DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1 – 25, 28, 29, 31, 32, 35 – 37 and 45 have been amended.
Claim 44 has been cancelled.
Claim 46 has been newly added.
Claims 38 – 43 are withdrawn.
Claims 1 – 43, 45 and 46 are pending.
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.
This subject matter eligibility analysis follows the latest guidance for Patent Subject Matter Eligibility Guidance.
Claims 1 – 37, 45 and 46 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
Under Step 1 of the analysis, it is noted that the claims are directed towards eligible categories of subject matter.
Step 2A:
Prong 1: Does the Claim recite an Abstract idea, Law of Nature, or Natural Phenomenon?
Claims 8 and dependent claims 9 - 37 are exemplary because they require substantially the same operative limitations of the remaining claims (reproduced below.) Examiner has underlined the claim limitations which recite the abstract idea, discussed in detail in the paragraphs that follow.
8. (Currently Amended) A method, comprising:
executing a video game to render a virtual interactive environment for presentation;
collecting gameplay data for the video game of one or more users interacting with the virtual interactive environment;
identifying, based at least in part on the gameplay data, a pattern associated with player difficulty with the video game;
identifying a data subset of the gameplay data associated with the pattern;
identifying, based at least in part on the data subset, a game world location in the virtual interactive environment that is associated with the pattern; and
updating presentation of the virtual interactive environment to present, at the game world location, a message requesting feedback to one or more players at the game world location associated with regarding the pattern.
The claims recite italicized limitations that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, namely, Mental processes.
More specifically, under this grouping, the italicized limitations represent concepts performed in the human mind (including an observation, evaluation, judgment, opinion). For example, the italicized limitations are directed towards playing of a virtual game, the collection of gameplay data, analyzing the data to determine a pattern, identification of a subset of data, matching the pattern to an in-game location and presenting a message to the game players requesting feedback at the location.
Prong 2: Does the Claim recite additional elements that integrate the exception into a practical application of the exception?
Although the claims recite additional limitations, these limitations do not integrate the exception into a practical application of the exception. For example, the claims require additional limitations as follow, (emphasis added): first and second neural networks, memories, processors
These additional limitations do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a)). Nor do they apply the exception using a particular machine, (MPEP 2106.05(b)). Furthermore, they do not effect a transformation. (MPEP 2106.05(c)). Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea. Therefore, since the additional limitations, individually or in combination, are indistinguishable from a computer used as a tool to perform the abstract idea, the analysis continues to Step 2B, below.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to conventional and routine computer implementation and mere instructions for implementing the abstract idea on generic computing devices.
For example, as pointed out above, the claimed invention recites additional elements facilitating implementation of the abstract idea. Applicant has claimed first and second neural networks. However, all of these elements viewed individually and as a whole, are indistinguishable from conventional computing elements known in the art. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea.
As the Alice court cautioned, citing Flook, patent eligibility cannot depend simply on the draftsman’s art. Here, amending the claims with generic computing elements does not (in this Examiner’s opinion), confer eligibility.
Applicant’s own specification establishes that theses addition elements are generic:
The block diagram shown in FIG. 1 depicts a non-limiting example of an implementation of a location-based player feedback system 100, according to some aspects of the present disclosure. In implementation depicted, the system 100 includes a data collection module 110 configured to collect gameplay data for a video game, a pattern recognition module 120 configured to analyze the collected gameplay data to identify a pattern associated with player difficulty with the video game, a localization module 130 configured to associate a game world location with the identified pattern, and a feedback module 140 configured to present a message to players at the game world location associated with the identified pattern requesting feedback. Components of the system 100 may be configured to communicate with other devices over a Network 150, e.g., through a suitably configured network interface. For example, the data collection module 110 may retrieve gameplay data over the network 150 from a remote gameplay data database 160. The gameplay data database 160 may, in turn, collect gameplay date from an arbitrary number N of client devices 1701, 1702, 1703...170N, which may be gaming consoles, portable gaming devices, desktop computers, laptop computers or mobile devices, such as tablet computers or cell phones that are configured to allow players to play the game. The gameplay database 160 may additionally receive gameplay data from a game server 180 that is configured to perform computations and other functions that allow the players to play the game via the client devices 1701, 1702, 1703...170N-
Regarding the Berkheimer decision, Lang et al US 2019/0258953 establishes that these additional elements are generic:
[0143] In an example of the present invention where the simulation is fully automated, a neural network is trained by running through many iterations of attack tree executions using conventional reinforcement learning (or similar) machine learning techniques. For example, the environment could be implemented as a game with OpenAI Gym compatible APIs. The actions are the selection of a particular child node in an attack tree relative to the current node (i.e. stepping to the child node). The rewards are whether the exploit at that node succeeded or not, or is unknown. Defender and environment are triggered by the attacker (e.g. simulated) action execution, or could run as autonomous threads. Using actions and rewards, a neural network is trained using well-known methods. After some/many iterations, the attacker neural net will have been trained to select the most likely to win path through the attack tree.
