DETAILED ACTION
Remarks
This non-final office action is in response to the application filled on 09/15/2024. Claims 1-17 are pending and examined below.
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 .
Information Disclosure Statement
As of date of this action, IDS filled has been annotated and considered.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a) ‐ (d). The certified copy has been filed in parent Application No. EP 2022/22165905.5, filed on 03/31/2022. PCT/EP2023/058096 was filled on 09/15/2024.
Claim Rejections - 35 USC § 112
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.
Claim(s) 1-17 is/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 pre-AIA the applicant regards as the invention.
Regarding claim 1, which recites “measurement results having a limited confidence” is not clear. [0032] of PGPub of submitted specification describe one reason of limited confidence could be due to sensing noise or distortion of the environment. However, specification does not mention that noise/distortion is the one or only reason of limited confidence. Limited confidence can be interpreted broadly. For example, environment measurement results having less confidence for doing an action by the automated device. Also, limited confidence does not clearly set forth the metes and bounds of the patent protection desired.
Also recites, “a state” line 5 is not clear since “a state” is also recited on line 2. It is not clear whether both states are same or different.
Dependent claim(s) 2-17 is/are also rejected because they do not resolve their parent deficiencies.
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, 4-7, 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0182772 (“Funke”), and further in view of IEEE transactions on neural networks and learning systems, vol 30, No 6; title “Exploiting generalization in the subspaces for faster model-based reinforcement learning”, by (“Maryam”).
Regarding claim 1, as best understood in view of indefiniteness rejection explained above, Funke discloses a method for determining an action for an automated device based on a state of an environment of the automated device (see at least fig 3, where operation of an autonomous vehicle is altered based on environment state), the method comprising
obtaining information on environmental measurement results (see at least [0067], where “At operation 306, example process 300 may comprise receiving sensor data from one or more sensors…the perception component may determine the perception data, which may include determining a curvature of a roadway, detecting the presence of passenger(s) in the vehicle, detecting weather and/or traffic conditions, a jerk and/or acceleration experienced by the vehicle…The vehicle 202 may additionally or alternatively detect an object in the environment”), the measurement results having a limited confidence (see at least [0069], where “The ML model may use such data to determine the safety confidence score, which may be a logit, a number between zero and 1”; see also [0071], where “At operation 310, example process 300 may comprise determining whether the safety confidence score meets or exceeds a score threshold”; confidence score less than threshold is interpreted as limited confidence);
estimating information on a state of the environment based on the information on the environmental measurement results (see at least [0067], where curvature of a roadway, presence of passenger, detecting weather and traffic condition, detect an object are estimated. So, state of the environment based on information is determined), the information on the state of the environment comprising information on a confidence of the state of the environment (see at least [0068], where based on the information on the state of the environment (e.g., existence of crosswalk) safety confidence score is determined. Safe or unsafe environment can be state of the environment. See also [0015], [0036] and [0054]);
determining a representation for the state of the environment based on the information on the state of the environment and based on the information on the confidence of the state of the environment (see at least [0067], where curvature of a roadway, presence of passenger, detecting weather and traffic condition, detect an object are estimated. So, representation for the state of the environment is determined based on state of the environment. Representation for the state of the environment may be safe or unsafe. The safety confidence score is also determined. When safety confidence score meets or exceeds a threshold indicating a safety condition. See also [0071]), the representation of the state of the environment comprising (see at least fig 4, where multiple separate regions are shown. multiple regions state together representing the state of the environment; vehicle 202 is passing through states comprising bicyclist and vehicle 408) and the confidence of the state of the environment (see at least [0081]); and
determining information on the action for the automated device based on the representation (see at least [0076], where “At operation 314, example process 300 may comprise altering operation of the vehicle. Altering operation of the vehicle may comprise altering the first trajectory, determining a new trajectory, altering operation of a component of the vehicle, and/or transmitting a notification to a passenger.”).
Funke does not explicitly disclose the following limitation:
two or more intermediary states representing the state of the environment.
However, Maryam discloses a method wherein two or more intermediary states representing the state of the environment (see at least fig 2, where subspace 1…N. decision is made using confidence degree model based on environmental feedback.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Funke to incorporate the teachings of Maryam by including the above feature for expediting the learning process, see page 1636, right col, 4th para of Maryam.
Regarding claim 2, Funke further discloses a method comprising determining confidence information for the action (see at least fig 3, where vehicle operation is determined based on confidence score).
Regarding claim 4, Funke further discloses a method comprising using one or more policies to obtain two or more intermediary actions based on the two or more intermediary states (see at least fig 4, where vehicle 202 is arriving pickup location, 414. See also [0081], where avoid unsafe condition is policy. The trajectory to 414 is determined based on intermediary state e.g. bicyclist and vehicle, 408).
