DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This is a final office action in response to the amendment filed 18 December 2025. Claims 31, 34-35, 40, and 50 have been amended. Claims 1-30 are canceled. Claims 31-50 remain pending and have been examined.
Response to Amendment
Applicant’s amendment to claims 31, 34-35, 40, and 50 has been entered.
Applicant’s amendment is sufficient to overcome the 35 U.S.C. 112(b) rejection of claim 50. The 35 U.S.C. 112(b) rejection of claim 50 is respectfully withdrawn.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the claims, as amended, do not recite an abstract idea, but “recites a specific improvement to machine learning technology and thereby integrates the alleged abstract idea into a practical application” because the “trained machine learning model … receives user-specific sporting event parameters and generates a schedule for a future series of live sporting events based on the one or more user-specific sporting event parameters” in a manner that enables users to customize how the model applies its own training when generating schedules by assigning weights that indicate the importance of user-specific target features. Applicant further asserts that the amended claims are similar to the claims of Ex parte Desjardins and integrate any alleged abstract idea into a practical application by reciting specific improvements to machine learning technology. Examiner respectfully disagrees.
In Ex parte Desjardines, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (precedential), the claimed invention improves how the machine learning model itself operates. In Desjardines, the Board held eligible a recited method of training a machine learning model, where (1) the model was trained on a first machine learning task using first training data to determine first values of machine learning model parameters, where a respective measure of performance was determined for a parameter of the first task and assigned to each parameter, and (2) the machine learning model was trained on a second machine learning task with second training data to adjust the first parameter values to optimize the machine learning model’s performance on the second machine learning task while protecting the model’s performance on the first machine learning task. See Desjardines at 2–3. In arriving at its eligibility conclusion, the Board noted that the claimed invention’s adjustment of the first values of plural parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task constituted an improvement to how machine learning model itself operates. See id. at 9. That is not the case here.
The claimed weights are applied to the one or more user-specific target features indicating an importance of each target feature used as input to the machine learning model, and is used to improve the quality of the data output, not the trained machine learning model. The step for training the machine learning model to identify data relationships using historical data is part of the abstract idea itself. Training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). Further, as acknowledged in Recentive, requirements that a machine learning model be iteratively trained or dynamically adjusted do not represent a technological improvement, because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025). The Specification herein provides no evidence that the claimed techniques provide a benefit to a computer technology or other technology or technical field as was found in Desjardins. Although a specification need not explicitly set forth the improvement, such improvement must be readily apparent to the ordinarily skilled artisan. MPEP § 2106.05(a). Here, the claims recite automating the process of optimizing a schedule for a series of live sporting events based on user-specific event parameters and weights assigned to the one or more user-specific target features. This may be performed in the human mind. As a result, the claims recite an abstract idea and do not integrate the abstract idea into a practical application. The 35 U.S.C. 101 rejection is proper, maintained, and updated below, as necessitated by amendment.
Claim Objections
Applicant is advised that should claim 41 be found allowable, claim 49 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Applicant is advised that should claim 31 be found allowable, claim 50 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non-statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a non-statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 31-50 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-28 of U.S. Patent No. 12,093,861. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 31-50 of the instant application are common to the invention of claims 1-28 of U.S. Patent No. 12,093,861, claims 1-20 of U.S. Patent 11,537,960, and claims 1-20 of U.S. Patent No. 11,386,367. See the following table for details:
The instant application:
18/799908
US Patent 12,093,861 (18/064131)
US Patent 11,537,960
(17/857597)
US Patent 11,386,367
(17/332144)
Claim 31. A computer-implemented method of dynamically generating a sporting event schedule, the method comprising: training, by a computer, a machine learning (ML) model to identify relationships between one or more sporting event parameters and one or more target features based on historical data corresponding to one or more previous series of live sporting events, wherein the one or more target features comprise at least one of travel distance and travel time; receiving, from a user, one or more user-specific sporting event parameters for a future series of live sporting events, wherein the future series of live sporting events is associated with a plurality of sporting teams; receiving, from the user, one or more user-specific target features for each sporting team of the plurality of sporting teams; assigning weights to the one or more user-specific target features indicating an importance of each target feature to the user, wherein the assigned weights customize operation of the trained ML model to the user's preferences; providing the one or more user-specific sporting event parameters and the one or more user-specific target features to the trained ML model; and generating, via the trained ML model, a schedule for the future series of live sporting events based on the one or more user-specific sporting event parameters, wherein the schedule is optimized in accordance with the assigned weights for the plurality of user-specific target features.
Claim 1: A computer-implemented method of dynamically generating a sporting event schedule, the method comprising: providing one or more sporting event parameters for series of live sporting events to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model; iteratively training the ML model to identify relationships between the one or more sporting event parameters and a popularity level of live sporting events using historical data corresponding to one or more previous series of live sporting events, wherein such iterative training improves the accuracy of the ML model; receiving, from a user, one or more user-specific sporting event parameters for a future series of live sporting events, the user-specific sporting event parameters including scheduling information for one or more future sporting events associated with the user; providing the one or more user-specific sporting event parameters to the trained ML model; determining a popularity level of each live sporting event in the future series of live sporting events based, at least in part, on the one or more user-specific sporting event parameters; generating, via the trained ML model, a schedule for the future series of live sporting events that is optimized based on the popularity level of each live sporting event in the future series of live sporting events, wherein the trained ML model is programmed to generate the schedule by assigning the live sporting events to broadcast time slots in the schedule to maximize (i) in-person attendance for the future series of live sporting events and (ii) revenue associated with remote attendance for the future series of live sporting events; displaying the schedule via a graphical user interface (GUI), the GUI including at least one of an optimal button, a reset button, a comma-separated values (CSV) button, a save button, and a load button; detecting a real-time change to the one or more user-specific sporting event parameters; providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and updating, via the trained ML model, the schedule for the future series of live sporting events such that the schedule remains optimized based on the popularity level of each live sporting event in view of the real-time change to the one or more user-specific sporting event parameters.
