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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention are directed to abstract ideas without significantly more.
Regarding Claim 1:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim recites the abstract ideas:
a prediction unit that predicts a difficulty level of a result prediction of the target race using a model in which a relationship between information regarding the race organization and a difficulty level of a race result prediction is machine-learned and the information regarding the race organization of the target race: - This limitation recites the abstract idea of mathematical concepts, as when given the broadest reasonable interpretation in light of the specification, the model that predicts the difficulty level of the result prediction of the target race uses the learning algorithm based on the factorized asymptotic Bayesian inference which is an algorithm based on mathematical calculations, therefore, this limitation amounts to mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
acquire information regarding race organization in a target race of a public competition: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
acquire information regarding race organization in a target race of a public competition: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II).
Regarding Claim 2:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection on claim 1).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
output the difficulty level of the target race to an output device: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
output the difficulty level of the target race to an output device: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II).
Regarding Claim 3:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 2 which included an abstract idea (see rejection on claim 2).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
output information regarding the race organization in a past race: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
output information regarding the race organization in a past race: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II).
Regarding Claim 4:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 3 which included an abstract idea (see rejection on claim 3).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
output information regarding the race organization in the past race selected based on a difficulty level of the target race: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
output information regarding the race organization in the past race selected based on a difficulty level of the target race: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II).
Regarding Claim 5:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 3 which included an abstract idea (see rejection on claim 3).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
acquire a target difficulty level of the race result prediction in the target race: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
output information regarding the race organization in the past race selected based on the target difficulty level: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
acquire a target difficulty level of the race result prediction in the target race: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II);
output information regarding the race organization in the past race selected based on the target difficulty level: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II).
Regarding Claim 6:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 6 which included an abstract idea (see rejection on claim 6).
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
create a program change proposal in which at least a part of information regarding the race organization in the target race is changed: - This limitation is directed to selection of a particular data source or type of data to be manipulated as it is merely adding data, being insignificant extra-solution activity (see MEPEP n2106.05(g));
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
create a program change proposal in which at least a part of information regarding the race organization in the target race is changed: - This limitation is analogous to electronic recordkeeping because it’s adding data to the training set and keeping record of it (see MPEP 2106.05(d) II (iii)).
Regarding Claim 7:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 6 which included an abstract idea (see rejection on claim 6). The additional elements:
predict the difficulty level of at least one of the created program change proposals: - This limitation recites the abstract idea of mathematical concepts, as when given the broadest reasonable interpretation in light of the specification, the prediction of the difficulty level of at least one of the created program change proposals is done using the learning algorithm based on the factorized asymptotic Bayesian inference which is an algorithm based on mathematical calculations, therefore, this limitation amounts to a mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
acquire a target difficulty level of a race result prediction in the target race: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
and output, to an output device, the program change proposal in which the predicted difficulty level matches the target difficulty level among the created program change proposals: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
acquire a target difficulty level of a race result prediction in the target race: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II);
and output, to an output device, the program change proposal in which the predicted difficulty level matches the target difficulty level among the created program change proposals: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II).
Regarding Claim 8:
Step 1 - Is the claim directed to a process, a machine, manufacture or composition of matter? - Yes, the claim is directed to an apparatus.
Step 2A - Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon? - Yes, the claim is dependent on claim 1 which included an abstract idea (see rejection on claim 1). The additional elements:
and generate a prediction model that predicts the difficulty level based on the information regarding the race organization by learning a relationship between the information regarding the race organization and the difficulty level of the race result prediction using the training data: - This limitation recites the abstract idea of mathematical concepts, as when given the broadest reasonable interpretation in light of the specification, the prediction model that predicts the difficulty level based on the information regarding the race organization is done using the learning algorithm based on the factorized asymptotic Bayesian inference which is an algorithm based on mathematical calculations, therefore, this limitation amounts to a mathematical concepts.
Step 2A - Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? - No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
acquire training data indicating a relationship between information regarding race organization in a race of the public competition and a difficulty level of result prediction of the race: - This limitation is directed to mere data gathering and outputting. The courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) have recognized mere data gathering and outputting as insignificant extra-solution activity (see MPEP 2106.05(g)(3)) and therefore fails to integrate the exception into a practical application;
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? - No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements:
acquire training data indicating a relationship between information regarding race organization in a race of the public competition and a difficulty level of result prediction of the race: - This limitation is directed to receiving or transmitting data over a network. The courts (as per Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362) have recognized receiving or transmitting data over a network as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (see MPEP 2106.05(d) II).
