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 Claims
This action is in reply to the action filed on 10/21/2025.
Claims 1-11 have been amended.
Claims 12-20 have been added.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
Claim Objections
Claim 16 is objected to for being dependent on itself. For the purposes of compact prosecution, Examiner will interpret Claim 16 as being dependent on Claim 15. Appropriate correction is required.
Claim Rejections 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 is rejected under 35 USC 112(b) for reciting “the tracking application”. This limitation lacks antecedent basis within Claim 20 or in Claim 1 upon which Claim 20 depends. For the purposes of compact prosecution, Examiner will interpret Claim 20 as being dependent on Claim 4, as claim 4 introduces “tracking application”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites “to analyze structural and semantic relationships”. There is no support for this limitation in the specification. Dependent Claims 2-20 are further rejected as being dependent on a rejected claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC § 101 as being directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 Analysis:
Independent Claim 1 is within the four statutory categories. Claim 1 is directed to a system. Dependent Claims 2-20 are also drawn to a system and therefore also fall into one of the four statutory categories.
Step 2A Analysis- Prong One:
Claim 1, which is indicative of the inventive concept, recites the following:
A system for facilitating patient-provider matching in dental care using treatment-specific ontologies, the system comprising:
a server; and a memory operably coupled to the server and storing instructions that, when executed by the server, cause the system to:
receive patient-specific input data via a user interface;
generate a treatment-specific ontology for the patient based on the input data, the ontology comprising at least clinical components, site components, and customer service components;
estimate a price range associated with the patient’s treatment-specific ontology;
receive provider-specific input data from a plurality of dental service providers;
generate a treatment-specific ontology for each provider based on the respective input data, each provider ontology comprising at least clinical components, site components, and customer service components;
estimate a price range associated with each provider’s treatment-specific ontology;
compare the patient’s ontology to the provider ontologies within a defined geographic region;
compute a similarity score for each provider using one or more computational models, including machine learning or rule-based algorithms, configured to analyze structural and semantic relationships between the patient’s ontology and each provider’s ontology, and to compare associated price ranges;
identify a subset of providers having the highest similarity scores;
present the identified providers to the patient via the user interface;
enable the patient to initiate an appointment request with a selected provider;
and transmit appointment information to the selected provider.
The series of steps as described in underline above, given the broadest reasonable interpretation, cover the abstract idea of certain methods of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teachings, and following rules or instructions- in this case, receiving data, generating ontologies, estimating a price, comparing ontologies, selecting the best match, and informing the provider), e.g., see MPEP 2106.04(a)(2). Any limitations not identified as part of the abstract idea are deemed “additional elements” and will be discussed in further detail below.
Dependent Claims 2, and 4-20 include other limitations directed toward the abstract idea. For example, Claim 2 recites price range estimates are performed, Claim 4 recites monitoring patient treatment progress, Claim 5 recites generating clinical recommendations based on patient-specific input data, Claim 6 recites enabling the patient to initiate and complete payment transactions for scheduled appointments, Claim 7 recites analyzing the patient’s treatment-specific ontology to identify suitable treatment options, Claim 8 recites each provider's treatment-specific ontology is analyzed to compute a reputation score for the provider based on clinical components, site components, and customer service components, Claim 9 recites the similarity score for each provider is computed to increase the increasing reliability of provider selection, Claims 10 and 11 recite taking pictures of a dental arch, Claim 12 recites each treatment-specific ontology is updated based on ground truth data received from subject matter experts, Claim 13 recites the similarity score is computed using a two-layer methodology comprising lexical mapping and conceptual mapping between the patient's treatment-specific ontology and each provider's treatment-specific ontology, Claim 14 recites presenting treatment-specific questionnaires tailored to the patient's selected specialty, Claim 15 recites generating a patient-specific workflow comprising pre-appointment actions including digital form completion, consent review, and payment processing, Claim 16 recites the patient-specific workflow includes generation of follow-up appointments, Claim 17 recites using patient feedback to generate actionable insights for performance improvement, Claim 18 recites the provider reputation score is computed using a weighted aggregation of clinical quality metrics, site attributes, and customer service evaluations, Claim 19 recites reconstructing a three-dimensional model of the patient's dental arch, Claim 20 recites tracking tooth movement over time. These limitations only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in the dependent clams not addressed above are part of the abstract idea and will be further addressed below. Hence, dependent Claims 2 and 4-20 are nonetheless directed toward fundamentally the same abstract idea as Claim 1.
