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 .
Response to Amendment
Examiner acknowledges receipt of Applicant’s amendments and arguments filed 07/22/2026. The arguments set forth are addressed herein below.
Applicant’s IDS submission is acknowledged and provided herewith.
Claims 1-5 and 7-16 are now pending.
Claim Objections
Claim 11 is objected to because of the following informalities.
Claim 11 recites “…selected form a group…” It should instead read “… selected from a group…” Appropriate correction is required.
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.
Claims 1-5 and 7-16 are 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.
Claims 1, 15 and 16 recites the limitation “… the increase of assets gained by the at least on player…” in line 18, line 21, and line 19, respectively. There is insufficient antecedent basis for this limitation in the claim. Dependent claims 2-5, and 7-14 inherit this discrepancy by nature of their dependencies. Appropriate correction is required.
Claims 1 and 15 recites the limitation “the client device of at least one of the plurality of players.” There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claim 10 recites the limitation “the at least one failed sequence.” There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claim 16 recites the limitation “the client device of the at least one of the plurality of players.” There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
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 1-5, and 7-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. Each of Claims 1-5, and 7-16 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 1
Step 1 of the two step-analysis considers whether the claims fall into one of the four statutory categories of invention such as a process, machine, manufacture, or composition of matter. Claims 1-5 and 7-14 recite a method of improving user experience in conjunction with a computer game, claim 15 recites a system for improving user experience in conjunction with a computer game, and claim 16 recites a computer program product … comprising a non-transitory medium storing thereon computer program instructions. As such, the claimed invention under claims 1-5 and 7-16 falls into the broad statutory categories of invention (Step 1 = Yes).
Step 2A, Prong 1
Under prong 1 of step 2A, the examiner considers whether the claim recites an abstract idea, law of nature or natural phenomenon. The term “abstract idea” is not interpreted as a layperson might. Instead, the term “abstract idea” is interpreted as described in legal opinions by courts.
According to MPEP 2106.04(a):
the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Under the broadest reasonable interpretation, the limitations of the independent claims describe deciding which reward to give which player based on how players behave. The limitations describe a series of steps of certain methods of organizing human activity – managing personal behavior and interactions between people, including following rules or instructions, and it sets forth rule for conducting a game. See MPEP 2106.04(a)(2), subsection II. The “long-term” factors are economic measures as recited in claim 5 below. Allocating incentives while managing such measures is a fundamental economic practice and a commercial interaction, which also falls under certain methods of organizing human activity. The limitations of the claims also recite observation, evaluation, and judgment, which falls under mental processes (see MPEP 2106.04(a)(2), subsections III). Identifying a pattern of behavior, proposing a reward, and choosing a reward that satisfied a threshold and a range are evaluations a person can perform in the mind or with pen and paper. Comparing values to threshold is a mental process. Electric Power Group V. Alstom, S.A., 830 F.3d 1350, 1353-54 (Fed. Cir. 2016). The limitations of the independent and dependent claims include the following abstract ideas below in italicized claim language:
Independent Claim 1 recites “1. (Currently Amended) A method of improving user experience in conjunction with a computer game long-term factors by controlling rewards to players, comprising: using at least one processor for executing a recommendation engine adapted to:
communicate with a game engine of a computer game (additional element) to receive a plurality of behavior parameters relating to in-game actions of a plurality of players engaged in the computer game using a plurality of client devices (additional element) (abstract idea: managing personal behavior and following rules or instructions),
analyze the plurality of behavior parameters to identify at least one first pattern indicative of a user experience of at least one of the plurality of players and at least one second pattern indicative of at least one long-term factor of the computer game (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
balance between the user experience of at least one of the plurality of players and the at least one long-term factor of the computer game by generating, based on the analysis of the plurality of behavior parameters, at least one reward recommendation for allocating at least one reward to at least one of the plurality of players for at least one in-game action made by the at least one player (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment), and selecting, from the at least one reward recommendation, at least one reward for allocation estimated to best satisfy a plurality of constraints defining at least that: the increase of assets gained by the at least one player exceeds a predefined threshold, and the at least one long-term factor is within a predefined range (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment), and
cause the game engine to adjust a graphic user interface (GUI) of the client device of at least one of the plurality of players to reflect the at least one selected reward (additional element),
…. comprising applying at least one trained machine learning (ML) model adapted to generate the at least one reward recommendation (additional element),