Claim 1. A computer-implemented method of dynamically generating an event schedule, the method comprising: receiving one or more event parameters for one or more series of live events, wherein the one or more event parameters comprise scheduling information for one or more performances by one or more performers; receiving one or more event target features associated with the series of live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof; providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model; iteratively training the ML model to identify relationships between the one or more event parameters and the one or more event target features using historical data corresponding to one or more previous series of live events, wherein such iterative training improves the accuracy of the ML model; receiving, from a user, one or more user-specific event parameters for a future series of live events associated with a first performer, the user-specific event parameters including scheduling information for one or more future performances by at least one second performer; receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the future series of live events; providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model; generating, via the trained ML model, a schedule for the future series of live events that is optimized relative to the one or more prioritized event target features; detecting a real-time change to the scheduling information for the one or more future performances by the at least one second performer; providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and updating, via the trained ML model, the schedule for the future series of live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the scheduling information for the one or more future performances by the at least one second performer.
Claim 1. A computer-implemented method of dynamically generating an event schedule, the method comprising: receiving one or more event parameters for series of live events, wherein the one or more event parameters comprise at least one of venue availability, venue locations, proposed ticket prices, performer fees, venue fees, scheduled performances by one or more performers, or any combination thereof; receiving one or more event target features associated with the series of live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof; providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model; iteratively training the ML model to identify relationships between different event parameters and the one or more event target features using historical data corresponding to one or more previous series of live events, wherein such iterative training improves the accuracy of the ML model; receiving, from a user, one or more user-specific event parameters for a future series of live events to be held in a plurality of geographic regions; receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the future series of live events; providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model; generating, via the trained ML model, a schedule for the future series of live events that is optimized relative to the one or more prioritized event target features; detecting a real-time change to the one or more user-specific event parameters; providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and updating, via the trained ML model, the schedule for the future series of live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the one or more user-specific event parameters.
Claim 32. The method of claim 31, wherein the one or more user-specific target features comprise at least one of a travel distance and a travel time to be traveled by each of the plurality of sports teams for the future series of live sporting events.
Claim 2. The method of claim 1, wherein the one or more user-specific sporting event parameters comprise a tentative schedule for the future series of live sporting events.
Claim 2. The method of claim 1, wherein the one or more user-specific event parameters comprise a tentative schedule for the future series of live events.
Claim 2. The method of claim 1, wherein the one or more user-specific event parameters comprise a tentative schedule for the future series of live events.
Claim 33. The method of claim 31, wherein the one or more user-specific sporting event parameters comprise a tentative schedule for the future series of live sporting events.
Claim 3. The method of claim 1, wherein the future series of live sporting events comprises a series of sporting events to be played within a single week.
Claim 3. The method of claim 1, wherein the future series of live events comprises a tour for at least one of a band, a music group, an author, a performer, a comedian, a politician, an activist, or a speaker.
Claim 3. The method of claim 1, wherein the future series of live events comprises a tour for at least one of a band, a music group, an author, a performer, a comedian, a politician, an activist, or a speaker.
Claim 34. The method of claim 31, wherein the one or more user-specific sporting event parameters comprise tentative locations for each of the future series of live sporting events.
Claim 4. The method of claim 1, wherein the future series of live sporting events comprises a series of sporting events to be played within a single day.
Claim 4. The method of claim 1, wherein generating the schedule for the future series of live events that is optimized relative to the one or more prioritized target features comprises generating a schedule that maximizes at least one of a revenue or a total attendance for at least one of the future series of live events or a single event from the future series of live events.
Claim 4. The method of claim 1, wherein generating the schedule for the future series of live events that is optimized relative to the one or more prioritized target features comprises generating a schedule that maximizes at least one of a revenue or a total attendance for at least one of the future series of live events or a single event from the future series of live events.
Claim 35. The method of claim 31, wherein the one or more user-specific sporting event parameters comprise venue availability for a plurality of venues associated with the plurality of sporting teams.
Claim 5. The method of claim 1, wherein generating the schedule for the future series of live sporting events that is optimized based on the popularity level of each live sporting event in the future series of live sporting events comprises generating a schedule that maximizes a total revenue for at least one of the future series of live sporting events or a single sporting event from the future series of live sporting events, wherein the total revenue includes revenue associated with in-person and remote attendance.
Claim 5. The method of claim 1, wherein generating the schedule for the future series of live events that is optimized relative to the one or more prioritized target features comprises generating a schedule that maximizes at least one of a revenue or a total attendance at the future series of live events for a particular demographic segment.
Claim 5. The method of claim 1, wherein generating the schedule for the future series of live events that is optimized relative to the one or more prioritized target features comprises generating a schedule that maximizes at least one of a revenue or a total attendance at the future series of live events for a particular demographic segment.