Therefore, these elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Moreover, the claims do not recite improvements to another technology or technical field. Nor, do the claims improve the functioning of the underlying computer itself -- they merely recite generic computing elements. Furthermore, they do not effect a transformation of a particular article to a different state or thing: the underlying computing elements remain the same.
Concerning preemption, the Federal Circuit has said in Ariosa Diagnostics, Inc., V. Sequenom, Inc., (Fed Cir. June 12, 2015):
The Supreme Court has made clear that the principle of preemption is the basis for the judicial exceptions to patentability. Alice, 134 S. Ct at 2354 (“We have described the concern that drives this exclusionary principal as one of pre-emption”). For this reason, questions on preemption are inherent in and resolved by the § 101 analysis. The concern is that “patent law not inhibit further discovery by improperly tying up the future use of these building blocks of human ingenuity.” Id. (internal quotations omitted). In other words, patent claims should not prevent the use of the basic building blocks of technology—abstract ideas, naturally occurring phenomena, and natural laws. While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility. In this case, Sequenom’s attempt to limit the breadth of the claims by showing alternative uses of cffDNA outside of the scope of the claims does not change the conclusion that the claims are directed to patent ineligible subject matter. Where a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot. (Emphasis added.)
For these reasons, it appears that the claims are not patent-eligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2,3 and 7, 8, 9-18, 21 – 23, 26 and 28 – 35 and 45 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zaghetto et al (US 2023/0078380) in view of Allen (2015/0231502) in view of Mattar et al (US 2022/0032202).
As per claim 1,
Zaghetto discloses a system (Zaghetto, Fig. 7A-B; ABSTRACT)comprising:
One or more memories and; (Zaghetto Fig 2)
one or more processors storing instructions that, upon execution by the one or more memories, configure the system to: (Zaghetto Fig 2)
execute a video game to render a virtual interactive environment for presentation; (Zaghetto Fig 5C)
collect gameplay data for the video game of one or more users interacting with the virtual interactive environment (Zaghetto, Fig. 7A; para [0121] store first gameplay of a specific duration);
identify, based at least on the gameplay data, a pattern associated with player difficulty with the video game (Zaghetto, para [0121], "The system 102 may apply the one or more AI models 106 on the stored first gameplay. Based on the application of the one or more AI models 106 on the stored first gameplay, the circuitry 202 may be configured to control the execution of the video game. For instance, in a shooter video game, a pattern of one or more enemies, in a recorded video, may be recognized to determine enemies that pose a greater threat to the player 708 as compared to other enemies in the game" being defeated by a specific enemy);
identify… a game world location in the virtual interactive environment that is associated with the pattern (Fig. 7B - the boss location of the specific enemy in the game world; (Zaghetto, para [0120]) with the identified pattern (Zaghetto, para [0121] - being defeated by a specific enemy in a specific level or location);
update presentation of the virtual interactive environment to present at the game world location a message (Zaghetto, para [0121], "in such a scenario, a hint may be displayed to the player 708 in terms of a message, a picture of 'your worst enemy', or a replay of the scenes that may be causing a systematic failure of the player 708. This may help the player 708 to decide if and when the player 708 needs to avoid unnecessary confrontation with specific enemies") to players at the game world location (Fig. 7B the boss location of the specific enemy in the game world; para [0120]), and additional presenting feedback gives the player means to select an option to skip a level (Zaghetto 0075)
Zaghetto fails to disclose:
Identify a data subset of the gameplay data associated with the pattern;
..based at least in part on the data subset…
…requesting feedback to the pattern.