wherein the plurality of behavior parameters relating to in-game actions of the plurality of players comprise at least one member of a group comprising: engagement time, a churn rate, a growth in number of new players, a retention rate of new players, an in-game action, advancement of players within the computer game, a player interaction, and a value of rewards aggregated by the plurality of players (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
wherein the at least one reward comprises at least one member of a group of: an asset, a token, an experience point (XP), a gaming clue, a level advancement, an in-game advantage, an in-game skill, and a monetary value (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
wherein the at least one long-term factor is expressed by at least one parameter selected from a group of: a value of assets aggregated by the players, a value of assets spent by the players, a ratio between the aggregated assets and the spent assets, a rate of asset purchasing by the players, an inflation rate of assets gained by the players, and a ratio between assets gained by at least some of the plurality of players (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
wherein the recommendation engine is further adapted to generate a sequence of reward recommendations estimated to comply with the plurality of constraints (abstract idea: mental processes: observation, evaluation, and judgment),
wherein the recommendation engine is further adapted to test a plurality of alternative sequences of reward recommendations to identify and select an optimal sequence of the plurality of alternative sequences (abstract idea: mental processes: observation, evaluation, and judgment),
wherein the recommendation engine is further adapted to generate an additional sequence of reward recommendations responsive to a failure of at least one previous sequence to comply with the plurality of constraints (abstract idea: mental processes: observation, evaluation, and judgment),
further comprising using the additional sequence and/or the at least one failed sequence to further train at least one ML model adapted to generate the sequence of reward recommendations (additional element),
wherein the recommendation engine is further adapted to apply at least one adverse result limitation constraint to the balancing, the at least one adverse result limitation constraint is selected form a group consisting of: a predefined engagement time limit, and a predefined monetary value spending limit (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
wherein the recommendation engine is further adapted to:
group the plurality of players into a plurality of player groups (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
collect a plurality of sets of behavior parameters each relating to respective one of the plurality of player groups (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
compute a combination of reward recommendations comprising at least one respective reward recommendation for each of the plurality of player groups generated based on a respective one of the plurality of sets (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
cause the game engine to allocate at least one reward to at least one player of each of the plurality of player groups according to the respective at least one reward recommendation (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment),
wherein the recommendation engine is further adapted to generate the combination of reward recommendations based on mutual impact between players of different groups (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment), and
wherein the recommendation engine is further adapted to generate the reward recommendations according to at least one distribution of rewards among the plurality of player groups (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment).
Independent Claim 15 recites: “15. (Currently Amended) A system for improving user experience in conjunction with a computer game long-term factors by controlling rewards to players, comprising:
at least one processor adapted to execute a code of a recommendation engine (additional element), the code comprising:
code instructions to communicate with a game engine of a computer game (additional element) to receive a plurality of behavior parameters relating to in-game actions of a plurality of players engaged in the computer game using a plurality of client devices (additional element) (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment);
code instructions to analyze the plurality of behavior parameters to identify at least one first pattern indicative of a user experience of at least one of the plurality of players and at least one second pattern indicative of at least one long- term factor of the computer game (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment);
code instructions to balance between the user experience of at least one of the plurality of players and the at least one long-term factor of the computer game by generating, based on the analysis of the plurality of behavior parameters, at least one reward recommendation for allocating at least one reward to at least one of the plurality of players for at least one in-game action made by the at least one player (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment), and selecting, from the at least one reward recommendation, at least one reward for allocation estimated to best satisfy a plurality of constraints defining at least that: the increase of assets gained by the at least one player exceeds a predefined threshold, and the at least one long-term factor is within a predefined range (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment); and
code instructions to cause the game engine to adjust a graphic user interface (GUI) of the client device of at least one of the plurality of players to reflect the at least one selected reward (additional element).”
