DETAILED ACTION
Status of the Application
This Non-Final Office Action is in response to Application Serial 18/646,419. Claims 1-20 are examined below.
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 . 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.
Information Disclosure Statement
Applicant did not submit an information disclosure statement (IDS) for consideration by the examiner.
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.
Claim 1-10 are process.
Claims 11-20 are machine.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim 11 (and similarly claim 1 ) recite, “… providing … receive query inputs and generate response outputs associated with scheduling target assignments performed by at least one service provider; receiving first data characterizing a query input provided to …, the query input including a request for at least one service provider to perform target assignment at a target assignment time; determining second data characterizing a response output contextually relevant to the query input, the response output including one or more appointments corresponding to the target assignment and the target assignment time; and providing the response output including the one or more appointments....” Claims 1-20 in view of the claim limitations, are an abstract idea of scheduling and appointment are, and thus, the claims are directed to certain methods of organizing human activity at Step 2A prong one.
This judicial exception are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “by a data processor of a multi-provider scheduling platform, at least one conversational agent configured to,” in claim 1. “A system comprising: at least one data processor of a multi-provider scheduling platform; a memory coupled to the at least one data processor, the memory storing instructions to cause the at least one data processor to perform operations comprising:”, “at least one conversational agent configured to”, at claim 11; however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05 (f)
Claim 2, 12: a natural language interface configured to
Claim 4, 14: a predictive model, a machine learning process
Claim 4, 10, 16: an application programming interface between an operator device client application in communication with the processor via the network connection.
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 transformation or reduction of a particular article to a different state or thing.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
At step 2B, it is MPEP 2106.05 (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).
Dependent claims 2-10 further narrow the abstract idea of independent claim 1. Dependent claims 12-20 further narrow the abstract idea of independent claim 11. The claims 1-20 are not patent eligible.
Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-10 and 12-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
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 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 3, 11, 12, 13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gharaybeh (US 2024/0,069,963 A1).
Regarding Claim 11, (and similarly claim 1)
A system comprising: at least one data processor of a multi-provider scheduling platform; a memory coupled to the at least one data processor, the memory storing instructions to cause the at least one data processor to perform operations comprising: providing at least one conversational agent configured to receive query inputs and generate response outputs associated with scheduling target assignments performed by at least one service provider;
Gharaybeh [004] teaches a computing system receives a request to schedule an appointment for a client. ; [042] teaches scheduling system 118 may include a plurality of modules. For example, scheduling system 118 may include one or more of fill the class engine 202, fill the available appointment engine 204, messaging engine 206, frequency engine 208, best appointment time engine 210, chat bot 240, virtual assistant 242, and orchestration engine 250. [Figure 2]
Gharaybeh [0092] Chat bot 240 may be configured to provide customer devices 102 with a customer facing scheduler. In some embodiments, chat bot 240 may interact with customer devices 102 via text message, phone, email, or as a virtual assistant through a website associated with a service provider system 106. Chat bot 240 may be configured to identify a desired booking time period for the customer, and may recommend appointment times for the customer to choose. In some embodiments, the appointment time options may be suggestions generated by best appointment times engine, such that the appointment times also optimize the service provider's schedule. In essence, chat bot 240 may automate the tasks typically associated with a front desk scheduling person.
receiving first data characterizing a query input provided to the at least one conversational agent, the query input including a request for at least one service provider to perform target assignment at a target assignment time;
Gharaybeh [0092] and Gharaybeh [033] discloses an event template may include properties, such as, but not limited to, a start and an end time of the event, duration, day(s) of the week, the cadence at which to create events (e.g., daily, weekly, bi-weekly, monthly, etc.), which service or services are associated with the event, which resources are assigned to the event, and the start and end dates that the events created fall within.
determining second data characterizing a response output contextually relevant to the query input, the response output including one or more appointments corresponding to the target assignment and the target assignment time; and providing the response output including the one or more appointments to the conversational agent.
Gharaybeh [0092] and
Gharaybeh [093] discloses Chat bot 240 may utilize one or more natural language processing techniques, such as speech-to-text algorithms to capture sentences during phone conversations and text-to-speech algorithms to read results back to the customer.