Step 2A Analysis- Prong Two:
Claim 1 is not integrated into practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the server, memory, user interface, computational models, and machine learning/rules based algorithms) are recited at a high level of generality (i.e. as a generic processor performing generic computer functions) such that they amount to no more than mere instructions to apply an exception using generic computer parts. For example, Applicant’s specification explains that the server may provide an application or web-based data processing service and interface to a computer, server, or other wired or wireless communication devices (e.g., mobile phone, tablet computer, wearable devices, etc.) of one or more practice groups,…[0035]. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing,…[0059]. The analytical engine 101 uses deep learning or machine learning to construct a full arch representation from the received images [0047]. Furthermore, the additional element of the user interface was found to be generally linking to a technological field of use. Applicant’s specification explains that the user interface module 104 may use the treatment-specific ontologies and a database of questions and information forms to render interfaces for soliciting information from the stakeholders,…[0039]. MPEP 2106.04(d)(1) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into practical application because they do not impose any meaningful limits on the abstract idea. Therefore, independent Claim 1 is directed to an abstract idea without practical application.
Dependent Claims 2-8, 10-11, 14-15, 17, and 19-20 recite additional elements. Claim 2 recites previously recited elements of the memory and server as well as new elements of deep learning algorithms, a decision support module, and Claim 2 specifies price range estimation is performed by a decision support module utilizing deep learning algorithm trained on historical treatment and cost data, wherein the module is stored in memory and executed by the server. Claim 3 recites the previously recited user interface and new elements of an online portal and kiosk device and specifies the user interface is accessible via an online portal operated on a kiosk device. Claim 4 recites a new element of a tracking application and specifies the tracking application monitors patient treatment progress and transmits patient progress to a selected dental service provider. Claim 5 recites the server and a new element of artificial intelligence techniques and specifies the server generates clinical recommendations based on patient-specific input data using AI techniques. Claim 6 recites the previously recited user interface and specifies the user interface enables the patient to complete online payment transactions for scheduled appointments. Claim 7 recites the previously recited server and artificial intelligence techniques and specifies the server analyzes the patient treatment-specific ontology using AI techniques to identify suitable treatment options. Claim 8 recites a new element of artificial intelligence models and specifies the patient’s ontology is analyzed using artificial intelligence models to compute a reputation score. Claim 10 recites the previously recited tracking app and a new element of a camera-enabled device, and Claim 10 specifies the tracking application captures images of the patient dental arch using a camera-enabled device. Claim 11 recites the previously recited tracking app and a new element of a camera-enabled kiosk and specifies the tracking application captures images of the dental arch using a camera-enabled kiosk. Claim 14 recites the previously recited user interface and specifies the user interface presents dynamically generated treatment-specific questionnaires tailored to the patient’s selected dental specialty. Claim 15 recites the previously recited server and specifies the server generates a patient-specific workflow comprising pre-appointment actions. Claim 17 recites the previously recited user interface and a new element of natural language processing and specifies the user interface is parsed using natural language processing techniques to generate actionable insights for provider improvement. Claim 19 recites the previously recited user interface and server and specifies the server reconstructs a three-dimensional model of the patients dental arch based on image data submitted through the user interface. Claim 20 recites a new element of a tracking application and specifies the tracking application performs image-based tracking of tooth movement over time by applying temporal analysis to sequential dental arch images submitted by the patient. However, these additional elements are used in their expected fashion, so they do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on the abstract idea. These additional elements amount to no more than mere instructions to apply an exception, and hence, do not integrate the aforementioned abstract idea into practical application.
Step 2B Analysis:
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of the server, memory, user interface, computational models, and machine learning/rules based algorithms of Claim 1 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). MPEP 2106.05(I)(A) indicates that merely stating “apply it” or equivalent to the abstract idea cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of the at user interface was considered to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the ‘significantly more’ analysis and has been found insufficient to provide significantly more. MPEP2106.05 (A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide an inventive concept (‘significantly more"). Accordingly, even in combination, these additional elements do not provide significantly more. As such, Claim 1 is not patent eligible.
