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 .
This non-final office action is in response to the applicant’s communication received on 1/28/2026 (“Amendment”).
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/28/2026 has been entered.
Status of Claims
Claims 1, 8, and 15 (independent claims) have been amended.
Claims 2, 9, and 16 had been canceled.
Claims 1, 3-8, 10-15, and 17-20 are pending.
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, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
MPEP 2106 provides step(s) in determining eligibility under 35 U.S.C. § 101. Specifically, 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. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any additional elements in the claim must integrate the judicial exception into a practical application. If not, the inquiry continues to see whether any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include mathematical concepts, mental processes, and certain methods of organizing human activities.
Under Step 1, claims 1 and 3-7 are directed to a method (i.e. process), claims 8 and 10-14 are directed to a system, while claims 15 and 17-20 are directed to a non-transitory computer-readable storage medium. Thus, the claimed inventions are directed towards one of the four statutory categories under 35 USC § 101. Nevertheless, the claims also fall within the judicial exception of an abstract idea without significantly more.
Step 2A, 1st prong:
Claim 1 recites: A computer-implemented method comprising:
a) continuously generating a plurality of worker segments from a pool of worker user accounts, wherein a worker segment comprises worker user accounts, having similar features and/or attributes;
b) continuously generating a plurality of business segments from a pool of business user accounts, wherein a business segment comprises business user accounts, having similar features and/or attributes;
c) receiving features of a worker user account;
d) receiving features of a business user account;
e) generating a prediction, for one of a plurality of distinct sequential time ranges occurring prior to a start of an event, regarding a likelihood of an occurrence of an unattendance by one or more individuals at the event, each individual represented by a respective user account, wherein each time range is associated with a distinct machine learning model, wherein each distinct machine learning model outputs the likelihood of the occurrence of the unattendance by an individual,
wherein the generating of the prediction further comprises:
receiving an indication of the event during a first time range prior to the start of the event;
predicting a first unattendance likelihood for a possible first booking of the event by a first user account prior to actual receipt of a booking request from the first user account;
predicting a second unattendance likelihood for a possible second booking of the event by a second user account prior to actual receipt of a booking request from the second user account;
f) training the distinct machine learning models with a neural net based algorithm, comprising one or more of an artificial neural network training algorithm, deep learning training algorithm; linear regression, comprising random sample consensus, Huber regression, Theil-Sen estimator; a kernel based approach, comprising a support vector machine and kernel ridge regression; a tree-based algorithm, comprising classification and regression tree, random forest, extra tree, gradient boost machine, alternating model tree and naive Bayes classifier;
g) feeding the received features to one or more of the distinct machine learning models, associated with the time ranges;
h) with the trained distinct machine learning models, performing inference operations comprising: generating an output prediction for each of the one or more distinct machine learning models based at least in part on the received features;
i) with the trained distinct machine learning models weighing the received features, based on a risk appetite parameter, associated with the time range corresponding to the distinct machine learning model;
j) based on the prediction, triggering one or more booking actions specific to a particular time range of the prediction;
k) triggering one or more notifications in accordance with the one or more booking actions for the event;
l) wherein the triggering the one or more booking actions specific to a particular time range of the prediction comprises: due to the first and the second unattendance likelihoods, concurrently providing the second user account access permission to initiate interaction activity with the event while denying user interaction activity of the first user account with the event; and due to the second unattendance likelihood, allowing user interaction activity of the second user account with the event; and
m) wherein the triggering of the one or more notifications comprises: sending a notification of the event to the second user account.
(Bold emphasis added on the additional element(s))
Under the broadest reasonable interpretation, the claim recites a process of j) triggering of one or more booking actions specific to a particular time range of the prediction, time range in which prediction applied, based on a prediction, i.e., likelihood of an occurrence of an unattendance by one or more individual at the event (i.e., job shift), the triggering of one or more booking actions comprising l) concurrently allowing a second user who has been predicted to be more reliable to fulfill attendance to the event to initiate interaction activity with the event while denying user interaction activity with the event to a first user who has been predicted to be less reliable to fulfill attendance and k), m) triggering one or more notifications in accordance with the one or more booking actions for the event, i.e., , and sending a notification of the event to the second user account. The instant original written disclosure (hereinafter “Specification”) describes the problem that some individuals may book a particular job shift on the online platform via a corresponding worker user account but may not, in reality, show up to actually perform occupational services and/or professional services during that booked job shift. Instant claim solves this business centric issue by predicting likelihood of an occurrence of an unattendance by one or more individual at the event and based on the prediction triggering of one or more booking actions specific to a particular time range of the prediction. As such, the claim recites an abstract idea, i.e., a certain method of organizing human activities, i.e., scheduling/booking, and/or mental activities that can be performed with human mind with pen and paper.
