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 .
Acknowledgments
Claims 1-26 are pending.
Applicant provided information disclosure statement.
Allowable Subject Matter
Claims 13 and 26 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Karnati (US20160379173A1) in further view of Kogan (US20230005607A1) in further view of Wicaksono (US20160173692A1) in further view of Alqabandi (US20220262468A1) who teaches a surcharge (i.e. cost not covered by insurance) in para 0072. However, with respect to exemplary claim 13 and 26, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 13 and 26.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-26 are directed to a system and method.
Regarding step 2A-1, Claims 1-26 recite a Judicial Exception. Exemplary independent claim 1 and similarly claim 14 recite the limitations of
…receiving…a request to schedule a medical appointment for a patient; determining…for each particular available appointment slot of one or more available appointment slots for scheduling the requested medical appointment, a corresponding price, wherein a training process trains the… model by: obtaining first training data comprising first historical data representing a first plurality of past medical appointments; training, based on the first training data, the…model in a first stage to teach the…model to determine a corresponding price for each particular appointment slot of an inputted schedule to achieve a target utilization rate for each particular appointment slot of the inputted schedule, the target utilization rate representing a percentage of time that an appointment is scheduled in the particular appointment slot; obtaining second training data comprising second historical data representing a second plurality of past appointments scheduled based on prices determined using the …model; and training, based on the second training data, the…model in a second stage to teach the…model to determine additional work and/or wage premiums to offer to staff to ensure adequate staffing for over utilized appointment slots in the second historical data; and providing… the one or more available appointment slots and the corresponding prices.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of receiving, determining, obtaining, training, and providing data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and load leveling machine learning model, the claim language encompasses simply receiving a request about an appointment, determining an appointment price that includes training a model and obtaining data, determine additional work/wage premiums for staff with respect to appointments, and providing available appointments to a requestor. These are mere data manipulation steps that do not require a computer. For example, a manager at a medical office can determine an appointment price without the use of a computer and by mere data analysis. In addition, training a model can also be done without a computer. Training merely means to picking the best variable for a model. The claimed invention is merely automating a manual process.
The claims also recite determining an appointment price and determining wage/work for staff. The specification also recites resource allocation with respect to needs of customers as seen in para 0002. These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (sales activity, fundamental economic principles or practices; business relations). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims recite the additional elements of data processing hardware, user interface, load leveling machine learning model, system, and memory hardware.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using 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 more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe additional variables the appointment price is based on such as ratings for medical providers. In addition, the dependent claims further describe what the rating comprises such as case mix rating. In addition, the dependent claims further describe what the training of machine learning model does such as increase revenue.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites data processing hardware, user interface, load-leveling machine-learning (ML) model
Claim 10 and 23 recite ratings ML model.
Claim 14 recites system, processing hardware, memory hardware, user interface, load-leveling machine-learning (ML) model,
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states a general purpose processor as seen in para 0065.
When looking at the additional elements in combination, the Applicant’s specification merely states a general purpose processor as seen in para 0065. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-26 are rejected under 35 U.S.C. 101.
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.
Claim(s) 1-12 and 14-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karnati (US20160379173A1) in further view of Kogan (US20230005607A1) in further view of Wicaksono (US20160173692A1).
Regarding claim 1 and similarly claim 14, Karnati teaches
A computer-implemented method (See abstract-Methods and systems are described for providing a marketplace between consumers (e.g., patients, parents, guardians, pet owners, and/or other users) and providers of services, such as health services.) This teaches a method.
