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, 4, 8, 15, and 20 have been amended.
Claims 1 – 20 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 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
Claims 1 – 7 are drawn to a system.
Claims 8 – 14 are drawn to a method.
Claims 15 – 20 are drawn to a CRM.
Thus, initially, 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 1 - 7 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.
1. (Currently Amended) A system, comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to:
receive gameplay data associated with playstyle of a player in one or more games;
receive persona data associated with the player and the gameplay data, wherein the persona data is derived from gameplay data collected across multiple games of different genres;
receive motion-capture data associated with an actor or athlete;
receive a request for personalized animation based on the playstyle of the player;
input the motion-capture data associated with the actor or athlete and at least a portion of the gameplay data including the playstyle of the player into a dynamic generation algorithm or model;
generate an animation for the player based on the gameplay data associated with the playstyle of the player in the one or more games using an animation modifier that applies mapping data from an animation style mapping datastore;
dynamically generate, based at least in part on a portion of the playstyle of the player games and based at least in part on a result of the dynamic generation algorithm or model, content including personalized animation, wherein the content including personalized animation is dynamically generated personalized content associated with the player; and
transmit the content including personalized animation for presentation in a game associated with the player.
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 For example, the italicized limitations are directed toward the concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The claims are directed towards the process of receiving gameplay data and persona data associated with a players playstyle, receiving motion capture data, receiving a request for a personalized animation, generating the animation based upon the playstyle to create a personalized animation and transmitting the animation for presentation to a player.
Prong 2: Does the Claim recite additional elements that integrate the exception in to 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): processors, and storage media.
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 computer processors, and storage media. 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.
Regarding the Berkheimer decision, Applicant’s own specification establishes that these additional elements are generic:
[0091] Computer-executable program instructions can be loaded onto a general- purpose computer, a special-purpose computer, a processor, or other programmable data processing apparatus to produce a particular machine, such that the instructions that execute on the computer, processor, or other programmable data processing apparatus implement one or more functions specified in the flowchart block or blocks. These computer-program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction that implement one or more functions specified in the flow diagram block or blocks. Examples of the disclosure provide for a computer program product, comprising a computer usable medium having a
computer-readable program code or program instructions embodied therein, the computer- readable program code adapted to be executed to implement one or more functions specified in the flow diagram block or blocks. The computer-program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions that execute on the computer or other programmable apparatus provide elements or steps for implementing the functions specified in the flow diagram block or blocks.
[0092] It will be appreciated that each of the memories and data storage devices described herein can store data and information for subsequent retrieval. The memories and databases can be in communication with each other and/or other databases, such as a centralized database, or other types of data storage devices. When needed, data or information stored in a memory or database can be transmitted to a centralized database capable of receiving data, information, or data records from more than one database or other data storage devices. In some embodiments, the databases shown can be integrated or distributed into any number of databases or other data storage devices. [0093] Many modifications and other embodiments of the disclosure set forth herein will be apparent having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the disclosure is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eatedali et al (US 2020/0197811) in view of “How Forza’s Drivatar Actually Works” by Thompson in view of “FIFA 22 HyperMotion Motion Capture” circa 2021 (hereinafter “FIFA 22”) in view of Le Chevalier (US 2023/0162420)
As per claim 1, Eatedali discloses:
one or more processors; and (Eatedali Fig 1, #112)
one or more computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to: (Eatedali Fig 1, #112, #114)
receive gameplay data associated with playstyle of a player in one or more games; (Eatedali discloses the receiving of gameplay data that is associated with a player playstyle) (Eatedali 0010, 0067)
receive persona data associated with the player and the gameplay data…; (Eatedali disclose the receiving of persona data associated with the playstyle, such as aggressiveness or preferences to be a sniper etc.) (Eatedali 0069)
generate an animation for the player based on the gameplay data associated with the playstyle of the player in the one or more games…; (Eatedali discloses the user being able to operate player controlled game characters in a game wherein this comprises providing game character animations that are displayed in FPS or first person shooter games) (Eatedali 0073)
dynamically generate, based at least in part on a portion of the playstyle of the player games and based at least in part on a result of dynamic generation algorithm or model, content including personalized animation, wherein the content including personalized animation is dynamically generated personalized content associated with the player; and (Eatedali discloses the use of animations in FPS video game (Eatedali 0073), the generation of personalized NPC’s that are dynamically generated based upon the a players gameplay behaviors (i.e. play style), wherein the NPC’s are designed to have personalized gameplay that closely mimics the gameplay of the player character) (Eatedali 0077, 0079, 0080), wherein the personalized animation is generated based upon a machine learning algorithm (Eatedali 0014, 0024)
transmit the content including personalized animation for presentation in a game associated with the player. (Eatedali discloses the transmitting of the personalized NPC data for presentation) (Eatedali 0010, 0020, 0081)
Eatedali fails to specifically discloses :
…wherein the persona data is derived from gameplay data collected across multiple games of different genres.