Regarding claim 5, Funke further discloses a method comprising using the same policy to obtain one intermediary action for each of the intermediary states (see at least fig 4 and [0081]).
Regarding claim 6, Funke further discloses a method wherein the one or more policies involve a non-linear transform to obtain the two or more intermediary actions based on the two or more intermediary states (see at least [0052] and fig 4).
Regarding claim 7, Funke further discloses a method comprising determining statistical properties of a distribution of the two or more intermediary actions (see at least [0060] and [0069]).
Regarding claim 16, Funke further discloses a non-transitory computer readable medium storing a computer program having program code for performing the method according to claim 1, when the computer program is executed on a computer, a processor, or a programmable hardware component (Refer at least to claim 1 for reasoning and rationale; see also [0050]).
Regarding claim 17, Funke further discloses an apparatus for controlling an automated device comprising a control module for performing the method of claim 1 (Refer at least to claim 1 for reasoning and rationale; see also fig 2).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0182772 (“Funke”), and in view of IEEE transactions on neural networks and learning systems, vol 30, No 6; title “Exploiting generalization in the subspaces for faster model-based reinforcement learning”, by (“Maryam”), as applied to claim 1 above, and further in view of US 2020/0263996 (“Gokhale”).
Regarding claim 3, Maryam further discloses a method wherein each of the two or more intermediary states comprises (see at least fig 2; selecting some statistical properties associated with each subspace is inherent to the estimation of the model).
Funke in view of Maryam does not explicitly disclose one or more sigma points representing statistical properties.
However, Gokhale discloses a method comprising one or more sigma points representing statistical properties (see at least [0031], where “The algorithm generates arbitrary sigma points for prediction as shown in FIG. 5.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Funke in view of Maryam to incorporate the teachings of Gokhale by including the above feature for providing more reliable properties so that safety during travelling increased.
Claim(s) 8-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0182772 (“Funke”), and in view of IEEE transactions on neural networks and learning systems, vol 30, No 6; title “Exploiting generalization in the subspaces for faster model-based reinforcement learning”, by (“Maryam”), as applied to claim 7 above, and further in view of US 2022/0161767 (“Sevensson”).
Regarding claim 8, Funke further discloses a method wherein emergency vehicle is identified and take taction based on the identification, see at least [0069]. Maryam further discloses a method wherein two or more intermediary regions/ actions are determined, see at least fig 2.
Funke in view of Maryam does not explicitly disclose using an unscented transform for determining the statistical properties.
However, Sevensson discloses a method comprising using an unscented transform for determining the statistical properties (see at least [0069], where “performing nonlinear modelling (unscented prediction) on the input parameters”; unscented transform is used for triggering an emergency stop, per [0051] of PGPub of submitted specification.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Funke in view of Maryam to incorporate the teachings of Sevensson by including the above feature for providing higher accuracy and faster computation so that safety during travelling increased.
Regarding claim 9, Maryam further discloses a method wherein the determining of the information on the action is based on the statistical properties of the distribution of the two or more intermediary actions, and wherein the statistical properties of the distribution of the two or more intermediary actions comprise confidence information on the intermediary actions (see at least page 1637, right col, page 1644 left col and fig 2).
Regarding claim 10, Funke further discloses a method wherein the determining of the information on the action is based on the confidence information of the intermediary actions (see at least fig 6 and [0083]).
Regarding claim 11, Funke further discloses a method comprising determining information on a safety action as information on the action if the confidence information for the intermediary actions indicates confidence levels of the intermediary actions below a predefined confidence threshold (see at least fig 3).
Regarding claim 12, Funke further discloses a method comprising training the policies, wherein the training of the policies is influenced by intermediary states identified for intermediary actions exhibiting a predefined statistical characteristic (see at least [0061] and fig 3, where confidence above threshold is interpreted as predefined statistical characteristic).
Regarding claim 13, Funke further discloses a method wherein the predefined statistical characteristic is a confidence threshold (see at least fig 3, block 310).
Regarding claim 14, Funke further discloses a method wherein the automated device is an autonomous vehicle or an industrial robot and wherein the action is a controlled action for the autonomous vehicle or the industrial robot (see at least [0003]).
Regarding claim 15, Funke further discloses a method wherein the action comprises one or more elements of the group of a maneuver, a motion, an acceleration, a deceleration, a steering command, a stop command, and an emergency command (see at least [0011] and [0015]).
Conclusion
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/SOHANA TANJU KHAYER/Primary Examiner, Art Unit 3657