Claim 36. The method of claim 31, wherein the future series of live sporting events comprises at least two live sporting events to be played within a single week.
Claim 6. The method of claim 1, wherein generating the schedule for the future series of live sporting events that is optimized based on the popularity level of each live sporting event in the future series of live sporting events comprises generating a schedule that maximizes a total attendance for at least one of the future series of live sporting events or a single sporting event from the future series of live sporting events, wherein the total attendance includes in-person and remote attendance.
Claim 6. The method of claim 1, wherein generating the schedule for the future series of live events that is optimized relative to the one or more prioritized target features comprises choosing at least one of a venue, a date, or a time for each live event in the future series of live events.
Claim 6. The method of claim 1, wherein generating the schedule for the future series of live events that is optimized relative to the one or more prioritized target features comprises choosing at least one of a venue, a date, or a time for each live event in the future series of live events.
Claim 37. The method of claim 31, wherein the future series of live sporting events comprises at least two live sporting events to be played within a single day.
Claim 7. The method of claim 1, wherein the schedule maximizes remote attendance in one or more geographic regions for at least one of the future series of live sporting events or a single live sporting event from the future series of live sporting events.
Claim 7. The method of claim 1, wherein the at least one second performer includes at least one performer that competes with the first performer in at least one geographic region.
Claim 7. The method of claim 1, wherein the geographic regions comprise at least one of a country, a state, or a city.
Claim 38. The method of claim 31, wherein generating the schedule for the future series of live sporting events comprises selecting a location, a date, and a time for each live sporting event of the future series of live sporting events.
Claim 39. The method of claim 31, further comprising: determining at least one of a projected revenue and a projected attendance for at least one of the live sporting events of the future series of live sporting events based on the schedule.
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 31-50 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of collecting users parameters and features, analyzing historical event data, and outputting a sporting event schedule, without significantly more. Independent claims 31 recites a process, independent claim 40 recites a device, and independent claim 50 recites a process of dynamically generating a sporting event schedule. Independent claims 31, 40, and 50 recite substantially similar limitations.
Taking independent claim 31 as representative, claim 31 recites at least the following limitations:
training, by a computer, a machine learning (ML) model to identify relationships between one or more sporting event parameters and one or more target features based on historical data corresponding to one or more previous series of live sporting events, wherein the one or more target features comprise at least one of travel distance and travel time;
receiving, from a user, one or more user-specific sporting event parameters for a future series of live sporting events, wherein the future series of live sporting events is associated with a plurality of sporting teams;
receiving, from the user, one or more user-specific target features for each sporting team of the plurality of sporting teams;
assigning weights to the one or more user-specific target features indicating an importance of each target feature to the user, wherein the assigned weights customize operation of the trained ML model to the user's preferences;
providing the one or more user-specific sporting event parameters and the one or more user-specific target features to the trained ML model; and
generating, via the trained ML model, a schedule for the future series of live sporting events based on the one or more user-specific sporting event parameters, wherein the schedule is optimized in accordance with the assigned weights for the plurality of user-specific target features.
Under Step 1 independent claim 31 recites at least one step or act, including receiving one or more user-specific target features for each sporting team of the plurality of sporting teams. Thus the claims fall within one of the statutory categories of invention.
Under Step 2A Prong One, the limitations for training a machine learning (ML) model to identify relationships between parameters and target features including travel distance and travel time, receiving one or more user-specific sporting event parameters, receiving one or more user-specific target features, assigning weights to the one or more user-specific target features, providing the one or more user-specific sporting event parameters and the one or more user-specific target features to the trained ML model, and generating an optimized schedule for the future series of live sporting events, as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (collecting, analyzing, and outputting a schedule based on user constraints). An event planner could determine an optimized sporting event schedule based on user specific parameters and target features without the use of a computer. Therefore, the limitations fall into the mental processes grouping and accordingly the claims recite an abstract idea.
The claim limitations describe a process or scheduling a live events using one or more input parameters related to the travel time and travel distance. When applying the broadest reasonable interpretation of the claim language, the limitations fall within the certain methods of organized human activity category of abstract concepts because the claims are directed to managing personal behavior of a user or members of a sports team related to scheduling live sporting events based on user specific parameters such as travel distance and time.
Additionally, training a learning model constitutes a mathematical concept, such as the concept of using known data to set and adjust coefficients and mathematical relationships of variables that represent some modeled characteristic or phenomenon. The MPEP expressly recognizes mathematical concepts including mathematical relationships as constituting an abstract idea. MPEP § 2106.04(a). The steps for receiving event parameters and user-specific target features, and providing the one or more parameters and features to the trained ML model are data gathering steps to provide data for processing, and as a result are construed as insignificant extra-solution data.
Under Step 2A Prong Two, the judicial exception is not integrated into a practical application. The claims recites a processor and storage device for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0053] states: “Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h).
The machine learning model is an additional element under Step 2A Prong Two. The use of the trained machine learning model to process user specific parameters and target features to output an event schedule does not confer patent eligibility because the machine learning model is merely processing data by receiving a specific input in the form of parameters and user-specific features and outputting data in the form of a schedule. The claim limitations do not include technical details on how the model is trained in a manner that amounts to a technical improvement. As a result the applied machine learning model is construed as merely a field of use application. The Specification at paragraph [0005] describes the inventive concept as “improved techniques for scheduling live events,” paragraph [0042] calls out “a significant improvement in computer functionality … improve the accuracy and/or automation of data processing.” Improving the accuracy of data processing is an improvement to the abstract idea of analyzing data to generate an output. An improvement in the abstract idea of generating a schedule using user-specific features and parameters is not an improvement in the functioning of a computer or an improvement to any other technology. See MPEP 2106.05(a).
