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 .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/452,920, filed on April 4, 2025. In response to Examiner’s Office Action of August 21, 2023, Applicant, on July 11, 2025, amended claims 1, 3, 5, 6, and 7; cancelled claims 2 and 4; and added claims 8-9. Claims 1, 3, 5-9 are pending in this application and have been rejected below.
Priority
3. The Examiner has noted this claims Priority from JP2022-132543 filed August 23, 2022.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding the 35. U.S.C. § 101 rejection, Applicant’s arguments have been
considered and is insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to applicants’ amendments. Updated 35 U.S.C. § 103 rejections have been applied to amended claims. Please refer to the § 103 rejection for further explanation and rationale.
Response to Arguments
Applicant’s arguments filed July 11, 2025 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed July 11, 2025.
On Pg. 8-9 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states the additional elements of implement transmitting the generated feedback data for service provider to one or more terminals of the one or more service providers and transmitting the feedback data for the person to one or more terminals of the one or more persons transforms the claims, as a whole, are directed to subject matter that is significantly more than an abstract idea. In response, the claims primarily recite the additional element of using computer components to perform each step. The “data processing device”, “computer”, “terminal” and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
On Pg. 10-11 of the Remarks, regarding 35 U.S.C. § 103 rejections. Applicant states Cinnor in view of Orumchian fails to disclose or suggest the features of amended independent claim 1 regarding transmitting the feedback data for service provider to one or more terminals of the one or more service providers and transmitting the feedback data for the person to one or more terminals of the one or more persons, wherein the step of generating, the feedback data for the service provider and the feedback data for the person comprises the steps of performing a future utilization forecast for the one or more services by a specific person among the one or more persons based on the dataset; specifying a service predicted to be used by the specific person in the future and at least one of a location and a time at which the service is predicted to be used, based on the utilization forecast data; and specifying, from among the one or more service providers, a service provider that provides the service predicted to be used by the specific person in the future, wherein the feedback data for the service provider includes data of the location at which the specified service is predicted to be used, and wherein, in the step of transmitting the feedback data for the person, the feedback data for the service provider is transmitted to a terminal of the specified service provider. In response, Cinnor in view of Orumchian discloses the elements of rolled up claims 2 and 4. Specifically Orumchian discloses in Par. 17-18-“ The information database is to embody the forecast data and to receive essentially real-time updates to the forecast data through the user interface. The user interface is to display the forecast data through the user interface with feedback as the data changes over time. In still another embodiment, the invention is a method of providing feedback from members of an organization on forecasts to sales people entering data for the forecasts. The method includes receiving the data for the forecasts from the sales people in a computer.
Please see additional 103 analysis below for additional detail.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, and 5 - 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 3, and 5 - 9 are directed to city management support.
Claim 1 recites a method for city management support, Claim 6 recites a device for city management support and Claim 7 recites an article of manufacture for city management support, which include obtaining service relevant data indicating data generated in relation to the one or more services; organizing the service relevant data based on at least one of location data and time data included in the service relevant data; performing a future supply forecast for the one or more services by the one or more service providers based on the organized service relevant data; obtaining person relevant data indicating data generated in relation to the one or more persons; organizing the person relevant data based on at least one of location data and time data included in the person relevant data; performing a future demand forecast for the one or more services by the one or more persons based on the organized person relevant data; associating supply forecast data indicating data of the future supply forecast with demand forecast data indicating data of the future demand forecast by using at least one of location data and time data as a parameter; generating feedback data for service provider and feedback data for person based on a dataset including the supply forecast data and the demand forecast data that are associated with each other; and transmitting the feedback data for service provider to one or more terminals of the one or more service providers and transmitting the feedback data for person to one or more terminals of the one or more persons, wherein the step of generating the feedback data for the service provider and the feedback data for the person comprises the steps of: performing a future utilization forecast for the one or more services by a specific person among the one or more persons based on the dataset; specifying a service predicted to be used by the specific person in the future and at least one of a location and a time at which the service is predicted to be used, based on the utilization forecast data; and specifying, from among the one or more service providers, a service provider that provides the service predicted to be used by the specific person in the future, wherein the feedback data for the service provider includes data of the location at which the specified service is predicted to be used, and wherein, in the step of transmitting the feedback data for the person, the feedback data for the service provider is transmitted to a terminal of the specified service provider.