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 Status
After the amendments filed 05/20/2026, claims 1-12 remain pending, of which, 1-5, 7, and 9-10 were amended.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to at least one of abstract idea groupings, according to the 2019 Revised Patent Subject Matter Guidelines (Mathematical Concepts, Mental Processes and/or Certain Methods of Organizing Human Activity). Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
More specifically, regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a system and/or process, which is are statutory categories of invention.
Step 2A-1 of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims are analyzed to determine whether it is directed to a judicial exception.
Independent claim 1 recites the following, with the abstract ideas highlighted in bold, including an indication as to the abstract idea grouping(s) to which the indicated limitations belong to, according to the 2019 Revised Patent Subject Matter Guidelines. Independent claim 10, having substantially similar features, was also analyzed and to which the following conclusion is also applicable:
1. A method for combat control, comprising:
determining, according to current game status data and communication information output by a target object toward at least one virtual object or other team member, a communicated strategic intention of the target object, wherein the communicated strategic intention characterizes a combat target expressed by the communication information (Mental Processes);
determining, independently from the communicated strategic intention and according to the game status data, an initial strategic intention of a virtual object in response to the game status data (Mental Processes);
determining, as a result of comparing the initial strategic intention with the communicated strategic intention, a target strategic intention of the virtual object (Mental Processes);
determining a response action of the virtual object based on the target strategic intention, and controlling the virtual object to perform the response action (Mental Processes).
The limitations in claim 1 (as well as claim(s) 10) recite an abstract idea included in the groupings of Mental Processes, connected to technology only through application thereof using generic computing elements (e.g., one or more processors, one or more memories, etc.) and/or insignificant extra-solution activity. According to the 2019 Revised Patent Subject Matter Guidelines:
Mental Processes include concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
Specifically, the instant claims include functions/limitations, as highlighted in the independent claim above, that constitute at least:
A. Concepts performed in the human mind: The steps of the instant claims (e.g., “determining, according to current game status data and communication information of the target object, a communicated strategic intention of a target object…”, “determining an initial strategic intention according to game status data”, “determining a target strategic intention as a result of comparing the initial strategic intention with the communicated strategic intention, and determining a response action based on the target strategic intention”), are mental processes involving observation, evaluation, judgement, and decision making. Specifically, these steps recite a basic process of human game evaluation and team coordination that a human player can perform mentally or with pen and paper. For example, during gameplay a human mentally: observing the game environment (e.g., observing current player positions, game status, and listening to/reading a teammates communication), evaluating a teammates objective (e.g., forming a mental evaluation of the teammates intention), assessing the current game state to determine a response action (e.g., evaluating their own status to determine an action), comparing the two plans (e.g., weighing their own plan against the teammate’s request to select a final intention), and deciding on an action to execute (e.g., mentally choosing the specific response to take). These limitations are interpreted as at least Mental Processes insomuch as the claim limitations are directed to performing the concepts in the human mind, while only generically connected to interaction with a computer utilizing non-special purpose generic computing elements and/or insignificant extra-solution activity as set forth in the claims.
Regarding dependent claims 2-9 and 11-12:
Each claim is dependent either directly or indirectly from the independent claim identified above and includes all the limitations of said independent claim. Therefore, each dependent claim recites the same abstract idea as identified above. Each of the dependent claim further describes additional aspects of the abstract idea, i.e., additional aspects to the Mental Processes. For example, some dependent claims merely provide additional Mental Processes to be performed and/or additional insignificant extra-solution activity, without anything more significant to establish eligibility under 35 U.S.C. 101.
Step 2A-2 of the 2019 Revised Patent Subject Matter Eligibility Guidance
The second prong of step 2a is the consideration if the claim limitations are directed to a practical application.
Limitations that are indicative of integration into a practical application:
-Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
-Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
-Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
-Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
-Applying or using 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 Vanda Memo
Limitations that are not indicative of integration into a practical application:
-Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)
-Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
-Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Claims 1-12 clearly do not improve the functioning of a computer, as they only incorporate generic computing elements, do not effect a particular treatment, and do not transform or reduce a particular article to a different state or thing. Similarly, there is no improvement to a technical field. In addition the claims do not apply the judicial exception with, or by use of a particular machine. The claims do not apply or use the judicial exception in a meaningful way. The claimed invention does not suggest improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05 (a)).