Under Step 2B the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor, storage device, and trained machine learning model amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept. See MPEP 2106.05.
Dependent claims 32-39 and 41-49 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process/certain method of organizing human activity by describing specific data used as input for the data processing steps and characteristics of the live sporting event for which the schedule is generated. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly independent claims 40 and 50 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 31 above. Therefore claims 31-50 are ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 non-obviousness.
Claims 31-34, 39-44, and 49-50 are rejected under 35 U.S.C. 103 as being unpatentable over Bellary et al. (US 2019/0164135) in view of Smith (US 2020/0380572).
Regarding Amended Claim 31, Bellary et al. discloses a computer-implemented method of dynamically generating a sporting event schedule, the method comprising: (… a computer-implemented method for selecting an optimal date for a planned event is provided. A data processing system receives event input data that includes at least one of a location for a planned event, a range of dates for the planned event, attributes of a target audience for the planned event, and attributes of the planned event. Bellary et al. [para. 0004, 0009, 0028]. … scheduling an event, such as a concert, conference, charitable event, sporting event. Bellary et al. [para. 0044]);
While Bellary et al. discloses processing event schedule data using machine learning techniques (Bellary et al. [para. 0033-0034, 0050-0052; Fig. 2-3]), Bellary et al. fails to explicitly recite training, by a computer, a machine learning (ML) model to identify relationships between one or more sporting event parameters and one or more target features based on historical data corresponding to one or more previous series of live sporting events. Smith discloses this limitation. (The computing system generates a predictive model using a machine learning model by generating a plurality of input data sets based on historical pricing information. Smith [para. 0004-0006]. … Machine learning module 124 may use one or more of a … artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, … and the like to train the prediction model. …dynamic pricing agent 116 may generate a plurality of training sets and a plurality of testing sets to train a prediction model corresponding to the respective team. Smith [para. 0030-0035, 0044-0050]. … Database 110 may include sports leagues 128 … Sport leagues 128 may be representative of any sports league for which organization computing system … Each sport league 128 may include a plurality of teams 130 and a generic prediction model 133. … each specific prediction model 132 may be tuned in a way that accounts for rivals of a given team 130, city in which the respective team plays, type of stadium the team plays in, and the like. … Each event may include one or more parameters associated therewith. For example, each event may include…, a location of the event, a type of facility associated with the event… and the like. Smith [para. 0040-0047]). It would have been obvious to one of ordinary skill in the art of data analytics and machine learning before the effective filing date of the claimed invention to modify the machine learning steps of Bellary et al. to include training, by a computer, a machine learning (ML) model to identify relationships between one or more sporting event parameters and one or more target features based on historical data corresponding to one or more previous series of live sporting events as disclosed by Smith to provide a computing system that generates a predictive model using a machine learning model by generating a plurality of input data sets (Smith [0004]), in a manner that would have generated predictable results at the relevant time.
wherein the one or more target features comprise at least one of travel distance and travel time. (… illustrative embodiments may generate a pool of target attendees using retrieved social media data. The social media data may include, for example: … information that would indicate that certain people are unable to physically attend the currently planned event based on geography (e.g., live beyond a defined threshold distance from the proposed location of the currently planned event). Bellary et al. [para. 0047]).
receiving, from a user, one or more user-specific sporting event parameters for a future series of live sporting events, (Event input data 220 represent different parameters, features, aspects, or characteristics corresponding to the planned event. In this example, event input data 220 include event attributes 222 and audience attributes 224. However, it should be noted that event input data 220 may include any type of event information. Bellary et al. [para. 0028-0029; 0033-0034, 0048-0049]. …Data processing system 302 utilizes event manager 304 to intake and process event input data 306, such as event input data 220 in FIG. 2. In this example, event input data 306 includes event location 308, target data range 310, event attributes 312, and target audience attributes 314, which correspond to the planned event. Bellary et al. [para. 0055-0057; Fig. 2]);
wherein the future series of live sporting events is associated with a plurality of sporting teams. Smith discloses this limitation. (Each sport league 128 may include a plurality of teams 130 and a generic prediction model 133. … each specific prediction model 132 may be tuned in a way that accounts for rivals of a given team 130, city in which the respective team plays, type of stadium the team plays in, and the like. … Each event may include one or more parameters associated therewith. For example, each event may include…, a location of the event, a type of facility associated with the event… and the like. Smith [para. 0040-0047]). It would have been obvious to one of ordinary skill in the art of data analytics and machine learning before the effective filing date of the claimed invention to modify the received input data of Bellary et al. to include the future series of live sporting events is associated with a plurality of sporting teams as disclosed by Smith to provide a computing system that generates a predictive model using a machine learning model by generating a plurality of input data sets (Smith [0004]), in a manner that would have generated predictable results at the relevant time.