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “data processing device”, “computer”, “terminal” and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “data processing device”, “computer”, “terminal” and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses 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. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in data analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “data processing device”, “computer” . “terminal” and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 3 and 5-9 recite wherein the step of generating feedback data for service provider and feedback data for person comprises the steps of: wherein: when the parameter is the location, the feedback data for person includes data of the location at which the specified service is available; and when the parameter is the time, the feedback data for person includes data of the time during which the specified service is available; wherein the step of generating the feedback data for service provider and the feedback data for person comprises the steps of: performing a future utilization forecast for the one or more services by a specific person among the one or more persons based on the dataset; specifying a service predicted to be used by the specific person in the future and at least one of a location and a time at which the service is predicted to be used, based on the utilization forecast data; and specifying, from among the one or more service providers, a service provider that provides the service predicted to be used by the specific person in the future, wherein the feedback data for service provider includes data of the location at which the specified service is predicted to be used, and wherein, in the step of transmitting the feedback data for person, the feedback data for service provider is transmitted to a terminal of the specified service provider; wherein when the parameter is the location, the feedback data for service provider includes data of the location at which the specified service is predicted to be used and data of the specific person who is predicted to use the specified service, and when the parameter is the time, the feedback data for service provider includes data of time at which the specified service is predicted to be used and data of the specific person who is predicted to use the specified service; receiving, from the one or more terminals of the one or more service providers or the one or more terminals of the one or more persons, a request for the association of the supply forecast data with the demand forecast data; and automatically transmitting, in response to the request for the association of the supply forecast data with the demand forecast data, an instruction for the association every time a predetermined period of time elapses; wherein the one or more service providers are a natural person or a corporation, and the one or more persons are the natural person that are not the one or more service providers; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 6 and 7. Regarding Claim, 8, and the additional elements of “terminal” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
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 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, and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Cinnor, US Publication No. 20200151634A1, [hereinafter Cinnor], in view of Orumchian et al., US Publication No. 20200019570 A1, [hereinafter Orumchian].
Regarding Claim 1,
Cinnor teaches
A method for supporting city management in which one or more services provided by one or more service providers are provided to one or more persons, the method comprising the steps of: obtaining service relevant data indicating data generated in relation to the one or more services (Cinnor Par. 6-“Apparatus and associated methods relate to scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.; Par. 20-21-“In FIG. 1, the exemplary demand/supply matching process data flow 100 models provider, consumer, and time slot interactions to obtain optimal service outcomes for a user.”);
organizing the service relevant data based on at least one of location data and time data included in the service relevant data (Cinnor Par. 6-“ Apparatus and associated methods relate to scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. In an illustrative example, the service consumer may be a client. The service provider may be, for example, a professional offering service in an available time slot. In some examples, individual client, provider, and time slot scores may be calculated as functions of predictive variables associated with a client population and the provider practice environment.”; Par. 31);
performing a future supply forecast for the one or more services by the one or more service providers based on the organized service relevant data (Cinnor Par. 10-11- FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores”; Par. 20- operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. [ ... ] a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of noshows, delays, or cancellations; practice resource utilization/ allocation preferences; demand forecasting; and, other related factors);
obtaining person relevant data indicating data generated in relation to the one or more persons (Cinnor Par. 20-21-“ FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.”);
organizing the person relevant data based on at least one of location data and time data included in the person relevant data (Cinnor Par. 6; Par. 20-21; Par. 31);
performing a future demand forecast for the one or more services by the one or more persons based on the organized person relevant data (Cinnor Par. 10-11- FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores”; Par. 20- operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. [ ... ] a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of noshows, delays, or cancellations; practice resource utilization/ allocation preferences; demand forecasting; and, other related factors);