This judicial exception is not integrated into a practical application because the claimed invention merely applies the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform the abstract idea (MPEP 2106.05 (f)) and/or generally links the use of the judicial exception to a particular technology or field of use (MPEP 2106.05 (h)). The claimed computer components are recited at a level of generality and are merely invoked as tool to perform the abstract idea. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
For the reasons as discussed above, the claim limitations are not integrated to a practical application.
Step 2b of the 2019 Revised Patent Subject Matter Eligibility Guidance
Next, the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because no element or combination of elements is sufficient to ensure any claim of the present application as a whole amounts to significantly more than one or more judicial exceptions, as described above. For example, the recitations of utilization of “one or more processors, one or more memories”, etc. used to apply the abstract idea merely implements the abstract idea at a low level of generality and fail to impose meaningful limitations to impart patent-eligibility. These elements and the mere processing of data using these elements do not set forth significantly more than the abstract idea itself applied on general purpose computing devices. The recited generic elements are a mere means to implement the abstract idea. Thus, they cannot provide the “inventive concept” necessary for patent-eligibility. “[I]f a patent’s recitation of a computer amounts to a mere instruction to ‘implement]’ an abstract idea ‘on ... a computer,’... that addition cannot impart patent eligibility.” Alice, 134 S. Ct. at 2358 (quoting Mayo, 132 S. Ct. at 1301). As such, the significantly more required to overcome the 35 U.S.C. 101 hurdle and transform the claimed subject matter into a patent-eligible abstract idea is lacking. Accordingly, the claims are not patent-eligible.
Further, the claims would require structure that is beyond generic, such as structure that can be interpreted analogous to a general purpose structure and general purpose computing elements in that they represent well-understood, routine, conventional elements that do not add significantly more to the claims. See Alice Corp. v. CLS Bank International, 134 S. Ct. at 2358-59. The elements of one or more processors and one or more memories are well known conventional devices used to electronically implement a game as evidenced by U.S. 2011/0216060, which discloses that a conventional gaming machine comprises elements such as one or more processors and one or more memories to control the overall operation of the gaming machine (¶100). See Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
The dependent claims do not add “significantly more” for at least the same reasons as directed to their respective independent claims, at least based on the position, as discussed above, that each of the dependent claims merely provide additional limitations to further expand the abstract idea of the independent claims, without adding anything which would establish eligibility under 35 U.S.C. 101.
Consequently, consideration of each and every element of each and every claim, both individually and as an ordered combination, leads to the conclusion that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1, 2, 7 and 10-12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Blok (U.S. 2023/0233935).
Regarding claims 1 and 10-12, Blok discloses:
a method for combat control (¶11-13, ¶69), comprising:
determining, according to current game status data (¶62-63, current state of the entity) and communication information output by a target object toward at least one virtual object or other team member (¶3, ¶12, ¶26, ¶62, inputs from a player of the video game which select actions directed at in-game opponents (e.g., inputs indicating a move to be performed on an opponent), a communicated strategic intention of the target object (¶62, ¶67, based on the current state of the entity and the player input an intent to transition to an outcome state is determined), wherein the communicated strategic intention characterizes a combat target expressed by the communication information (¶62-63, ¶67, each transition represents a movement of the entity from a current grappling position to a subsequent grappling position with the intent to cause a submission of an opponent);
determining, independently from the communicated strategic intention and according to the game status data, an initial strategic intention of a virtual object in response to the game status data (¶17, ¶21, ¶44, 65, the system initializes a graph representing game states determines path distances, and pre-scores nodes based on game status data prior to runtime outcome selection);
determining, as a result of comparing the initial strategic intention with the communicated strategic intention, a target strategic intention of the virtual object (¶65-66, based on the determined score and the player input an optimal path is chosen such that the transition distance is the shortest possible); and
determining a response action of the virtual object based on the target strategic intention, and controlling the virtual object to perform the response action (¶13-14, ¶67, actions associated with the nodes along the path which results in the shortest transition distance between the current state and the target state are performed).