Independent Claim 16 recites: “16. (Currently Amended) A computer program product of a recommendation engine adapted to improve user experience in conjunction with a computer game long-term factors by controlling rewards to players, comprising a non-transitory medium storing thereon computer program instructions (additional element) which, when executed by at least one hardware processor (additional element), cause the at least one hardware processor to:
communicate with a game engine of a computer game to receive a plurality of behavior parameters relating to in-game actions of a plurality of players using a plurality of client devices to play the computer game (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment);
analyze the plurality of behavior parameters to identify at least one first pattern indicative of a user experience of at least one of the plurality of players and at least one second pattern indicative of at least one long-term factor of the computer game (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment);
balance between the user experience of at least one of the plurality of players and the at least one long-term factor of the computer game by generating, based on the analysis of the plurality of behavior parameters, at least one reward recommendation for allocating at least one reward to at least one of the plurality of players for at least one in-game action made by the at least one player (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment), and selecting, from the at least one reward recommendation, at least one reward for allocation estimated to best satisfy a plurality of constraints defining at least that: the increase of assets gained by the at least one player exceeds a predefined threshold, and the at least one long-term factor is within a predefined range (abstract idea: managing personal behavior and following rules or instructions, mental processes: observation, evaluation, and judgment); and
cause the game engine to adjust a graphic user interface (GUI) of the client device of the at least one of the plurality of players to reflect the at least one selected reward (additional element).”
The steps/limitations identified above are akin to certain methods of organizing human activity – fundamental economic practice and following a set of rules for a competition, mental processes including observation, evaluation, and judgment, and mathematical concepts, which falls within the groupings of abstract ideas. Therefore, the claims recite an abstract idea.
Step 2A, Prong 2
Under prong 2 of step 2A, the examiner considers whether the additional elements in the claims integrate the abstract idea into a practical application. To do so, the examiner looks to the following exemplary considerations, looking at the elements individually and in combination:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field (see MPEP 2106.05(a));
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda Memo);
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim (see MPEP 2106.05(b);
an additional element effects a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e)).
Conversely, considerations not indicative of integration include adding words “apply it” (or equivalent) with the judicial exception or mere instructions to implement the abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (MPEP 2106.05(f)); adding insignificant extra-solution activity (MPEP 2106.05(g)), or generally linking the use of the abstract idea to a particular technological environment or field of use (MPEP 2106.05(h)).
Here, the abstract idea is not integrated into a practical application. The additional elements of the claims are at least one processor, a recommendation engine executed by the processor, communicating with a game engine of a computer game, a plurality of client devices, at least on trained machine learning (ML) model (Claim 2), and causing the game engine to adjust a graphic user interface (GUI) of a client device to reflect the selected award, and a non-transitory medium. The processor, recommendation engine, game engine, client devices and non-transitory medium are recited at a high level of generality and are used as tools to perform the abstract idea. MPEP 2106.05(f). Communicating with the game engine to receive behavior parameters is insignificant pre-solution data gathering. MPEP 2106.05(g). Causing the game engine to adjust a GUI to reflect the selected award is insignificant post-solution activity: it displays the result of the abstract idea. No particular GUI structure or display technique is claimed. MPEP 2106.05(g). Limiting the idea to a computer game played on client devices is field of use limitation. MPEP 2106.05(h). The claims do not improve the functioning of a computer, or any other technology or technical field. The processor, network, game engine and GUI operate in their ordinary manner; only the reward decision differs. The at least one trained machine learning (ML) to generate the recommendation is generically recited without any particular architecture or training technique, and merely applies the abstract idea using a generic tool. A generically recited machine-learning component invoked to perform a task, without any improvement to the model or to the computer, is a tool applied to the abstract idea rather than an improvement in the functioning of a computer. MPEP 2106.05(f) and 2106.05(a). Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025).
Furthermore, the additional elements do not serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Accordingly, the claims as a whole does not integrate the recited judicial exception into a practical application and these claims are directed to the judicial exception. Therefore, the claims are directed to an abstract idea.
Step 2B
Finally, under step 2B, the examiner evaluates whether the additional elements are indicative of an inventive concept (aka “significantly more”) by the following considerations:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field (see MPEP 2106.05(a));
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda Memo);
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim (see MPEP 2106.05(b);
an additional element effects a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e)).; and
The examiner also evaluates whether the additional elements add a specific limitation other than what is well-understood, routine, conventional activity in the field (MPEP 2106.05(d)), which is indicative that an inventive concept may be present.