associating supply forecast data indicating data of the future supply forecast with demand forecast data indicating data of the future demand forecast by using at least one of location data and time data as a parameter (Cinnor ([0020]: [ ... ] the depicted demand/supply matching process data flow 100 supports probabilistic demand/ supply matching determined as a function of an adaptive algorithm optimizing scheduling efficiency based on matching service consumers, service providers, and time slots”);
generating feedback data for service provider and feedback data for person based on a dataset including the supply forecast data and the demand forecast data that are associated with each other (Cinnor Par. 34- “tracking key performance indexes (KPIs) considering real-time data and client feedback; dynamic tracking and real-time update of variables, scores, and applicable parameters depending on past, prevailing, and/or projected conditions”);
and transmitting the feedback data for service provider to one or more terminals of the one or more service providers and transmitting the feedback data for person ... (Cinnor [0007]: notifications sent to clients encouraging the client to seek service in an available time slot. [0026]: the processor 305 notifying patients about availability of an open provider/time slot combination. Then, the method continues at step 725, with the processor 305 confirming. [0036]: notifying clients of availability of slot(s) and tracking responses”)
…wherein the generating the feedback data for the service provider and the feedback data for the person further causes the computer to execute processing to: perform a future utilization forecast for the one or more services by a specific person among the one or more persons based on the dataset; (Cinnor Par. 22- “Demand/ Supply Matching Engine (DSME) configured to execute a process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot”);
specify a service predicted to be used by the specific person in the future and at least one of a location and a time at which the service is predicted to be used, based on the utilization forecast data (Cinnor Par. 22- FIG. 3 depicts a structural view of an exemplary computing device adapted with an embodiment Demand/Supply Matching Engine (DSME) configured to execute a process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.”),
and specify, from among the one or more service providers, a service provider that provides the service predicted to be used by the specific person in the future, wherein the feedback data for the service provider includes data of the location at which the specified service is predicted to be used, and wherein, in the transmitting the feedback data for the person, …(Cinnor Par. 34- “tracking key performance indexes (KPIs) considering real-time data and client feedback; dynamic tracking and real-time update of variables, scores, and applicable parameters depending on past, prevailing, and/or projected conditions”; [0007]: notifications sent to clients encouraging the client to seek service in an available time slot. [0026]: the processor 305 notifying patients about availability of an open provider/time slot combination. Then, the method continues at step 725, with the processor 305 confirming. [0036]: notifying clients of availability of slot(s) and tracking responses”) (Cinnor Par22-23; Par. 31; Par. 35- identifying a weighting factor for each of the key factors contributing to time slot's score and adjusting the score of each of the time slot based on the weighting factors; identifying a weighting factor for practice's preference for providers, clients, and/or time slots and adjusting the score of each client, provider, and/or time slot based on the weighting factors; identifying a weighting factor for practice's preference for providers, clients, and/or time slots and adjusting the score of each client, provider, and/or time slot based on the weighting factors; identifying a weighting factor for client's preference for providers and/or time slots and adjusting the score of each client, provider, and/ or time slot based on the weighting factors; identifying a weighting factor for internal and/or external environmental and other constraints that simultaneously or in combination influence providers, clients, and/or time slots performance and adjusting the score of each of client, provider, and/or time slot based on the weighting factors; assigning a score to each client, provider, and time slot based on outcomes of a scheduled event; assigning a score to each client, provider, and time slot based on changes of key variables over time);
Cinnor teaches analysis and the feature is expounded upon by Orumchian:
…transmitting the feedback data for the person to one or more terminals of the one or more persons (Orumchian Par. 17-18-“ The information database is to embody the forecast data and to receive essentially real-time updates to the forecast data through the user interface. The user interface is to display the forecast data through the user interface with feedback as the data changes over time. In still another embodiment, the invention is a method of providing feedback from members of an organization on forecasts to sales people entering data for the forecasts. The method includes receiving the data for the forecasts from the sales people in a computer.
…the feedback data for the service provider is transmitted to a terminal of the specified service provider (Orumchian Par. 17-18-“ The information database is to embody the forecast data and to receive essentially real-time updates to the forecast data through the user interface. The user interface is to display the forecast data through the user interface with feedback as the data changes over time. In still another embodiment, the invention is a method of providing feedback from members of an organization on forecasts to sales people entering data for the forecasts. The method includes receiving the data for the forecasts from the sales people in a computer.”)