receiving, from the user, one or more user-specific target features for each sporting team of the plurality of sporting teams; (Event input data 220 represent different parameters, features, aspects, or characteristics corresponding to the planned event. In this example, event input data 220 include event attributes 222 and audience attributes 224. However, it should be noted that event input data 220 may include any type of event information. Bellary et al. [para. 0028-0029]. Bellary et al. [para. 0033, 0048-0049]. …Data processing system 302 utilizes event manager 304 to intake and process event input data 306, such as event input data 220 in FIG. 2. In this example, event input data 306 includes event location 308, target data range 310, event attributes 312, and target audience attributes 314, which correspond to the planned event. Bellary et al. [para. 0055-0057; Fig. 2]);
assigning weights to the one or more user-specific target features indicating an importance of each target feature to the user, wherein the assigned weights customize operation of the trained ML model to the user's preferences; (Event manager 218 utilizes cognitive machine learning component 238 to receive and analyze event attendance record 240. … Cognitive machine learning component 238 compares event attendance record 240 with pervious event attendance records for same or similar events to improve date selection for future events. … cognitive machine learning component 238 may generate future event recommendations 244 for recurring events, such as parades, festivals, and fairs, based on event attendance record 240 and attribute weights 242. … cognitive machine learning component 238 may generate updates 246 for future event recommendations 244 on a predetermined time interval basis. Bellary et al. [para. 0033-0034, 0050-0052; Fig. 2-3]);
providing the one or more user-specific sporting event parameters and the one or more user-specific target features to the trained ML model; and generating, via the trained ML model, a schedule for the future series of live sporting events based on the one or more user-specific sporting event parameters, wherein the schedule is optimized in accordance with the assigned weights for the plurality of user-specific target features. (… the attributes used in the cognitive learning algorithm have different weights applied for each different attribute (i.e., making one attribute more important than another). Bellary et al. [para. 0050-0052] … Event manager 304 generates event output data 320, such as event output data 232 in FIG. 2, based on analysis of event input data 306, web search data 316, and social media data 318. Event output data 320 results in list of ranked event dates 322. Event manager 304 ranks the dates from highest to lowest in list of ranked event dates 322 by which dates will result in a highest level of attendance by a target audience for the planned event. Bellary et al. [para. 0057; see also para. 0031; Fig. 2-4]).
Regarding Claim 32, Bellary et al. and Smith combined disclose the method, wherein the one or more user-specific target features comprise at least one of a travel distance and a travel time to be traveled by each of the plurality of sports teams for the future series of live sporting events. Smith et al. discloses this limitation. (Each sport league 128 may include a plurality of teams 130 and a generic prediction model 133. … each specific prediction model 132 may be tuned in a way that accounts for rivals of a given team 130, city in which the respective team plays, type of stadium the team plays in, and the like. … Each event may include one or more parameters associated therewith. For example, each event may include…, a location of the event, a type of facility associated with the event… and the like. Smith [para. 0017, 0040-0049]). It would have been obvious to one of ordinary skill in the art of data analytics and machine learning before the effective filing date of the claimed invention to modify the received input data of Bellary et al. to include one or more user-specific target features comprise at least one of a travel distance and a travel time to be traveled by each of the plurality of sports teams for the future series of live sporting events as disclosed by Smith to provide a computing system that generates a predictive model using a machine learning model by generating a plurality of input data sets (Smith [0004]), in a manner that would have generated predictable results at the relevant time.
Regarding Claim 33, Bellary et al. and Smith combined disclose the method, wherein the one or more user-specific sporting event parameters comprise a tentative schedule for the future series of live sporting events. (… cognitive machine learning component 238 may generate future event recommendations 244 for recurring events. Bellary et al. [para. 0034]. … Data processing system 302 utilizes event manager 304 to intake and process event input data 306, such as event input data 220 in FIG. 2. In this example, event input data 306 includes event location 308, target data range 310, event attributes 312, and target audience attributes 314, which correspond to the planned event. Bellary et al. [para. 0055]).
Regarding Amended Claim 34, Bellary et al. and Smith combined disclose the method, wherein the one or more user-specific sporting event parameters comprise tentative locations for each of the future series of live sporting events. (A data processing system receives event input data that includes at least one of a location for a planned event, a range of dates for the planned event, attributes of a target audience for the planned event, and attributes of the planned event. Bellary et al. [para. 0004, 0009, 0028]. … scheduling an event, such as a concert, conference, charitable event, sporting event. Bellary et al. [para. 0044]).
Regarding Claim 39, Bellary et al. and Smith combined disclose the method, further comprising: determining at least one of a projected revenue and a projected attendance for at least one of the live sporting events of the future series of live sporting events based on the schedule. (Illustrative embodiments retrieve and analyze unstructured data, such as, for example, web searches and social media content, and structured data, such as, for example, city calendars, business calendars, and previous event attendance records, to automatically select the best or optimal date and time in which to hold a planned event to maximize attendance by a target audience. Bellary et al. [para. 0044]. … Event manager 304 generates event output data 320, such as event output data 232 in FIG. 2, based on analysis of event input data 306, web search data 316, and social media data 318. Event output data 320 results in list of ranked event dates 322. Event manager 304 ranks the dates from highest to lowest in list of ranked event dates 322 by which dates will result in a highest level of attendance by a target audience for the planned event. Bellary et al. [para. 0057]).