Regarding claim 2, Blok discloses that which is discussed above, and further discloses that:
determining, independently from the communicated strategic intention and according to the game status data, an initial strategic intention of a virtual object in response to the game status data comprises:
inputting the game status data and the communicated strategic intention into a pre-trained strategy model, to obtain the initial strategic intention of the virtual object in response to the game status data (¶65-66, a current state of the entity is determined and a score for the current state is then calculated according to the intent by inputting game status data and player input strategic intention data into a pretrained intent-based model); and
determining a response action of the virtual object based on the target strategic intention comprises:
performing an action decision according to the target strategic intention and the game status data, to obtain a response action of the virtual object (¶13-14, ¶67, a current state of the entity is determined and a score for the current state is then calculated according to the intent and based on the determined score and the player input a path is selected).
Regarding claim 7, Blok discloses that which is discussed above, and further discloses that:
the communication information comprises at least one of the following:
inputted text or audio information, signal information inputted for a shortcut communication control, and preset communication information corresponding to a specified behavior (¶62, ¶67, input from a player of the video game includes for example information corresponding to a specified movement of the entity);
the game status data characterizing game related description information, comprising at least one of the following:
game description information of the target object, game description information of the virtual object, and game environment description information (¶65, a current state of the entity is determined, for example a location of the entity withing the game).
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.
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Blok (U.S. 2023/0233935) in view of Wang et al (U.S. 2018/0357286).
Regarding claim 3, Blok discloses that which is discussed above, however, does not specifically disclose that:
determining a target strategic intention of the virtual object according to the communicated strategic intention and the initial strategic intention comprises:
identifying the communicated strategic intention based on the strategy model, to obtain a response probability for the communicated strategic intention;
if the response probability is greater than or equal to a preset threshold, determining a target strategic intention of the virtual object according to the communicated strategic intention.
Wang teaches:
a system which utilizes pre-trained models that receive current state data and user input to select appropriate outputs (¶51-52), wherein the system calculates a response probability (¶52-53, ¶70, the trained model determines a probability value for each response based on the inputs) and selects the output based on the probability meeting or exceeding a threshold (¶52-53, ¶70, the emotional chatbot 220 selects those emotions(s) 334 that meet or exceed a probability threshold).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply Wang’s threshold based selection logic to the initial and communicated intentions of Blok in order to yield the predictable result of ensuring that the virtual object only acts on high-confidence inputs, leading to a more balanced and realistic interaction, thereby providing the user with more relevant and engaging selected actions (Wang, ¶2).
Regarding claim 4, Blok discloses that which is discussed above, however, does not specifically disclose that:
determining a target strategic intention of the virtual object according to the communicated strategic intention and the initial strategic intention comprises:
identifying the communicated strategic intention based on the strategy model, to obtain a response probability for the communicated strategic intention;
if the response probability is less than a preset threshold, determining the initial strategic intention as the target strategic intention of the virtual object.
Wang teaches:
a system which utilizes pre-trained models that receive current state data and user input to select appropriate outputs (¶51-52), wherein the system calculates a response probability (¶52-53, the trained model determines a probability value for each response based on the inputs) and assigns a weighting factor to candidate outputs based on the probabilities (¶55, ¶87, weighting factors are applied to responses based on the probabilities), wherein responses which do not meet the probability threshold are not provided a weighting factor (¶55, ¶87, if the emotion of a response is not similar to the user emotion 234 (i.e., does not meet the probability threshold) the response is not given a weighting factor), and responses are selected based on a ranking of the weighted responses (¶55-56, ¶70, responses are ranked based on their weighted factor, wherein responses which meet or exceed the probability threshold are ranked higher than those which do not meet the probability threshold ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply Wang’s threshold based selection logic to the initial and communicated intentions of Blok, thus leaving the initial strategic intention of Blok as the sole remaining basis for the entities target action (Blok, ¶65-66), thereby yielding the predictable result of ensuring that the virtual object only acts on high-confidence inputs, leading to a more balanced and realistic interaction, thereby providing the user with more relevant and engaging selected actions (Wang, ¶2).