Conversely, considerations not indicative of integration include adding words “apply it” (or equivalent) with the judicial exception or mere instructions to implement the abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (MPEP 2106.05(f)); adding insignificant extra-solution activity (MPEP 2106.05(g)); generally linking the use of the abstract idea to a particular technological environment or field of use (MPEP 2106.05(h)); or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP 2106.05(d) and Berkheimer Memo)).
The present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements identified under Prong 2, are generically claimed. The additional elements amount to no more than generic computer implementation, data gathering, display of results, and a field-of-use limitation. Receiving and transmitting data over a network and presenting information on a display are well-understood, routine, and conventional functions, as recognized by court decisions listed in MPEP 2106.05(d), subsection II, Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016)). The machine learning model and its training are recited at a high level of generality, with no detail about the model, training, or how it operates. Applying generic machine learning to a new environment does not integrate the exception into an inventive concept. Recentive Analytics, Inc., v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025).
Additionally, taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in the claims do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to conduct an activity specified at a high level of generality in a particular technological environment. The above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
For at least the above reasons, the claims are directed to applying an abstract idea (e.g., mental process, certain methods of organizing human activity) on a general purpose computer without (i) improving the performance of the computer itself (as in McRO, Bascom and Enfish), or (ii) providing a technical solution to a problem in a technical field (as in DDR).
Therefore, claims 1-5 and 7-16 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
AIA Notice
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-5, 7, and 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2013/0316779 A1 to Vogel et al. (Vogel) in view of U.S. Patent Application Publication 2016/0300446 A1 to Burgin et al. (Burgin) and further in view of U.S. Patent Application Publication 2022/0215721 A1 to Chan.
Regarding Claim 1, and similarly recited Claims 15 and 16, (Currently Amended) A method of improving user experience in conjunction with a computer game long-term factors by controlling rewards to players (¶ [0011] discloses a gaming system that optimizes the player's game play experience in a way that is mutually beneficial to the player and the enterprise operating the game), comprising:
using at least one processor for executing a recommendation engine adapted to (¶¶ [0031], [0037]-[0038] discloses central processing unit 412 that executes program code 431 and also engagement processor 108):
communicate with a game engine of a computer game to receive a plurality of behavior parameters relating to in-game actions of a plurality of players engaged in the computer game using a plurality of client devices (fig. 1, ¶¶ [0013] discloses game events caused by players’ actions … ¶¶ [0019]-[0021] discloses game engine 204 that manages player state such as health, experience levels, and possessions, and provides game events to event filter 104 and prediction modeler 106; ¶¶ [0018] discloses players access the game from laptops, smartphones, tablets);
analyze the plurality of behavior parameters to identify at least one first pattern indicative of a user experience of at least one of the plurality of players (¶¶ [0015], [0028]-[0029] discloses prediction modeler 106 applies predictive algorithms to game events and state to predict outcomes including the player quitting; ¶¶ [0033] discloses players leave when bored or frustrated) and at least one second pattern indicative of at least one long-term factor of the computer game (¶¶ [0029] discloses predicting that the player will purchase game items or make payments; ¶¶ [0035] discloses the assessor tracks success rates of offers and the pricing structure across players);
balance between user experience of at least one of the plurality of players and the at least one long-term factor of the computer game by generating, based on the analysis of the plurality of behavior parameters, at least one reward recommendation for allocating at least one reward to at least one of the plurality of players … (¶¶ [0011], [0030], [0034], [0036] discloses tuning the game to provide the best gaming experience for players while improving the revenue stream … to generate engagement activities, such as presenting one or more recommendations or suggestions, offers to purchase real or virtual items, and so on with which to engage the player).
Vogel does not explicitly disclose “… for at least one in-game action made by the at least one player,” “selecting, from the at least one reward recommendation, at least one reward for allocation estimated to best satisfy a plurality of constraints defining at least that: the increase of assets gained by the at least one player exceeds a predefined threshold, and the at least one long-term factor is within a predefined range” and cause the game engine to adjust a graphic user interface (GUI) of the client device of at least one of the plurality of players to reflect the at least one selected reward.
