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 pro----visions of the AIA .
Restriction
Restriction to one of the following inventions is required under 35 U.S. C. 121:
Group I: Claims 1-14 are drawn to a system od for , by a computing system, a trigger event to execute a workflow to fill an open appointment with a provider, responsive to detecting the trigger event, generating, by a recurrent neural network, a list of clients for the open appointment based on constraints of a service being offered at the open appointment and historical booking information associated with the list of clients, for each client in the list of clients, generating, using a generative adversarial network, a customized communication, determining, by the computing system using contextual bandwidth algorithms, a frequency at which to send each customized communication; identifying, by an online machine learning model of the computing system, a best time of day to deliver the customized communication to each client in the list of clients based on a context of the customized communication and sending, by the computing system, each customized communication, classified in class 705, subclass in 7.19.
Group II: Claims 15-20 are drawn to a system comprising an orchestration
engine configured to receive communications from a client device of a client and a provider device of a provider, the orchestration engine configured to execute workflows across a plurality of artificial intelligence engines, the plurality of artificial intelligence engines comprising: a first set of artificial intelligence engines configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device, a second set of artificial intelligence engines configured to work in conjunction to execute a rebooking workflow for process a rebooking request received upon detecting a first trigger event, and a third set of artificial intelligence engines configured to work in conjunction to execute an event filling workflow to optimize a schedule of the provider, classified in class 705, subclass in 7.19.
The inventions are distinct, each from the other because of the following reasons:
Inventions I-II are unrelated. Inventions are unrelated if it can be shown that they
are not disclosed as capable of use together and they have different modes of operation, different functions, or different effects (MPEP § 806.04, MPEP § 808.01). In the instant case the different inventions are unrelated because:
In the invention of Group I, it is not necessary to include:
functions of "a second set of artificial intelligence engines configured to work in
conjunction to execute a rebooking workflow for process a rebooking request received upon detecting a first trigger event, and a third set of artificial intelligence engines configured to work in conjunction to execute an event filling workflow to optimize a schedule of the provider", as required in Group II.
In the invention of Group II, it is not necessary to include:
functions of "determining, by the computing system using contextual bandwidth
algorithms, a frequency at which to send each customized communication; identifying, by an online machine learning model of the computing system, a best time of day to deliver the customized communication to each client in the list of clients based on a context of the customized communication and sending, by the computing system, each customized communication", as required in Group I.
Inventions 1-20 are directed to related apparatuses and/or systems. The related
inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, (i.e. each invention as claimed requires a mutually exclusive characteristic not required for the other invention); and (3) the inventions as claimed are not obvious variants. See MPEP § 806. 050). In the instant case, the inventions as claimed can have a materially different design, mode of operation, function or effect, do not overlap in scope and they are not obvious variants as noted above.
Restriction for examination purposes as indicated is proper because all these
inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and examination burden if restriction were not required because one or more of the following reasons apply:
Because these inventions are distinct for the reasons given above and have
acquired a separate status in the art because of their recognized divergent subject matter, restriction for examination purposes as indicated is proper.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable on the elected invention.
If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103(a) of the other invention.
During a telephonic interview, Applicant’s representative, Stefan Greenewald (Registration No. 79199), elected the invention of group I (Claims 1-14).
Drawings
The drawings filed on February 28, 2024 are accepted.
Information Disclosure Statement
The Information Disclosure Statements filed on September 18, 2025 have been considered. An initialed copy of the Form 1449 is enclosed herewith.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-14 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.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Under step 1 of the analysis, claims 1-8 are directed to system claims. Claims
9-14 are directed to method claims. The claims all fall under one of the four statutory classes of invention.
If the claims do fall within one of the statutory categories, they must then
be determined whether the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A, Prong One, the claimed invention is directed to an abstract idea without significantly more.
Representative claim 1 recites the abstract idea in non-bold and the
additional elements in bold:
an orchestration engine configured to receive communications from a client device of a client and a provider device of a provider, the orchestration engine configured to execute workflows across a plurality of artificial intelligence engines, the plurality of artificial intelligence engines comprising:
a first set of artificial intelligence engines configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device, a second set of artificial intelligence engines configured to work in conjunction to execute a rebooking workflow for process a rebooking request received upon detecting a first trigger event, and a third set of artificial intelligence engines configured to work in conjunction to execute an event filling workflow to optimize a schedule of the provider.