A system comprising:data processing hardware; and memory hardware in communication with the data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations comprising (See fig. 1) This shows a computer that includes processor and memory. (See para 0105-Such computer programs, when executed, may enable the computer system 500 to perform the features in accordance with aspects of the present invention, as discussed herein. )
executed on data processing hardware that causes the data processing hardware to perform operations comprising (See fig. 1) This show a system with data processing hardware. (See para 0105-Such computer programs, when executed, may enable the computer system 500 to perform the features in accordance with aspects of the present invention, as discussed herein. )
receiving, from a person via a user interface, a request to schedule a medical appointment for a patient (See para 0022-FIG. 10 illustrates a consumer interface for selecting a service in accordance with aspects of the present disclosure.) This shows a person who is the patient making a request to schedule a medical appointment such as selecting what service they want.
Determining…for each particular available appointment slot of one or more available appointment slots for scheduling the requested medical appointment, a corresponding price (See para 0044-When determining prices corresponding to available appointments, the system 10 may apply the third party payer schedule of payments to provider-specified prices to determine prices to be paid by the consumer 30.) This shows the system determines prices for having appointments. The prices are with respect appointment slots as seen in fig. 14.
providing, to the person via the user interface, the one or more available appointment slots and the corresponding prices. (See figure 14).
Even though Karnati teaches determining prices, it doesn’t teach this is done with respect to machine learning, however Kogan teaches
determining, using a load-leveling machine-learning (ML) model…(See para 0021- The predictive model generated by the appointment optimization and route planning system (AORPS) comprises, for example…expected costs… In an embodiment, the AORPS utilizes one or more of decision trees, machine learning models, and regression models for generating and executing the predictive model and for generating the appointment schedule) This shows that the system uses load leveling machine learning model which corresponds to a machine learning model to determine costs such as expected costs with respect to medical appointments.
Karnati and Kogan are analogous art because they are from the same problem solving are of appointment schedules which include medical appointments. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's invention by incorporating the method of Kogan because Karnati can also implement a machine learning model to determine prices. The machine learning model would help the system of Karnati handle complex data sets and help the user select an optimized appointment time. Optimized appointment times would lead to less cancellations and less revenue loss for the medical service providers. The machine learning model would also provide a more accurate calculation for appointment time pricing since machine learning models get more accurate as they learn more data.
Kogan further teaches wherein a training process trains the load-leveling ML model by: obtaining first training data comprising first historical data representing a first plurality of past medical appointments (See para 0018- The AORPS generates a predictive model for appointments, capitation, and return on investment for delivering patient care based on training data comprising, for example, appointment history) (See para 0055- The historical data comprises, for example, feedback from the patients, the healthcare providers, the onsite care coordinators, and the client about fulfilled home-visit appointments, and data about acceptance or rejection of scheduled appointments) This shows that the machine learning model is trained on historical data such as accepted past medical appointments.
training, based on the first training data, the load-leveling ML model in a first stage to teach the load-leveling ML model to determine a corresponding price for each particular appointment slot of an inputted schedule (See para 0021-The predictive model generated by the appointment optimization and route planning system (AORPS) comprises, for example…expected costs) (See para 0055-As appointments are scheduled and fulfilled, the output generation module 106 C obtains historical data from collected feedback and appointment history and uses the historical data as part of the training data to generate the predictive model and as part of the optimization factors to generate the appointment schedule with the travel routes accordingly.) This shows that the training of the predictive model (i.e. machine learning model) is with respect to making an optimized appointment schedule and it determines expects costs associated with that schedule. The schedule includes time slots as seen in para 0119, so the system is determining costs with respect to those slots. In addition, Karnati already teaches determining price for time slots.
Karnati and Kogan are analogous art because they are from the same problem solving area of appointment schedules which include medical appointments. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's invention by incorporating the method of Kogan because Karnati can also implement a machine learning model to determine prices. The machine learning model would help the system of Karnati handle complex data sets and help the user select an optimized appointment time. Optimized appointment times would lead to less cancellations and less revenue loss for the medical service providers. The machine learning model would also provide a more accurate calculation for appointment time pricing since machine learning models get more accurate as they learn more data.