In a related invention, Burgin discloses “for at least one in-game action made by the at least one player” (¶¶ [0056], [0064]-[0065] discloses reward module 412 determines a custom incentive reward, such as virtual currency, energy, or virtual objects, for the player’s in-game interaction, based on the player’s attributes; ¶¶ [0074] discloses selecting a reward type other than in-game currency for players who regularly buy in-game currency, which balances the player’s reward against the game economy), selecting, from the at least one reward recommendation, at least one reward for allocation …(¶¶ [0019], [0072] discloses selecting a reward type from a predetermined plurality of benefit types … and ¶¶ [0076]-[0078] discloses selecting one of several candidate reward amounts (18, 12, or 6 units) according to the player’s predicted interaction-probability band and each selection is based on a statistically estimated outcome), and cause the game engine to adjust a graphic user interface (GUI) of the client device of at least one of the plurality of players to reflect the at least one selected reward (Fig. 7, UI 1000 displays benefit type icon 1015 and amount icon 1010 for the reward, ¶¶ [0069], [0079] discloses reward information displayed on client device 312).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Vogel to include the custom reward determination as disclosed by Burgin as Vogel discloses engagement activity already includes offering game times to players to reduce churn. Including the custom rewards would further increase player engagement during game play (Burgin, ¶¶ [0082], [0084]).
The combination of Vogel and Burgin does not explicitly disclose selecting, …, estimated to best satisfy a plurality of constraints defining at least that: the increase of assets gained by the at least one player exceeds a predefined threshold, and the at least one long-term factor is within a predefined range.
In a related invention, Chan discloses selecting, …, estimated to best satisfy a plurality of constraints defining at least that: the increase of assets gained by the at least one player exceeds a predefined threshold (Table 1, ¶¶ [0054]-[0057], [0066]-[0071] and equation 3 discloses each reward as a reference value multiplied by the inflation factor for the player’s level … the factor is 1.00 at the initial level and increases stepwise at predefined levels; for a player who has passed the first step-up level, the reward credited therefore exceeds the reference value, a value set in advance), and the at least one long-term factor is within a predefined range (¶¶ [0006]-[0008], [0054]-[0057], [0167]-[0172] discloses inflation factors to rewards, minimum expenditures and the money conversion rate, so that the ratio between reward and expenditure is consistent across levels and in-game purchasing power is maintained).
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 combined system of Vogel and Burgin with the inflation reward valuation of player awards as disclosed by Chan so that the reward selected for a player is valued to grow the player’s assets as the player advances. Doing so would further increase continued player satisfaction, enjoyment, and player engagement during gameplay (Chan, ¶¶ [0005]).
Regarding Claim 2, (Original) Vogel in view of Burgin and Chan discloses the method of claim 1, further comprising applying at least one trained machine learning (ML) model adapted to generate the at least one reward recommendation (Vogel, ¶¶ [0028], [0029], [0036] discloses prediction modeler applies known machine learning algorithms such as linear regression, decision trees, developed from training data; Burgin, ¶¶ [0081] discloses reward system is a self-learning through a feedback loop that updates its empirical data).
Regarding Claim 3, (Original) Vogel in view of Burgin and Chan discloses the method of claim 1, wherein the plurality of behavior parameters relating to in-game actions of the plurality of players comprise at least one member of a group comprising: engagement time, a churn rate, a growth in number of new players, a retention rate of new players, an in-game action, advancement of players within the computer game, a player interaction, and a value of rewards aggregated by the plurality of players (Vogel, ¶¶ [0029], [0033] discloses behavior parameters include churn, [0026] discloses engagement time such as a player having played for six straight hours, [0013], [0026] discloses in-game actions and advancement such as leveling up).
Regarding Claim 4, (Original) Vogel in view of Burgin and Chan discloses the method of claim 1, wherein the at least one reward comprises at least one member of a group of: an asset, a token, an experience point (XP), a gaming clue, a level advancement, an in-game advantage, an in-game skill, and a monetary value (Burgin, ¶¶ [00129], [0064] discloses rewards include virtual currency, energy, virtual objects, and discounts).