Regarding Claims 40-44 and 49, claims 40-44 and 49 recite substantially similar limitations to those of claims 31-34 and 39 respectively (examiner notes that claims 41 and 49 are duplicates and both recite substantially similar limitations to those of claim 39), and are therefore rejected based upon the same prior art combination, reasoning, and rationale. Claims 40-45 and 49 are directed to a system comprising one or more computer systems programmed to perform operations, which is taught by Bellary et al. at paragraph [0009, 0024]: “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. …Data processing system 200 is an example of a computer, such as sever 104 in FIG. 1, in which computer readable program code or instructions implementing processes of illustrative embodiments may be located. ”
Regarding Amended Claim 50, Bellary et al. discloses a computer-implemented method of dynamically generating a sporting event schedule, the method comprising: (… a computer-implemented method for selecting an optimal date for a planned event is provided. A data processing system receives event input data that includes at least one of a location for a planned event, a range of dates for the planned event, attributes of a target audience for the planned event, and attributes of the planned event. Bellary et al. [para. 0004, 0009, 0028]. … scheduling an event, such as a concert, conference, charitable event, sporting event. Bellary et al. [para. 0044]);
While Bellary et al. discloses processing event schedule data using machine learning techniques (Bellary et al. [para. 0033-0034, 0050-0052; Fig. 2-3]), Bellary et al. fails to explicitly recite training, by a computer, a machine learning (ML) model to identify relationships between one or more sporting event parameters and one or more target features based on historical data corresponding to one or more previous series of live sporting events. Smith discloses this limitation. (The computing system generates a predictive model using a machine learning model by generating a plurality of input data sets based on historical pricing information. Smith [para. 0004-0006]. … Machine learning module 124 may use one or more of a … artificial neural network model, deep learning model, inductive logic programming model, support vector machine model, … and the like to train the prediction model. …dynamic pricing agent 116 may generate a plurality of training sets and a plurality of testing sets to train a prediction model corresponding to the respective team. Smith [para. 0030-0035, 0044-0050]. … Database 110 may include sports leagues 128 … Sport leagues 128 may be representative of any sports league for which organization computing system … Each sport league 128 may include a plurality of teams 130 and a generic prediction model 133. … each specific prediction model 132 may be tuned in a way that accounts for rivals of a given team 130, city in which the respective team plays, type of stadium the team plays in, and the like. … Each event may include one or more parameters associated therewith. For example, each event may include…, a location of the event, a type of facility associated with the event… and the like. Smith [para. 0040-0047]). It would have been obvious to one of ordinary skill in the art of data analytics and machine learning before the effective filing date of the claimed invention to modify the machine learning steps of Bellary et al. to include training, by a computer, a machine learning (ML) model to identify relationships between one or more sporting event parameters and one or more target features based on historical data corresponding to one or more previous series of live sporting events as disclosed by Smith to provide a computing system that generates a predictive model using a machine learning model by generating a plurality of input data sets (Smith [0004]), in a manner that would have generated predictable results at the relevant time.
wherein the one or more target features comprise at least one of travel distance and travel time. (… illustrative embodiments may generate a pool of target attendees using retrieved social media data. The social media data may include, for example: … information that would indicate that certain people are unable to physically attend the currently planned event based on geography (e.g., live beyond a defined threshold distance from the proposed location of the currently planned event). Bellary et al. [para. 0047]).
receiving, from a user, one or more user-specific sporting event parameters for a future series of live sporting events, (Event input data 220 represent different parameters, features, aspects, or characteristics corresponding to the planned event. In this example, event input data 220 include event attributes 222 and audience attributes 224. However, it should be noted that event input data 220 may include any type of event information. Bellary et al. [para. 0028-0029; 0033-0034, 0048-0049]. …Data processing system 302 utilizes event manager 304 to intake and process event input data 306, such as event input data 220 in FIG. 2. In this example, event input data 306 includes event location 308, target data range 310, event attributes 312, and target audience attributes 314, which correspond to the planned event. Bellary et al. [para. 0055-0057; Fig. 2]);
wherein the future series of live sporting events is associated with a plurality of sporting teams; Smith discloses this limitation. (Each sport league 128 may include a plurality of teams 130 and a generic prediction model 133. … each specific prediction model 132 may be tuned in a way that accounts for rivals of a given team 130, city in which the respective team plays, type of stadium the team plays in, and the like. … Each event may include one or more parameters associated therewith. For example, each event may include…, a location of the event, a type of facility associated with the event… and the like. Smith [para. 0040-0047]). It would have been obvious to one of ordinary skill in the art of data analytics and machine learning before the effective filing date of the claimed invention to modify the received input data of Bellary et al. to include the future series of live sporting events is associated with a plurality of sporting teams as disclosed by Smith to provide a computing system that generates a predictive model using a machine learning model by generating a plurality of input data sets (Smith [0004]), in a manner that would have generated predictable results at the relevant time.