In addition, the art of Karnati is determining prices to make sure the time slots are utilized, Karnati teaches to achieve a target utilization rate for each particular appointment slot of the inputted schedule, the target utilization rate representing a percentage of time that an appointment is scheduled in the particular appointment slot The provider of Karnati is trying to make sure all appointments slots are filled all the time (i.e. target utilization rate). This is why the provider in Karnati provides discounts for slow appointment times so that they may be filled all the time (i.e. achieve target utilization rate). (See para 0049- As another example, the discount may be based on the time of day. For example, if a provider 20 typically has a time of day that is slow (e.g., from 1 pm to 2 pm), the provider 20 may offer a higher discount on services provided during the slow time than prices offered on services during a peak time of day. )
However Karnati doesn’t teach training data such as second training data, however Kogan teaches obtaining second training data comprising second historical data representing a second plurality of past appointments scheduled based on prices determined using the load-leveling ML model Kogan already teaches determining expected costs with respect to appointments as taught above. Karnati teaches different kinds of historical data in addition to the first training data (i.e. accepted appointments). The second historical training data corresponds to details about the whether the user actually went to the appointments in the appointment history (i.e. appointments that were rejected/cancelled/absent from). (See para 0055- The historical data comprises, for example, feedback from the patients, the healthcare providers, the onsite care coordinators, and the client about fulfilled home-visit appointments, and data about acceptance or rejection of scheduled appointments, visit cancellation, absence for a scheduled home visit).
Karnati and Kogan are analogous art because they are from the same problem solving area of appointment schedules which include medical appointments. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's invention by incorporating the method of Kogan because Karnati can also implement a machine learning model to determine prices. The machine learning model would help the system of Karnati handle complex data sets and help the user select an optimized appointment time. Optimized appointment times would lead to less cancellations and less revenue loss for the medical service providers. The machine learning model would also provide a more accurate calculation for appointment time pricing since machine learning models get more accurate as they learn more data.
Even though Kogan teaches this second training data, it unclear if it used to update (i.e. retrain the machine learning model). However another section of Kogan teaches
and training, based on the… training data, the load-leveling ML model in a second stage to teach the load-leveling ML model (See para 0054-The output generation module 106 c generates the predictive model by analyzing the training data using algorithms comprising, for example, decision trees, machine learning, and regression models. The output generation module 106 c also executes artificial intelligence algorithms for generating and updating the predictive model in an agile environment) This teaches the training is done again (i.e. second stage) on the predictive model (i.e. ML model) since the predictive model is updated. This update can be with respect to additional data as seen here (See para 0054-The output generation module 106 c uses this feedback along with medical records of the patients, appointment and patient history, and healthcare data as training data for model training to subsequently generate the predictive model.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the declined appointment data (i.e. second training data) also as feedback data to retrain the predictive model. The predictive model would be able to utilize which appointments the user did not attend and retrain the model to not make an appointment schedule with respect to the days and times the user did not go to the appointments. This would ensure more appointments are attended by the patients which makes the art of Kogan more robust.
In addition, even though Karnati teaches historical data and over utilized appointment slots (i.e. appointment slots that are high in demand, See para 0050-0051 in Karnati) and Kogan teaches second training data, they do not teach to determine additional work and/or wage premiums to offer to staff to ensure adequate staffing for over utilized appointment slots in the second historical data
However Wicaksono teaches to determine additional work and/or wage premiums to offer to staff to ensure adequate staffing for over utilized appointment slots in the second historical data (See para 0176- During busy periods, more staff may be allocated to handling phone/chat contact types if they need more staff in order to achieve respective service goals. Optimization of when to re-allocate staff from Email/casework to phone/chat and how many staff should be re-allocated results.) This teaches determining additional work during busy periods to offer staff such as reallocating them to phone/chat when that sector needs more staff.