Regarding claims 9-16, these claims are directed to a method and are rejected on the same basis as claims 1-8 respectively, since they are analogous.
Regarding claims 17-19, these claims are directed to a non-transitory recording medium and are rejected on the same basis as claims 1-3 respectively, since they are analogous.
Regarding claim 20, this claim is directed to a non-transitory recording medium and is rejected on the same basis as claim 8 since they are analogous.
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 nonobviousness.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over McKeever et al (US-20200105107-A1 - hereinafter McKeever) in view of Lee et al (KR-102479631-B1- hereinafter Lee).
Referring to Claim 1, McKeever teaches an apparatus:
and a prediction unit that predicts a difficulty level of a result prediction of the target race using a model in which a relationship between information regarding the race organization and a difficulty level of a race result prediction is machine-learned and the information regarding the race organization of the target race (see McKeever at Pg. 62: “The horse win percentage calculator 240 may calculate predicted win percentages 114 of the horses in each of a plurality of scheduled races. As noted above, the predicted win percentage 114 of a horse may represent the likelihood that the horse will win a given race as determined according to any algorithm (e.g. machine learning algorithms) on the basis of publicly available information. For example, relevant data may be obtained from a provider of horse racing data such as TrackMaster® and stored in the race data storage 230. Based on such data, the horse win percentage calculator 240 may input any combination of various data features (e.g. age of horse, horse's track record, jockey's track record, trainer's track record, etc.) into an algorithm whose output is the predicted win percentage 114. For example, past race data can be used to process past horses into arrays of data features (e.g. evaluated to −1, 0, and 1), and the race results can be used to train a Support Vector Machine (SVM) to predict horse finish order based on the data features. The SVM can then be used to predict future horse finish order and generate predicted win percentages 114 based on the same data features as applied to future races. A corresponding predicted win percentage 114 may thus be provided to the ticket updater 220 for each horse in each of the lists 110 generated by the list generator 222, such that the predicted win percentages 114 may be displayed on the horse race betting GUI 100 as shown in FIG. 1. The horse win percentage calculator 240 may further provide the predicted win percentages 114 to the horse selector 250 for use in the automatic selection of horses by the horse race betting apparatus 200”. Examiner interprets the SVM being used to generate the predicted win percentages 114 (interpreted as difficulty level of a result prediction of the target race) based on the same data features such as age of horse, horse's track record, and jockey's track record (interpreted as information regarding the race organization) as applied to future races to be equivalent as the claimed “and a prediction unit that predicts a difficulty level of a result prediction of the target race using a model in which a relationship between information regarding the race organization and a difficulty level of a race result prediction is machine-learned and the information regarding the race organization of the target race”).
However, McKeever fails to teach:
acquire information regarding race organization in a target race of a public competition.
Lee teaches, in analogous system,
acquire information regarding race organization in a target race of a public competition (see Lee at Pg. 148: “In the present invention, the necessary data for horse racing prediction modeling may include, in addition to racehorse behavior observation information, at least one of racehorse personal information, racehorse performance information, jockey performance information, track environment information, or previous race result information. At this time, the above-mentioned necessary data can be collected through at least one of observation equipment, information provided by the racetrack, and external weather information”. Examiner interprets the collection of racehorse personal information, racehorse performance information or previous race result information to be equivalent as the claimed “acquire information regarding race organization in a target race of a public competition”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of McKeever with the above teachings of Lee by predicting the difficulty level of the result prediction of the target race using a model in which the relationship between information regarding the race organization and the difficulty level of the race result prediction is machine-learned and the information regarding the race organization of the target race, as taught by McKeever, and acquiring information regarding race organization in the target race of a public competition, as taught by Lee. The modification would have been obvious because one of ordinary skill in the art would be motivated to extract training and validation datasets from the collected necessary data (as suggested by Lee at Pg. 150: “Data extraction step for extracting training and validation datasets from collected necessary data”).
