DETAILED ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 13 recites the limitation "the other object" in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4, 6, 8, 10-12 and 14-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Specifically the claims are directed to the abstract idea of a Mental Process. Claims 1 and 14-15 recite “an acquisition unit that acquires subject state information representing a game state at a timing for evaluating a game play to be evaluated; a search unit that extracts the reference state information similar to the subject state information from among the reference data; an evaluation unit that evaluates the tide of a game at the timing for evaluating the game play to be evaluated on the basis of the game results associated with the reference state information extracted; and an output unit that outputs the result of the evaluation.” This recites a process whereby a person could observe a game to determine its state and the search, either from memory or based on gathered data for a similar historical game state which the person could then use as the basis of a prediction regarding the outcome of a game which they then state.” This could be as simple as the observe noting that one player has a higher life total or more game pieces remaining and that this tends to suggest that they are in a winning position in the game.
This judicial exception is not integrated into a practical application because the additional elements such as a computer or program are mere instructions to implement the abstract idea on a computer, see MPEP 2106.05(f). Further recitation of data storage for game state reference data as best represents insignificant extra solution data-gathering activity, See MPEP 2106.05(g).
Further Dependent Claims 2, 4, 6, 8, and 10-11 represent recitation of additional abstract features of the gathered data, while Claim 12es teach insignificant extra solution data gathering activity.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because recitation of a “computer” or “program” is simply recitation of well-understood, routine and conventional computer elements. Further, the courts have held that “Storing and retrieving information in memory” (See Versata Dev. Group, Inc. v. SAP Am., Inc.) and “Electronic Recordkeeping” (See Alice Corp.) represent well-understood, routine, and conventional computer activity. Further, See Kawamoto et al., which teaches where it is conventional to gather data from game logs of players for machine learning (Par. 164). As such, even when considered as a whole, the claims fail to recite significantly more than the abstract idea.
Further Dependent Claims 2, 4, 6, 8, and 10-11 represent recitation of additional abstract features of the gathered data, while Claim 12 teach well-understood data gathering as described above. Therefore these limitations fail to recite significantly more than the abstract idea.
In Reference to Claim 15
Given that the broadest reasonable interpretation of "a program for causing a computer to function" covers signals per se, Claim 15 must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). Applicant should consider amending Claims 15 to include the limitation “non-transitory computer readable medium storing a program…” or similar language reciting a non-transitory computer-readable medium in order to narrow the claim to cover only statutory embodiments.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 and 10-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hitar-Garcia et al., IEEE Transactions on Games journal article titled “Machine Learning Methods for Predicting League of Legends Game Outcome”. Please see the cited IEEE Citation page that notes that the earliest date of publication for this article was “23 February 2022.”
In Reference to Claims 1, 14, and 15
Hitar-Garcia et al. teaches a tide evaluation device, method and program comprising a reference-data storage unit that stores reference data, the reference data being data generated on the basis of log data of a plurality of game plays executed in the past, and the reference data including a plurality of combinations of reference state information representing a game state in a game play and a game result determined following the game state (See Page 3-4 “Dataset” “Pre-processing” and “Feature Selection” which teaches a dataset which “consists of observations corresponding to professional games from various leagues and championships played between 2014 and 2018.” And “Once cleaned and prepared, the dataset includes 241 teams, 1470 players, and 139 champions. It has 7583 instances, with a binary class variable, indicating the team that wins the game, and 26 features , corresponding to the year, season, league, and type of match, as well as the name of the team playing on
each side (2features,1perside), its composition of champions in each of the five roles (10features,5perside), and its composition of players in each of the five roles (10 features, 5 per side).” Which teach both game state feature such as the champion used in game, as well as game winner); an acquisition unit that acquires subject state information representing a game state at a timing for evaluating a game play to be evaluated (Page 6 “Partition and Preprocess” which teaches acquiring a test set of data distinct from the training data set); a search unit that extracts the reference state information similar to the subject state information from among the reference data (Page 4-5 “Classification” and Page 8 “Classification” which teach “With the preprocessed data, binary classification algorithms are trained to find the patterns present in the training set, allowing generalization to new observations to predict their class.” See also Page 10 “In this article we have seen how it is possible to create a classifier for determining the winning team in professional LoL games that, with a limited number of observations and features, obtains good results;”) an evaluation unit that evaluates the tide of a game at the timing for evaluating the game play to be evaluated on the basis of the game results associated with the reference state information extracted (Page 4-5 “Classification” and Page 8 “Classification” and Page 10 as described above which teach using a classifier to predict a game winner from a set of features extracted from game logs); and an output unit that outputs the result of the evaluation (Page 8-9 “Results” and Page 10 which teaches “is possible to create a classifier for determining the winning team in professional LoL games” which teaches that the system outputs a classification of the game winner).