Regarding Claim 12, (and similarly claim 2),
The system of claim 11, wherein the conversational agent includes a natural language interface configured to receive the query inputs and generate the response outputs in audible format.
Gharaybeh [093] discloses Chat bot 240 may utilize one or more natural language processing techniques, such as speech-to-text algorithms to capture sentences during phone conversations and text-to-speech algorithms to read results back to the customer.
Regarding 13, (and similarly claim 3),
The system of claim 11, wherein the multi-provider scheduling platform includes a plurality of conversational agents, each conversational agent associated with a respective service provider of a plurality of service providers configured in the multi-provider scheduling platform and configured to generate contextually relevant response outputs based on the respective service provider and the query input.
Gharaybeh [092]- [093].
Gharaybeh [041] discloses scheduling system 118 may utilize a suite of artificial intelligence engines to intelligently automate and optimize a service provider's scheduling processes. ; [068] teaches scheduling insights engine 220 may be configured to identify the contribution of different variables to the predictions. Such interpretable insights may be presented in natural language form to the service provider.
Claim Rejections - 35 USC § 103
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4, 7, 8, 9, 10, 14, 17, 18, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gharaybeh (US 2024/0,069,963 A1) in view of Saha (US 2023/0401538 A1).
Regarding Claim 14, (and similarly claim 4)
The system of claim 11, wherein the at least one conversational agent is configured to generate response outputs based on a predictive model associated with the at least one service provider, the predictive model trained in a machine learning process using training data including one or more of target assignment types performed by the at least one service provider, schedule data associated with the at least one service provider, and resources associated with the at least one service provider.
Gharaybeh [092]- [093].
Saha [010] utilizes a two-tier predictive module (e.g., software module(s)) to perform a first predictive categorization followed by a second predictive categorization. The first predictive categorization may aid the establishment determine whether the pre-scheduled appointments for the future time period are predicted to be kept appointments or missed appointments.
Saha [071] instructions 414 may be included in and/or accessed by the computer-readable medium 412 of FIG. 4 may include code, pseudo-code, algorithms, models (e.g., machine-learned models), software modules and/or so forth and are executable by the processor 410.
Saha [081] based on the second plurality of appointment data 302 for the future time period and/or historical data (e.g., the first plurality of appointment data 202 of the past time period), in addition to the prediction of the percentage or the count of the predicted delayed appointment(s) 312, the delay predictor 420 may also offer insights as to which of the predicted kept appointment(s) 308 are most likely to be predicted delayed appointment(s) 312 and which of the predicted kept appointment(s) 308 are most likely to be predicted on-time appointment(s) 310.
Regarding Claim 17, (and similarly claim 7)
The system of claim 11, wherein determining the response output further comprises determining the one or more appointments based on an anchor parameter defining a window of time between a start time or an end time in which the one or more appointments can be scheduled and for which an amount of downtime before or after the one or more appointments is minimized.
Gharaybeh [053] teaches best appointment time engine 210 attempts to reduce the number of small, unused gaps by analyzing a service provider's schedule and appointment resource requirements and generating a list of start times based on a schedule's resource availability. Best appointment time engine 210 may list the start times based on how well those times prevent schedule defragmentation.
Saha [082] in FIG. 5, the blocks “delay predictor 420,” “predicted to keep the appointment(s) on time? 506,” and/or “predicted delayed appointment(s) 312” can help the establishment 102 to over-book appointments to counterbalance (or cancel out) the predicted delayed appointment(s) 312. For example, even though an appointment(s) may be predicted to be a predicted kept appointment(s) 308, the appointment(s) may be predicted to be time-lag appointment(s) or time-lead appointment(s) for, for example, a specific time slot (e.g., 10:30 AM) for the future time period. In such a case the establishment 102 can over-book appointments for the specific time slot (e.g., 10:30 AM) for the future time period. The lag and lead time are measures of downtime.
Regarding Claim 18, (and similarly claim 8)
The system of claim 17, wherein the anchor parameter corresponds to a blocking event, a non-blocking event, a daily start time, a daily end time, or a previously scheduled appointment.
