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 2-3 and 10-19 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 2, this claim recites “a route benchmark”. It is unclear if “a route benchmark” in claim 2 refers to “a route benchmark” in claim 1 or is a new separate unclaimed recitation of “a route benchmark”, therefore this claim is indefinite. For the purposes of examination, Examiner has interpreted “a route benchmark” in claim 2 to mean “the route benchmark”.
Regarding claim 3, this claim depends from claim 2 and is therefore rejected for the same reason as claim 2 above, as it does not cure the deficiencies of claim 2 noted above.
Regarding claim 10, this claim recites “identify, by a computerized system”, however this claim also recites “cause the processing device to:”. It is unclear if it is the computerized system or the processing device that is identifying road segments artificial intelligence models, therefore this claim is indefinite. For the purposes of examination, Examiner has interpreted the identifying to be done by any system.
Regarding claims 11-18, these claims depend from claim 10 and are therefore rejected for the same reason as claim 10 above, as they do not cure the deficiencies of claim 10 noted above.
Regarding claim 11, this claim recites “a route benchmark”. It is unclear if “a route benchmark” in claim 11 refers to “a route benchmark” in claim 10 or is a new separate unclaimed recitation of “a route benchmark”, therefore this claim is indefinite. For the purposes of examination, Examiner has interpreted “a route benchmark” in claim 11 to mean “the route benchmark”.
Regarding claim 12, this claim depends from claim 11 and is therefore rejected for the same reason as claim 11 above, as it does not cure the deficiencies of claim 11 noted above.
Regarding claim 19, this claim recites “identify, by a computerized system”, however this claim also recites “processing device configured to:”. It is unclear if it is the computerized system or the processing device that is identifying road segments artificial intelligence models, therefore this claim is indefinite. For the purposes of examination, Examiner has interpreted the identifying to be done by any system.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 10 , and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 10 recites:
“a non-transitory computer readable medium storing instructions that, when executable by at least one processing device, cause the processing device to:
identify, by a computerized system, road segments artificial intelligence models, based on a similarity metric between different road segments along one or more different driving routes and in accordance with a route benchmark,
wherein the road segments artificial intelligence models are generated each in association with a road segment for the driving route, by collecting driving data relating directly to the road segment and reflecting behavioral data of drivers captured along the road segment, to provide a decision making that is adaptive to the road segment; and
create a general artificial intelligence model for at least a portion of the driving route, by automatically merging at least a portion of the road segments artificial intelligence models for the different road segments of the one or more different driving routes based on, at least in part, the similarity metric, to provide a decision making that complies with the different road segments along the one or more different driving routes”.
The limitations of identifying, generating, and creating artificial intelligence models, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting by a processing device, nothing in the claim elements precludes the steps from practically being performed in the mind. For example, the processing device identifying, generating, and creating in the context of this claim encompasses the user manually performing the steps of learning and creating a model in his mind, and deciding a model to use. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim recites using a processing device to perform identifying, generating, and creating artificial intelligence models. The processing device in these steps is recited at a high-level of generality (i.e., as a generic processor performing generic computer functions of identifying, generating, and creating) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of a non-transitory computer readable medium, a processing device, a computerized system, and artificial intelligence models, to perform identifying, generating, and creating, amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Dependent claims 2-9 and 11-18 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claims are not directed to an abstract idea. The additional elements in the dependent claims are not sufficient to amount to significantly more than the judicial exception for the same reasons as with claim 10.
Office Note: In order to overcome this rejection, the Office suggests further defining the limitations of the independent claim, for example by controlling movement of the vehicle based on the artificial intelligence models or linking the claims to an improvement for computers. Limitations such as these suggested above would further bring the claimed subject matter out of the realm of an abstract idea without significantly more.
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 1-6, 10-15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Noce (US 12298953 B1) in view of Benisch (US 20210191407 A1).
Regarding claim 1, Noce discloses a method of Al models generalization for driving, the method comprising (Col. 11 Line 64 – Col. 12 Line 45, Claim 16);
identifying, by a computerized system, road segments artificial intelligence models, based on a similarity metric between different road segments along one or more different driving routes and in accordance with a route benchmark (Col. 10 Line 28 – Col. 11 Line 62; “querying for the best performing models of a given region (for example, using a predetermined threshold for a given metric value)… and then compute the distance from the mobile edge node to all known points to arrive at the closest one and thus the best model”; Similarity metric is mapped to distance; Route benchmark is mapped to predetermined threshold);
by collecting driving data relating directly to the road segment and reflecting behavioral data of drivers captured along the road segment (Col. 7 Line 14 – Col. 7 Line 22, Col. 8 Line 18 – Col. 8 Line 33; “collect substantial amounts of heterogeneous data to navigate long distances where the network can vary along the way. In these scenarios, the autonomous vehicle would have to adapt to different conditions in the route such as new weather or traffic distributions”);
to provide a decision making that is adaptive to the road segment (Col. 11 Line 64 – Col. 12 Line 63, Claim 16);
and creating a general artificial intelligence model for at least a portion of the driving route, by automatically merging at least a portion of the road segments artificial intelligence models for the different road segments of the one or more different driving routes based on, at least in part, the similarity metric (Col. 11 Line 64 – Col. 12 Line 45, Col. 13 Line 19 – Col. 13 Line 23, Claim 16; “perform a new model merge, the mobile edge device merges the previous best model with the next best model. In some implementations this merge is done by weighing 510 the merge using the distance 512a, 512b"; It is obvious that the server can create the general artificial intelligence model because it has all the necessary data and reduces processing at the mobile edge device);
to provide a decision making that complies with the different road segments along the one or more different driving routes (Col. 11 Line 64 – Col. 12 Line 45, Claim 16; “a smooth transition between models as the mobile edge device traverses different sub-regions 506a, 506b that might exhibit possible differences in data distribution”).