Karnati, Kogan, and Wicaksono are analogous art because they are from the same problem solving area of personnel management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's and Kogan’s invention by incorporating the method of Wicaksono because Karnati could also make it so to make sure the provider has adequate staff when a patient books an appointment. This would make the system of Karnati more sophisticated since it adds another layer of analysis to make sure the patient appointment goes smoothly. Kogan could also use the staff data by the provider to make sure the appointment optimization guides a patient to a provider that has adequate staff to accommodate them. This also makes the system of Kogan more sophisticated since it adds another layer of analysis. This would also ensure the patients give positive feedback data to the provider when the predictive model is updated with such data.
Regarding claims 2 and similarly claim 15, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Karnati further teaches
wherein: at least one of the available appointment slots for scheduling of the requested medical appointment comprises corresponding available appointments slots for one or more medical providers that can perform the medical appointment (See fig. 14) This shows available appointments slots for the medical provider.
determining the corresponding price for the at least one of the available appointment slots comprises determining a corresponding price for each of the one or more medical providers for the at least one the available appointment slots (See fig. 14) This shows the corresponding price that is determined.
and presenting, in the user interface for the person, the one or more available appointment slots and the corresponding prices comprises presenting the corresponding available appointment slots for the one or more medical providers and the corresponding price for each of the one or more medical providers for the at least one of the available appointment slots. (See fig. 14) This shows that the medical calendar is shown to the user on a user interface. This shows the corresponding price and available time slots. (See para 0026- FIG. 14 illustrates a consumer interface for displaying a detailed provider schedule in accordance with aspects of the present disclosure).
Regarding claims 3 and similarly claim 16, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Kogan further teaches
wherein the first historical data comprises, for each past appointment of the first plurality of past appointments, at least one of:a date;a day of a week;a day of a month;a month of a year;a scheduled start time;an actual start time;a duration;a current procedural terminology (CPT) code;a procedure description;a practitioner identifier;an equipment identifier;a location identifier;a room identifier; or a surcharge amount. (See para 0018- The AORPS generates a predictive model for appointments, capitation, and return on investment for delivering patient care based on training data comprising, for example, appointment history) This shows historical data with respect to appointment history, the history includes location identifier such as at home visits. (See para 0040- The appointment optimization and route planning system (AORPS) 106 optimizes home-visit appointments )
Karnati and Kogan are analogous art because they are from the same problem solving are of appointment schedules which include medical appointments. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's invention by incorporating the method of Kogan because Karnati can also implement a machine learning model to determine prices. The machine learning model would help the system of Karnati handle complex data sets and help the user select an optimized appointment time. Optimized appointment times would lead to less cancellations and less revenue loss for the medical service providers. The machine learning model would also provide a more accurate calculation for appointment time pricing since machine learning models get more accurate as they learn more data.
Regarding claims 4 and similarly claim 17, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Kogan further teaches
wherein the first historical data represents past appointments for a plurality of medical providers. (See para 0054- The output generation module 106 c uses this feedback along with medical records of the patients, appointment and patient history, and healthcare data as training data for model training to subsequently generate the predictive model). (See para 0056- The predictive model that the output generation module 106 c generates analyzes the history of home-visit appointments by the healthcare providers and occupational healthcare consultants and the history of hospital and emergency room (ER) visits by the patients.) This shows the historical data shows past appointments from plurality of healthcare workers.
Karnati and Kogan are analogous art because they are from the same problem solving are of appointment schedules which include medical appointments. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's invention by incorporating the method of Kogan because Karnati can also implement a machine learning model to determine prices. The machine learning model would help the system of Karnati handle complex data sets and help the user select an optimized appointment time. Optimized appointment times would lead to less cancellations and less revenue loss for the medical service providers. The machine learning model would also provide a more accurate calculation for appointment time pricing since machine learning models get more accurate as they learn more data.