However, Allen discloses a system (Allen Fig. 1-4; ABSTRACT) for location-based player feedback for video games, the system comprising:
a localization module configured to associate a game world location (Allen 210, Fig. 2 - at particular section of game; para [0026]) with the identified pattern (para [0020]);
a feedback module configured to present a message (Allen 230, Fig. 2; para [0028]) to players at the game world location (210, Fig. 2 at particular section of game; para [0026]) associated with the identified pattern (para [0020]) requesting feedback (para [0028]).
Accordingly, it would have been obvious to one of ordinary skill in the art to have modified the feedback module of Zaghetto with the feedback module of Allen as to request and receive feedback to allow for game adjustment of difficulty based on the user's feedback to create a more enjoyable video game experience.
However, in a similar field of endeavor, Mattar teaches the use of collecting game data such as telemetry data and biometric data, analyzing the collected gameplay data to identify patterns in the data that match game events, wherein the event identification system may analyze a subset of the total events that are associated with the pattern (Mattar 0062).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Zaghetto in view of Mattar to collect use collected game data to identify events that are associated with a pattern and to further identify a data subset of that data. This would be beneficial as gaming sessions may be lengthy in nature and the amount of data analyzed may become voluminous. By only utilizing a subset of that data to determine a pattern, the system may be able to save memory space and use existing memory more efficiently.
As per claim 2, Zaghetto and Allen disclose the modified system of claim 1, Zaghetto further discloses wherein the pattern is identified by using a neural network (Zaghetto 106, Fig. 2; para [0024], para [0047]) trained to detect patterns in gameplay data that are associated with player difficulty with video games (Zaghetto para [0121], "The system 102 may apply the one or more AI models 106 on the stored first gameplay. Based on the application of the one or more Al models 106 on the stored first gameplay, the circuitry 202 may be configured to control the execution of the video game. For instance, in a shooter video game, a pattern of one or more enemies, in a recorded video, may be recognized to determine enemies that pose a greater threat to the player 708 as compared to other enemies in the game" - being defeated by a specific enemy).
As per claim 3, Zaghetto and Allen disclose the modified system of claim 1, Zaghetto further discloses wherein the game world location is identified by using a neural network (Zaghetto 106, Fig. 2; para [0099]) configured to identify game world locations (Zaghetto Fig. 7B the boss location of the specific enemy in the game world; para [0120]) from patterns of gameplay data.
As per claim 7, Zaghetto and Allen disclose the modified system of claim 1, Zaghetto further discloses wherein the gameplay data is collected (Zaghetto Fig. 7A; para [0121] store first gameplay of a specific duration) over a network (Zaghetto 110, Fig. 1; para [0030]) from a plurality of video game devices (para [0077]),
Independent claim(s) 8 and 45 is/are made obvious by the combination of Zaghetto, Allen and Mattar based on the same analysis set forth for claim(s) 1, which are similar in claim scope.
As per claim 9, wherein the collected gameplay data includes data relating to location of one or more player characters in the virtual interactive environment. (Zaghetto 0062)
As per claim 10, wherein the gameplay data includes data relating to a game level for the video game. (Zaghetto 0062)
As per claim 11, wherein the gameplay data includes time a player has spent in a particular region of the virtual interactive environment (Zaghetto 0062)
As per claim 12, wherein the gameplay data includes an amount of time a player has failed to complete a game level of the video game. (Zaghetto 0062)
As per claim 13, wherein the gameplay data includes an amount of time a player has failed to complete a game task of the video game. (Zaghetto 0062)
As per claim 14, wherein the gameplay data relates to a game activity occurring in a game level of the video game. (Zaghetto 0062)
As per claim 15, wherein the gameplay data relates to an amount of time spent by a player on a game activity in the video game. (Zaghetto 0062)
As per claim 16, wherein the gameplay data includes equipment associated with one or more player characters. (Zaghetto disclose the change in a player ability of the character or an item they control as collected gameplay data) (Zaghetto 0072, 0079)
As per claim 17, wherein the gameplay data includes a player rank of one or more players. Zaghetto disclose the increase in the player ability such as strength (i.e. rank) of the character or an item they control as collected gameplay data) (Zaghetto 0072, 0079)
As per claim 18, wherein the gameplay data includes data corresponding to one or more controller inputs. (Zaghetto 0062)
As per claim 21, wherein identifying the game world location includes determining one or more game world locations where players have exited a part of a game. (Zaghetto discloses the tracking of patterns that show a player failing the game (I.e. exiting)) (Zaghetto 0062)
As per claim 22, wherein identifying the game world location includes determining one or more game world locations where players have exited a part of a game as a result of failing a task. (Zaghetto discloses the tracking of patterns that show a player failing the game (I.e. exiting)) (Zaghetto 0062)
As per claim 23, wherein identifying the game world location includes determining one or more game world locations where players have exited a part of a game by voluntarily giving up. (Zaghetto discloses the tracking of patterns or locations that show a player failing the game (I.e. exiting)) (Zaghetto 0062)
As per claim 26, further comprising responding to player feedback. (Zaghetto discloses the player providing feedback to confirm they would like to skip a level or area and the game responding to the player feedback by proceeding to skip the level or area) (Zaghetto 0075).