In Reference to Claim 2
Hitar-Garcia et al. teach where the evaluation unit measures the game results associated with the reference state information extracted; and the output unit outputs the result of the measurement as the result of the evaluation (Page 8-9 “Results” and Page 10 which teach predicting the winning team).
In Reference to Claim 3
Hitar-Garcia et al. teach where the search unit extracts the reference state information similar to the subject state information from among the reference data by way of nearest neighbor search or approximate nearest neighbor search (Page 5 and 8 “kNN Algorithm” “It is based on the idea of searching
for “k” observations in the training data that have the smallest Euclidean distance to a new observation (nearest neighbors), assigning it the most repeated class of those training observations.”).
In Reference to Claims 4 and 5
Hitar-Garcia et al. teach where the reference data are grouped on the basis of characteristics of the game plays; the reference-data storage unit stores the reference data on a per-group basis; and the search unit identifies the group to which the game play to be evaluated belongs on the basis of characteristics of the game play to be evaluated, and extracts the reference state information similar to the subject state information from among the reference data that belong to the group identified (See page 4 Table I and Page 8 Table II which teaches where the dataset uses characteristics from game such as champion used. See Pages 6-8 particularly Page 7 “Once the task of creating new features has been completed, the categorical features are one-hot encoded to convert them into numerical ones. The next preprocessing performed is feature selection. As a result of the previous operations, the number of features increased to 4257. For this reason, a filter type selection, since it requires less computational resources, is made with ANOVA statistical test and p − value =0.05 as threshold. In this way, a first subset of 705 features is obtained. Then, and over this subset, a wrapper selection is applied using random forest [41] as training algorithm and recursive backward selection as search algorithm. Thus, the best metric in the cross-validation with repetition is obtained for a set composed of 28 features” which teach that the datasets are converted in a list of features with normalized numerical values, i.e. vectors. See also Page 5 “kNN Algorithm” which teaches selecting values that are nearby in Euclidean distance, and then choosing the “k” observations in the reference data that have the smallest Euclidean distance. Examiner considers the feature vectors of the game to be grouped and stored on a per-group basis since the values of the feature list define their location in Euclidian space, and the search unit identifies the group from which the outcome is evaluated to choose a winner).
In Reference to Clam 10
Hitar-Garcia et al. teach where the game plays are plays of a game in which a first player and a second player each select a character; the reference data are grouped on the basis of the combination of the character selected by the first player and the character selected by the second player; the acquisition unit further acquires information
indicating the character selected by the first player and the character selected by the second player in the game play to be evaluated; and the search unit extracts the reference state information similar to the subject state information from among the reference data that belong to the group corresponding to the combination of the character selected by the first player and the character selected by the second player in the game play to be evaluated (Page 8 Table II “vsChampion”. See also Par. 2 “Likewise, players must also choose a champion from the 139 available. These champions have unique abilities and characteristics, making the choice champion/role composition of the team an important strategic factor that can determine the final outcome of the match.” And where stored normalized selected feature values of the data points represent grouping in the examiner’s opinion as described above in reference to Claims 4-5).
In Reference to Claim 11
Hitar-Garcia et al. teaches where the reference data are grouped on the basis of the combination of player characteristics of a first player and player characteristics of a second player; the acquisition unit further acquires the player characteristics of the first player and the player characteristics of the second player in the game play to be evaluated; and the search unit extracts the reference state information similar to the subject state information from among the reference data that belong to the group corresponding to the combination of the player characteristics of the first player and the player characteristics of the second player in the game play to be evaluated (Page 8 Table II “vsPlayer”. See also Par. 2 “Likewise, players must also choose a champion from the 139 available. These champions have unique abilities and characteristics, making the choice champion/role composition of the team an important strategic factor that can determine the final outcome of the match.” And where stored normalized selected feature values of the data points represent grouping in the examiner’s opinion as described above in reference to Claims 4-5).