receive motion-capture data associated with an actor or athlete;
receive a request for personalized animation based on the playstyle of the player;
input the motion-capture data associated with the actor or athlete and at least a portion of the gameplay data including playstyle of the player into a dynamic generation algorithm or model;
…using an animation modifier that applies mapping data from an animation style mapping datastore.
In the event that it could be argued that Eatedali fails to specifically discloses the use of animations for player character and NPC’s, and the receiving of a request for personalized animations based upon the playstyle of the player, the Examiner directs attention to Thompson wherein Thompson discloses the use a Drivatar’s which are avatar character animations that a player will race against wherein the Drivatar’s animation as displayed in a race is based upon a player’s previous playstyle and behavior (Thompson page 2, par 1). Thompson discloses that the Drivatar’s a hosted online in the cloud thus the Drivatar is downloaded to a user device when requested to be used. (Thompson page 8, par 2 – 3)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Eatedali in view of Thompson to use a known technique to modify similar devices in the same way to specifically provide personalized animations that are displayed based upon player character behaviors or playstyles. This would be beneficial as it would enable the video game to be more realistic as displayed NPC’s would adapt to a player character’s behavior and actions.
In a similar field of endeavor, that of providing more realistic player animations and NPC behaviors, FIFA 22 discloses a system wherein motion capture data of a real game match played by real-world players was sensed and captured to thereby create new animations while a player is playing the game. FIFA 22 teaches the capturing of motion capture data that power over 4000 animations (FIFA 22 page 1, par 3 – page 2, par 4).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Eatedali in view of FIFA 22 to use a known technique to modify similar devices in the same way by means of incorporating motion capture data associated with real-world athlete and creating in-game animations based upon that motion capture data. This would be beneficial as it would make the animations more realistic and immersive to the end user player.
In a similar field of endeavor, Le Chevalier teaches the alteration of a avatars animation by the application of an animation modifier stored in an animation data store wherein an avatar is animated according to tracked user data such as a user’s actions across different types of activities when using an online platform wherein the activities may be of different types (i.e. genres or categories) such as reading, taking notes or testing (Le Chevalier 0058 – 0063).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Eatadali in view of Le Chevalier to use a known technique to improve similar device in the same way by modifying an avatar’s animation based upon a user’s tracked behavior or activities across different types of activities while using an electronic platform. This would be beneficial as it would provide a more personalized virtual environment to further attract and engage the user and hold their interest longer in an attempt to get them to use the game system longer.
As per claim 2, Eatedali discloses:
wherein the content including the personalized animation is dynamically generated personalized content in a game currently being played by the player. (Eatedali discloses the generation of personalized NPC within the game being played by the player) (Eatedali 0070)
As per claim 3, Eatedali discloses:
wherein the content including personalized animation is further based on additional gameplay data associated with additional gameplay of the player in one or more additional games. (Eatedali discloses personalized NPC’s based upon additional gameplay in one or more games) (Eatedali 0070, 0080)
As per claim 4, Eatedali discloses:
wherein the content including personalized animation is further based on the animation modifier. (Eatedali discloses the NPC’s are generated based upon modified NPC’s to develop new baseline NPC’s (i.e. modifiers) (Eatedali 0077)
As per claim 5, Eatedali discloses:
when executed by the one or more processors, further cause the one or more processors to: access the dynamic generation algorithm or model from a datastore, the dynamic generation algorithm or model associated with the gameplay based on the persona of the player; input at least a portion of the gameplay data to the dynamic generation algorithm or model; receive a second result of the dynamic generation algorithm or model; and transmit the second result of the dynamic generation algorithm or model as part of the content including the personalized animation for presentation in the game associated with the player. (Eatedali discloses the utilization of machine learning (ML) algorithms to dynamically generate NPC data that dictates how the NPC will behave and react in the video game according to the human player behaviors and playstyle. Eatedali discloses a system that utilizes ML algorithms throughout a game session such as the AI engine making determinations of how NPC’s respond to multiple events that occur in a game session. Thus, Eatedali anticipates the generation of first and second results that are used to personalize the animations associated with NPC behavior and how they are depicted within the game) (Eatedali 0070).
