodology of the present disclosure. Based on the feedback, the personality-matching algorithm may be modified, for example, as shown at 122, as necessary to shape the experience better". Therefore, interpreting service locations as other doctors, that have been filtered to be within a predetermined distance, we can interpret the feedback provided and stored to be reused for matching to disclose this limitation.)
Abedini combined with Valdizan discloses a system for utilizing machine learning to match users to healthcare providers. Zebarjadi discloses a system meant to match doctors with patients seeking medical care Each reference discloses a means to pair patients seeking medical care with doctors. Extending the system recorded in Abedini combined with Valdizan to incorporate the limitation of using data collected within a preset distance range as mentioned in Zebarjadi is applicable to Abedini combined with Valdizan as they are aimed at the same goal; we already have support for third party data integration in Zebarjadi, so constraining the source of that data to a specific distance range, as Zebarjadi does, is not doing something fundamentally novel.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to allow for the distance constraint as taught in Zebarjadi and apply that to the system of Abedini combined with Valdizan. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting such a restraint would enable users to incorporate data that may be most immediately relevant to a user, for example constraining the results to the most recent appointments where the patient may have been within a certain distance radius.
Claims 11 and 17 are rejected as presenting substantially similar limitations as Claim 2.
Claims 3, 12, 18
As to claim 3, Abedini combined with Valdizan and Zebarjadi teaches all the limitations of claim 2 as discussed above.
Abedini teaches:
The system of claim 2, wherein the instructions further cause the processor to generate the at least one feature vector based on the plurality of features, the historical care providers' matching data combined with the care provider matching-related data from the at least one 3d-party database.
(In [0060], "One or more hardware processors 304 may communicate with one or more servers 310 over a computer communications network 312 to obtain data associated with one or more of the user and the health care providers. One or more servers may include, but are not limited to, one or more of social media server, social network server, electronic mail server, and text messaging server....One or more hardware processors 304 may cluster the data into a predefined set of features as related to the user and the health care providers based on the preferences, for example, and build a computer-implemented machine learning model comprising user preference for a predefined set of features associated with the user's health condition and health care provider preference for the predefined set of features in treating the user's health condition.)
Claims 12 and 18 are rejected as presenting substantially similar limitations as Claim 3.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Abedini(US 20170177814 A1) in view of Valdizan(US 20180322944) in further view of Huang(TW202107477A).
Claim 7
As to Claim 7, Abedini combined with Valdizan teaches all the limitations of Claim 1 as discussed above.
Abedini combined with Valdizan does not expressly disclose the remaining limitations.
However, Huang teaches:
The system of claim 1, wherein the instructions further cause the processor to record the at least one care provider recommendation parameter on a blockchain ledger along with the user profile data corresponding to the care provider request.
(On pg.25, it is given that in the “decentralized Ethereum or EOS blockchain in the first specific example of the cash flow model of the present invention… With respect to the dementia patient 1A, the third matching record block 3113 of the matched person C of the first scheduled matching data link 3110 is linked to the second matching record block 3112 of the matched person B of the first scheduled matching data link 3110, during which the matched person C and the dementia case XX sign the matching smart contract 31131 of the smart contract 1AC”.
Abedini combined with Valdizan discloses a system for utilizing machine learning to match users to healthcare providers. Huang discloses a system that utilizes deep learning to match patients with health care providers. Each reference discloses a system intended to use patient data and provide compatible healthcare providers. Extending the usage of the blockchain as recorded in Huang to the system disclosed in Abedini combined with Valdizan is feasible as Huang demonstrates that such integration is both possible and advantageous .
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to use blockchain technology as taught in Huang and apply that to the system of Abedini combined with Valdizan. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting blockchain technology would enable users to ensure the immutability and integrity of stored data, while simultaneously reducing the susceptibility of the data to bad actors and attackers.
Claim 8
As to Claim 8, Abedini combined with Valdizan and Huang teaches all the limitations of Claim 7 as discussed above.
Abedini combined with Valdizan does not expressly disclose the remaining limitations.
Huang teaches:
The system of claim 7, wherein the instructions further cause the processor to retrieve the at least one care provider recommendation parameter from the blockchain responsive to at least a consensus among provider nodes and a user of the at least one user device.
(On pg.25, it is given that in the “decentralized Ethereum or EOS blockchain in the first specific example of the cash flow model of the present invention… With respect to the dementia patient 1A, the third matching record block 3113 of the matched person C of the first scheduled matching data link 3110 is linked to the second matching record block 3112 of the matched person B of the first scheduled matching data link 3110, during which the matched person C and the dementia case XX sign the matching smart contract 31131 of the smart contract 1AC”. In this reference, matching analogizes to recommendations. On pg. 23, we are further provided that "Other users can use a smart phone 232 or a laptop 233, 234 to connect to the Internet 24 through a WiFi sharer 2329 to query and process blockchain data or matching data". Regarding the “consensus among provider nodes”, citing Introducing Blockchain Applications p.102, "As you have already seen, when a transaction is created and signed, before being transmitted to the network, it is validated locally through the local Ethereum node." It is explained on p.104 that this is automatically performed with the Proof of Work Algorithm, and more recently in Ethereum 2.0, the Proof of Work algorithm; this means to say that the underlying Ethereum architecture of Huang facilitates the maintenance of consensus. Examiner notes that Introducing Blockchain Applications is cited above, not as a teaching reference to teach additional subject matter, but instead, as extrinsic evidence to show that the underlying Ethereum architecture expressly taught in Huang necessarily includes maintenance of consensus among provider nodes, as claimed. See MPEP 2131.01.)
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to use the blockchain technology as taught in Huang and apply that to the system of Abedini. Motivation to do so comes from the same rationale as outlined above with respect to Claim 7.
Claim 9
As to Claim 9, Abedini combined with Valdizan and Huang teaches all the limitations of Claim 8 as discussed above.
Huang also teaches:
The system of claim 8, wherein the instructions further cause the processor to execute a smart contract to record data reflecting the care provider recommendation on the blockchain for future audits.
(pg.25 of Huang discloses the structure of the decentralized Ethereum blockchain - noting that records are maintained regarding "a total set of contract matching records", with for an example patient "matched person A and dementia case provider XX signed a matching smart contract 31111 of smart contract 1AA...")
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to leverage blockchain technology as taught in Huang and apply that to the system of Abedini combined with Valdizan. Motivation to do so comes from the same rationale as outlined above with respect to Claim 7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE L XIE whose telephone number is (571)272-7102. The examiner can normally be reached M-F 9-5.
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/THEODORE XIE/Examiner, Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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