Claim(s) 5-6 and 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Blok (U.S. 2023/0233935) in view of Somers et al (U.S. 2019/0354759).
Regarding claim 5, Blok discloses that which is discussed above, however, does not specifically disclose:
training the strategy model by:
obtaining game combat data;
obtaining a training sample set according to the game combat data, wherein the training sample set comprises a plurality of first training samples, each first training sample at least comprising a first game status data sample and a strategic intention label and an action label in response to the first game status data sample;
training the strategy model based on the training sample set, to obtain a trained strategy mode;
wherein training the strategy model based on the training sample set to obtain a trained strategy model comprises:
inputting a first game status data sample of the training sample set into the strategy model to obtain a predicted strategic intention in response to the first game status data sample;
obtaining a corresponding predicted response action, according to the first game status data sample and the predicted strategic intention or the strategic intention label, and training the strategy model according to the predicted strategic intention, the predicted response action and the strategic intention label and action label in the first training sample, until a target loss function of the strategy model is minimized;
wherein the target loss function comprises a first loss function and a second loss function, the first loss function is a loss function between the predicted strategic intention and the strategic intention label, and the second loss function is a loss function between the predicted response action and the action label.
Somers teaches:
a system which utilizes pre-trained models that receive current state data and user input to select appropriate outputs for controlling characters in a video game (¶20-21), wherein a strategy model is trained (¶31, ¶55-56, AI models are trained by AI model training system 140) by obtaining combat data (¶66, the model inputs are received as video gameplay logs or otherwise collected during gameplay); obtaining a training sample set according to the game combat data (¶56, model inputs for each state where a user provides control input which are provided to the machine learning system), wherein the training sample set comprises a plurality of first training samples, each first training sample at least comprising a first game status data sample and a strategic intention label and an action label in response to the first game status data sample (¶69-70, ¶140-141, situational data, such as game state data, and human control inputs, such as extracted button inputs, are categorized (i.e., labeled) for training the AI model);
training the strategy model based on the training sample set, to obtain a trained strategy model (¶119, the model inputs are provided to train the machine learning system);
wherein training the strategy model based on the training sample set to obtain a trained strategy model comprises:
inputting a first game status data sample of the training sample set into the strategy model to obtain a predicted strategic intention in response to the first game status data sample (¶44-45, ¶119, during runtime of the game inputs are captured by the system and provided to the AI training system for processing);
obtaining a corresponding predicted response action, according to the first game status data sample and the predicted strategic intention or the strategic intention label (¶119-121, a first set of model inputs are input to the machine learning system to determines controls for an NPC to execute, such as movements of button actions), and training the strategy model according to the predicted strategic intention, the predicted response action and the strategic intention label and action label in the first training sample, until a target loss function of the strategy model is minimized (¶118-119, the system is trained until a threshold level of AI effectiveness is achieved by training the AI model using situation data and control inputs);
wherein the target loss function comprises a first loss function and a second loss function, the first loss function is a loss function between the predicted strategic intention and the strategic intention label, and the second loss function is a loss function between the predicted response action and the action label (¶118-119, AI models are grouped by category into “bins” which are processed using different techniques and refining the AI models until a threshold level of AI effectiveness is achieved ).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply Somer’s machine learning training system to the combat control method as taught by Blok in order to yield the predictable result of minimizing errors when implementing Blok’s strategy model, thereby improving the realism and responsiveness of the virtual entities.
Regarding claim 6, Blok discloses that which is discussed above, however, does not specifically disclose:
determining a communicated strategic intention of a target object according to a current game status data and communication information of the target object comprises:
inputting the game status data and the communication information of the target object into a pre-trained intention prediction model to obtain a communicated target strategic intention of the target object;
wherein, the intention prediction model is trained with a plurality of second training samples, each second training sample comprising a second game status data sample, a communication information sample, and a communicated strategic intention label.
