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 Interpretation
The claims are replete with recitations of the word “may.” The Examiner notes that each and every instance of this word triggers the presumption that the subject is optional for purposes of claim interpretation. In other words, if a claim limitation “may” be present, it is interpreted to mean that it may not be present, and hence it is only an optional feature that is not positively recited or in any way required by the claims.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7, 14, 21, and 28 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for modelling one or more of objects, people, weather systems, terrains, animals, and vehicles which may be present in a given environment, does not reasonably provide enablement for modelling all objects, people, weather systems, terrains, animals, and vehicles which may be present in a given environment. It is a physical impossibility for a system to account for every animal or object in a given environment, as this would essentially require an infinite amount of computing power for the infinite resolution of capturing a given environment. Similarly, the specification does not reasonably provide enablement for modelling objects, people, weather systems, terrains, animals, and vehicles which may not be present in a given environment. It is unclear what meaning the system could possibly give to all objects not present in a subject environment, but it would also be an infinite set of objects, animals, people, etc. which would require infinite computing power to model. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make or use the invention commensurate in scope with these claims. Appropriate correction is required.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-28 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “the first plurality of programming instructions.” There is insufficient antecedent basis for this limitation in the claims. Appropriate correction is required. Claims 2-7 are rejected based on their dependency on claim 1.
Claims 2, 9, 16, and 23 recite the limitation “the past and current positions.” There is insufficient antecedent basis for this limitation in the claims. Appropriate correction is required. Claims 3-6, 10-13, 17-20 and 24-27 are rejected based on their dependency upon claims 2, 9, 16, and 23.
Claims 21 and 28 recite the limitation “the plurality of models for vehicles, operators, and environments.” There is insufficient antecedent basis for this limitation in the claims. Appropriate correction is required.
Claims 15 and 22 recite the limitation “the user device” on the last line of each claim. There is insufficient antecedent basis for this limitation in the claims. Appropriate correction is required. Claims 16-21 and 23-28 are rejected based on their dependency upon claims 15 and 22.
Claims 18 and 25 recite the limitation “the simulated user avatar.” There is insufficient antecedent basis for this limitation in the claims. Appropriate correction is required. Claims 19, 20, 26, and 27 are rejected based on their dependency upon claims 18 and 25.
Claims 7, 14, 21, and 28 are rejected because they fail to particularly point out and distinctly claim the subject matter regarded as the invention. As discussed above, the claims encompass modelling objects, people, weather systems, terrains, animals, and vehicles which may not be present in a given environment. It is unclear what meaning the system could possibly give to all objects not present in a subject environment. Appropriate correction is required.
The term “optimal” in claims 15 and 22 is a relative term which renders the claim indefinite. The term “optimal” is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In essence, there is no way to discern the metes and bounds of the claims because there is no clear definition for what is or is not an optimal prediction. Claims 16-21 and 23-28 are rejected based on their dependency upon claims 15 and 22. Appropriate correction is required.
Claims 1 and 8 recite the limitation “a plurality of vehicles, operators, and environments.” It is unclear what the scope of this feature includes. It is reasonably clear that the claim could be met with two or more vehicles, two or more operators, and two or more environments. However, it is not clear if the claim necessarily requires all of these features. For example, it is not clear whether the claim may be met with two or more vehicles and one operator. This issue flows from the fact that “plurality” is not clearly tethered to each and every one of the vehicles, operators, and environments. Because a plurality could be met by choosing one of each of these categories, it is unclear whether one of each would be encompassed by the claim or not. Appropriate correction/clarification is required. Claims 2-7 and 9-14 are rejected based on their dependency upon claims 1 and 8.
Claims 19 and 26 recite the phrase “the plurality of modeled vehicles, operators, and environments” and claims 21 and 28 recite the phrase “the plurality of models for vehicles, operators, and environments.” There is insufficient antecedent basis for these limitations. Appropriate correction is required. Claims 20 and 27 are rejected based on their dependency.
Claims 3, 10, 17, and 24 recite the phrase “gradually return after throughout a plurality of user inputs.” First, the term “gradually” is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Second, it is not clear what “after throughout” means. It appears that the claims should be amended to delete “throughout.” Appropriate correction is required. Claims 4-6, 11-13, 18-20, and 24-27 are rejected based on their dependency.
Given the unusually large number of indefinite claim limitations, the Examiner respectfully suggests a full review of the claim language to ensure full compliance with 35 U.S.C. § 112. For instance, every use of the terms “the” or “said” should clearly and unambiguously refer to a previous recitation of the subject feature, either in the subject claim itself or in a claim upon which the subject claim depends. Additionally, the metes and bounds of the claims should be clearly set forth the invention in a way that would be understood by one of ordinary skill in the art. Applicant should review all pending claims and correct all deficiencies in the next reply.
