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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4-6, 8-10, 12-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Publication 2021/0090410 A1 to Albright et al. (hereinafter Albright) in view of US Publication 2022/0370908 A1 to Kipnis (hereinafter Kipnis).
Concerning claim 1,
Albright discloses an apparatus comprising: at least one processor system (0016, 0046, Figure 4) configured to:
using an output of the ML model (element 104), generate a tactile signal using a haptics generator (element 106, 108) (0016-0017, 0035-0036, Figure 1, Figure 2).
Albright does not disclose generating text describing computer game data and/or real world environment data and/or virtual world environment data and/or player motion data; and
inputting the text to at least one machine learning (ML) model.
Kipnis teaches generating text (element 300) describing computer game data (the shooting of the zombie and it’s defeated state is considered computer game data) and/or real world environment data and/or virtual world environment data and/or player motion data (0028-0030, 0039, Table 1, Figure 3); and
inputting the text to at least one machine learning (ML) model (0042, Figure 5-6).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the text based input to a machine learning model of Kipnis with the machine learning haptic control apparatus of Albright as both take player’s experience, input them into a machine learning model, and influence the player’s experience as a result of the machine learning model. Incorporating the text based machine learning input of Kipnis would make the gaming content input into the machine learning model of Albright more multifaceted and robust.
Concerning claim 2,
Albright does not disclose the text describes computer game data.
Kipnis teaches the text describes computer game data (0028-0030, 0039, Table 1, Figure 3, the shooting of the zombie and it’s defeated state is considered computer game data).
Concerning claim 4,
Albright does not disclose the text describes virtual world environment data.
Kipnis teaches the text describes virtual world environment data (0032-0033, Table 1, Figure 3).
Concerning claim 5,
Albright does not disclose the text describes player motion data.
Kipnis teaches the text describes player motion data (0032, Table 1, Figure 3, wherein the player walking is considered player motion data).
Concerning claim 6,
Albright does not disclose the text describes at least two of computer game data, real world environment data, virtual world environment data, player motion data.
Kipnis teaches the text describes at least two of computer game data, real world environment data, virtual world environment data, player motion data (0032-0033, Table 1, Figure 3, wherein the player walking is considered player motion data, and the virtual world environment data is clearly disclosed, with the rain given as an example).
Concerning claim 8,
Albright discloses the haptics generator is on a computer game controller (0040, 0042, 0050).
Concerning claims 9 and 17, see the rejection of claim 1.
Concerning claim 10 and 18, see the rejection of claim 2.
Concerning claim 12 and 19, see the rejection of claim 4.
Concerning claim 13 and 20, see the rejection of claim 5.
Concerning claim 14, see the rejection of claim 6.
Concerning claim 16, see the rejection of claim 8.
Claim(s) 3, 7, 11, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Publication 2021/0090410 A1 to Albright et al. in view of US Publication 2022/0370908 A1 to Kipnis and further in view US Publication 2021/0001225 A1 to Panec (hereinafter Panec).
Concerning claim 3,
Albright does not disclose the text describes real world environment data.
Kipnis teaches the text describes environment data (0032-0033, Table 1, Figure 3).
Panec teaches real world environment data and virtual environment data being simultaneously outputted in AR gaming (0025-0026, 0088, Figure 1, Figure 3).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to incorporate the text based input to a machine learning model of Kipnis with the machine learning haptic control apparatus of Albright as both take player’s experience, input them into a machine learning model, and influence the player’s experience as a result of the machine learning model. It would further be obvious to incorporate the gaming based text of Kipnis with the augmented reality gaming apparatus of Paned as both concern the playing of a video game. Incorporating the text based machine learning input of Kipnis would make the gaming content input into the machine learning model of Albright more multifaceted and robust. Incorporating the augmented reality gaming apparatus of Paned into the game based text generation tracking apparatus of Kipnis would allow for the additional medium of augmented reality gaming to be integrated, allowing for Kipnis to address a wider gaming market.
Concerning claim 7,
Albright does not disclose the text describes all of computer game data, real world environment data, virtual world environment data, player motion data.
Kipnis teaches the text describes all of computer game data, virtual world environment data, player motion data (0028-0030, 0032-0033, 0039, Table 1, Figure 3, the shooting of the zombie and it’s defeated state is considered computer game data. The player walking is considered player motion data, and the virtual world environment data is clearly disclosed, with the rain given as an example).
Panec teaches real world environment data and virtual environment data being simultaneously outputted in AR gaming (0025-0026, 0088, Figure 1, Figure 3).
Concerning claim 11, see the rejection of claim 3.
Concerning claim 15, see the rejection of claim 7.
Conclusion
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/I.S./Examiner, Art Unit 3715
/DMITRY SUHOL/Supervisory Patent Examiner, Art Unit 3715
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