Regarding Claim 5, (Original) Vogel in view of Burgin and Chan discloses the method of claim 1, wherein the at least one long-term factor is expressed by at least one parameter selected from a group of: a value of assets aggregated by the players, a value of assets spent by the players, a ratio between the aggregated assets and the spent assets, a rate of asset purchasing by the players, an inflation rate of assets gained by the players, and a ratio between assets gained by at least some of the plurality of players (Burgin, ¶¶ [0074] discloses determining the rate at which player purchases in-game currency with out-of-game currency, which is a rate of asset purchasing; Chan, Table 1 ¶¶ [0006]-[0008] discloses and displays inflation factor governs the inflation rate of assets gained by players).
Regarding Claim 7, (Currently Amended) Vogel in view of Burgin and Chan discloses the method of claim wherein the recommendation engine is further adapted to generate a sequence of reward recommendations estimated to comply with the plurality of constraints (Chan, Table 1, fig. 4 operations 402-406, ¶¶ [0065]-[0067] displays and discloses a schedule of inflation factors applied to rewards at successive predefined levels and each scheduled value is calculated so that the player’s balance grows while purchasing power stays consistent).
Regarding Claim 11, (Original) Vogel in view of Burgin and Chan discloses method of claim 1, wherein the recommendation engine is further adapted to apply at least one adverse result limitation constraint to the balancing, the at least one adverse result limitation constraint is selected form a group consisting of: a predefined engagement time limit, and a predefined monetary value spending limit (Burgin, ¶¶ [0074] discloses how much a player spends during a play).
Regarding Claim 12, (Original) Vogel in view of Burgin and Chan discloses the method of claim 1, wherein the recommendation engine is further adapted to:
group the plurality of players into a plurality of player groups (Vogel, ¶¶ [0014], [0036] discloses event filter may identify a group of players and engagements may be applied to certain groups),
collect a plurality of sets of behavior parameters each relating to respective one of the plurality of player groups (Burgin, ¶¶ [0051] discloses targeting player segments based on demography and behavior),
compute a combination of reward recommendations comprising at least one respective reward recommendation for each of the plurality of player groups generated based on a respective one of the plurality of sets (Burgin, ¶¶ [0061] discloses using different sets of player attributes for different types of players), and
cause the game engine to allocate at least one reward to at least one player of each of the plurality of player groups according to the respective at least one reward recommendation (Burgin, ¶¶ [0064] discloses computing a reward for each player according to that player’s attribute set).
Regarding Claim 13, (Original) Vogel in view of Burgin and Chan discloses the method of claim 12, wherein the recommendation engine is further adapted to generate the combination of reward recommendations based on mutual impact between players of different groups (Vogel, ¶¶ [0032] discloses generating engagement actions based on the actions of other players in the player’s social network; Burgin, ¶¶ [0062] discloses basing player attributes on the interaction behavior of the player’s friends).
Regarding Claim 14, (Original) Vogel in view of Burgin and Chan discloses the method of claim 12, wherein the recommendation engine is further adapted to generate the reward recommendations according to at least one distribution of rewards among the plurality of player groups (Burgin, ¶¶ [0078] discloses banded table distributes rewards among groups of players – 18 units for lowest interaction probability band, 12 for the middle band, and 6 for the highest band).
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2013/0316779 A1 to Vogel et al. (Vogel) in view of U.S. Patent Application Publication 2016/0300446 A1 to Burgin et al. (Burgin) and U.S. Patent Application Publication 2022/0215721 A1 to Chan, as applied to Claim 7 above, and further in view of U.S. Patent Application Publication 2022/0126208 A1 to Salakka et al. (Salakka).
Regarding Claim 8, (Original) Vogel, Burgin, and Chan discloses the method of claim 7, but does not explicitly disclose wherein the recommendation engine is further adapted to test a plurality of alternative sequences of reward recommendations to identify and select an optimal sequence of the plurality of alternative sequences.
In a related invention, Salakka discloses a system for designing, simulating, and adjusting a video game economy. Salakka discloses wherein the recommendation engine is further adapted to test a plurality of alternative sequences of reward recommendations to identify and select an optimal sequence of the plurality of alternative sequences (fig. 3, operations 306, 308, and 314, ¶¶ [0045]-[0048], [0072]-[0074] discloses several player profiles and comparing results and describing a means to experiment with and optimize the game economy). 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 combination Vogel, Burgin and Chan to include Salakka’s simulate and compare approach in order to select the sequence that best meets the constraints of the gaming system to perform the reward selection for players of the game to further improve on player engagement and gaming incentives during game play.