As per claim 28, wherein the message asks whether the video game is too difficult at the game world location associated with the identified pattern. (Zaghetto discloses the presenting a message enabling the player skip an area if it is too hard or difficult) (Zaghetto 0075)
As per claim 29, wherein the message requesting feedback includes an offer of help with the video game at the game world location associated with the pattern. (Zaghetto discloses the presenting a message of help if it is too hard or difficult) (Zaghetto 0075)
As per claim 30, wherein the offer of help includes an offer to guide a player through a difficult part of the video game. (Zaghetto discloses the presenting a message to guide if it is too hard or difficult) (Zaghetto 0075)
As per claim 31, herein the offer of help includes an offer to show a player video of a successful attempt by another player to complete a task at the game world location associated with the pattern. (Zaghetto discloses the displaying of replay videos that may help a player complete the tack if they are determined to be unsuccessful) (Zaghetto 0121).
As per claim 32, wherein presenting the message includes classifying a difficulty with the video game from the pattern and/or game world location. (Zaghetto discloses a message classifying the most difficult enemy detected by pattern) (Zaghetto 0121).
As per claim 33, further comprising receiving feedback from one or more players in response to the message. (Zaghetto discloses the presenting a message enabling the player skip an area if it is too hard or difficult) (Zaghetto 0075)
As per claim 34, wherein the feedback includes recording detailed gameplay data as player plays the video game. (Zaghetto discloses the display of recorded replay videos that may help a player complete the tack if they are determined to be unsuccessful) (Zaghetto 0121).
As per claim 35, wherein the feedback includes recording detailed gameplay data as player plays the video game at the game world location associated with the pattern. (Zaghetto discloses the displaying of recorded replay videos that may help a player complete the tack if they are determined to be unsuccessful) (Zaghetto 0121).
Claim(s) 4-6, and 46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zaghetto et al (US 2023/0078380) in view of Allen (2015/0231502) in view of Mattar et al (US 2022/0032202) in view of Simon (US 2018/0070872)
As per claim 4 – 6, Zaghetto fails to disclose:
wherein presentation is updated by using a neural network trained to classify a difficulty with the video game from the pattern. [claim 4]
wherein the presentation is updated by using a neural network trained to classify a difficulty with the video game from the game world location. [claim 5]
wherein the presentation is updated by using a neural network trained to classify a difficulty with the video game from the identified pattern and game world location. [claim 6]
However, Simon teaches the determination or the classification of a difficulty level based upon detected patterns of spatial-temporal of a game. Specifically “[0038] Rather than adopting a common game development paradigm that divides a game into levels of difficulty and uses informal methods to determine the content, “feel” and difficulty of each level, the system uses an adaptive-programming methodology to implement a factor that distinguishes it from existing games: that of player-dependent stimulation of the spatial and temporal information-processing systems based specifically on the player's current spatial and/or temporal crowding thresholds. In general terms, adaptive game development is common and dates back to the beginning of video games and even typing tutors. Thus, it can be thought of as an aspect of the system that does not need to be developed or proved. Variants of adaptive game design are referred to as “Dynamic Game Balancing” or Game “Artificial Intelligence” (AI). While adaptive AI methods, like genetic algorithms or artificial neural networks can be used, alternate adaptive methods entail using common adaptive functions that are used in psychophysical experimentation, such as the “Parameter Estimation by Sequential Testing (PEST)” technique (see Leek, M. R.: Adaptive procedures in psychophysical research. Perception & Psychophysics 63, 1279-1292 (2001)) may also prove just as effective.”