Somers teaches:
a system which utilizes pre-trained models that receive current state data and user input to select appropriate outputs for controlling characters in a video game (¶20-21), wherein a strategy model is trained (¶31, ¶55-56, AI models are trained by AI model training system 140) by obtaining combat data (¶66, the model inputs are received as video gameplay logs or otherwise collected during gameplay), wherein determining a communicated strategic intention of a target object according to a current game status data and communication information of the target object comprises:
inputting the game status data and the communication information of the target object into a pre-trained intention prediction model (¶43-45, ¶58, captured video game state data and player inputs are captured via a state capture application) to obtain a communicated target strategic intention of the target object (¶26, ¶29, AI models are trained to emulate human behavior in NPCs);
wherein, the intention prediction model is trained with a plurality of second training samples, each second training sample comprising a second game status data sample, a communication information sample, and a communicated strategic intention label (¶44-45, model inputs are generated from gameplay logs and the AI models are trained using situation data (i.e., status) and control inputs (i.e., communication) and the resulting behavior (i.e., the label)).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply Somer’s machine learning training system to the combat control method as taught by Blok in order to yield the predictable result of minimizing errors when implementing Blok’s strategy model, thereby improving the realism and responsiveness of the virtual entities.
Regarding claim 8, Blok discloses that which is discussed above, however, does not specifically disclose:
determining the communication target strategic intention of the target object according to a current game status data and communication information of the target object comprises:
during a game combat, determining the communicated strategic intention of the target object according to a current game status data and communication information of the target object, wherein the game combat at least comprises an own team and an enemy team, the own team comprising one or more target objects and one or more virtual objects, and the enemy team comprising a plurality of game objects.
Somers teaches:
a system which utilizes pre-trained models that receive current state data and user input to select appropriate outputs for controlling characters in a video game (¶20-21), wherein a strategy model is trained (¶31, ¶55-56, AI models are trained by AI model training system 140) by obtaining combat data (¶66, the model inputs are received as video gameplay logs or otherwise collected during gameplay), during a game combat, determining the communicated strategic intention of the target object according to a current game status data and communication information of the target object (¶43-45, during video gameplay sessions character interaction data is captured), wherein the game combat at least comprises an own team and an enemy team (¶67, teammates and members of the opposing team), the own team comprising one or more target objects and one or more virtual objects (¶102-103, wherein the own team includes a teammate open to receive an object), and the enemy team comprising a plurality of game objects (¶67, ¶102-103, the opposing team including a plurality of opponent teammates).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply Somer’s machine learning training system to the combat control method as taught by Blok in order to yield the predictable result of minimizing errors when implementing Blok’s strategy model, thereby improving the realism and responsiveness of the virtual entities.
Regarding claim 9, Blok discloses that which is discussed above, however, does not specifically disclose:
determining a communicated strategic intention of the target object according to a current game status data and communication information of the target object comprises:
when the communication information of a plurality of target objects is obtained during the game combat, determining a communicated strategic intention of each target object respectively;
determining a target strategic intention of the virtual object according to the initial strategic intention and the communicated strategic intention comprises: determining a target strategic intention of the virtual object for each target object according to the initial strategic intention and the communicated strategic intention of each target object; determining a response action of the virtual object based on the target strategic intention and controlling the virtual object to perform the response action, comprises: determining, according to a preset rule, the response action of the virtual object based on a target strategic intention of the virtual object for each target object respectively, and controlling the virtual object to perform a response action for each target object respectively.