Dependent Claims 9, 12-13, 16, and 18 do not recite any additional elements and only narrow the abstract idea. Claim 9 recites the similarity score for each provider is computed to increase the increasing reliability of provider selection, Claim 12 recites each treatment-specific ontology is updated based on ground truth data received from subject matter experts, Claim 13 recites the similarity score is computed using a two-layer methodology comprising lexical mapping and conceptual mapping between the patient's treatment-specific ontology and each provider's treatment-specific ontology, Claim 16 recites the patient-specific workflow includes generation of follow-up appointments, Claim 18 recites the provider reputation score is computed using a weighted aggregation of clinical quality metrics, site attributes, and customer service evaluations.
Dependent Claims 2-3, 5-7, 10-11, 14-15, 17, and 19 recite previously recited additional elements, which are not eligible for the reasons stated above, and further narrow the abstract idea. Claim 2 recites previously recited elements of the memory and server as well as new elements of deep learning algorithms, a decision support module, and Claim 2 specifies price range estimation is performed by a decision support module utilizing deep learning algorithm trained on historical treatment and cost data, wherein the module is stored in memory and executed by the server. Claim 3 recites the previously recited user interface and new elements of an online portal and kiosk device and specifies the user interface is accessible via an online portal operated on a kiosk device. Claim 5 recites the previously recited server and a new element of artificial intelligence techniques and specifies the server generates clinical recommendations based on patient-specific input data using AI techniques. Claim 6 recites the previously recited user interface and specifies the user interface enables the patient to complete online payment transactions for scheduled appointments. Claim 7 recites the previously recited server and artificial intelligence techniques and specifies the server analyzes the patient treatment-specific ontology using AI techniques to identify suitable treatment options. Claim 10 recites the previously recited tracking app and a new element of a camera-enabled device, and Claim 10 specifies the tracking application captures images of the patient dental arch using a camera-enabled device. Claim 11 recites the previously recited tracking app and a new element of a camera-enabled kiosk and specifies the tracking application captures images of the dental arch using a camera-enabled kiosk. Claim 14 recites the previously recited user interface and specifies the user interface presents dynamically generated treatment-specific questionnaires tailored to the patient’s selected dental specialty. Claim 15 recites the previously recited server and specifies the server generates a patient-specific workflow comprising pre-appointment actions. Claim 17 recites the previously recited user interface and a new element of natural language processing and specifies the user interface is parsed using natural language processing techniques to generate actionable insights for provider improvement. Claim 19 recites the previously recited user interface and server and specifies the server reconstructs a three-dimensional model of the patients dental arch based on image data submitted through the user interface.
Dependent Claims 4, 8, and 20 recite new additional elements. Claim 4 recites a new element of a tracking application and specifies the tracking application monitors patient treatment progress and transmits patient progress to a selected dental service provider. Claim 8 recites a new element of artificial intelligence models and specifies the patient’s ontology is analyzed using artificial intelligence models to compute a reputation score. Claim 20 recites a new element of a tracking application and specifies the tracking application performs image-based tracking of tooth movement over time by applying temporal analysis to sequential dental arch images submitted by the patient. These additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Hence, Claims 1-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination does not add anything that is already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 USC § 101 as being directed toward non-statutory subject matter.
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, 3, 6, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Brown et al. (US 20220198573 A1) in view of Gu et al. (US 20180330231 A1) and Cashman et al. (US 20130173287 A1).
