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 .
Election/Restrictions
Applicant’s election without traverse of Species II (as identified in the Requirement for Restriction dated 1/27/2026) in the reply filed on 2/18/2026 is acknowledged.
Claim 2 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to nonelected Species I (as identified in the Requirement for Restriction dated 1/27/2026), there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 2/18/2026.
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 5 and 7-8 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.
Regarding claim 5, “the dataset” lacks antecedent basis, therefore this claim is indefinite. For the purposes of examination, Examiner has interpreted “the dataset” to mean any dataset.
Regarding claim 7, “the second artificial model” lacks antecedent basis, therefore this claim is indefinite. For the purposes of examination, Examiner has interpreted “the second artificial model” to mean “the second artificial intelligence model”.
Regarding claim 8, this claim depends from claim 7 and is therefore rejected for the same reason as claim 7 above, as it does not cure the deficiencies of claim 7 noted above.
Regarding claim 8, “the second artificial intelligence mode” lacks antecedent basis, therefore this claim is indefinite. For the purposes of examination, Examiner has interpreted “the second artificial
intelligence mode” to mean “the second artificial intelligence model”.
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.
Examiner notes that the independent claims recite a practical application of generating artificial intelligence models to provide a decision making that is adaptive to a road segment.
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 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.
Claims 1, 3, 6-7, 12-17 are rejected under 35 U.S.C. 103 as being unpatentable over Yan (CN 109685214 A) in view of Bagschik (US 20250208620 A1).
Claim 1
Yan teaches:
A method of Al models generalization across different road segments, the method comprises (Yan - Paragraphs 0043-0053) “target driving model generalization capability, for example, so that the target driving model of stronger adaptive capacity, can adapt more driving road”
wherein the first road segment is associated with a first artificial intelligence model, such that the first artificial intelligence model is generated in association with the first road segment by collecting driving data relating directly to the first road segment and reflecting behavioral data of drivers captured along the first road segment to provide a decision making that is adaptive to the first road segment (Yan - Paragraphs 0044-0053) “the basic driving model is using first scene obtained by training the sample data of the driver model”
and generating, by the computerized system, a second artificial intelligence model in association with the second road segment, based on, at in part at least one of: the first artificial intelligence model, or a first dataset fed to the first artificial intelligence model during a generating of the first artificial intelligence model (Yan - Paragraph 0057) “transfer learning in the basic driving model (transferlearning) to obtain the driving model”
to provide a decision making that is adaptive to the second road segment (Yan - Paragraph 0057) “a first scenario is an open road, the second scene is a closed community”
Yan does not teach:
Identifying a similarity metric between a first road segment and a second road segment.
However, Bagschik teaches:
identifying, by a computerized system, a similarity metric between a first road segment and a second road segment (Bagschik - Paragraphs 0160-0163) “determining a first similarity metric between the target road feature and the first road feature meets or exceeds a threshold similarity; and determining a second similarity metric between the target road feature and the second road feature is less than the threshold similarity”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Yan with identifying a similarity metric between a first road segment and a second road segment of Bagschik with a reasonable expectation of success. One of ordinary skill in the art would understand that both Yan and Bagschik discuss machine learning models for vehicles. One would have been motivated to combine as this improves safety and general operating behavior (Bagschik – Paragraph 0013).
Claim 3
The combination of Yan and Bagschik teaches all of the limitations of claim 1 as seen above.
Yan further teaches:
the first road segment and the second road segment are for a similar driving route (Yan - Paragraph 0047-0048) “the first scene and the second scene has common road characteristic, namely with partial road characteristics are the same in the two scenarios”
Claim 6
The combination of Yan and Bagschik teaches all of the limitations of claim 1 as seen above.
Yan further teaches:
the generating of the second artificial intelligence model comprises initializing at least a part of the second artificial intelligence model to a corresponding at least part of the first artificial intelligence model (Yan - Paragraph 0057) “transfer learning in the basic driving model (transferlearning) to obtain the driving model”
Claim 7
The combination of Yan and Bagschik teaches all of the limitations of claim 1 as seen above.
Yan further teaches:
the generating of the second artificial intelligence model comprises feeding to the second artificial model, during a training of the second artificial intelligence model, a second dataset associated with the second road segment (Yan - Paragraphs 0046-0048) “using the second scene of the sample data to train the basic driving model to obtain for the second scene in driving target driving model”
the second dataset being at least a portion of the first dataset of the first artificial intelligence model (Yan - Paragraphs 0046-0048) “the first scene and the second scene has common road characteristic, namely with partial road characteristics are the same in the two scenarios”
Claim 12
Yan teaches:
A non-transitory computer readable medium storing instructions that, when executable by at least one processing device, cause the processing device to (Yan - Paragraphs 0028-0029) “a processor, a memory, and stored on the memory and running on the processor, the computer program”
All of the other limitations have been examined with respect to claim 1. Please see the rejection above.
Claims 13-14
All of the limitations have been examined with respect to claims 6-7. Please see the rejection above.
Claim 15
Yan teaches:
A system of Al models generalization for driving, the system comprising at least one processing device configured to (Yan - Paragraphs 0028-0029, 0097-0101) “a terminal device of the structure graph, as shown in FIG. 6, the terminal device 600 comprises a processor 601, a memory 602 and stored on the memory 602 and running on the processor of the computer program”
All of the other limitations have been examined with respect to claim 1. Please see the rejection above.