Referring to Claim 2, McKeever teaches the apparatus of claim 1:
output the difficulty level of the target race to an output device (see McKeever at Pg. 45: “As noted above, the horse race betting GUI 100 may serve an advising function. To this end, in association with each horse of a given list 110, a predicted win percentage 114 of the horse may be displayed. For example, in the first list 110 shown in FIG. 1, “FROSTY ZEN” has a predicted win percentage 114 of only 1%, while “J K'S GIRL” has a predicted win percentage 114 of 45%. As shown, the horses of each list 110 may be ordered according to their predicted win percentages 114, with the higher predicted win percentages 114 appearing first. The predicted win percentage 114 of a horse may represent the likelihood that the horse will win the race as determined according to any algorithm (e.g. any machine learning algorithm) on the basis of publicly available information. By reviewing the predicted win percentages 114 of the horses running one or more races, the user may make a more informed decision when selecting horses to win each race. The user may, for example, compare the predicted win percentages 114 to morning line odds and/or live odds in an effort to find horses that are being undervalued (overlays) or overvalued (underlays) by the public”. Examiner interprets displaying the predicted win percentage 114 (interpreted as difficulty level) in association with each horse of the list 110 (interpreted as the target race) to be equivalent as the claimed “output the difficulty level of the target race to an output device”).
Referring to Claim 3, McKeever teaches the apparatus of claim 2:
output information regarding the race organization in a past race (see McKeever at Pg. 78: “A trouble indicator 516 may also appear in association with some of the horses in the list 510. The trouble indicator 516 may indicate that the corresponding horse experienced some unusual difficulty in its previous race, such as tiring early, falling down, or losing a rider. Unlike conventional race statistics, which might include descriptions of such unusual difficulties together with uneventful descriptions for other horses (e.g. even pace, good finish, etc.), the trouble indicator 516 may be provided only for unusual difficulty (e.g. for horses 3 and 7 in FIG. 5) while being absent for horses that experienced no unusual difficulty (e.g. horses 1, 2, 4-6, and 8). To this end, a list of significant words or phrases may be stored in a database of the horse race betting apparatus 200, and the horse race betting GUI 500 may display the trouble indicator 516 only when published information about a horse's past race includes a word or phrase on the list. In this way, the user can more quickly and easily notice which horses might require special consideration. As shown, the trouble indicator 516 itself might be only a symbol (e.g. an exclamation point), which may then be clicked or rolled-over to navigate to a details page (e.g. pop-up window) showing the details of the trouble that the horse experienced”. Examiner interprets the horse race betting GUI 500 displaying the trouble indicator that indicates some unusual difficulty experienced by a horse in its previous race such as tiring early, falling down, or losing a rider to be equivalent as the claimed “output information regarding the race organization in a past race”).
Referring to Claim 4, McKeever teaches the apparatus of claim 3:
output information regarding the race organization in the past race selected based on a difficulty level of the target race (see McKeever at Pg. 45: “As noted above, the horse race betting GUI 100 may serve an advising function. To this end, in association with each horse of a given list 110, a predicted win percentage 114 of the horse may be displayed. For example, in the first list 110 shown in FIG. 1, “FROSTY ZEN” has a predicted win percentage 114 of only 1%, while “J K'S GIRL” has a predicted win percentage 114 of 45%. As shown, the horses of each list 110 may be ordered according to their predicted win percentages 114, with the higher predicted win percentages 114 appearing first. The predicted win percentage 114 of a horse may represent the likelihood that the horse will win the race as determined according to any algorithm (e.g. any machine learning algorithm) on the basis of publicly available information. By reviewing the predicted win percentages 114 of the horses running one or more races, the user may make a more informed decision when selecting horses to win each race. The user may, for example, compare the predicted win percentages 114 to morning line odds and/or live odds in an effort to find horses that are being undervalued (overlays) or overvalued (underlays) by the public”. Examiner interprets the horse race betting GUI 100 displaying the horse name (for example FROSTY ZEN) based on its predicted win percentage (1%) which represents the likelihood that the horse will win the race as determined according to any algorithm to be equivalent as the claimed “output information regarding the race organization in the past race selected based on a difficulty level of the target race”).