Regarding Claim 1, Brown discloses the following:
A system for facilitating patient-provider matching in dental care using treatment-specific ontologies, the system comprising: a server; and a memory operably coupled to the server and storing instructions that, when executed by the server, cause the system to: (Brown discloses embodiments of the present invention relate, in general, to systems and methods for administering dental care benefit programs and coordinating dental care for patients [0002]. The Matching Tool uses information about the enrollee, including the enrollee's address, demographics, medical and dental information, the dental services the enrollee requires, and other relevant information as inputs for its matching algorithm [0038]. Computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine such that the instructions that execute on the computer or other programmable apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory [0032].)
receive patient-specific input data via a user interface; (Brown discloses with reference to FIG. 7A, a standard listing of search results output by the Matching Tool is depicted as a graphical user input display. The search results include dental care providers ranked according to each provider's % Match score. The results display 700a may include a compact Matching Tool interface 710. The compact search interface 710 includes a button for setting the user's category weighting preferences 711, a button for setting the search location 712, and a search button 713 for initiating the search [0051]. The patient's dental preferences used in the Match score are interpreted as patient-specific input data.)
generate a treatment-specific ontology for the patient based on the input data, the ontology comprising at least clinical components, site components, and customer service components; (Brown discloses the beneficiary can choose to elevate location to the highest rank, and customer reviews to third, so that the beneficiary's ranking becomes as follows: 1) location, 2) price, 3) average customer review, 4) services offered, 5) hours of operation [0049]. Services offered are interpreted as clinical components, location and hours of operation are interpreted as site components, and average customer review is interpreted as customer service.)
estimate a price range associated with the patient’s treatment-specific ontology; (Brown discloses the application may also provide a default range for each criterion, e.g., a desirable price range,...The beneficiary may also adjust the default ranges for one or more criteria, e.g., lowering the maximum price for a cleaning visit,…[0049].)
receive provider-specific input data from a plurality of dental service providers; (Brown discloses the enrollee initiates the search by clicking the Search button 540, and the application generates a list of dental care providers, rank ordered by percent (%) match with the customer's criteria ranking, i.e., % Match score. The weighted categories include whether a practitioner is a participant in an IOP 611, whether the practitioner offers certain regular services 612 or optional services 613. Regular services may be specified by inputting the services into the regular services listing 630, and optional services can be specified by adding the services to the optional services listing 640. The services included in either category may be adjusted based on trends in dental care services. The Matching Tool assesses the practitioner's service offerings in relation to the inputted regular 630 and optional 640 services, and provides a score for the practitioner,…[0049-50].)
generate a treatment-specific ontology for each provider based on the respective input data, each provider ontology comprising at least clinical components, site components, and customer service components; (Brown discloses the beneficiary can choose to elevate location to the highest rank, and customer reviews to third, so that the beneficiary's ranking becomes as follows: 1) location, 2) price, 3) average customer review, 4) services offered, 5) hours of operation [0049]. Services offered are interpreted as clinical components, location and hours of operation are interpreted as site components, and average customer review is interpreted as customer service.)
estimate a price range associated with each provider’s treatment-specific ontology; (Brown discloses pricing information 764 is shown. In some embodiments, regional price guidance, see FIG. 2, 116, is accessed by clicking the pricing information 764 to display a comparison of the practitioner's offered prices to regional averages [0052]. The provider’s listed prices are an estimated price range.)
compare the patient's ontology to the provider ontologies within a defined geographic region;…compute a similarity score for each provider (Brown discloses the first component of the disclosed system is a Matching Tool or WPMS 110, which allows beneficiaries to match with dental services providers. The Matching Tool comprises a dynamic matching algorithm that accounts for pricing, customer reviews of a dentist or dental practice, location of a dental practice, hours of operation, types of services provided, and dentist or appointment availability. The weight provided to each category may be set by algorithm, adjusted by a user, adjusted by an administrator, or may be customized for a particular geographic area [0037]. The enrollee initiates the search by clicking the Search button 540, and the application generates a list of dental care providers, rank ordered by percent (%) match with the customer's criteria ranking, i.e., % Match score [0049].)
…and to compare associated price ranges; (Brown discloses the Matching Tool 110 also accounts for the dental care landscape for the enrollee, including information about dentists or dental practices in the enrollee's geographic area, such as… pricing,…[0039].)
compare the patient's estimated price range to the provider price ranges within the defined geographic region; (Brown discloses the application may also provide a default range for each criterion, e.g., a desirable price range, a desirable range of distances from the beneficiary's residence, etc. The beneficiary can choose to elevate location to the highest rank, and customer reviews to third, so that the beneficiary's ranking becomes as follows: 1) location, 2) price,… [0049].)