Steps a)-e) and g)- i) recite the process of the prediction. For example, a) and b) recite continuously generating a plurality of worker segment and plurality of business segment based on accounts having similar features (i.e., grouping) from each of pools. Steps c) and d) recite receiving of feature of a worker user account and feature of a business user account. These steps may be practically performed in the human mind using observation, evaluation, judgement, and opinion. Step e) recites generating a prediction, for one of plurality of distinct sequential time ranges occurring prior to a start of an event regarding a likelihood of an occurrence of an unattendance by one or more individual at the event using each distinct machine learning model associated with each time range. Under its broadest reasonable interpretation steps e), g), h), and i) encompass performing evaluation, judgement, and opinion to make determination about a likelihood of an occurrence of an unattendance by one or more individual at the event using model(s) for particular time range(s). Steps h) and i) recite use of the trained distinct machine learning mode, i.e., generating an output prediction for each of the one or more distinct machine learning models based at least in part on the received features and weighting the received features associated with the time range corresponding to the distinct machine learning model based on a risk parameter. The examiner submits that the modeling is also mathematical concept.
Steps f) recites training of the distinct machine learning models with a neural net based algorithm and recites the particular algorithm. The plain meaning when given broadest reasonable interpretation is optimization algorithms for the purpose of training. The particular algorithm used in training encompasses mathematical concept.
In view of the finding, the claim recites an abstract idea, i.e., combination of certain method of organizing human activity (managing relationships or interactions between people or business relations), mental activities, and mathematical concept.
The other independent claims, i.e., claims 8 and 15, are significantly similar to claim 1. As such, claims 8 and 15 also recite abstract idea.
Under the Step 2A (prong 2), this judicial exception is not integrated into a practical application. Specifically, the additional elements in the claim(s), i.e., computer-implemented, machine learning model comprising neural net based algorithm, system comprising one or more processors, and a non-transitory medium storing instructions, amount to no more than mere instructions (i.e., machine learning) to implement the abstract idea and/or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). Here, the claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the claim(s) as a whole, taken individually and in combination, do not provide an inventive concept. As explained above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed judicial exception amount to no more than mere instructions to implement the abstract idea and/or merely uses a computer as a tool to perform an abstract idea. Mere instructions to implement the abstract idea on a computer, or merely using the computer as a tool to perform an abstract idea to apply the exception using a generic computer component cannot provide an inventive concept. Looking at the limitations as a combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of the elements improves the functioning of the recited system or the system’s component(s) that is responsible for performing the step(s).
The dependent claims 3-7, 10-14, and 17-20 expand further on the abstract idea related to certain method of organizing human activities and/or mental activities without reciting further additional elements.
Response to the Argument/Amendment
112
112 rejection(s) have been withdrawn in light of the claim amendment(s).
101
The 101 rejections have been modified to accommodate the claim amendment(s). The applicant asserts that claims do not recite methods of organizing human activity or managing interactions between people, nor do the claims recite any mathematical relationship, formulas, or calculations or do the claims recite mental processes. The examiner respectfully disagrees and points the applicant to the above analysis.
In regard to the applicant’s argument related to building and training artificial intelligence network models, the examiner points to the analysis in the 101 section, particularly the recitation of the training the machine learning model using artificial intelligence network algorithm is a mathematical concept.
The applicant asserts that claims are directed to using artificial intelligence technique in the field of determining likelihood of occurrence or non-occurrence of an event, hence include one or more practical applications. The examiner respectfully disagrees. The use of artificial intelligence is in terms of training of the machine learning model(s), particularly an algorithm. The use of particular algorithm in training of the machine learning model(s) encompasses mathematical concepts. There is no improvement upon the computer-implemented process or the computer (its components) performed the said steps.
Accordingly, the claims remain rejected under 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent No. 12002575 discloses computer-implemented method of increasing appointment attendance comprises providing a processor and a non-transitory memory including computer program code for one or more programs, the memory and the computer program code configured to, with the at least one processor, perform steps comprising obtaining an appointment data structure, comprising an attendee and a corresponding appointment for the attendee, obtaining a set of population data, obtaining a set of appointment data, obtaining a set of external data, obtaining environmental data, inferring, using the population data, the appointment data, the external data, and the environmental data in a machine learning algorithm, a probability that the attendee will attend the appointment, and when the probability of attendance is below a threshold, performing a mitigation step to increase the probability that the attendee will attend the appointment. The patent further discloses use of time windows, particularly calculation of a probability of one or more risk factor for some or all of the time windows, and the probability is used to schedule or suggest one or more mitigating steps at one or more of the time windows;
US Patent Publication No. 20230214373 discloses granting or denying access for concurrent requests to access data object;
US Patent Publication No. US 20210304304 discloses AI that automates prediction whether a person will keep a commitment;
US Patent Publication No. 20180174079 and 20230401538 discloses predicting a likelihood of occurrence of a no-show in which a user fails to attend a reservation/appointment;
US Patent No. 11309076 discloses systems, methods, and apparatus to generate and utilize predictive workflow analytics and inferencing are disclosed and described. An example predictive workflow analytics apparatus includes a data store to receive healthcare workflow data including at least one of a schedule or a worklist including a patient and an activity in the at least one of the schedule or the worklist involving the patient. The example apparatus includes a data access layer to combine the healthcare workflow data with non-healthcare data to enrich the healthcare workflow data for analysis with respect to the patient. The example apparatus includes an inferencing engine to generate a prediction including a probability of patient no-show to the activity by processing the combined, enriched healthcare workflow data using a model and triggering a corrective action proportional to the probability of patient no-show.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN S KIM whose telephone number is (571)270-5287. The examiner can normally be reached Monday -Friday: 7:00 - 3:30.
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/STEVEN S KIM/Primary Examiner, Art Unit 3698