Gharaybeh [0110] GUI 500 may illustrate a “New Reservation” screen. In some embodiments, GUI 500 may be presented to the service provider during checkout of an existing customer that finished an appointment. As shown, GUI 500 may include a date field 502 and a start time field 504. Although only shown on start time field 504, when a service provider selects date field 502, a dropdown menu may appear with “Best Suggested” dates.
Regarding Claim 19, (and similarly claim 9),
The system of claim 17, wherein determining the response output further comprises determining the one or more appointments based on the anchor parameter and a suitability factor corresponding to a likelihood the respective appointment will occur within the window of time defined by the anchor parameter.
Gharaybeh [0110] teaches reservations. Gharaybeh [0107] teaches Best appointment time engine 210 may utilize deep reinforcement learning techniques to recommend the best times for the potential appointment based on a balance between the best potential appointment times for the customer and the best potential appointment times for the service provider.
Saha further teaches:
a suitability factor corresponding to a likelihood the respective appointment
Saha [082]-[085] disclose appointment time predictability.
Gharaybeh teaches artificial intelligence engines is/are configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device. Saha teaches optimizing appointment scheduling. It would have been obvious to combine before the effective filing date, considering travel time when booking a service, as taught by Gharaybeh, with using an algorithm that considers weight, as taught by Saha to crease their revenue, profits, operational efficiency., Saha [0083].
Regarding Claim 20, (and similarly claim 10)
The system of claim 19, wherein the suitability factor is determined as a function of an amount of time a portion of the respective appointment will occur outside the window of time defined by the anchor parameter and a combined travel time for the respective appointment compared to a combined travel time for a second appointment of the one or more appointments.
Gharaybeh [0030] There may be different types of events, based on the event purpose, such as appointment, class, blocked times (e.g., employee lunch, personal appointment, outside of working hours, etc.), cleanup, travel, preparation work, administrative tasks, and the like. These different types of events may indicate whether the event has other characteristics, such as, but not limited to, whether the event has a service associated with it, whether the event's existence depends on customers being present, and whether the exact number of attendees or revenue generated from the event is known ahead of time. If, for example, there is a service associated with the event, there may be many additional attributes of the service that could impact the scheduling of that service for an event. Some examples of attributes may include, but are not limited to, even capacity (e.g., the number of people an event for that service can accommodate) and the default length of time of an event for this service. In some embodiments, resources may also have attributes that impact scheduling, such as whether a resource is allowed to be double-booked, whether the resource requires preparation time before and/or after the event, whether the resource is being assigned to multiple events that overlap, or whether the resource is assigned to multiple providers at the same time.
Claim(s) 5, 6, 15, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gharaybeh (US 2024/0,069,963 A1) in view of Saha (US 2023/0401538 A1) and in further view of Rathod (WO 2016/113,602 A1 ).
Regarding Claim 15, (and similarly claim 5)
The system of claim 11, wherein determining the response output further comprises determining the one or more appointments based on …. determined based at least on a first ratio of a travel time before the respective appointment and a travel time after the respective appointment and a second ratio of a travel distance before the respective appointment and a travel distance after the respective appointment, and providing the response output including a ranked list of the one or more appointments based on the determined ….
Gharaybeh [046] teaches customer-to-time block recommender may match a customer. Gharaybeh [047] Fill the available appointment engine 204 may take a recurrent neural network based approach (e.g., LSTM network) to analyze customers' historical booking pattern and generate a list of candidate customers that may be expected to book an appointment on the relevant day. Fill the available appointment engine 204 may then rank and/or sort the list of candidate customers.
Gharaybeh [089] traveler engine 234 may be similar to best appointment time engine 210. For example, traveler engine 234 may include the additional enhancement of taking travel time into account by including an additional set of features that may include location information of the provider and/or the service and associated travel time in order to minimize travel time and reduce scheduling gaps due to traveling to and from services. For each request (e.g., at each timestep), traveler engine 234 may be trained to recommend the best appointment times such that the final schedule has minimum travel time without sacrificing revenue. Traveler engine 234 may utilize a travel time prediction engine to predict the travel time between two locations at any given time (e.g., in the future). Such output may be used by traveler engine 234 when making a recommendation.