Regarding claims 5 and similarly claim 18, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Kogan teaches machine learning model, however Karnati further teaches
wherein the training process trains the load-leveling ML model to increase a revenue associated with a particular appointment slot. (See para 0049-The system 10 may also allow a provider 20 to selectively discount the price for the services provided. For example, as illustrated in FIG. 6, a provider 20 may configure an express discount to target upcoming open appointment times. The provider 20 may offer a large discount for upcoming same-day appointments that would otherwise go unfilled.) This shows increasing revenue for an appointment slot that would otherwise go unfilled. The provider is trying to have all the appointment slots filled.
Regarding claims 6 and similarly claim 19, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Kogan teaches machine learning model, however Karnati further teaches
wherein the training process trains the load-leveling ML model to determine the corresponding price based on loyalty program information for the patient. (See para 0057- In an aspect, the system 10 may offer a discount, rewards, or credits instead of or in addition to a discount offered by a provider 20. For example, the system 10 may offer a discount based on repeated use of the system or repeated appointments with a provider 20. Alternatively, the system 10 may offer a discount after completing a requisite number of appointments while attaining a requisite average rating. For example, the system 10 may award credits and/or another form of rewards points to consumers 30 and providers 20 who use the system 10 frequently (e.g., book appointments, provide services, rate providers, or share experiences). This shows discounts are received based on loyalty program information such as reward points. Reward points are received when consumers/providers use the system frequently.
Regarding claims 7 and similarly claim 20, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Karnati further teaches
wherein the operations further comprise: receiving, from the person via the user interface, a selection of a selected appointment slot of the one or more available appointment slots (See fig. 14) This shows the person can select an appointment slot by selecting “book.” This is received from the person via the interface.
and allocating the corresponding price for the selected appointment slot to an account associated with the patient. The price/payment is allocated to the account of the patient and they have to pay as seen here (See para 0045- The payment system 80 may include any financial services network. The payment system 80 may allow consumers 30 to pay for services. In an aspect, the payment system 80 may first process a payment from a consumer 30 to the system 10. For example, the consumer 30 may make a payment via the system 10 upon completion of an appointment during a check-out process… For example, a payment from a consumer 30 via the system 10 may include use of a credit card, debit card, digital currency, online account (e.g., PayPal or Bitcoin) or other payment service or feature. A payment via the system 10 to a provider 20 may use a direct transfer, electronic check, automated clearing house (ACH), or other payment service or feature… A guest may not need to register an account with the system 10. Instead, for example, a guest may book an appointment with a provider 20 through the system 10 and pay via a credit card. ) This shows online payment account such as PayPal or bitcoin.
Regarding claims 8 and similarly claim 21, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, Kogan already teaches a machine learning model, however Karnati further teaches
wherein the training process trains the load-leveling ML model to determine the corresponding price for an available appointment slot based on a rating for a medical provider. (See para 0053- In an aspect, the discounts may be based on or correlated with a rating of a provider 20 and/or a rating of a consumer 30. For example, the system 10 may default a provider 20 with a relatively low ranking to offer a relatively large discount to attract customers. Conversely, the system 10 may default a relatively high ranking provider 20 to offer a smaller discount. ) This teaches that rating is tied to the price of the appointment with respect to rating of the provider.
Regarding claims 9 and similarly claim 22, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Karnati further teaches
wherein the rating for the medical provider comprises at least one of:an overall rating;a patient satisfaction score;a volume;a duration of service rating;an on-time start rating; or a case mix rating. (See fig. 20) This shows patient satisfaction score.
Regarding claims 10 and similarly claim 23, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Karnati further teaches
Determining…the rating for the medical provider… to determine one or more ratings for each of the plurality of medical providers. (See para 0060- and the provider's average rating (e.g., inputted ratings by consumers 30, as described further below). ) This teaches the system determines ratings for medical providers with respect to consumer inputs.
