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 § 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.
Claims 1 and 8 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.
Claims 1 and 8 recite multiple limitations that depend on actions and determinations of a “driving expert,” including generating driving guidance information, providing a result of recognition, intervening in driving, determining abnormalities, and reconfirming commands. However, the term “driving expert” is not defined in the claims and lacks objective structural or functional boundaries. The claims do not specify whether the driving expert is a human, a remote operator, a professional instructor, or an automated system, nor do they define how the expert’s recognition, determinations, or interventions are obtained or represented.
Because the identity, role, and operational interaction of the driving expert are unclear, one of ordinary skill in the art cannot reasonably determine the scope of the claims. A clarification is respectfully requested for these claims to be properly treated on their merit.
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.
Claim(s) 1-2 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeon (US 20200369294), in view of Ishioka (US 20190016339).
Claims 1 and 8. Jeon teaches a method for learning to drive growing autonomous vehicles in real time based on driver interaction performed by a system for learning to drive growing autonomous vehicles in real time based on driver interaction, the method comprising:
(a) an autonomous vehicle performing autonomous driving learning based on driving guidance information generated using information received from a driving expert , teaches learning an autonomous driving algorithm based on passenger driving manipulation, not expert guidance, para [0010]: learning an autonomous driving algorithm, para [0023]: learning based on passenger manipulation, “…learning an autonomous driving algorithm… by taking into consideration driving manipulation of a passenger…” (para [0010]) ;
(b) the autonomous vehicle performing external driving environment recognition matching on a result of recognition of a recognition system and a result of recognition of the driving expert, para (0005, 0055–0056) “…recognizing a surrounding environment using sensors…” (para [0005]), however not explicit on comparison between expert and the system recognition; however Ishioka teaches identifying surrounding vehicles and setting target locations based on human-interpretable environment modeling, para (0008, 0035, 0102)“…identify surrounding vehicles and set a target location…” (para [0035]), therefore, it would have found it obvious to compare system perception with expert-derived interpretation to improve perception reliability; and
(c) the autonomous vehicle performing reviewing and learning after driving training to improve a level of autonomous driving, Jeon para (0010, 0023, [0025)“…enable the learning of the autonomous driving algorithm…” (para 0023).
Claims 2 . The method according to claim 1, wherein the (a) an autonomous vehicle performing autonomous driving learning performs the autonomous driving learning using utterance information of the driving expert; Jeon is silent on disclosing utterance or speech based input; however Ishioka teaches human-originated instruction and decision inputs used in trajectory/action planning (para 0003, 0008), “…control according to an operation of a driver…” (para 0003); thus using spoken or verbal expert input is an obvious form of driver operation input.
Claim(s) 3-7, 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jeon (US 20200369294), in view of Ishioka (US 20190016339), further in view of NPL “ Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars”, Dated 2021, hereon NPL Wang.
Claims 3 and 9, Jeon teaches learning an autonomous driving algorithm based on driving manipulation of a passenger, including stopping autonomous control and learning when passenger intervention occurs (paras [0010], [0023]–[0026]). However, Jeon does not disclose utterance-based inputs, nor spoken instructions, rewards, penalties, or explanations, “…learning an autonomous driving algorithm… by taking into consideration driving manipulation of a passenger…” (para [0010]), however, he NPL Wang explicitly teaches voice utterances from a human coach that include movement instructions, rewards, penalties, confirmations, and explanations, Page 1–2: “We propose to give natural language voice instructions to train AV agents.” (p. 2), thus, It would have been obvious to incorporate the spoken coaching instructions, rewards, and penalties of Wang et al. into Jeon’s passenger-intervention-based learning framework in order to provide a more natural human-in-the-loop training signal, yielding predictable improvements in learning efficiency.
Claims 4 and 10, Jeon discloses learning and updating autonomous driving algorithms, but does not disclose converting speech to text or classifying utterances against predefined instruction data, however, the NPL Wang explicitly teaches a speech → text → classification → instruction mapping pipeline, Page 5 (Speech-to-Text Transcription): “We used Google’s speech-to-text (STT) to convert these audio clips into text transcripts…” Page 6: “We employ a … text classifier to classify each transcribed utterance … to one of the actions or reward sub-types.”, thus, applying NPL Wang’s known STT-and-classification pipeline to Jeon’s learning system would have been an obvious way to operationalize human input in a structured, machine-usable form.
Claims 5 and 11, Jeon teaches detecting and recording passenger driving manipulation, comparing it with autonomous control, and using it to trigger learning (paras [0023]–[0026], [0027]), “…determine whether driving manipulation of a passenger… has been involved… and enable the learning…” (para [0023]), However NPL Wang teaches human coach interventions that override or guide agent actions and are logged during training, Page 6 (Algorithm 1):“If voice action aₕ ≠ ∅ then a = aₕ.” this indicates explicit human intervention events that are part of the training data, thus, substituting Jeon’s “passenger” with NPL Wang’s expert coach is an obvious variation; both are human interventions recorded and used to improve learning.
Claims 6 and 12, Jeon teaches real-time recognition of the driving environment via sensors (paras [0005], [0061]–[0064]), but not expert recognition; furthermore, the NPL Wang teaches a human coach observing the same environment and issuing utterances that confirm or describe environmental states, Page 4, Table 1 (State / Describe): These utterances reflect human recognition of the environment aligned in time with system perception, thus, comparing sensor-based recognition with expert observations is an obvious human-in-the-loop validation technique.
Claims 7 and 13, Jeon teaches determining abnormal passenger state and altering control accordingly (paras [0035]–[0037]), furthermore, the NPL teaches expert evaluative feedback identifying mistakes, accidents, or bad behavior, and correcting actions, Page 4, Table 1: These utterances represent expert abnormality determinations and corrective confirmations, thus, replacing Jeon’s automated/passenger abnormality assessment with explicit expert verbal determinations is an obvious design choice that predictably improves training quality.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MASUD AHMED whose telephone number is (571)270-1315. The examiner can normally be reached M-F 9:00-8:30 PM PST with IFP.
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MASUD . AHMED
Primary Examiner
Art Unit 3657A
/MASUD AHMED/Primary Examiner, Art Unit 3657