Referring to Claim 5, McKeever teaches the apparatus of claim 3:
acquire a target difficulty level of the race result prediction in the target race (see McKeever at Pg. 45: “As noted above, the horse race betting GUI 100 may serve an advising function. To this end, in association with each horse of a given list 110, a predicted win percentage 114 of the horse may be displayed. For example, in the first list 110 shown in FIG. 1, “FROSTY ZEN” has a predicted win percentage 114 of only 1%, while “J K'S GIRL” has a predicted win percentage 114 of 45%. As shown, the horses of each list 110 may be ordered according to their predicted win percentages 114, with the higher predicted win percentages 114 appearing first. The predicted win percentage 114 of a horse may represent the likelihood that the horse will win the race as determined according to any algorithm (e.g. any machine learning algorithm) on the basis of publicly available information. By reviewing the predicted win percentages 114 of the horses running one or more races, the user may make a more informed decision when selecting horses to win each race. The user may, for example, compare the predicted win percentages 114 to morning line odds and/or live odds in an effort to find horses that are being undervalued (overlays) or overvalued (underlays) by the public”. Examiner interprets the user reviewing the displayed predicted win percentages (interpreted as target difficulty level) of the horses running one race to be equivalent as the claimed “acquire a target difficulty level of the race result prediction in the target race”);
output information regarding the race organization in the past race selected based on the target difficulty level (see McKeever at Pg. 52: “The user may wish to produce a hybrid manually/automatically generated ticket by introducing desired constraints for the horse race betting GUI 100 to take into consideration when automatically generating selections. To this end, the horse race betting GUI 100 may include, in association with each horse, a horse lock element 116 by which the user may lock the horse's marked/unmarked status. For example, after automatically generating the ticket shown in FIG. 1 using the automatic ticket generation button 190, the user may decide to make some changes upon reviewing the selections. For example, the user may have done some independent research on “PICKY BITS” in Leg 1 and may feel that the predicted win percentage 114 of 16% should be higher. The user may therefore click the win selection element 112 associated with “PICKY BITS” to mark “PICKY BITS” as selected to win and also click the horse lock element 116 associated with “PICKY BITS” to lock the marked status of this individual horse. At the same time, the user may dislike the name “SCOTTISH DEVIL” in Leg 2 and may therefore unmark “SCOTTISH DEVIL” using the associated win selection element 112 and lock the horse's unmarked status using the horse lock element 116”. Examiner interprets the user clicking the win selection element 112 and clicking the horse lock element 116 associated with PICKY BITS (PICKY BITS is displayed by the horse race betting GUI 100 and interpreted as information regarding the race organization in the past race) based on the predicted win percentage 114 of 16% (interpreted as target difficulty level) to be equivalent as the claimed “output information regarding the race organization in the past race selected based on the target difficulty level”).
Referring to Claim 6, McKeever teaches the apparatus of claim 1:
create a program change proposal in which at least a part of information regarding the race organization in the target race is changed (see McKeever at Pg. 52: “The user may wish to produce a hybrid manually/automatically generated ticket by introducing desired constraints for the horse race betting GUI 100 to take into consideration when automatically generating selections. To this end, the horse race betting GUI 100 may include, in association with each horse, a horse lock element 116 by which the user may lock the horse's marked/unmarked status. For example, after automatically generating the ticket shown in FIG. 1 using the automatic ticket generation button 190, the user may decide to make some changes upon reviewing the selections. For example, the user may have done some independent research on “PICKY BITS” in Leg 1 and may feel that the predicted win percentage 114 of 16% should be higher. The user may therefore click the win selection element 112 associated with “PICKY BITS” to mark “PICKY BITS” as selected to win and also click the horse lock element 116 associated with “PICKY BITS” to lock the marked status of this individual horse. At the same time, the user may dislike the name “SCOTTISH DEVIL” in Leg 2 and may therefore unmark “SCOTTISH DEVIL” using the associated win selection element 112 and lock the horse's unmarked status using the horse lock element 116”. Examiner interprets the changes make upon reviewing the selections to be equivalent as the claimed “create a program change proposal“. For example, producing changes like: marked “PICKY BITS” (“PICKY BITS” is interpreted as information regarding the race organization in the target race) as selected to win and also locked “PICKY BITS” marked status; as well as unmarked “SCOTTISH DEVIL” (“SCOTTISH DEVIL” is interpreted as information regarding the race organization in the target race) and lock “SCOTTISH DEVIL” unmarked status; is interpreted as an example of “create a program change proposal in which at least a part of information regarding the race organization in the target race is changed”).
