DETAILED ACTION
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 Application
The following is a Final Office Action. In response to Examiner's communication on 05/30/2025, Applicant on 08/28/2025, amended Claims 1, 10, 16, and canceled Claims 5-6, 14-15, 20. 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 render moot the 35 USC 102 rejections set forth in the previous action in view of new and updated grounds for rejection necessitated by Applicants’ amendments. Therefore, these rejections are withdrawn in view of the new grounds for rejection necessitated by Applicants’ amendments, as set forth below.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 argues that even if the claims involve an abstract idea, which Applicant disputes, the claims are integrated into a practical application and additionally represent significantly more per Step 2B of the analysis because while some steps may arguably recite an abstract idea, in view of the ordered combination of the steps of using machine learning (ML) techniques in an inventive manner and the underlying computer components that are fundamental to the claim, the claim as a whole is directed to an improvement to in computer technology. Examiner respectfully disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
That is, only after determining whether the claims recite limitations that, individually or in combination, that fall within one of the enumerated subject matter groups of abstract ideas in the first prong of Step 2B, under the second prong of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. However, the steps referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, for the reason detailed in the following paragraphs, the limitations referred to by Applicant are part of and directed to the recited abstract idea because they are recitations of mental
processes that can be practically performed mentally and merely use generic computer components as a tool (i.e., “a machine learning model”) to implement the mental processes.
As set forth in the MPEP, mere automation of a manual or mental process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Merely requiring that the claims use generic computer components, of which the claimed predictive modeling capability and illustrated network architecture, to implement the recited abstract idea does not make the claims directed to an improvement in technology or otherwise transform the abstract idea into a patent eligible invention. Independently analyzing abstract ideas and the means by which they are implemented is not an artificial breakage of the Claims but rather essential parts of standard 35 USC 101 analysis. Further, respectfully noted for the sake of responding to Applicant’s Remarks, this analysis is carried out in accordance with the language of the claim. Applicant at various points in the Remarks notes that implementation details found in the specification suggest an implicit impossibility of performing the claimed limitations in the human mind. However, claims are interpreted according to the broadest reasonable interpretation in light of the specification; such details from the specification cannot implicitly be understood to be part of the language of the claim, and therefore do not render the claim eligible subject matter.
The steps referred to by Applicant do not recite an improvement in technology, but rather, the steps referred to by Applicant are recitation of mental processes that can be practically performed mentally and merely use a generic computer components as a tool (i.e., a “machine learning module”, “the predictive model” in Claim 1) to implement the mental process. In fact, aside from the generic component used as a tool to implement the steps, the steps referred to by Applicant are not additional elements beyond the recited abstract idea, but, as noted above, they are recitations of mental processes that recite an abstract idea.
Viewing the limitations in combination per the pen and paper test recited in MPEP 2106.04(a)(2)(iii), a human can receive inputted patient requests to be matched to a care provider, mentally analyze the request to determine salient patient details, mentally parse historical care data, mentally represent such information in a vector(as a non-limiting but concrete example, by means of one-hot encoding qualitative attributes), and mentally use analyzed data to perform a judgement in the form of a care provider recommendation. Further, the patient can continuously monitor streams of incoming data for the sake of assessing changes, and accordingly update or revise recommendations on that basis.
In combination, these steps do not reflect an improvement in computer technology, but rather a mental process of deciding how to assign care providers to patients.
Like in Electric Power Group, the claims are not focused on a specific improvement in computers, but on certain independently abstract ideas that simply use computers as tools. Electric Power Group, LLC v. Alstom S.A,, et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
As detailed below with respect to the second prong of Step 2A, the recited abstract idea is not integrated into a practical Application because the additional elements beyond the recited abstract idea merely use generic computer components as a tool to apply the recited abstract idea.
As set forth in the MPEP, mere automation of a manual or mental process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Merely requiring that the claims use generic computer components, such as the generically recited machine learning model, to implement the recited abstract idea does not make the claims directed to an improvement in technology or otherwise transform the abstract idea into a patent eligible invention.
Like in Electric Power Group, the claims are not focused on a specific
improvement in computers, but on certain independently abstract ideas that
simply use computers as tools. Electric Power Group, LLC v. Alstom S.A,,
et al., No. 2015-1778, slip op. at 8 (Fed. Cir. Aug. 1, 2016); MPEP 2106.05(a).
Similarly, the limitations referred to by Applicant in independent Claims 10, 16 are merely recitations of mental processes that can be performed by a human mentally observing various data salient to a patient care requests and mentally performing a judgement using the observed information, with recited additional limitations failing to both integrate the abstract ideas into a practical application and amount to significantly more.
Response to Arguments – 35 USC § 102 and 35 USC § 103
Applicant’s arguments, see Pages 8-11 of Remarks, filed 08/28/2025, with respect to the rejection(s) of Claims 1, 10 and 16 under 102(a)(1) have been fully considered but are rendered moot by submitted amendments. However, upon further consideration, a new ground(s) of rejection is made in view of 35 USC 103.
Applicant notes that Abedini additionally does not teach the limitation of amended Claim 1 pertaining to “responsive to the at least one medical record having been added or removed from the user profile data, gen- erate 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.”. Examiner respectfully disagrees.
In [0041], it's stated "The machine learning based matching engine, for example, at 226, may continuously learn from previous observations and users and/or doctors' feedback. In this mechanism in one embodiment of the present disclosure, the rule based learning techniques can adopt hierarchical methods that can support complex decision making systems such as treatment philosophy". Additionally, in [0061,0062], "One or more hardware processors 304 may modify the machine learning model to retrain the model based on the feedback....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."
Abedini does not expressly disclose the remaining newly introduced amended limitations. However, Valdizan teaches:
“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;”
In [0032], the technique of continuously scanning the electronic medical repository is taught to examine for changes. " In some embodiments, EMR message monitor application 114 periodically scans EMR application 112 (e.g., every few minutes) searching for changes in a time stamp (e.g., an EPOCH time stamp), the patient treatment team and/or regimen (e.g., additional staff to the treatment team, staff removed from the treatment team, treatment updates or changes, etc.), changes in patient location, etc".
In light of this, while the 35 USC 102 rejection has been withdrawn, Claims 1, 4, 10, 13, 16, and 19 are rejected under 35 U.S.C. 103 over Abedini(US 20170177814 A1) in view of Valdizan(US20180322944).
Claims 2-3,11-12, 17-18 are now 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 7-9 are now 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 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".
Abedini does not expressly disclose the remaining limitations.
However, Valdizan teaches:
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.
(In [0032], the technique of continuously scanning the electronic medical repository is taught to examine for changes. " In some embodiments, EMR message monitor application 114 periodically scans EMR application 112 (e.g., every few minutes) searching for changes in a time stamp (e.g., an EPOCH time stamp), the patient treatment team and/or regimen (e.g., additional staff to the treatment team, staff removed from the treatment team, treatment updates or changes, etc.), changes in patient location, etc.")
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.
(In [0041], it's stated "The machine learning based matching engine, for example, at 226, may continuously learn from previous observations and users and/or doctors' feedback. In this mechanism in one embodiment of the present disclosure, the rule based learning techniques can adopt hierarchical methods that can support complex decision making systems such as treatment philosophy". Additionally, in [0061,0062], "One or more hardware processors 304 may modify the machine learning model to retrain the model based on the feedback....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.")
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.)
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 same rationale as outlined above with respect to Claim 2.
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.
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
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 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