DETAILED ACTION
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 1-15 are rejected under 35 U.S.C. 101 as being directed to ineligible subject matter.
Claim 1 is directed to a method of providing adaptive training to a user in a training environment. The method calls for receiving a profile of a user with personality and biometric information, and developing a training scenario. The user is then presented with the training scenario, wherein the training is adapted in real-time, based upon behavioral data from the user. The above claim limitations are directed to an abstract idea, in that they are directed to the organization of human behavior, i.e. teaching, which is an identified judicial exception to patent eligibility. However, the analysis is not yet complete, as the claims also require a computer with machine learning. Applicant’s specification identifies wherein the system operates on standard established computer technology (YY), thus there is no special purpose computing system required, as the claimed method is directed to education, and not computer technology itself. Strictly speaking, a computer is not required to perform the broader abstract claim method. User characteristics could be observed and recorded manually, and needed adjustments to training could be performed manually. Thus the computer does not add significantly more to the claim, nor does it limit the claims to a practical application. Thus claim 1 is rejected under 35 U.S.C. 101 as being ineligible, for being directed to an abstract idea, which is a judicial exception to patent eligibility. The same reasoning holds for independent claims 14 and 15.
Claims 2-6 require changing the behavior of NPCs based upon assessed user behavior exceeding a threshold. This could also be accomplished manually, e.g. by observation and reaction of trainers to various user data (weighted as preferred) towards reaching a training goal. These limitations are a part of the broader abstract idea, as described above with regard to claim 1. They pertain to the abstract education component, are encompassed therein, and are do not render the claims patent eligible.
Claim 7 requires a VR training environment. However, this could just be a simple computer interface, and it does require special use the computer beyond anything established, i.e. the computer displays data, provides I/O and communication, makes conditional adaptions, and so forth. This manner of expected computer use does not add significantly more to the claim, and it is thus ineligible patent subject matter similar to claim 1.
Claims 8-13 require changing the training based upon pre-set modifications, such as adapting task difficulty, based upon user behavior such as correctness, and wherein future training sessions are also modified based upon updated user data from various devices (i.e. a keyboard), and wherein the data includes such things characterizations as ‘stressed’. These limitations all pertain directly to the abstract educational process, and they are encompassed therein. They do not render the claims patent eligible.
Claim Rejections - 35 USC § 102
(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 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aslan et al. (US 10,013,892).
Regarding claims 1 and 14-15, Aslan discloses a training system, which receives biometric and personality characteristics of a user, and generates a training scenario based thereon. See col. 4: 14-32. Aslan discloses wherein the user is provided with the training scenario, and the system modifies the scenario in real-time based upon the user’s behavior in the session. See col. 5: 1-5 and col. 5: 20-32. Aslan discloses wherein the system utilizes machine learning and functions as an agent in that it presents learning material to the user. See col. 12: 36-41 and col. 5: 52-57.
Regarding claim 2, Aslan discloses wherein the system controls non-player characters in the training scenario, and modifications to the scenarios include modifications to NPC behavior. See col. 11: 32-53 (there are machines and peer users, both reasonably construed as NPCs in that they are not the learner).
Regarding claims 3-4 and 6, Aslan discloses wherein the system modifications are threshold based with regard to assessments of user behavior towards reaching a training goal. See col. 5: 20-32.
Regarding claims 5 and 8, Aslan discloses wherein the user characteristics are weighted for analysis and the scenario is adapted according to pre-set options. See col. 9: 30-45 (i.e. stepping up or down a level would be a preset option).
Regarding claim 7, Aslan discloses wherein the system is VR and the training session and modifications etc. occur in a VR environment. See col. 10: 4-10.
Regarding claim 9, Aslan discloses wherein the modifications include changing the complexity of tasks. See col. 9: 45-52 (note that the threshold values can be unique, or ‘weighted’ with regard to particular user’s and parameters.
Regarding claim 10, Aslan discloses wherein the scenario includes a plurality of tasks to be completed by the user, and correctness is a monitored parameter. See col. 10: 45-57.
Regarding claim 11, Aslan discloses wherein the profiles are updated and taken into account in subsequent training sessions. See col. 11: 32-44.
Regarding claim 12, Aslan discloses wherein the biometric profile is updated based upon data collected from user devices. See col. 7: 37-51.
Regarding claim 13, Aslan discloses wherein engaged is a personality characteristic for the profile. See col. 6: 47-64.
Conclusion
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TIMOTHY A. MUSSELMAN
Primary Examiner
Art Unit 3715
/TIMOTHY A MUSSELMAN/Primary Examiner, Art Unit 3715