Referring to Claim 7, McKeever teaches the apparatus of claim 6:
acquire a target difficulty level of a race result prediction in the target race (see McKeever at Pg. 45: “As noted above, the horse race betting GUI 100 may serve an advising function. To this end, in association with each horse of a given list 110, a predicted win percentage 114 of the horse may be displayed. For example, in the first list 110 shown in FIG. 1, “FROSTY ZEN” has a predicted win percentage 114 of only 1%, while “J K'S GIRL” has a predicted win percentage 114 of 45%. As shown, the horses of each list 110 may be ordered according to their predicted win percentages 114, with the higher predicted win percentages 114 appearing first. The predicted win percentage 114 of a horse may represent the likelihood that the horse will win the race as determined according to any algorithm (e.g. any machine learning algorithm) on the basis of publicly available information. By reviewing the predicted win percentages 114 of the horses running one or more races, the user may make a more informed decision when selecting horses to win each race. The user may, for example, compare the predicted win percentages 114 to morning line odds and/or live odds in an effort to find horses that are being undervalued (overlays) or overvalued (underlays) by the public”. Examiner interprets the user reviewing the displayed predicted win percentages (interpreted as target difficulty level) of the horses running one race to be equivalent as the claimed “acquire a target difficulty level of a race result prediction in the target race”);
predict the difficulty level of at least one of the created program change proposals (see McKeever at Pg. 52: “The user may wish to produce a hybrid manually/automatically generated ticket by introducing desired constraints for the horse race betting GUI 100 to take into consideration when automatically generating selections. To this end, the horse race betting GUI 100 may include, in association with each horse, a horse lock element 116 by which the user may lock the horse's marked/unmarked status. For example, after automatically generating the ticket shown in FIG. 1 using the automatic ticket generation button 190, the user may decide to make some changes upon reviewing the selections. For example, the user may have done some independent research on “PICKY BITS” in Leg 1 and may feel that the predicted win percentage 114 of 16% should be higher. The user may therefore click the win selection element 112 associated with “PICKY BITS” to mark “PICKY BITS” as selected to win and also click the horse lock element 116 associated with “PICKY BITS” to lock the marked status of this individual horse. At the same time, the user may dislike the name “SCOTTISH DEVIL” in Leg 2 and may therefore unmark “SCOTTISH DEVIL” using the associated win selection element 112 and lock the horse's unmarked status using the horse lock element 116”. Examiner interprets generating after reviewing the selections, the following change: marked “PICKY BITS” as selected to win and also locked “PICKY BITS” marked status, based on the user feeling that the predicted win percentage of 16% (16% is interpreted as difficulty level) for “PICKY BITS” should be higher, to be equivalent as the claimed “predict the difficulty level of at least one of the created program change proposals”);
and output, to an output device, the program change proposal in which the predicted difficulty level matches the target difficulty level among the created program change proposals (see McKeever at Pg. 51: “If the user would like the horse race betting GUI 100 to advise on which horses to select for an optimal ticket, the user may click the automatic ticket generation button 190 to request an automatic selection of horses. In response, the horse race betting GUI 100 may mark one or more horses of each of the first, second, third, fourth, fifth, and sixth lists 110 as selected to win based on the predicted win percentages 114 of the horses. In a simple case, the automatically generated selections may maximize the predicted ticket win percentage 170 without causing the number of bets times the bet amount 140 to exceed the maximum ticket price 150. So, in the example shown in FIG. 1, where the user has input $2.00 as the bet amount and $200 as the maximum ticket price, the horse race betting GUI 100 may select a combination of horses that maximizes the predicted ticket win percentage 170 without exceeding 100 bets (since more than 100 bets times $2.00/bet would exceed $200). More generally, the automatically generated selections may maximize some function of the predicted ticket win percentage 170, as described in more detail below, within various constraints.
In the example of FIG. 1, the user has clicked the automatic ticket generation button 190 and, as a result, the horse race betting GUI 100 has selected the first two horses in Leg 1, the first horse in Leg 2, the first four horses in Leg 3, the first five horses in Leg 4, the first two horses in Leg 5, and the first four horses in Leg 6, resulting in a ticket cost 160 of $192.00 and a predicted ticket win percentage 170 of 4.07%. The automatically selected horses may be indicated by their respective win selection elements 112, e.g., by displayed checkmarks as shown, allowing the user to easily review the selections. For convenience, the number of selections in each list 110 of horses may also be displayed, e.g., in the corresponding list info portion 130 as shown (e.g. “2 horse(s)”). The user may then modify the automatic selections as he/she sees fit. For example, the user may unmark and/or mark additional win selection elements 112. The user may also clear an entire leg (i.e. remove all selections from a particular list 110) using a clear leg button 134 in the corresponding list info portion 130. The user may clear the entire ticket (i.e. remove all selections from all lists 110) using a clear ticket button 180. As the user makes changes, the ticket cost 160 and predicted ticket win percentage 170 may be automatically updated to reflect the newly selected combination of horses”. Examiner interprets the changes made by the user to the displayed predicted ticket win percentage (interpreted as the target difficulty level among the created program change proposals) to reflect the newly selected combination of horses to be equivalent as the claimed “and output, to an output device, the program change proposal in which the predicted difficulty level matches the target difficulty level among the created program change proposals”; the new combination of horses are selected to win based on the predicted win percentages (interpreted as the predicted difficulty level) of the horses).