As per claim 6, Eatedali discloses:
receive the result of the dynamic generation algorithm or model; and transmit the result of the dynamic generation algorithm or model as part of the content including the personalized animation for presentation in the game associated with the player. (Eatedali discloses the use of animations in FPS video game (Eatedali 0073), the generation of personalized NPC’s that are dynamically generated based upon the players gameplay behaviors (i.e. play style), wherein the NPC’s are designed to have personalized gameplay that closely mimics the gameplay of the player character) (Eatedali 0077, 0079, 0080), wherein the personalized animation is generated based upon a machine learning algorithm (Eatedali 0014, 0024)
As per claim 7, Eatedali discloses:
wherein the dynamic generation algorithm or model is configured to map at least a portion of the playstyle of the player to at least a portion of the motion-capture data associated with the actor or athlete. (The combination of Eatedali, Thompson and FIFA 22 wherein Eatedali and Thompson discloses the use of animations that are mapped or associated with a player style of the player as applied to claim 1, and FIFA 22 teaches a game wherein a player utilizes avatars that are associated with motion capture data of real-world athletes, as applied to claim 1)
Independent claim(s) 8 and 15 is/are anticipated by Eatedali Thompson, FIFA 22 and Le Chevalier, based on the same analysis set forth for claim(s) 1, which are similar in claim scope.
As per claim 14, creating an additional animation associated with a plurality of persona dimensions, wherein the content including the personalized animation is based on the additional animation. (Eatedali discloses the NPC can be modified by “NPC modification engine 122 for its purpose. In one implementation, a game profile may be generated for a gameplay session based on gameplay information. Gameplay information may describe various game characteristics of a gameplay session that may influence the quality of gameplay. For example, gameplay information may include, without limitation, a number of players, types of roles (e.g., snipers), types of in-game items used or purchased (e.g., weapons, vehicles, armor, custom suits, custom paint, tires, engine modifications, etc.), composition of teams (e.g., number and/or types of roles in each team), maps or game levels played (e.g., battle zones, racetracks, sporting arenas, etc.), duration of gameplay (e.g., how duration of a given gameplay session), player skill levels, player styles (e.g., aggressive, prefers to be a sniper, etc.), types of matches (e.g., team death match, capture the flag, etc.), and/or other information related to a gameplay session.” (Eatedali 0069). The Examiner notes that the underlined portions above correspond to the “persona dimensions” that Applicant claims and describes in the Applicant’s specification in paragraph 0011)
Dependent claim(s) 9, 10 and 11 is/are made obvious by the combination of Eatedali Thompson, FIFA 22 and Le Chevalier based on the same analysis set forth for claim(s) 2, 3 and 5 which are similar in claim scope.
Dependent claim(s) 12 and 13 is/are made obvious by the combination of Eatedali Thompson, FIFA 22 and Le Chevalier based on the same analysis set forth for claim(s) 6 and 7 which are similar in claim scope.
Dependent claim(s) 16, 17 and 18 is/are made obvious by the combination of Eatedali Thompson, FIFA 22 and Le Chevalier based on the same analysis set forth for claim(s) 2, 3 and 5 which are similar in claim scope.
Dependent claim(s) 20 is/are made obvious by the combination of Eatedali Thompson, FIFA 22, Le Chevalier based on the same analysis set forth for claim(s) 14 which is similar in claim scope.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 - 20 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 in view of newly found references to Le Chevalier.
Conclusion
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/RAW/Examiner, Art Unit 3715
3/5/2026
/KANG HU/Supervisory Patent Examiner, Art Unit 3715