identify a subset of providers having the highest similarity scores; (Brown discloses the application then presents a list of dental practices in order of each practice's % Match score [0049]. Fig. 5 shows an example GUI with an ordered display. Fig. 7B depicts a GUI displaying the best dentists based upon the match score.)
present the identified providers to the patient via the user interface; (Brown discloses the search results include dental care providers ranked according to each provider’s % Match score. The results display 700a may include a compact Matching Tool interface 710. The compact search interface 710 includes a button for setting the user's category weighting preferences 711, a button for setting the search location 712, and a search button 713 for initiating the search. The results page 700a also includes entries 720a, 721, 722, 723 for dentists or dental practices ranked based on their % Match score 730. The displayed entries may either be expanded 720a as indicated by a caret button 740 oriented toward the top of the page, or compact 721, as indicted by a caret button oriented toward the bottom of the page 741…All entries display basic information, such as the name of the dentist or dental practice, their % Match score 730, … [0049].)
enable the patient to initiate an appointment request with a selected provider; (Brown discloses expanded entries 720a may also include contact information 762 for the practitioner,…as well as buttons for requesting an appointment 750,…[0051].)
Brown does not disclose computing the similarity score by an algorithm to analyze relationships which is met by Gu:
compute a similarity score for each provider using one or more computational models, including machine learning or rule-based algorithms, configured to analyze structural and semantic relationships… (Gu teaches the clustering may be performed using a similarity metric (e.g., how alike, a homogeneous score, based on attributes of the data such as syntax, semantics, etc.) and based on analyzing the set of event data using the IMTM technique…Clustering techniques may include a … algorithm for performing statistical data analysis with respect to the set of event data [0060]. Computer modules that question analyzer 314 may include, but are not limited to a tokenizer 316, part-of-speech (POS) tagger 318, semantic relationship identification 320, and syntactic relationship identification 322 [0040]. Syntactic relationship identification 322 can determine the grammatical structure of sentences, for example, which groups of words are associated as “phrases” and which word is the subject or object of a verb. In certain embodiments, syntactic relationship identification 322 can conform to a formal grammar [0044]. The syntactic relationship is interpreted as a structural relationship. )
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed inventio to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a similarity score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate the similarity score being computed using an algorithm to analyze structural and semantic relationships as taught by Gu. This modification would create a system and methods capable of managing data as efficiently as possible (see Gu, ¶ 0001-2).
Brown and Gu do not teach transmitting appointment information to the provider which is met by Cashman:
and transmit appointment information to the selected provider (Cashman teaches a Provider Application that is designed to enable the medical provider to navigate through the appoint process during the patient appointment. The Provider Application can be designed to allow the medical provider to a) display appointment, b) display patient information, c) … the Provider Application can be designed to enable the medical provider to view appointments that have been completed by the medical provider, … and/or which are future appointments. [0022].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate informing the provider about booked appointments as taught by Cashman. This modification would create a system and methods capable of providing medical services in a more convenient, desirable, timely, and cost effective manner (see Cashman, ¶ 0008).
Regarding Claim 3, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown further discloses:
… the user interface… (Brown discloses the application includes a graphical user interface to facilitate interaction with a user, and performs a search for dental services providers according to a set of user criteria (abstract).)
Brown and Gu do not teach the following limitation met by Cashman:
wherein the…interface is accessible via an online portal and configured to operate on a kiosk device located at a remote site. (Cashman teaches the novel…for providing medical services, diagnoses, health, and/or wellness advice to individuals can include one of more software applications (e.g., …patient portal, provider portal, etc.) [0016]. The schedule system allows a patient to schedule an appointment with a medical provider when the patient uses the medical kiosk [0015]. The method and apparatus …to create a conference link between a patient located in the medical kiosk and a medical provider that is located remotely to the medical kiosk [0034].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate the patient accessing an online portal at a remote kiosk to book an appointment as taught by Cashman. This modification would create a system and methods capable of providing medical services in a more convenient, desirable, timely, and cost effective manner (see Cashman, ¶ 0008).
Regarding Claim 6, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown further discloses:
the user interface… (Brown discloses the application includes a graphical user interface to facilitate interaction with a user, and performs a search for dental services providers according to a set of user criteria (abstract).)