Regarding claims 1 and 9, the limitations of “receive”, and “determine”,
are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Claim 2 further recites a first artificial intelligence engine comprising a deep reinforcement learning algorithm trained to identify a best appointment time for the client based on historical booking information of the client and an existing schedule of the provider.
Accordingly, the steps or functions of “assign”, involves generic computer
functions and are similar to a mental step.
Claim 3 further recites a second artificial intelligence engine comprising a deep reinforcement learning network configured to identify a best appointment time for the provider by reducing travel time to a client location.
Accordingly, the steps or functions of “identify”, involve generic computer
functions and are similar to mental steps.
Claim 4 further recites wherein the first set of artificial intelligence engines
comprises: a second artificial intelligence engine comprising a deep reinforcement learning network configured to identify the best appointment time by minimizing fragmentation in the existing schedule of the provider.
Accordingly, the steps or functions of “identify”, involve generic computer
functions and are similar to mental steps.
Claim 5 further recites a second artificial intelligence engine comprising a
neural network trained to move an existing reservation by reconciling conflicts with the existing schedule of the provider and minimizing schedule fragmentation.
Accordingly, the step or function of “move”, involves generic computer
functions and is similar to a mental step.
Claim 6 further recites a first artificial intelligence engine comprising a recurrent neural network trained to identify a list of clients for an open appointment slot based on constraints of a service being offered at the open appointment slot and historical booking information associated with the list of clients.
Accordingly, the step or function of “identify”, involves generic computer
functions and is similar to a mental step.
Claim 7 further recites a recurrent neural network trained to predict a number
of days between a current appointment for the client and a future appointment for the client based on service type and time slot; and a second artificial intelligence network comprising a deep reinforcement learning network trained to identify a set of best appointment times for the provider based on the predicted number of days.
Accordingly, the steps or functions of “identify” and “predict”, involve generic computer
functions and is similar to a mental step.
Claim 8 further recites a first artificial intelligence engine comprising a recurrent neural network trained to generate a list of clients most likely to book an appointment at a given time slot;
a second artificial intelligence agent comprising a generative adversarial network coupled with a deep learning keyword extractor trained to generate a customized communication for each client in the list of clients;
a third artificial intelligence agent contextual bandit algorithms configured to determine, for each client in the list of clients, a frequency at which to send the customized communication; and
a fourth artificial intelligence engine comprising an online machine learning model trained to identify, for each client in the list of clients, a best time of day to deliver the customized communication to the client based on a context of the customized communication.
Claim 9 recites receiving, by a computing system, a request to schedule
an appointment for a client, wherein the appointment is for a service with a service provider;
generating, by a deep reinforcement learning network of the computing
system, a set of recommend times for the service, wherein set of recommend times minimizes fragmentation in a schedule of the service provider; causing, by the computing system, the set of recommended times to be displayed to the client; and
responsive to receiving a selection from the client, generating, by the
computing system, an appointment event for the client.
Claim 10 further recites detecting, by a computing system, a trigger
event, wherein the trigger event indicates that the appointment event for the client is over; and
responsive to the detecting, executing, by the computing system, a rebooking workflow to book a future appointment event for the client by:
predicting, by a recurrent neural network, a number of days between the appointment event for the client and the future appointment event based on service type and historical booking information associated with the client, and based on the predicted number of days,
generating, by the deep reinforcement learning network, a set of best appointment times for the future appointment event, wherein the set of best appointment times minimizes fragmentation in a schedule of the service provider.
Claim 11 further recites causing, by the computing system, the set of best appointment times to be displayed to the client; and
responsive to receiving a further selection from the client, generating, by the
computing system, a new appointment event for the client.
Claim 12 further recites receiving, by the computing system from the client, a
further request to move the appointment event; and
responsive to the further request, identifying, by the computing system, a new proposed appointment event by reconciling conflicts with existing appointments in the schedule and further minimizing fragmentation of the schedule.
Claim 13 further recites receiving, by the computing system, a second request to create a second appointment event from a provider computing system; and
based on the second request, identifying, a recurrent neural network, a list of
clients for an open appointment slot based on constraints of a service being offered at the open appointment slot and historical booking information associated with the list of clients.
Claim Rejections - 35 USC§ 102
7. 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.
8. 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)(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.
9. Claims 1 and 6 are rejected under 35 USC 102(a1) as being anticipated by US Cinnor et al (2020/0151634 hereinafter, Cinnor).