Cinnor and Orumchian are directed to forecast analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Cinnor, as taught by Orumchian, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Cinnor with the motivation of allowing for improved future forecasting and for understanding of expected changes in forecasts. (Orumchian Par. 186).
Regarding Claim 2, Cinnor in view of Orumchian - Cancelled
Regarding Claim 3
wherein: when the parameter is the location, the feedback data for person includes data of the location at which the specified service is available; and when the parameter is the time, the feedback data for person includes data of the time during which the specified service is available. (Cinnor Par22; Par. 23- “The depicted method 400 begins at step 405 with the processor 305 creating an N×M matrix representing N providers and M time slots. Then, the method continues at step 410 with the processor 305 computing a probability score for each entry (Ni, Mj) for no-shows, delays, or cancellation. In various embodiment designs, the method may repeat.”; [0007]: notifications sent to clients encouraging the client to seek service in an available time slot. [0026]: the processor 305 notifying patients about availability of an open provider/time slot combination. Then, the method continues at step 725, with the processor 305 confirming. [0036]: notifying clients of availability of slot(s) and tracking responses”); Par. 31; Par. 35);,
Regarding Claim 4,- Cancelled
Regarding Claim 5,
wherein when the parameter is the location, the feedback data for the service provider includes data of the location at which the specified service is predicted to be used and data of the specific person who is predicted to use the specified service, and when the parameter is the time, the feedback data for the service provider includes data of time at which the specified service is predicted to be used and data of the specific person who is predicted to use the specified service. (Cinnor Par. 22-23; Par. 31-“ For example, to maximize effective matching, a practice would need to keep a candidate list/wish list of clients with probabilistic estimates. Such a probabilistic estimate may contain weighting factors such as lead time, convenience, time flexibility, distance, transportation options, age, insurance coverage, previous no-show history, demographic and socio-economic factors, and the like. Such a system may best be served by a GPS functionality, which clients can opt into, to track client's current location and movement towards the practice. “; Par. 34-“ defining, storing, and tracking quantifiable performance indicators over time showing trends and patterns in no-shows, cancelations, delays, and other similar undesirable outcomes; evaluating historical data to identify key variables that have impact on outcomes under consideration (for example, no-shows, cancellations, delays, and the like) including client, provider, and practice attributes as well as broad parameters such as population level geographic and socioeconomic indicators, weather, traffic patterns, and other relevant variables, wherein examples of data points may include, but not limited to, scheduled Day, Clinic Appointment Day, Clinic Appointment Time (AM or PM), Gender, Age, Marital Status, Number of Children, Distance to clinic, New referral vs. old, Insurance coverage, Recent insurance change, Urgency of care needed, Chronic conditions, Days since the last visit, Previous no-show history, Number of prior visits, Notification/reminder sent or not, Type of notification/reminder, Confirmation received or not, Clinic appointment outcome, and the like;”)
Regarding Claim 6,
Cinnor teaches
A device for supporting city management in which one or more services provided by one or more service providers are provided to one or more persons, comprising: a data processing device, wherein the data processing device is configured to execute: processing to obtain service relevant data indicating data generated in association with the one or more services (Cinnor Par. 6-“Apparatus and associated methods relate to scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.; Par. 20-21-“In FIG. 1, the exemplary demand/supply matching process data flow 100 models provider, consumer, and time slot interactions to obtain optimal service outcomes for a user.”);
processing to organize the service relevant data based on at least one data of location and time included in the service relevant data (Cinnor Par. 6-“ Apparatus and associated methods relate to scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. In an illustrative example, the service consumer may be a client. The service provider may be, for example, a professional offering service in an available time slot. In some examples, individual client, provider, and time slot scores may be calculated as functions of predictive variables associated with a client population and the provider practice environment.”; Par. 31);
processing to perform a future supply forecast for the one or more services by the one or more service providers based on the organized service relevant data (Cinnor Par. 10-11- FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores”; Par. 20- operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. [ ... ] a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of noshows, delays, or cancellations; practice resource utilization/ allocation preferences; demand forecasting; and, other related factors);
processing to obtain person relevant data indicating data generated in relation to the one or more persons (Cinnor Par. 20-21-“ FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.”);
processing to organize the person relevant data based on at least one data of location and time included in the person relevant data (Cinnor Par. 6; Par. 20-21; Par. 31);