Although highly suggested, Gharaybeh does not explicitly teach:
… an appointment score determined for respective appointments of the one or more appointments, the appointment score …
Sara teaches:
… an appointment score determined for respective appointments of the one or more appointments, the appointment score …
Saha [083-086] disclose a light gradient boosting machine (LightGBM or LGBM)), LGBM classifier, that considers a weighted average expression used to predict cancelations and used to predict delay, and thus, Saha discloses calculating an appointment score.
Gharaybeh teaches artificial intelligence engines is/are configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device. Saha teaches optimizing appointment scheduling. It would have been obvious to combine before the effective filing date, considering travel time when booking a service, as taught by Gharaybeh, with using an algorithm that considers weight, as taught by Saha to crease their revenue, profits, operational efficiency., Saha [0083].
Rathod further teaches:
… response output including a ranked list of the one or more appointments based on the determined appointment score.
Rathod [p. 41 line 25 -34] teaches determine starting location based on location of both requested or interest showing service consumer(s) or service provider(s), determine ending of location based on different location after said same location of both said service consumer(s) or service provider(s), determine cancel status based on identify static location or offline status of service consumer(s) and/or service provider(s) or sending or accepting of other users request by service consumer(s) or service provider(s) or starting of providing of service to other users by service provider(s), determine in-process status based on starting and ending of status.
Rathod [p.42 line 1-5] discloses providing rank- wise list of available service providers based on online status, availability status, calculated or estimated approximate price or fare or rate, real-time updated distance to arrive or reach, real-time updated calculated or estimated approximate time or duration to arrive or reach, ratings, preferences, calculated or estimated approximate time to reach at destinations or drop-off location or providing service(s) and like.
Gharaybeh teaches artificial intelligence engines is/are configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device. Rathod teaches on demand services providers. It would have been obvious to combine before the effective filing date, considering travel time when booking a service, as taught by Gharaybeh, with real time present or show & update listed on demand service providers including online & available or available within particular duration, as taught by Rathod to enable[e] service provider(s) to select user(s) or prospective service consumer(s) based on service provider's preferences or after reviewing or analyzing or auto matching ., Rathod [p.4 lines 22-24].
Regarding Claim 16, (and similarly claim 6)
The system of claim 15, wherein determining the appointment score further comprises determining … for respective appointments of the one or more appointments, … determined based on a combined travel time for the respective appointment compared to a combined travel time for a second appointment of the one or more appointments, … further determined based on a combined travel factor provided by the at least one service provider and configured to prioritize the respective appointment having a lower combined travel time compared to the second appointment.
Gharaybeh [0107] teaches best appointment time engine 210 may utilize deep reinforcement learning techniques to recommend the best times for the potential appointment based on a balance between the best potential appointment times for the customer and the best potential appointment times for the service provider.
Gharaybeh [0088] teaches traveler engine 234 may be configured to recommend best appointment times based on appointment location and travel time, such that total travel time may be minimized when the schedule is realized. traveler engine 234 may be trained to recommend the best appointment times such that the final schedule has minimum travel time without sacrificing revenue. Traveler engine 234 may utilize a travel time prediction engine to predict the travel time between two locations at any given time (e.g., in the future). Such output may be used by traveler engine 234 when making a recommendation., Gharaybeh [0088] –[089].
Although highly suggested, Gharaybeh does not explicitly teach:
… a weighted efficiency score … the weighted efficiency score …
Saha [083] discloses a cancelation predictor 418 and delay predictor 420 that may use binary algorithms. The binary algorithms may be machine learning algorithms. Saha [085-086] disclose a light gradient boosting machine (LightGBM or LGBM)), LGBM classifier, that considers a weighted average expression used to predict cancelations and used to predict delay.
Gharaybeh teaches artificial intelligence engines is/are configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device. Saha teaches … It would have been obvious to combine before the effective filing date, considering travel time when booking a service, as taught by Gharaybeh, with …, as taught by Saha to prevent inefficiencies and resource and revenue loss., Gharaybeh [002].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
Vasileiou (2022, The health chatbots in telemedicine: intelligent dialog system for remote support.) teaches chatbots and scheduling.
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/THEA LABOGIN/Examiner, Art Unit 3624