Regarding claim 1, Cinnor discloses a system comprising: an orchestration engine configured to receive communications from a client device of a client and a provider device of a provider, the orchestration engine configured to execute workflows across a plurality of artificial intelligence engines, the plurality of artificial intelligence engines comprising: a first set of artificial intelligence engines configured to work in conjunction to execute a booking workflow for processing a booking request received from the client device or the provider device, a second set of artificial intelligence engines configured to work in conjunction to execute a rebooking workflow for process a rebooking request received upon detecting a first trigger event, and a third set of artificial intelligence engines configured to work in conjunction to execute an event filling workflow to optimize a schedule of the provider (Abstract, Figures 3-8, Paragraphs [0020]-[0022], and [0030]),
Regarding claim 6, Cinnor discloses the system of claim 1, wherein the first set of artificial intelligence engines comprises: a first artificial intelligence engine comprising a recurrent neural network trained to identify a list of clients for an open appointment slot based on constraints of a service being offered at the open appointment slot and historical booking information associated with the list of clients
(Paragraphs [0026], [0031], [0034]).
Claim Rejections - 35 USC § 103
10. 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.
11. 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
12. Claims 2-5 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Cinnor et al (US Publication No. 2020/0151634 hereinafter Cinnor) in view of Krystek (US Publication No. 20190340579).
Regarding claim 2, Cinnor teaches the system of claim 1, wherein the first set of artificial intelligence engines comprises: a first artificial intelligence engine comprising a deep learning algorithm trained to identify a best appointment time for the client based on historical booking information of the client and an existing schedule of the provider (Abstract, Paragraphs [0020], [0028], [0034]). Easy manage fails to explicitly discloses that the deep learning algortihm is a reinforcement algorithm. However, in an analogous art, Krystek does teach a reinforcement learning algorithm (Paragraph [00851).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ a deep reinforcement learning algorithm as in Krystek with the system of Cinnor with the motivation to improve scheduling system performance.
Regarding claim 3, Cinnor teaches the system of claim 2, wherein the first set of artificial intelligence engines comprises: a second artificial intelligence engine comprising a deep reinforcement learning network configured to identify a best appointment time for the provider by reducing travel time to a client location (para [0020], [0031], [0034]).
Regarding claim 4, Cinnor further teaches wherein the first set of artificial intelligence engines comprises: a second artificial intelligence engine comprising a deep reinforcement learning network configured to identify the best appointment time by minimizing fragmentation in the existing schedule of the provider (para [0024], [0034]).
Regarding claim 5, Cinnor further teaches the system of claim 2, wherein the first set of artificial intelligence engines comprises: a second artificial intelligence engine comprising a neural network trained to move an existing reservation by reconciling conflicts with the existing schedule of the provider and minimizing schedule fragmentation (Paragraphs [0024]-[0025], [0031], and [0034]).
Regarding claim 7, Cinnor further teaches the system of claim 1, wherein the second set of artificial intelligence engines comprises: a first artificial intelligence engine comprising a recurrent neural network trained to predict a number of days between a current appointment for the client and a future appointment for the client based on service type and time slot (Paragraphs [0024] and [0034]); and a second artificial intelligence network comprising a deep learning network trained to identify a set of best appointment times for the provider based on the predicted number of days (Abstract, Paragraphs [0020], [0028] and [0034]). Easy manage fails to explicitly discloses that the deep learning algortihm is a reinforcement algorithm. However, in an analogous art, does teach a reinforcement learning algorithm (Paragraph [0085]}. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ a deep reinforcement learning algorithm as in Krystek with the system of Cinnor with the motivation to improve the scheduling system performance.
Regarding claim 8, Cinnor further teaches the system of claim 1, wherein the third set of artificial intelligence engines comprises: a first artificial intelligence engine comprising a recurrent neural network trained to generate a list of clients most likely to book an appointment at a given time slot (Paragraphs [0022] and [0034]); a third artificial intelligence agent contextual bandit algorithms configured to determine, for each client in the list of clients, a frequency at which to send the customized communication (Paragraphs [0029] and [0034]); and a fourth artificial intelligence engine comprising an online machine learning model trained to identify, for each client in the list of clients, a best time of day to deliver the customized communication to the client based on a context of the customized communication (Paragraphs [0029] and [0034]).