processing to perform a future demand forecast for the one or more services by the one or more persons based on the organized person relevant data (Cinnor Par. 10-11- FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores”; Par. 20- operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. [ ... ] a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of noshows, delays, or cancellations; practice resource utilization/ allocation preferences; demand forecasting; and, other related factors);
processing to associate supply forecast data indicating data of the future supply forecast with demand forecast data indicating data of the future demand forecast by using at least one of location data and time data as a parameter (Cinnor ([0020]: [ ... ] the depicted demand/supply matching process data flow 100 supports probabilistic demand/ supply matching determined as a function of an adaptive algorithm optimizing scheduling efficiency based on matching service consumers, service providers, and time slots”);
processing to generate feedback data for service provider and feedback data for person based on a dataset including the supply forecast data and the demand forecast data that are associated with each other (Cinnor Par. 34- “tracking key performance indexes (KPIs) considering real-time data and client feedback; dynamic tracking and real-time update of variables, scores, and applicable parameters depending on past, prevailing, and/or projected conditions”);
and processing to transmit the feedback data for service provider to one or more terminals of the one or more service providers and to transmit the feedback data for the person ... (Cinnor [0007]: notifications sent to clients encouraging the client to seek service in an available time slot. [0026]: the processor 305 notifying patients about availability of an open provider/time slot combination. Then, the method continues at step 725, with the processor 305 confirming. [0036]: notifying clients of availability of slot(s) and tracking responses”)
…wherein the processing to generate the feedback data for the service provider and the feedback data for the person comprises: processing to perform a future utilization forecast for the one or more services by a specific person among the one or more persons based on the dataset;; (Cinnor Par. 22- “Demand/ Supply Matching Engine (DSME) configured to execute a process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot”);
processing to specify a service predicted to be used by the specific person in the future and at least one of a location and a time at which the service is predicted to be used, based on the utilization forecast data (Cinnor Par. 22- FIG. 3 depicts a structural view of an exemplary computing device adapted with an embodiment Demand/Supply Matching Engine (DSME) configured to execute a process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.”),
and processing to specify, from among the one or more service providers, a service provider that provides the service predicted to be used by the specific person in the future, wherein the feedback data for the service provider includes data of the location at which the specified service is predicted to be used,, and wherein, in the processing to transmit the feedback data for the person, …(Cinnor Par. 34- “tracking key performance indexes (KPIs) considering real-time data and client feedback; dynamic tracking and real-time update of variables, scores, and applicable parameters depending on past, prevailing, and/or projected conditions”; [0007]: notifications sent to clients encouraging the client to seek service in an available time slot. [0026]: the processor 305 notifying patients about availability of an open provider/time slot combination. Then, the method continues at step 725, with the processor 305 confirming. [0036]: notifying clients of availability of slot(s) and tracking responses”) (Cinnor Par22-23; Par. 31; Par. 35- identifying a weighting factor for each of the key factors contributing to time slot's score and adjusting the score of each of the time slot based on the weighting factors; identifying a weighting factor for practice's preference for providers, clients, and/or time slots and adjusting the score of each client, provider, and/or time slot based on the weighting factors; identifying a weighting factor for practice's preference for providers, clients, and/or time slots and adjusting the score of each client, provider, and/or time slot based on the weighting factors; identifying a weighting factor for client's preference for providers and/or time slots and adjusting the score of each client, provider, and/ or time slot based on the weighting factors; identifying a weighting factor for internal and/or external environmental and other constraints that simultaneously or in combination influence providers, clients, and/or time slots performance and adjusting the score of each of client, provider, and/or time slot based on the weighting factors; assigning a score to each client, provider, and time slot based on outcomes of a scheduled event; assigning a score to each client, provider, and time slot based on changes of key variables over time);
Cinnor teaches analysis and the feature is expounded upon by Orumchian:
… transmit the feedback data for the person to one or more terminals of the one or more persons (Orumchian Par. 17-18-“ The information database is to embody the forecast data and to receive essentially real-time updates to the forecast data through the user interface. The user interface is to display the forecast data through the user interface with feedback as the data changes over time. In still another embodiment, the invention is a method of providing feedback from members of an organization on forecasts to sales people entering data for the forecasts. The method includes receiving the data for the forecasts from the sales people in a computer.