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Zaghetto in view of Simon to utilize a known technique of utilizing a neural network to classify a difficulty based upon detected game play data that is related to spatial-temporal areas of a game such as game world location and an identified pattern. This would be beneficial as it would help the developer to adaptively balance the difficulty of the game thus ensuring the that game is not too difficult or too easy.
As per claim 46, Zaghetto discloses:
one or more collection instructions configured to collect gameplay data for a video game, when executed; (Zaghetto, Fig. 7A; para [0121] store first gameplay of a specific duration);
one or more pattern recognition instructions configured to analyze the collected gameplay data with a first trained neural network to identify a pattern associated with player difficulty with the video game, when executed; (Zaghetto, para [0121], "The system 102 may apply the one or more AI models 106 on the stored first gameplay. Based on the application of the one or more AI models 106 on the stored first gameplay, the circuitry 202 may be configured to control the execution of the video game. For instance, in a shooter video game, a pattern of one or more enemies, in a recorded video, may be recognized to determine enemies that pose a greater threat to the player 708 as compared to other enemies in the game" being defeated by a specific enemy);
one or more localization instructions configured to analyze the identified pattern … to associate a game world location associated with the identified pattern, when executed; and (Zaghetto, para [0120]) with the identified pattern (Zaghetto, para [0121] - being defeated by a specific enemy in a specific level or location);
one or more instructions messaging configured to present a message (Zaghetto, para [0121], "in such a scenario, a hint may be displayed to the player 708 in terms of a message, a picture of 'your worst enemy', or a replay of the scenes that may be causing a systematic failure of the player 708. This may help the player 708 to decide if and when the player 708 needs to avoid unnecessary confrontation with specific enemies") to players at the game world location (Fig. 7B the boss location of the specific enemy in the game world; para [0120]), and additional presenting feedback gives the player means to select an option to skip a level (Zaghetto 0075)
Zaghetto fails to disclose:
…with a second neural network…
… requesting feedback to one or more players at the game world location associated with the identified pattern, when executed.
However, Allen discloses a localization module configured to associate a game world location (Allen 210, Fig. 2 - at particular section of game; para [0026]) with the identified pattern (para [0020]);
a feedback module configured to present a message (Allen 230, Fig. 2; para [0028]) to players at the game world location (210, Fig. 2 at particular section of game; para [0026]) associated with the identified pattern (para [0020]) requesting feedback (para [0028]).
Accordingly, it would have been obvious to one of ordinary skill in the art to have modified the feedback module of Zaghetto with the feedback module of Allen as to request and receive feedback to allow for game adjustment of difficulty based on the user's feedback to create a more enjoyable video game experience.
However, Simon teaches the determination or the classification of a difficulty level based upon detected patterns of spatial-temporal of a game (i.e. locations). Specifically “[0038] Rather than adopting a common game development paradigm that divides a game into levels of difficulty and uses informal methods to determine the content, “feel” and difficulty of each level, the system uses an adaptive-programming methodology to implement a factor that distinguishes it from existing games: that of player-dependent stimulation of the spatial and temporal information-processing systems based specifically on the player's current spatial and/or temporal crowding thresholds. In general terms, adaptive game development is common and dates back to the beginning of video games and even typing tutors. Thus, it can be thought of as an aspect of the system that does not need to be developed or proved. Variants of adaptive game design are referred to as “Dynamic Game Balancing” or Game “Artificial Intelligence” (AI). While adaptive AI methods, like genetic algorithms or artificial neural networks can be used, alternate adaptive methods entail using common adaptive functions that are used in psychophysical experimentation, such as the “Parameter Estimation by Sequential Testing (PEST)” technique (see Leek, M. R.: Adaptive procedures in psychophysical research. Perception & Psychophysics 63, 1279-1292 (2001)) may also prove just as effective.”
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Zaghetto in view of Simon to utilize a known technique of utilizing a neural network to classify a difficulty based upon detected game play data that is related to spatial-temporal areas of a game such as game world location and an identified pattern. This would be beneficial as it would help the developer to adaptively balance the difficulty of the game thus ensuring that the game is not too difficult or too easy.