However, Karnati doesn’t teach this is done with respect to obtaining EHR records and training a model. However Kogan teaches
determining, using a ratings ML model… wherein a second training process trains the rating ML model by: obtaining records from an electronic health records (EHR) system associated with a plurality of medical providers; and training the rating ML model, using the records Kogan teaches a second training process since it teaches updating a machine learning model (See para 0054-The output generation module 106 c generates the predictive model by analyzing the training data using algorithms comprising, for example, decision trees, machine learning, and regression models. The output generation module 106 c also executes artificial intelligence algorithms for generating and updating the predictive model in an agile environment) This teaches the training is done again (i.e. second stage) on the predictive model (i.e. ML model) since the predictive model is updated. (See para 0046-The output generation module 106 c obtains healthcare data, for example, electronic health records (EHR), ) (See para 0047- In an embodiment, the output generation module 106 c utilizes one or more of decision trees, machine learning models, and regression models for generating and executing the predictive model and for generating the appointment schedule.) This shows that this data which is also historical data is used to train machine learning models. This is also seen here (See para 0055-In an embodiment, the training data for the generation of the predictive model and the optimization factors for the generation of the appointment schedule with the travel routes comprise, among other data, historical data. The historical data comprises). The ratings ML model also corresponds to the predictive model as seen in Kogan.
Karnati and Kogan are analogous art because they are from the same problem solving area of appointment schedules which include medical appointments. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's invention by incorporating the method of Kogan because Karnati can also implement a machine learning model to determine provider ratings. The machine learning model would provide a more accurate calculation for the provider rating since machine learning models get more accurate as they learn more data. This would help the user make the best decision on which provider to choose.
Regarding claims 11 and similarly claim 24, Karnati, Kogan, and Wicaksono teach the limitations of claims 10 and 23, however Karnati further teaches
obtains current ratings information for each of the plurality of medical providers… to determine the one or more ratings for each of the plurality of medical providers. (See para 0023-FIG. 11 illustrates a consumer interface for selecting search filters in accordance with aspects of the present disclosure.) This shows that when the consumer is selecting filters, the results will show the current ratings for the providers. The system determines the ratings based on the system filters.
However Karnati doesn’t teach that this is done with respect to machine learning and EHR records, however Kogan teaches wherein the second training process…and trains the rating ML model, using the records…information Kogan teaches a second training process since it teaches updating a machine learning model (See para 0054-The output generation module 106 c generates the predictive model by analyzing the training data using algorithms comprising, for example, decision trees, machine learning, and regression models. The output generation module 106 c also executes artificial intelligence algorithms for generating and updating the predictive model in an agile environment) This teaches the training is done again (i.e. second stage) on the predictive model (i.e. ML model) since the predictive model is updated. (See para 0046-The output generation module 106 c obtains healthcare data, for example, electronic health records (EHR), ) (See para 0047- In an embodiment, the output generation module 106 c utilizes one or more of decision trees, machine learning models, and regression models for generating and executing the predictive model and for generating the appointment schedule.) This shows that this data which is also historical data is used to train machine learning models. The ratings ML model also corresponds to the predictive model as seen in Kogan.
Karnati and Kogan are analogous art because they are from the same problem solving area of appointment schedules which include medical appointments. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Karnati's invention by incorporating the method of Kogan because Karnati can also implement a machine learning model to determine provider ratings. The machine learning model would provide a more accurate calculation for the provider rating since machine learning models get more accurate as they learn more data. This would help the user make the best decision on which provider to choose.
Regarding claims 12 and similarly claim 25, Karnati, Kogan, and Wicaksono teach the limitations of claims 1 and 14, however Karnati further teaches
wherein presenting, in the user interface for the person, the one or more available appointment slots and the corresponding prices comprises presenting a schedule, wherein the schedule comprises each available appointment slot the corresponding price. (See fig. 14) This shows a schedule that is presented to the user interface.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Alqabandi (US20220262468A1) who teaches a surcharge (i.e. cost not covered by insurance) in para 0072.
Merle (20140039961) who teaches forecasting demand for labor in an organization.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
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