…the feedback data for the service provider is transmitted to a terminal of the specified service provider. (Orumchian Par. 17-18-“ The information database is to embody the forecast data and to receive essentially real-time updates to the forecast data through the user interface. The user interface is to display the forecast data through the user interface with feedback as the data changes over time. In still another embodiment, the invention is a method of providing feedback from members of an organization on forecasts to sales people entering data for the forecasts. The method includes receiving the data for the forecasts from the sales people in a computer.”)
Cinnor and Orumchian are directed to forecast analysis. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have improve upon data analysis of Cinnor, as taught by Orumchian, by utilizing additional analysis with a reasonable expectation of success of arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make the modification to the teachings of Cinnor with the motivation of allowing for improved future forecasting and for understanding of expected changes in forecasts. (Orumchian Par. 186).
Regarding Claim 7,
Cinnor teaches
A non-transitory computer-readable storage medium for supporting city management in which one or more services provided by one or more service providers are provided to one or more persons, the computer-readable storage medium storing a program configured to cause a computer to execute processing to: obtain service relevant data indicating data generated in association with the one or more services (Cinnor Par. 22; Par. 6-“Apparatus and associated methods relate to scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.; Par. 20-21-“In FIG. 1, the exemplary demand/supply matching process data flow 100 models provider, consumer, and time slot interactions to obtain optimal service outcomes for a user.”);
organize the service relevant data based on at least one data of location and time included in the service relevant data (Cinnor Par. 6-“ Apparatus and associated methods relate to scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. In an illustrative example, the service consumer may be a client. The service provider may be, for example, a professional offering service in an available time slot. In some examples, individual client, provider, and time slot scores may be calculated as functions of predictive variables associated with a client population and the provider practice environment.”; Par. 31);
perform a future supply forecast for the one or more services by the one or more service providers based on the organized service relevant data (Cinnor Par. 10-11- FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores”; Par. 20- operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. [ ... ] a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of noshows, delays, or cancellations; practice resource utilization/ allocation preferences; demand forecasting; and, other related factors);
acquire person relevant data indicating data generated in relation to the one or more persons (Cinnor Par. 20-21-“ FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores.”);
organize the person relevant data based on at least one data of location and time included in the person relevant data (Cinnor Par. 6; Par. 20-21; Par. 31);
perform a future demand forecast for the one or more services by the one or more persons based on the organized person relevant data (Cinnor Par. 10-11- FIG. 1 depicts an illustrative operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores”; Par. 20- operational process scheduling service provider and service consumer interactions based on determining service consumer scores, service provider scores, and service time slot scores predicting outcomes for provider service to a consumer in a time slot, and automatically adapting demand and supply matched as functions of the scores. [ ... ] a consumer score may be determined as a function of the consumer's historical no-show probability, the consumer's physical condition, and the consumer's distance from the practice facility where service may be rendered in a given time slot. In some embodiments, a consumer, provider, or time slot score may be determined as a function of a probabilistic estimate of noshows, delays, or cancellations; practice resource utilization/ allocation preferences; demand forecasting; and, other related factors);
associate supply forecast data indicating data of the future supply forecast with demand forecast data indicating data of the future demand forecast by using at least one of location data and time data as a parameter (Cinnor ([0020]: [ ... ] the depicted demand/supply matching process data flow 100 supports probabilistic demand/ supply matching determined as a function of an adaptive algorithm opti