receiving, from the user, one or more user-specific target features for the plurality of sporting teams. (Event input data 220 represent different parameters, features, aspects, or characteristics corresponding to the planned event. In this example, event input data 220 include event attributes 222 and audience attributes 224. However, it should be noted that event input data 220 may include any type of event information. Bellary et al. [para. 0028-0029]. Bellary et al. [para. 0033, 0048-0049]. …Data processing system 302 utilizes event manager 304 to intake and process event input data 306, such as event input data 220 in FIG. 2. In this example, event input data 306 includes event location 308, target data range 310, event attributes 312, and target audience attributes 314, which correspond to the planned event. Bellary et al. [para. 0055-0057; Fig. 2]);
assigning weights to the one or more user-specific target features indicating an importance of each target feature to the user, wherein the assigned weights customize operation of the trained ML model to the user's preferences; (Event manager 218 utilizes cognitive machine learning component 238 to receive and analyze event attendance record 240. … Cognitive machine learning component 238 compares event attendance record 240 with pervious event attendance records for same or similar events to improve date selection for future events. … cognitive machine learning component 238 may generate future event recommendations 244 for recurring events, such as parades, festivals, and fairs, based on event attendance record 240 and attribute weights 242. … cognitive machine learning component 238 may generate updates 246 for future event recommendations 244 on a predetermined time interval basis. Bellary et al. [para. 0033-0034, 0050-0052; Fig. 2-3]);
providing the one or more user-specific sporting event parameters and the one or more user-specific target features to the trained ML model; and generating, via the trained ML model, a schedule for the future series of live sporting events based on the one or more user-specific sporting event parameters, wherein the schedule is optimized in accordance with the assigned weights for the plurality of user-specific target features. (… the attributes used in the cognitive learning algorithm have different weights applied for each different attribute (i.e., making one attribute more important than another). Bellary et al. [para. 0050-0052] … Event manager 304 generates event output data 320, such as event output data 232 in FIG. 2, based on analysis of event input data 306, web search data 316, and social media data 318. Event output data 320 results in list of ranked event dates 322. Event manager 304 ranks the dates from highest to lowest in list of ranked event dates 322 by which dates will result in a highest level of attendance by a target audience for the planned event. Bellary et al. [para. 0057; see also para. 0031; Fig. 2-4]).
Claims 35 and 45 are rejected under 35 U.S.C. 103 as being unpatentable Bellary et al. (US 2019/0164135) in view of Smith (US 2020/0380572), in further view of Booth (US 7,725,402).
Regarding Amended Claim 35, Bellary et al. and Smith combined fail to explicitly disclose the method, wherein the one or more user-specific sporting event parameters comprise venue availability for a plurality of venues associated with the plurality of sporting teams. Booth discloses this limitation. (a method for booking a performance venue is disclosed. Booth [col. 2, lines 37-67]. … Venue Module or Interface 230 may also include an Account Module or Interface 420 that enables a venue to enter information related to scheduling and availability information that may be of interest to performers or fans. For instance, Account Module or Interface 420 may be accessed by venues to input information and details about specific dates and times on which space is available or not available. Booth [col. 12, lines 50-67; col. 13, lines 1-25]). It would have been obvious to one of ordinary skill in the art of sports event scheduling before the effective filing date of the claimed invention to modify the input data of Bellary et al. and Smith combined to include venue availability for a plurality of venues associated with the plurality of sporting teams as disclosed by Booth for facilitating the booking of a venue (Booth [col. 2, lines 60-67]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 45, claim 45 recites substantially similar limitations to those of claim 35 and is therefore rejected based upon the same prior art combination, reasoning, and rationale. Claim 45 is directed to a system comprising one or more computer systems programmed to perform operations, which is taught by Bellary et al. at paragraph [0009, 0024]: “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. …Data processing system 200 is an example of a computer, such as sever 104 in FIG. 1, in which computer readable program code or instructions implementing processes of illustrative embodiments may be located. ”
Claims 36-37 and 46-47 are rejected under 35 U.S.C. 103 as being unpatentable Bellary et al. (US 2019/0164135) in view of Smith (US 2020/0380572), in further view of Graham et al. (US 2002/0059205).
Regarding Claim 36, Bellary et al. and Smith combined fail to explicitly disclose the method, wherein the future series of live sporting events comprises at least two live sporting events to be played within a single week. Graham et al. discloses this limitation. (The present invention may therefore comprise a method of creating a schedule for a sports competition using a computer comprising: having a database of teams; selecting the scheduling parameters for competition; automatically calculating the individual games between each team; and displaying the schedule. Graham et al. [para. 0019; 0033, Fig. 1]. … the manager is able to easily manage the league during the course of the season. … The manager also has the opportunity to schedule tournaments, such as a conventional bracket, or single elimination tournament, or a double elimination tournament, king of the hill tournament, or any other type of tournament one can imagine. Graham et al. [para. 0014-0016]. … The competition program may be a league wherein several teams compete against each other for a specific length of time or season. The competition program may be a tournament that includes a number of teams competing over a weekend. Further, the competition program may be a practice session, which is a period where only one team may be scheduled for a time slot. … The competition program may comprise multiple instances of scheduled programs, such as a tournament or a league. The competition program may comprise only one instance, such as a single practice session or a party. The term competition program is meant to include any period of time, including recurring periods of time that are scheduled for the facility and should not be constrained to include those of only a competitive nature. Graham et al. [para. 0037, 0066-0067; Fig. 8]. … The input parameters 200 may include such factors as the start date, end date and days of the week for scheduling of multiple games of a league, for example. … The venues may also be selected as an input to the schedule, and multiple venues may be selected for the same league. For example, a baseball park may have four fields for softball that are scheduled simultaneously for a softball league. Graham et al. [para. 0054]). It would have been obvious to one of ordinary skill in the art of sports event scheduling before the effective filing date of the claimed invention to modify the schedule recommendation functions of Bellary et al. and Smith