Brown and Gu do not teach the following limitations met by Cashman:
wherein the…interface is further configured to enable the patient to initiate and complete online payment transactions for scheduled appointments. (Cashman teaches a user/patient can be allowed to enter payment information at the registration station (e.g., swipes a card or debit card, etc.); however it can be appreciated that payment information can also or alternatively be entered…. Wirelessly or over a network via a smart phone or other device or by a computer connected to a network, etc. [0029]. A medical kiosk that includes a payment center than enables a user to pay for medical services….. prior to and/or after the user uses the medical kiosk….. The payment center can be in any form….. As can be appreciated, a user can be allowed to wirelessly connect to the medical kiosk or to some other computer network so as to wirelessly register and/or enter payment information for use of the medical kiosk [0038]. [P]atient appointment application can include one or more functions selected from the group consisting of…enabling a patient to schedule a new appointment,… 3) welcoming a patient that has already scheduled an appointment,… 18) requesting the patient to make a copay based the medical insurance or to fully pay for the medical visit,…(Claim 13).)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate online payment services being offered to the patient as taught by Cashman. This modification would create a system and methods capable of providing medical services in a more convenient, desirable, timely, and cost effective manner (see Cashman, ¶ 0008).
Regarding Claim 15, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown and Gu do not teach the following limitations met by Cashman:
the server is further configured to generate a patient-specific workflow comprising pre-appointment actions including digital form completion, consent review, and payment processing (Cashman teaches the use of a medical kiosk to enable the patient to conveniently communicate with the medical provider…The medical kiosk having an exterior check-in registration station can be used by the patient to enter/convey basic information about the patient. Such information includes, …n) consent forms [0029]. The schedule system allows a patient to schedule an appointment with a medical provider when the patient uses the medical kiosk. The scheduling system can be used to… enter a partial or fully payment for use of the medical kiosk, 8) enter medical insurance information, etc. [0015].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate a workflow comprising pre-appointment actions as taught by Cashman. This modification would create a system and methods capable of providing medical services in a more convenient, desirable, timely, and cost effective manner (see Cashman, ¶ 0008).
Claims 2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Brown, Gu, and Cashman as applied to Claim 1 above, and further in view of Mowery et al. (US 20200168334 A1).
Regarding Claim 2, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above.
… is stored in memory and executed by the server. (Gu teaches the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk,…[0084]. The computer systems may include server, desktop, laptop,…[0032].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a similarity score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate the use of a module being stored on a memory and operated on a server as taught by Gu. This modification would create a system and methods capable of managing data as efficiently as possible (see Gu, ¶ 0001-2).
Brown, Gu, and Cashman do not teach the following limitation met by Mowery:
wherein the price range estimation is performed by a decision support module utilizing deep learning algorithms (Mowery teaches the present invention that provides for an advanced predictive method for surgery, specifically to a mechanism for predicting…cost analysis using deep learning techniques [0013].)
trained on historical treatment and cost data, wherein the decision support module (Mowery teaches the chosen trained model which is dynamically calculated provides a predictive cost,…[0017]. [A] real time predictive system that optimizes surgical decisions based on historic patient data,… cost,…[0012].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate estimating prices using deep learning as taught by Mowery. This modification would create a system and methods capable of predicting real time costs and ensure they are as accurate as possible (see Mowery, ¶ 0002, 0012).
Regarding Claim 5, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown further discloses:
… based on patient-specific input data…(Brown discloses with reference to FIG. 7A, a standard listing of search results output by the Matching Tool is depicted as a graphical user input display. The search results include dental care providers ranked according to each provider's % Match score. The results display 700a may include a compact Matching Tool interface 710. The compact search interface 710 includes a button for setting the user's category weighting preferences 711, a button for setting the search location 712, and a search button 713 for initiating the search [0051]. The patient's dental preferences used in the Match score are interpreted as patient-specific input data.)
Brown, Gu, and Cashman do not teach the following limitation met by Mowery:
wherein the server is further configured to generate clinical recommendations …using artificial intelligence techniques. (Mowery teaches a mechanism for predicting surgical decisions, procedural success rates, surgical complications, pharmaceutical decisions,… using deep learning techniques [0013].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate estimating clinical decisions using artificial intelligence as taught by Mowery. This modification would create a system and methods capable of ensuring clinical data predications are as accurate as possible (see Mowery, ¶ 0002, 0012).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Brown, Gu, and Cashman as applied to Claim 1 above, and further in view of Muñoz et al. (US 20160339607 A1).