Claims 16-17
All of the limitations have been examined with respect to claims 6-7. Please see the rejection above.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Yan and Bagschik, as applied to claim 1 above, and further in view of Echigo (US 20250350539 A1).
Claim 4
The combination of Yan and Bagschik teaches all of the limitations of claim 1 as seen above.
Yan does not teach:
A learning of the first artificial intelligence (AI) model is based on the generating of the second AI model.
However, Echigo teaches:
a learning of the first artificial intelligence model is based on, at least in part, the generating of the second artificial intelligence model (Echigo - Paragraphs 0036, 0140, 0178, 0185) “The BS/UE of category 2 exchanges information of partial or complete models”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Yan with a learning of the AI model is based on the generating of the second AI model of Echigo with a reasonable expectation of success. One of ordinary skill in the art would understand that Yan and Echigo discuss AI models for vehicles. One would have been motivated to combine as this reduces AI model overhead (Echigo – Paragraph 0012).
Claim 5
The combination of Yan, Bagschik, and Echigo teaches all of the limitations of claim 4 as seen above.
Yan does not teach:
Feeding to the first AI model, a part of dataset from the second AI model.
However, Echigo teaches:
the learning involves feeding, to the first artificial intelligence model, at least a part of the dataset of the second artificial intelligence model (Echigo - Paragraphs 0036, 0140, 0178, 0185) “The BS/UE of category 3 exchanges information of data sets for training/validation/test”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Yan for the same reasons as seen in claim 4 above.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Yan and Bagschik, as applied to claim 1 above, and further in view of Jiang (US 20240001966 A1).
Claim 8
The combination of Yan and Bagschik teaches all of the limitations of claim 7 as seen above.
Yan does not teach:
Assigning first weights to the first dataset and second weights to the second dataset.
However, Jiang teaches:
assigning, during the generating of the second artificial intelligence mode, first weights to the first dataset (Jiang - Paragraphs 0017, 0038-0041) “weights of training dataset allocated to the driving scenarios 107, 111 and 115”
and assigning second weights to the second dataset, the second weights exceeding the first weights (Jiang - Paragraphs 0017, 0038-0041) “three sets of weights (0.1, 0.5, 0.4; 0.2, 0.2, 0.6; and 0.2, 0.1, 0.7) for the three driving scenarios 107, 111, and 115”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Yan with assigning first weights to the first dataset and second weights to the second dataset, the second weights exceeding the first weights of Jiang with a reasonable expectation of success. One of ordinary skill in the art would understand that both Yan and Jiang are in the field of autonomous vehicle models. One would have been motivated to combine as this prevents overfitting and performance degradation (Jiang – Paragraph 0003).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Yan and Bagschik, as applied to claim 1 above, and further in view of Muller (US 20240127062 A1).
Claim 9
The combination of Yan and Bagschik teaches all of the limitations of claim 1 as seen above.
Yan does not teach:
incorporating the second artificial intelligence model within a liquid arrangement of artificial intelligence models associated with different road segments.
However, Muller teaches:
incorporating the second artificial intelligence model within a liquid arrangement of artificial intelligence models associated with different road segments (Muller - Paragraphs 0024, 0039) “machine learning model(s) 108… liquid state machine”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Yan with a liquid arrangement of AI models of Muller with a reasonable expectation of success. One of ordinary skill in the art would understand that both Yan and Muller are in the field of machine learning models for vehicles. One would have been motivated to combine as this provides a system for autonomous driving without collisions (Muller – Paragraph 0003).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yan, Bagschik, and Muller, as applied to claim 9 above, and further in view of Dwivedi (US 20230097169 A1).
Claim 10
The combination of Yan, Bagschik, and Muller teaches all of the limitations of claim 9 as seen above.
Yan does not teach:
A correlation of AI models in the liquid arrangement.
However, Dwivedi teaches:
the incorporating is based on an artificial intelligence model representation correlation between the second artificial intelligence model and the artificial intelligence models of the liquid arrangement (Dwivedi – Paragraphs 0110-0111, Claim 19) “identify a plurality of neural network layers shared by a first trained model and a second trained model”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Yan with a correlation of AI models in the liquid arrangement of Dwivedi with a reasonable expectation of success. One of ordinary skill in the art would understand that Yan and Dwivedi discuss AI models of autonomous vehicles. One would have been motivated to combine as this reduces memory usage and computational overhead (Dwivedi – Paragraph 0058).
Claim 11
The combination of Yan, Bagschik, and Muller teaches all of the limitations of claim 9 as seen above.
Yan does not teach:
Sharing neurons in the liquid arrangement of AI models.
However, Dwivedi teaches:
with the liquid arrangement of the artificial intelligence models being implemented by neural networks, the incorporating involves having a shared plurality of neural neurons with at least a part of the neural networks (Dwivedi - Paragraphs 0110-0111, Claim 19) “identify a plurality of neural network layers shared by a first trained model and a second trained model”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention of Yan for the same reasons as seen in claim 10 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to Matthew Ho whose telephone number is (571) 272-1388. The examiner can
normally be reached on Mon-Thurs 9:00-5:30 EST.
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, Navid Z Mehdizadeh can be reached on (571)-272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW HO/ Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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