combined to include wherein the future series of live sporting events comprises at least two live sporting events to be played within a single week as disclosed by Graham et al. to provide an on line method for scheduling a series of sporting events wherein a tournament scheduler may us a computer network to create and maintain a database of participants, book time at one or more sporting facilities for the event, have automated assistance in creating a schedule of competition, and create and maintain a website for the sporting events (Graham et al. [para. 0010]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 37, Bellary et al. and Smith combined fail to explicitly disclose the method, wherein the future series of live sporting events comprises at least two live sporting events to be played within a single day. Graham et al. discloses this limitation. (The present invention may therefore comprise a method of creating a schedule for a sports competition using a computer comprising: having a database of teams; selecting the scheduling parameters for competition; automatically calculating the individual games between each team; and displaying the schedule. Graham et al. [para. 0019; 0033, Fig. 1]. … the manager is able to easily manage the league during the course of the season. … The manager also has the opportunity to schedule tournaments, such as a conventional bracket, or single elimination tournament, or a double elimination tournament, king of the hill tournament, or any other type of tournament one can imagine. Graham et al. [para. 0014-0016]. … The competition program may be a league wherein several teams compete against each other for a specific length of time or season. The competition program may be a tournament that includes a number of teams competing over a weekend. Further, the competition program may be a practice session, which is a period where only one team may be scheduled for a time slot. … The competition program may comprise multiple instances of scheduled programs, such as a tournament or a league. The competition program may comprise only one instance, such as a single practice session or a party. The term competition program is meant to include any period of time, including recurring periods of time that are scheduled for the facility and should not be constrained to include those of only a competitive nature. Graham et al. [para. 0037, 0066-0067; Fig. 8] … The input parameters 200 may include such factors as the start date, end date and days of the week for scheduling of multiple games of a league, for example. … The venues may also be selected as an input to the schedule, and multiple venues may be selected for the same league. For example, a baseball park may have four fields for softball that are scheduled simultaneously for a softball league. Graham et al. [para. 0054]). It would have been obvious to one of ordinary skill in the art of sports event scheduling before the effective filing date of the claimed invention to modify the schedule recommendation functions of Bellary et al. and Smith combined to include the future series of live sporting events comprises a series of sporting events to be played within a single day as disclosed by Graham et al. to provide an on line method for scheduling a series of sporting events wherein a tournament scheduler may us a computer network to create and maintain a database of participants, book time at one or more sporting facilities for the event, have automated assistance in creating a schedule of competition, and create and maintain a website for the sporting events, (Graham et al. [para. 0010]), in a manner that would have yielded predictable results at the relevant time.
Regarding claims 46-47, claims 46-47 recite substantially similar limitations to those of claims 36-37 respectively, and are therefore rejected based upon the same prior art combination, reasoning, and rationale. Claims 46-47 are directed to a system comprising one or more computer systems programmed to perform operations, which is taught by Bellary et al. at paragraph [0009, 0024]: “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. …Data processing system 200 is an example of a computer, such as sever 104 in FIG. 1, in which computer readable program code or instructions implementing processes of illustrative embodiments may be located. ”
Claims 38 and 48 are rejected under 35 U.S.C. 103 as being unpatentable Bellary et al. (US 2019/0164135) in view of Smith (US 2020/0380572), in further view of Mann et al. (US 2009/0106067).
Regarding Claim 38, Bellary et al. and Smith combined fail to explicitly disclose the method, wherein generating the schedule for the future series of live sporting events comprises selecting a location, a date, and a time for each live sporting event of the future series of live sporting events. Mann et al. discloses this limitation. (system and method are provided that allow users to identify and schedule events that are optimized based on user-entered selection criteria. Mann et al. [para. 0003]. … a plurality of filters are provided to the person scheduling the event. Mann et al. [para. 0030]. … . In one exemplary embodiment, the distance filter can be a filter that restricts the list of teams based on a distance parameter, such as a maximum distance from a selected location, a location associated with the scheduler, whether the scheduling request is for a home game or away game or other suitable distance parameters. Mann et al. [para. 0040]. … if a time and date have not been specified in the schedule request, available venues can be indicated with associated dates and time. Manne t al. [para. 0041]). It would have been obvious to one of ordinary skill in the art of event schedule management before the effective filing date of the claimed invention to modify the output of Bellary et al. and Smith combined to include generating the schedule for the future series of live sporting events comprises selecting a location, a date, and a time for each live sporting event of the future series of live sporting events as disclosed by Mann et al. r to generate schedule requests for games, such as for season schedule generation, to reschedule a cancelled game, or for other purposes (Mann et al. [para. 0044]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 48, claim 48 recite substantially similar limitations to those of claim 38 and is therefore rejected based upon the same prior art combination, reasoning, and rationale. Claim 48 is directed to a system comprising one or more computer systems programmed to perform operations, which is taught by Bellary et al. at paragraph [0009, 0024]: “The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. …Data processing system 200 is an example of a computer, such as sever 104 in FIG. 1, in which computer readable program code or instructions implementing processes of illustrative embodiments may be located. ”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
DiTomaso et al. (US 2020/0134514) - determining whether an apparent booking is a genuine booking or is a blocked period of unavailability that is not the result of a genuine booking. Bookings occur in all sorts of industries, such as travel, medical, entertainment, weddings, catering, and the like. In some examples, the method may include receiving content from a website that includes a listing for an object, identifying a period of unavailability of the object based on the content received from the website, predicting, via a machine learning model, whether the period of unavailability of the object is a blocked period that is not a result of a reservation of the object
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623