Claim Rejections - 35 USC § 102/103
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.
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.
Claims 1, 7, 8, 14, 15, 21, 22, and 28 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over US 20220347582 to Russo et al. (hereinafter Russo).
Regarding claims 1 and 8, Russo teaches a system and method for AI-enabled telematics and actuation for electronic entertainment, simulation, training and remote operations systems, comprising:
a computing device comprising at least a memory and a processor (see at least Fig. 6 showing one or more processors and memories);
a plurality of programming instructions stored in the memory and operable on the processor (see at least Fig. 6 showing one or more processors and memories storing program instructions), wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
collect a plurality of operating data from a plurality of vehicles, operators, and environments (e.g., other vehicles, buildings, pedestrians, trees, animals, roadways, weather conditions, road conditions, road topography, traffic signals and signs, etc. from the real world in ¶ 167) wherein operating data may include visual, acoustic, mechanical, and user control data (e.g., telematics data includes receiving telematics data associated with one or more real trips during which a user operated a real vehicle in ¶ 33 and throughout the reference);
train a machine learning system using the plurality of operating data on how to produce a plurality of models for vehicles, operators, and environments (e.g., other vehicles, buildings, pedestrians, trees, animals, roadways, weather conditions, road conditions, road topography, traffic signals and signs, etc. from the real world in ¶ 167);
produce a plurality of models using the machine learning system and a plurality of generative AI systems (e.g., the processor may generate the data model according to various data analysis techniques by analyzing the raw sensor data and generating a set of information (e.g., structured information) from which vehicle operation metrics may be identified or determined in ¶ 179; the processor may be trained using supervised machine learning and/or unsupervised machine learning, and the machine learning may employ an artificial neural network, which, for example, may be a convolutional neural network, a recurrent neural network, a deep learning neural network, a reinforcement learning module or program, or a combined learning module or program that learns in two or more fields or areas of interest in ¶ 264);
display the plurality of models to a user’s electronic video game or simulation system (e.g., display a game that uses the above machine learning model outcomes describing aspects of the real-world environment in a virtual setting; see at least ¶¶ 133-153 and throughout the reference); and
generate a simulated user avatar using the plurality of generative AI systems which may enable a user to interact with the plurality of models (e.g., the system further includes a presenting module configured to present the updated character profile to the user. In some examples, the presenting module is configured to present the updated vehicle condition of the virtual vehicle to the user in ¶ 31 and throughout the reference).
Regarding claims 15 and 22, Russo teaches a system and non-transitory computer-readable storage media having computer-executable instructions embodied thereon that, when executed by one or more processors of a computing system ((see at least Fig. 6 showing one or more processors and memories storing program instructions) employing an asset registry platform for AI–enabled telematics and actuation for electronic entertainment, simulation, training and remote operations systems, cause the computing system to:
collect a plurality of data from a plurality of data sources (the processor may generate the data model according to various data analysis techniques by analyzing the raw sensor data in at least ¶ 179);
combine similar data into a plurality of classed data (e.g., generating a set of information (e.g., structured information) from which vehicle operation metrics may be identified or determined in ¶ 179 and/or provide training data discussed in ¶ 265);
send a plurality of classed data through a generative AI system (e.g., machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as images, object statistics and information, historical estimates, and/or actual repair costs in ¶ 265);
process the plurality of classed data into a plurality of generative AI outputs (e.g., by providing output of the machine learning programs described above);
send the plurality of generative AI outputs, a plurality of game state data, and a plurality of user input data through a machine learning system (e.g., process the game data in at least ¶ 179 using a previously-trained machine learning model);
predict an optimal future game state based on the plurality of generative AI outputs, the plurality of game state data, and the plurality of user input data (e.g., when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output in ¶ 266);
generate a new game state based on the machine learning system’s prediction (e.g., when subsequent novel inputs are provided the processing element may, based upon the discovered rule, accurately predict the correct output in ¶ 266); and
send the plurality of generative AI outputs or the new game state to the user device (e.g., update the state of a virtual vehicle based on inputs from a real vehicle; see at least ¶¶ 247-249 and throughout the reference).
Further regarding claims 1, 8, 15, and 22, under an alternative interpretation of the prior art, Russo teaches the invention substantially as described above, including a telematics-based game and a plurality of machine learning artificial intelligence algorithms and methods for training and using said algorithms to predict various outcomes, but lacks in explicitly teaching using those algorithms to process the telematics-based game data. Regardless, it would have been obvious to one of ordinary skill in the art before the effective date to modify Russo to allow the disclosed plurality of machine learning artificial intelligence algorithms and methods for training and using said algorithms to predict various outcomes to be used on the telematics-based game data in order to control the game environment quickly, automatically, and/or with little or no human intervention necessary to program the game.