Regarding Claim 9, (Original) Vogel, Burgin, Chan and Salakka discloses the method of claim 7, wherein the recommendation engine is further adapted to generate an additional sequence of reward recommendations responsive to a failure of at least one previous sequence to comply with the plurality of constraints (Salakka, fig. 3, operations 310-314, ¶¶ [0073]-[0078] discloses at operation 314 of the method 300, data from one or more live games (e.g., or benchmark data) may be gathered and input to the simulations. The use of real data from one or more live games enables a designer of a game (e.g., a game developer) to quantify an accuracy of design assumptions made for the game based on the live game data).
Regarding Claim 10, (Original) Vogel in view of Burgin, Chan and Salakka discloses the method of claim 9, further comprising using the additional sequence and/or the at least one failed sequence to further train at least one ML model adapted to generate the sequence of reward recommendations (Vogel, ¶¶ [0028]-[0029], [0036] discloses prediction modeler uses machine learning models developed from training data that adapt over time as results are observed).
Response to Arguments/Remarks
Applicant’s arguments filed 07/22/2026 have been fully considered.
On page 9, regarding the indication in the prior action that claims 5-11 would be allowable if the rejection under 35 U.S.C. 101 were overcome, it is hereby withdrawn in view of the newly cited references applied above. Applicant’s incorporation of former claim 6 into the independent claim is noted.
On page 10 under section 1.1, Applicant argues that the claims do not recite rules for managing a game and that no step of the claimed pipeline is a game rule and that the Planet Bingo analogy does not apply. The Examiner respectfully disagrees. The claims decide what reward a player receives for an in-game action and require that the reward keep the player’s gains above a threshold and the game economy within a range. That is the substance of rule for conducting a rule. Expressing the rule as a “pipeline” executed by a “recommendation engine” does not change its character. Rules for conducting a game are abstract even implemented in new ways. In re Smith, 815 F.3d at 819.
On pages 10-11 under section 1.2, Applicant argues that “No human game designer monitors these parameters simultaneously across a live multi-player population at scale, identifies the two distinct types of patterns they express, generates reward recommendation candidates from those patterns, and evaluates those candidates against a defined threshold-and-range constraint criterion to select the best-satisfying candidate - in real time and across a distributed client device network.” Examiner respectfully disagrees as these arguments are not commensurate with the scope of the claims, as they do not recite real-time processing, any particular number of players or data volume, or simultaneity. Moreover, performing a mental process faster or at greater scale on a computer does not make it any less abstract. FairWarning IP, LLC v Iatric Sys., Inc., 839 F.3d 1089, 1098 (Fed. Cir. 2016).
Applicant also argues that the generate-and-select step is governed by a defined computational criterion with two specific, measurable conditions. Comparing candidates to a threshold and a range and choosing the best one is an evaluation. Specific criteria do not remove a step from the mental-process grouping; a more specific abstract idea is still an abstract idea. SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163 (Fed. Cir. 2018).
On pages 11-12 under section 1.3, Applicant argues that the claims are not directed to mere use of machine learning under Recentive Analytics and the amended independent claims do not recite machine learning. The Examiner agrees that the independent claims 1, 15, and 16 do not recite machine learning. However, the claims recite a “recommendation engine” that analyzes, generates, and selects without any detail of how, which is an even more generic tool than the machine learning addressed in Recentive. For claim 2, Applicant relies on the specification’s description of training datasets and objectives in paragraphs [0125]-[0128]. None to those details appear in the claims. The eligibility inquiry focuses on the claims and not on what appears in the specification. Applicant also argues that the task was not previously performed by humans (Remarks, p. 12). Whether a task was previously performed goes to novelty, not eligibility, and applying machine learning to a new data environment does not confer eligibility. Recentive, 124 F.4th 1205.
On page 12 under section 1.4, Applicant argues that the long-term factor is a defined technical property of the game system. The Examiner respectfully disagrees. Per claim 5 and paragraph [0100] of the specification, the long-term factor is expressed as value of assets aggregated by the players, value of assets spent by the players, a ratio between the aggregated assets and the spent assets, a rate of asset purchasing by the players, an inflation rate of assets gained by the players, and a ratio between assets gained by at least some of the plurality of players. These are economic measures. That the economy is virtual does not make its management technical. The claimed balance is an economic balance between rewarding players and preserving the value of the game’s assets.