Noce does not specifically state wherein the road segments artificial intelligence models are generated each in association with a road segment for the driving route
However, Benisch teaches wherein the road segments artificial intelligence models are generated each in association with a road segment for the driving route (Paragraphs 0031-0032, 0102; “The region-specific models have thus been trained specifically on their associated regions”).
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 Noce with generating AI models in association with a road segment for the driving route of Benisch with a reasonable expectation of success. One of ordinary skill in the art would understand that a vehicle may encounter unusual situations that a general AI model is not suited for. Training regional based models allows for better performance based on the location of the vehicle. One would have been motivated to combine Noce with Benisch as this achieves better AI model performance. As stated in Benisch, “using models trained for specific regions when the vehicle is located in those regions has advantages over generalized models trained for common regions because the region-specific models can generate correct results in unusual regions. The regions-specific models advantageously use localized training that would cause overfitting errors in generalized models, without the disadvantages of localized training” (Paragraph 0032).
Regarding claim 2, Noce discloses determining a route benchmark for the driving route (Col. 10 Line 28 – Col. 10 Line 41, Col. 11 Line 14 – Col. 11 Line 21; “querying for the best performing models of a given region (for example, using a predetermined threshold for a given metric value)”).
Regarding claim 3, Noce discloses providing an indication with respect to a requirement for a generation of a mature road segment artificial intelligence model, in accordance with the route benchmark (Col. 10 Line 28 – Col. 11 Line 21; “an example output includes a map 308 from the points P.sub.1 through P.sub.8 to the best model 302 in the sub-region 306 of a route 310”).
Regarding claim 4, Noce discloses road segment and general artificial intelligence models.
Noce does not specifically state generating another road segment artificial intelligence model for another road segment, using the general artificial intelligence model.
However, Benisch teaches generating another road segment artificial intelligence model for another road segment, using the general artificial intelligence model (Paragraph 0041, Fig. 1B; “each of the region-specific models may be created by copying the generalized model 121”).
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 Noce with generating a regional AI model based on the general AI model of Benisch with a reasonable expectation of success. One of ordinary skill in the art would understand that regional models perform better in their associated region than general models. These regional models can be created by copying the general model, so training is partially complete. One would have been motivated to combine Noce with Benisch as this improves model performance. As stated in Benisch, “using models trained for specific regions when the vehicle is located in those regions has advantages over generalized models trained for common regions because the region-specific models can generate correct results in unusual regions” (Paragraph 0032).
Regarding claim 5, Noce discloses the other road segment is along another driving route that is different from the one or more different driving routes (Col. 10 Line 45 – Col. 11 Line 13, Fig. 3; “best model 302 in the sub-region 306 of a route 310”).
Regarding claim 6, Noce discloses the other road segment is along at least one of the one or more different driving routes (Col. 10 Line 45 – Col. 11 Line 13, Fig. 3; “best model 302 in the sub-region 306 of a route 310”).
Regarding claim 10, Noce discloses a non-transitory computer readable medium storing instructions that, when executable by at least one processing device, cause the processing device to (Abstract, Col. 1 Line 44 – Col. 2 Line 31, Claims 1-2; Non-transitory computer readable medium and processing device is mapped to server);
all the other limitations have been examined with respect to claim 1. Please see the rejection above.
Regarding claims 11-15, all the limitations have been examined with respect to claims 2-6. Please see the rejection above.
Regarding claim 19, Noce discloses a system of Al models generalization for driving, the system comprising at least one processing device configured to (Abstract, Col. 1 Line 44 – Col. 2 Line 31, Claims 1-2; “a central server is equipped with the memory and the processor, and the central server and the mobile edge devices are configured to operate in a hybrid Federated Learning”);
all the other limitations have been examined with respect to claim 1. Please see the rejection above.
Allowable Subject Matter
Claims 7-9 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claims 16-18 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, as well as 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 7 recites:
“The method according to claim 1, further comprising incorporating the general artificial intelligence model within a liquid arrangement of artificial intelligence models”.
The prior art does not teach, disclose, or otherwise render obvious the above-noted features of the claims.
Noce discloses a general artificial intelligence model.
However, Noce does not specifically state a liquid arrangement of artificial intelligence models.
Li (US 20250342398 A1) teaches the ML models for automated driving can be liquid state machines (Paragraphs 0035-0036; "liquid state machines").
However, Li does not specifically state incorporating the general artificial intelligence model within a liquid arrangement of artificial intelligence models.
These differences between the subject matter of claim 7 and the prior art are not taught or otherwise rendered obvious by any available evidence in the remaining prior art. Accordingly, claim 7 recites allowable subject matter.
Claim 16 recites allowable subject matter because this claim recites similar allowable subject found in claim 7.
Claims 8-9 and 17-18 recite allowable subject matter based upon their dependency from one of claims 7 and 16.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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.
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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