Claim(s) 19, 20, 27, 36 and 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zaghetto et al (US 2023/0078380) in view of Allen (2015/0231502) in view of Mattar et al (US 2022/0032202) in view of Simon (US 2018/0070872) in view of “Bungie Weekly Update 11/9/07”, by Lukems (hereinafter “Lukems”)
As per claim 19, Zaghetto discloses:
…where players have requested an ability to tune one or more parameters of one or more features of the game world. (Zaghetto discloses players being able to request and accept the skipping of game levels (i.e. tune parameters) (Zaghetto 0075)
Zaghetto fails to disclose “wherein identifying the pattern includes generating a heat map of game world locations”
However in a similar field of endeavor, Lukems discloses the generation of a heatmap of game statistics showing locations wherein game players struggle or die repeatedly (Lukems 0004).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Zaghetto in view of Lukems to generate a heatmap showing game locations where players desire to skip levels or tune parameters. This would enable game developers to visualize where game maps may be too difficult.
As per claim 20, wherein the one or more features of the game world include one or more objects, non-player characters, or terrain features (Zaghetto 0062, 0079)
As per claim 27, wherein responding to the player feedback includes escalating the player feedback to a developer of the video game. (Combination of Zaghetto in view of Lukems wherein Lukems discloses the generation of a Heatmap that is accessible to the developer to thereby view and determine areas that are associated with various game telemetry (i.e. feedback) that is generated when a player is playing the game in a particular game level or map) (Lukems par 4).
As per claims 36, wherein the feedback includes recording detailed gameplay data as player plays the video game at the game world location associated with the pattern and sending the detailed gameplay data to a publisher of the video game. (Combination of Zaghetto in view of Lukems wherein Lukems discloses the generation of a Heatmap that is accessible to the developer to thereby view various recorded game telemetry (i.e. feedback) that is generated when a player is playing the game in a particular game level or map) (Lukems par 4).
As per claim 37, wherein the feedback includes a stream of metadata showing a problem with the video game at the game world location associated with the pattern. (Combination of Zaghetto in view of Lukems wherein Lukems discloses the generation of a Heatmap that is accessible to the developer to thereby view various recorded game telemetry (i.e. stream of metadata) that is generated when a player is playing the game in a particular game level or map) (Lukems par 4).
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zaghetto et al (US 2023/0078380) in view of Allen (2015/0231502) in view of Mattar et al (US 2022/0032202) in view of Mene et al (US 2023/0188569).
As per claim 24, Zaghetto fails to disclose:
further comprising analyzing player feedback in response to the message, wherein the analyzing includes classifying a player difficulty with game.
However, Mene teaches the determination of the types (i.e. classifying) of a user’s difficulty in response to associated feedback responses (Mene 0033).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Zaghetto in view of Mene to use a known technique to improve similar devices in the same way by means of determining the types of difficulties a user may be having based upon their feedback responses. This would enable a developer to understand exactly what parts of a game are too hard or too difficult for certain types of users of the system.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-37 and 45-46 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see above rejection I view of Mattar and Mene.
Regarding the rejection of the claim under 35 U.S.C. 101, the Applicant argues:
“Claim 1 further requires "identify[ing], based at least in part on the data subset, a game world location in the virtual interactive environment that is associated with the identified pattern," and "update[ing] presentation of the virtual interactive environment to present, at the game world location, a message." These claimed limitations are tied to a technical improvement in the functioning of the video game system and telemetry data processing.” (Remarks page 10). Applicant then points to the specification for support wherein the specification details “reducing the dimensionality of the data down to a localized data subset, the system saves processing power and time because the modules of the inference engine…do not need to operate on the entire” dataset or image” (Remarks page 11). The Examiner respectfully disagrees and notes that the mere reducing of the amount of data that is processed by a system amounts to the mere improving of the efficiency of the system. A human utilizing manual means such as pen and paper can reduce the amount of data needed to be processed by being more selective in only processing the most needed or more important pieces of data by manual means. The claim does not recite a particular technology mechanism that selects or processes the subset of data that improves the manner in which the computer stores transmits or processes data. The claims merely use the computer as tool to perform the data operations. The alleged reduction in processing time and saving of computational resources represent an efficiency arising from merely implementing the abstract idea on a computer versus a technological improvement that integrates the judicial exception into a practical application.
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 ROSS A WILLIAMS whose telephone number is (571)272-5911. The examiner can normally be reached Mon-Fri 8am - 4pm.
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/RAW/ Examiner, Art Unit 3715
10/3/2026
/KANG HU/ Supervisory Patent Examiner, Art Unit 3715