Somers teaches:
a system which utilizes pre-trained models that receive current state data and user input to select appropriate outputs for controlling characters in a video game (¶20-21), wherein a strategy model is trained (¶31, ¶55-56, AI models are trained by AI model training system 140) by obtaining combat data (¶66, the model inputs are received as video gameplay logs or otherwise collected during gameplay), wherein when the communication information of a plurality of target objects is obtained during the game combat, determining a communicated strategic intention of each target object respectively (¶42-46, the states of various objects are captured and provided as inputs to the models to determines the state of each object in the scene);
determining a target strategic intention of the virtual object according to the initial strategic intention and the communicated strategic intention comprises:
determining a target strategic intention of the virtual object for each target object according to the initial strategic intention and the communicated strategic intention of each target object (¶42-45, the model processes situational data for the various objects and characters to formulate the specific response strategies for each);
determining a response action of the virtual object based on the target strategic intention and controlling the virtual object to perform the response action, comprises:
determining, according to a preset rule, the response action of the virtual object based on a target strategic intention of the virtual object for each target object respectively, and controlling the virtual object to perform a response action for each target object respectively (¶130-133, the AI generated controls are checked against hard coded software rules to determines consequences of causing the character to execute the actions, wherein the character is caused to execute the action).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply Somers’ machine learning training system to the combat control method as taught by Blok in order to yield the predictable result of minimizing errors when implementing Blok’s strategy model, thereby improving the realism and responsiveness of the virtual entities.
Response to Arguments
Applicant’s arguments, see Remarks, filed 05/20/2026, with respect to the rejection(s) under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that the claimed steps do not fall within the “Mental Processes” grouping because a human cannot practically perform the claimed steps in the mind (See Remarks, pg. 10-11). The examiner must respectfully disagree. Under MPEP 2106.04(a), processes consisting of observation, evaluation, judgement, and decision making constitute mental processes regardless of whether they are executed on a computer. Gathering data, determining an “initial strategic intention”, determining “a communicated strategic intention”, and comparing the two to make a choice are basic mental evaluation steps. The fact that these computations occur in “real-time” or at a speed beyond human capability within a virtual game environment does not remove them from the mental processes category, as the Federal Circuit has established that “the speed or efficiency with which a device performs an abstract process does not impart eligibility to an otherwise ineligible claim” (Electric Power Group, LLC v. Alstom SA, 830F3d 1350,1354).
Applicant argues that the claims integrate the abstract idea into a practical application by solving a technical problem in gaming AI, specifically, improving AI cooperation, making virtual objects act more anthropomorphically, and preventing AI agents from blindly following human players (Se Remarks pgs. 11-12). The examiner must respectfully disagree. Under MPEP 2106.05(a), an improvement to a technical field requires a specific technical solution to a specific technical problem in the operation of the computer or underlying software infrastructure itself. Enhancing game logic, improving non-player character realism, or increasing user participation represents an improvement to game rules and user experience, not a technical improvement to computer functionality. Because the claims recite these functions at a high level of generality without specifying technical algorithms or specialized hardware, and merely restrict the execution to an in-game combat environment, the claim simply links the abstract idea to a specific technological environment without integrating it into a practical application (See MPEP 2106.05(h)).
Applicant argues that under Step 2B, the claims considered as an ordered combination, recite significantly more than any exception, because the combination recites elements which are novel, non-conventional, and absent from the prior art of record (See Remarks, pgs. 12-13). The examiner must respectfully disagree. Under MPEP 2106.05(d), novelty and non-obviousness under 35 U.S.C. 102 and/or 35 U.S.C. 103 are evaluated separately from eligibility under 35 U.S.C. 101. A claim limitation or ordered combination that is novel under 102/103 does not automatically supply an “inventive concept” under Step 2B if that limitation itself is part of the abstract idea. The claimed ordered combination consists entirely of a logical sequence of mental steps executed using conventional computing elements, which fails to provide “significantly more” that the abstract idea itself.
Applicant’s arguments, see Remarks, filed 05/20/2026, with respect to the rejection(s) under 35 U.S.C. 102 and 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant's arguments are substantially directed to newly added limitations. The Examiner respectfully submits that the prior art of record discloses these limitations, as discussed in the updated rejections as set forth above. Accordingly, the Applicant is directed to the rejection of the claims above for a detailed response to Applicant's arguments as to the applicability of the prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON PINHEIRO whose telephone number is (571)270-1350. The examiner can normally be reached M-F 8:00A-4:30P ET.
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, Dmitry Suhol can be reached at (571) 272-4430. 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.
/Jason Pinheiro/Examiner, Art Unit 3715
/JUSTIN L MYHR/Primary Examiner, Art Unit 3715
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