Regarding claims 7, 14, 21, and 28, Russo teaches or suggests wherein the plurality of models for vehicles, operators, and environments includes models for all objects, people, weather systems, terrains, animals, and vehicles which may or may not be present in a given environment (e.g., other vehicles, buildings, pedestrians, trees, animals, roadways, weather conditions, road conditions, road topography, traffic signals and signs, etc. from the real world in ¶ 167). It is noted that these claims are rejected under both interpretations of Russo.
Claims 2-6, 9-13, 16-20, and 23-27 are rejected under 35 U.S.C. 103 as being unpatentable over Russo as applied above, in view of US 2017/0252645 to McClive et al. (hereinafter McClive).
Regarding claims 2-3, 9-10, 16-17, and 23-24, Russo teaches or suggests the invention substantially as described above, but lacks in explicitly teaching that operating data further comprises the past and current positions of a plurality of operable actuators paired with the user’s electronic video game or simulation system, and wherein the machine learning system is further trained using the past and current positions of the plurality of actuators, wherein the machine learning system may establish a preferred actuator position where actuators may gradually return after a plurality of user inputs.
In a related disclosure, McClive teaches a mechanical center calibration of control sticks in user input devices such that the system processes movement data representative of the user manipulation to derive a mechanical center of the control stick mechanism from at least a sequence of qualified resting points associated with the control stick mechanism (see abstract). McClive explains that such user devices may be a variety of different types and may be associated with a gaming device (see ¶¶ 13, 17). McClive explains that when a user is not engaging the thumbstick, the thumbstick can responsively return to a central resting position using springs, bands, motors, servos, or other return elements and this resting position can vary over time within a device or from device-to-device depending upon manufacturing variability, component variation, wear levels of components, and other factors (see ¶ 14). McClive solves the variability of input devices by a calibration process that provides more accurate user input control of user interface elements in gaming systems and reduces or eliminates dead zones (see ¶ 21). The system monitors samples of signals representative user manipulation, determines a center, and dynamically calibrates movement data based on mechanical center and cardinal extreme references points and provides the dynamically calibrated movement data to the user’s system (see at least Fig. 2). In this way, McClive teaches the operating data comprising past and current positions of a plurality of operable actuators which is dynamically trained and updated, as claimed and lacking from Russo. As discussed in the grounds of rejection above, Russo teaches the use of machine learning models to train a model and apply it to the gaming system.
It would have been obvious to one of ordinary skill in the art before the effective date to modify the machine learning models of Russo to process the user input data of past and current positions of user operable actuators taught by McClive in order to allow a user to play the game on a hand-held controller that also provides more accurate user input control of user interface elements in gaming systems, as is beneficially taught by McClive.
Regarding claims 4, 11, 18, and 25, the combination of Russo and McClive teaches or suggests wherein the simulated user avatar may take the place of a selected modeled operator in a selected modeled vehicle while the selected modeled vehicle traverses through a selected modeled environment (e.g., the example interface presents a plurality of virtual characters, each selectable by a user, such as to be trained, to be sent onto a virtual trip, and to be played in the telematics-based game in ¶ 72 of Russo; note that the vehicle may be controlled manually by the player as taught in at least ¶¶ 87-89 of Russo).
Regarding claims 5, 12, 19, and 26, the combination of Russo and McClive teaches or suggests wherein a user may control the selected modeled vehicle and interact with the plurality of modeled vehicles, operators, and environments which the machine learning system or plurality of generative AI systems may update depending on the plurality of user inputs (e.g., the vehicle may be controlled manually by the player as taught in at least ¶¶ 87-89 of Russo).
Regarding claims 6, 13, 20, and 27, the combination of Russo and McClive teaches or suggests wherein the user’s ability to control the selected modeled vehicle is restricted depending on the difference in a first position where the selected modeled operator is controlling the selected modeled vehicle and a second position where the plurality of user inputs is controlling the selected modeled vehicle (e.g., the vehicle may be automatically controlled or may be controlled manually by the player, wherein the player is restricted in that certain inputs may result in a deduction in trip performance, as taught in at least ¶¶ 87-89 of Russo).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references to Crabtree et al. are commonly owned and encompass similar subject matter. US 2023/0241491 to Stafford pertains to determining materials in a real world environment, such as a bus, using various sensors and providing them in a virtual game world.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM H MCCULLOCH whose telephone number is (571)272-2818. The examiner can normally be reached M-F 9:30-5:30.
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, David Lewis can be reached at 571-272-7673. 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.
/WILLIAM H MCCULLOCH JR/Primary Examiner, Art Unit 3715
Prosecution Insights