Cinnor fails to explicitly teach a second artificial intelligence agent comprising a generative adversarial network coupled with a deep learning keyword extractor trained to generate a customized communication for each client in the list of clients. However, in an analogous art, Krystek does teach a second artificial intelligence agent comprising a generative adversarial network coupled with a deep learning keyword extractor trained to generate a customized communication for each client in the list of clients (Paragraphs [0067] and [0070]-[0071]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to employ a keyword extraction learning algorithm as in Krystek with the system of Cinnor for improved scheduling system communications.
13. Claims 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Sanderford et al (US Publication No. 2022/0198402, hereinafter Sanderford) in view of Krystek (US Publication No. 20190340579).
Regarding claim 9, Sanderford teaches a method, comprising: receiving, by a computing system, a request to schedule an appointment for a client, wherein the appointment is for a service with a service provider (Paragraph [0078]);
generating, by a deep learning network of the computing system, a set of recommend times for the service, wherein set of recommend times minimizes fragmentation in a schedule of the service provider (Paragraphs [0233] and [0254]); causing, by the computing system, the set of recommended times to be displayed to the client (Paragraphs [0278]-[0279]); and
responsive to receiving a selection from the client, generating, by the computing system, an appointment event for the client (Paragraph [0279]). Sanderford fails to explicitly disclose that the deep learning algortihm is a reinforcement algorithm. However, in an analogous art, Krystek does teach a reinforcement learning algorithm (Paragraph [0085]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ a deep reinforcement learning algorithm of Krystek with the system of Sanderford with the motivation to improve scheduling system performance.
Regarding claim 10, Sanderford further teaches the method of claim 9, further comprising: detecting, by a computing system, a trigger event, wherein the trigger event indicates that the appointment event for the client is over (Paragraph [0081]); and responsive to the detecting, executing, by the computing system, a rebooking workflow to book a future appointment event for the client by: predicting, by a recurrent neural network, a number of days between the appointment event for the client and the future appointment event based on service type and historical booking information associated with the client, and based on the predicted number of days, generating, by the deep learning network, a set of best appointment times for the future appointment event, wherein the set of best appointment times minimizes fragmentation in a schedule of the service provider (Figure 13F, Paragraphs [0233], [0257] and [0278]-[0279]). Sanderford fails to explicitly disclose that the deep learning algorithm is a reinforcement algorithm. However, in an analogous art, Krystek discloses a reinforcement learning algorithm (Paragraph [0085]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to employ a deep reinforcement learning algorithm of Krystek with the system of Sanderford for improved scheduling system performance.
Regarding claim 11, Sanderford teaches the method of claim 10, further comprising: causing, by the computing system, the set of best appointment times to be displayed to the client (Paragraphs [0278]-[0279]); and responsive to receiving a further selection from the client, generating, by the computing system, a new appointment event for the client (Paragraph [0279]).
Regarding claim 12, Sanderford teaches the method of claim 9, further comprising: receiving, by the computing system from the client, a further request to move the appointment event (Paragraphs [0278]-[0279]); and responsive to the further request, identifying, by the computing system, a new proposed appointment event by reconciling conflicts with existing appointments in the schedule and further minimizing fragmentation of the schedule (Paragraphs [0230] and [0278]-[0279]).
Regarding claim 13, Sanderford teaches the method of claim 9, further comprising: receiving, by the computing system, a second request to create a second appointment event from a provider computing system (Paragraphs [0278]-[0279]); and based on the second request, identifying, a recurrent neural network, a list of clients for an open appointment slot based on constraints of a service being offered at the open appointment slot and historical booking information associated with the list of clients (Paragraphs [0278]-[0279]).
Regarding claim 14, Sanderford teaches the method of claim 13, further comprising: recommending, by a second deep learning network, the open appointment slot be moved to a new appointment slot responsive .to determining that the new appointment slot reduces provider travel time to a client location (Paragraphs [0278]-[0279]). Sanderford fails to explicitly disclose that the deep learning algortihm is a reinforcement algorithm. However, in an analogous art, Krystek does teach a reinforcement learning algorithm (Paragraph [0085]). Therefore, it would have been obvious to one of ordinary skill at the time of the invention to employ a deep reinforcement learning algorithm of Krystek with the system of Sanderford for improved scheduling system performance.
Conclusion
14. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. As per attached PTO 892 form.
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Romain Jeanty whose telephone number is (571) 272-6732. The examiner can normally be reached M-F 9:00AM to 5:30PM.
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supervisor, Jerry O'Connor can be reached on 571 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RJ/
/ROMAIN JEANTY/Primary Examiner, Art Unit 3624