Referring to Claim 8, McKeever teaches the apparatus of claim 1:
acquire training data indicating a relationship between information regarding race organization in a race of the public competition and a difficulty level of result prediction of the race (see McKeever at Pg. 62: “The horse win percentage calculator 240 may calculate predicted win percentages 114 of the horses in each of a plurality of scheduled races. As noted above, the predicted win percentage 114 of a horse may represent the likelihood that the horse will win a given race as determined according to any algorithm (e.g. machine learning algorithms) on the basis of publicly available information. For example, relevant data may be obtained from a provider of horse racing data such as TrackMaster® and stored in the race data storage 230. Based on such data, the horse win percentage calculator 240 may input any combination of various data features (e.g. age of horse, horse's track record, jockey's track record, trainer's track record, etc.) into an algorithm whose output is the predicted win percentage 114. For example, past race data can be used to process past horses into arrays of data features (e.g. evaluated to −1, 0, and 1), and the race results can be used to train a Support Vector Machine (SVM) to predict horse finish order based on the data features. The SVM can then be used to predict future horse finish order and generate predicted win percentages 114 based on the same data features as applied to future races. A corresponding predicted win percentage 114 may thus be provided to the ticket updater 220 for each horse in each of the lists 110 generated by the list generator 222, such that the predicted win percentages 114 may be displayed on the horse race betting GUI 100 as shown in FIG. 1. The horse win percentage calculator 240 may further provide the predicted win percentages 114 to the horse selector 250 for use in the automatic selection of horses by the horse race betting apparatus 200”. Examiner interprets obtaining relevant data (interpreted as information regarding race organization) such as age of horse, horse's track record, jockey's track record, trainer's track record, from a provider of horse racing data, and input them into an algorithm whose output is the predicted win percentage (interpreted as difficulty level) to be equivalent as the claimed “acquire training data indicating a relationship between information regarding race organization in a race of the public competition and a difficulty level of result prediction of the race”);
and generate a prediction model that predicts the difficulty level based on the information regarding the race organization by learning a relationship between the information regarding the race organization and the difficulty level of the race result prediction using the training data (see McKeever at Pg. 62: “(see McKeever at Pg. 62: “The horse win percentage calculator 240 may calculate predicted win percentages 114 of the horses in each of a plurality of scheduled races. As noted above, the predicted win percentage 114 of a horse may represent the likelihood that the horse will win a given race as determined according to any algorithm (e.g. machine learning algorithms) on the basis of publicly available information. For example, relevant data may be obtained from a provider of horse racing data such as TrackMaster® and stored in the race data storage 230. Based on such data, the horse win percentage calculator 240 may input any combination of various data features (e.g. age of horse, horse's track record, jockey's track record, trainer's track record, etc.) into an algorithm whose output is the predicted win percentage 114. For example, past race data can be used to process past horses into arrays of data features (e.g. evaluated to −1, 0, and 1), and the race results can be used to train a Support Vector Machine (SVM) to predict horse finish order based on the data features. The SVM can then be used to predict future horse finish order and generate predicted win percentages 114 based on the same data features as applied to future races. A corresponding predicted win percentage 114 may thus be provided to the ticket updater 220 for each horse in each of the lists 110 generated by the list generator 222, such that the predicted win percentages 114 may be displayed on the horse race betting GUI 100 as shown in FIG. 1. The horse win percentage calculator 240 may further provide the predicted win percentages 114 to the horse selector 250 for use in the automatic selection of horses by the horse race betting apparatus 200”. Examiner interprets the SVM (age of horse, horse's track record, and jockey's track record are input into the SVM) being used to generate the predicted win percentages 114 based on the same data features as applied to future races to be equivalent as the claimed “and generate a prediction model that predicts the difficulty level based on the information regarding the race organization by learning a relationship between the information regarding the race organization and the difficulty level of the race result prediction using the training data”).
Referring to independent Claims 9, 17, these claims are rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to dependent Claims 10-16, these claims are rejected on the same basis as dependent claims 2-8 respectively, since they are analogous claims.
Referring to dependent Claims 18-19, these claims are rejected on the same basis as dependent claims 2-3 respectively, since they are analogous claims.
Referring to dependent Claim 20, this claim is rejected on the same basis as dependent claim 8, since they are analogous claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AWADAGBE G HOUNTON whose telephone number is (571)270-0670. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AWADAGBE G HOUNTON/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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