Regarding Claim 4, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown, Gu, and Cashman do not teach the following limitation met by Muñoz:
a tracking application configured to monitor patient treatment progress and transmit patient progress data to a selected dental service provider. (Muñoz teaches application 60B can also be designed to communicate with medical software packages or other similarly related smartphone applications via the internet 80B and/or a cloud network 80A. For example, in an embodiment, application 60B also allows the physician to provide personalized care for patients by providing, for example, online treatment design, monitoring and modification of the treatment process at any time, remote control and monitoring of therapeutic device 50, analysis of progress data for each patient, and the ability to conduct a remote assessment of the patient using the phone's camera [0058].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate a tracking app that sends a patient’s progress to the provider as taught by Muñoz. This modification would create a system capable of keeping the provider up to date on the patient’s status to guide treatment plans (see Muñoz, ¶ 0054).
Claims 7-9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Brown, Gu, and Cashman as applied to Claim 1 above, and further in view of Shrager et al. (US 20200411199 A1).
Regarding Claim 7, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown further discloses:
wherein the server is configured to analyze the patient’s treatment specific ontology… (Brown discloses the Matching Tool uses information about the enrollee, including the enrollee's address, demographics, medical and dental information, the dental services the enrollee requires, and other relevant information as inputs for its matching algorithm [0038]. In order to match, the algorithm analyzes the patient data.)
Brown, Gu, and Cashman do not teach the following limitation met by Shrager:
…using artificial intelligence techniques to identify suitable treatment options. (Shrager teaches a decision engine uses self-learning schemes to analyze cases in the knowledge base, for example, machine learning algorithms, artificial intelligence algorithms, … and/or other forms of self-learning schemes that are able to understand the information about the patient and/or the patient's treatment regimen, and derive a conclusion about the possible treatment options for the user based on this data [0119].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, analyzing and comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate using artificial intelligence to find treatment options as taught by Shrager. This modification would create a system and methods capable of efficiently searching the voluminous space of treatment options (see Shrager, ¶ 0007).
Regarding Claim 8, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown further discloses:
wherein a provider’s treatment-specific ontology is analyzed …(Brown discloses the Matching Tool uses information about the enrollee, including the enrollee's address, demographics, medical and dental information, the dental services the enrollee requires, and other relevant information as inputs for its matching algorithm [0038]. In order to match, the algorithm analyzes the patient data.)
based on clinical components, site components, and customer service components. (Brown discloses the application presents a default ranking of criteria, e.g., listed from most important 510 at the top to least important 511 at the bottom. These criteria include, for example: 1) price 520, 2) location 521, 3) average customer review 522, 4) hours of operation 523, and 5) services offered 524. In some embodiments, the application may also provide a default range for each criterion, e.g., a desirable price range [0049].)
Brown, Gu, and Cashman do not teach the following limitation met by Shrager:
wherein a provider’s ontology is analyzed …using one or more artificial intelligence models configured to compute a reputation score for the provider…(Shrager teaches where predictive treatment options are provided, such predictive treatment options may be assigned a predicted outcome score for each of one or more outcomes (e.g., 1 for 3 month survival, 0.8 for 5 year survival, 0.1 for 10 year survival, etc.) based on the cumulative training of the neural network, and recordation of actual outcome may be provided for comparison, the differential of which may be propagated back through the system to iteratively train the neural network for higher accuracy [0094].)
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have modified the system for receiving data from a patient, analyzing and comparing patient data to provider information, determining a score for the matching data, and displaying the best dental service to the patient as disclosed by Brown to incorporate using artificial intelligence to estimate a reputation score as taught by Shrager. This modification would create a system and methods capable of efficiently searching the voluminous space of treatment options and determining the optimal treatment (see Shrager, ¶ 0007).
Regarding Claim 9, Brown, Gu, and Cashman teach the limitations as shown in the rejection of Claim 1 above. Brown fur