In Reference to Claim 12
Hitar-Garcia et al. teaches a reference-data creation unit that extracts, from
among the log data of a plurality of game plays executed by a plurality of players in the past, the log data of game plays executed in the past by the players whose player characteristics satisfy a prescribed condition, and creates the reference data on the basis of the log data extracted (See Page 4 Table I which teaches that the Log Data includes data of player whose characteristics satisfy a prescribed condition. Such as team role and champion chosen).
In Reference to Claim 13
Hitar-Garcia et al. where game states in the game play are expressed in the form of a multi-dimensional vector (Page 8 Table II); the reference state information and the subject state information include the multi-dimensional vector (Page 8 Table II); the multi-dimensional vector includes a vector relating to an object that is used in the game (Page 8 Table II ); and in the case where the multi-dimensional vector included in the subject state information includes a vector relating to a new object, the search unit, by using prescribed transformation information, transforms a vector relating to the new object in the multi-dimensional vector into a vector relating to the other object that is different from the new object, and extracts the reference state information similar to the subject state information by using the multi- dimensional vector transformed (Page 3-4 “For the treatment of missing values an imputation with kNN[24] is performed. This algorithm searches the dataset for the “k” nearest neighbors of the observation with the missing value. Once these neighbors are found, their feature values are used to impute the missing value, using, for example, the mean or the mode.” Which teaches that if the data includes a missing value the system generates a replacement value from the average of the k nearest neighbors. Examiner is interpreting the missing value to constitute the “new object” and replacement value that is the kNN average of the nearest neighbors to constitute the transformed multi-dimensional vector related to other objects).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Hitar-Garcia et al., IEEE Transactions on Games journal article titled “Machine Learning Methods for Predicting League of Legends Game Outcome”, in view of Xiao et al., IEEE Conference Article “WP-GBDT: An Approach for Winner Prediction using Gradient Boosting Decision Tree.”
In Reference to Claims 6 and 7
Hitar-Garcia et al. teaches where the reference data are grouped on the basis of various criteria in the data set; the acquisition unit acquires the criteria at the timing for evaluating the game play to be evaluated; and the search unit extracts the reference state information similar to the subject state information from among the reference data that belong to the group corresponding to the criteria at the timing for evaluation (See above which teaches grouping the data based on various criteria as described above). Further, Hitar-Garcia et al. Page 10 teaches that “The approach presented in this article can be considered as a proof of concept for its application in other videogames (not
exclusively in MOBA) or even sports.” And Page 3 “As seen in the related work, in-game approaches generally perform better than pregame approaches. This is because these approaches have much more information than pregame ones (such as experience gained, turrets destroyed, or enemies defeated). Thus, as the game advances, the features allow more accurate prediction of the winning team.” However, Hitar-Garcia et al. does not teach where the game plays are plays of a game in which a first player and a second player alternately perform actions; or where the dataset includes criteria of the number of actions as counted beginning from the first action.
Xiao et al. teaches a system for predicting a winner in a game where the game plays are plays of a game in which a first player and a second player alternately perform actions (Page 2 “The task in this competition is to predict winners in the “Tactical Troops: Anthracite Shift” based on rich history of gameplay logs. “Tactical Troops: Anthracite Shift” is a turn-based science-fiction game of tactics and dexterity.”); and where the dataset includes criteria of the number of actions as counted beginning from the first action (Page 4-5 “Truncated Features: Truncated features are extracted
from truncated format logs. Truncated format logs contain the historical information of each game and have the most abundant information. Thus, we focus on extracting features that are based on historical information. We count the attributes of each team under the “Turns”, “States”, “Bullets”, “Damages”, and “RegionState” fields. The “Turns” field records the start time of each turn and the player belonging to this turn.” And Page 5 “We also extract six basic features from total features which are “version”, “map name”, “mode”, “t”, “turn no”, and “to move”” which teach turn number “turn_no” as an extracted feature from the data set).
It would be desirable to modify the system of Hitar-Garcia et al. to include turn based games and to include turn number among extracted features for predicting a game winner as taught by Xiao et al. in order to allow the system to be expanded for use on turn based style games in addition to League of Legends, and to improve the predictive accuracy of the system by including game progress data such as turn number as described in Page 3 of Hitar-Garcia et al.
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing of the invention to include turn based games and to include turn number among extracted features for predicting a game winner as taught by Xiao et al.
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 8 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARL V LARSEN whose telephone number is (571)270-3219. The examiner can normally be reached Monday through Friday; 10:00 am - 6:30 pm.
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/CARL V LARSEN/Examiner, Art Unit 3715
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