On pages 13-14 under section 2.1, Applicant analogizes the steps of the claims to the specific rules in MCRO in stating that “The constraint set -requiring simultaneously that asset gain increase exceeds a predefined threshold and that the long-term factor is within a predefined range - is the direct analog of the specific rules in MCRO. It is what makes the claimed process a specific technical implementation with a defined computational criterion rather than a functional claim to a desired result.” The Examiner respectfully disagrees. The MCRO rules improved a technological process, computer animation, by replacing an animator’s subjective judgment with specific rules that produced a technical result (synchronized lip movement). Here, the result of the rules is an economic and game-design outcome: which reward to give. Second, the MCRO claims specified how the result was achieved. The present claims recite result oriented language (“estimated to best satisfy”) with unspecified thresholds and ranges and no particular method of identifying patterns or generating candidates. Lastly, MCRO itself distinguished claims that merely recite a result rather than a specific means of achieving it.
Applicant analogizes Enfish and argues that the amended claims focus on a specific improvement in the capabilities of computer game systems. The Examiner respectfully disagrees. The claims do not improve the way a computer operates. There is no change to any data structures, memory, processing, or network operations in the claims. The computer is used as a tool to carry out the reward rules. The prior systems problems that Applicant describes is an economic problem, and its solution is an economic one.
On pages 14-15 under section 2.2, Applicant argues that the claims do more than improve user experience because the constraint-based selection criterion of the amended claims specifically requires that the selected recommendation satisfy both a user experience condition and a long-term game economy condition simultaneously. Examiner respectfully disagrees. Adding a second business objective (economy health) to the first user experience does not convert the claim into a technological improvement. Both are outcomes of the game’s design, not improvements to computer functionality. As to Electric Power Group, the claims fit its pattern closely such as collecting information (receiving behavior parameters), analyzing it (identifying patterns, generating and selecting recommendations), and displaying the results (adjusting the GUI to reflect the reward). 830 F.3d at 1353-54. Characterizing the analysis as a “computational transformation” does not change its nature.
On pages 15-16 under section 2.3, Applicant argues that the analyze step, generate-then-select structure, and the constraint-based selection criterion are substantive operations rather than contextual links. The Examiner agrees that they are not merely contextual. They are the abstract idea itself. Elements that are part of the abstract idea cannot supply integration into a practical application or an inventive concept. MPEP 2106.05(1). The only additional elements are the generic processor, game engine, client devices, and GUI display, addressed above.
On pages 16-17 under section 3, Applicant argues that the combination of steps (i)-(v) was not well-understood, routine, and conventional, relying on specification paragraphs [0039]-[0057] and Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018). However, Applicant’s argument misapplies the inquiry. The well-understood, routine, and conventional analysis applies to the additional elements, not to the abstract idea. The asserted novelty of the dual-pattern analysis and constraint-based selection is novelty of the abstract idea, which is not part of the analysis. The additional elements (receiving data over a network from a game engine and displaying information on client devices) are shown to be conventional by the court decisions cited in MPEP 2106.05(d), subsection II, satisfying the factual support requirement of Berkheimer.
Therefore, the rejection under 35 U.S.C. 101 is maintained.
On page 18 regarding the rejection under 35 U.S.C. 103, Applicant’s only argument against the prior art rejections is that the amendment incorporates former claim 6, which the prior action indicated as allowable subject matter once the 101 rejection was overcome. That argument is moot in view of the new grounds of rejection cited in the rejections above under newly found prior art of Chan, Salakka, and Ando. The prior indication of allowable subject matter has been withdrawn. Applicant did not otherwise address the rejections under the prior art of Vogel and Burgin.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure and is provided in the Notice of References cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAUNA-KAY HALL whose telephone number is (571)270-1419. The examiner can normally be reached M-F 9:00AM-5:00PM.
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, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.N.H/Examiner, Art Unit 3715
/JAMES B HULL/Primary Examiner, Art Unit 3715
