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 Status
This action is in response to claims filed on 11/17/2023. Claims 1-20 are considered in this office action. Claims 1-20 are pending examination.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it does not appear to pertain to the subject matter of the invention recited in the claims. Additionally, the phrases “image data may be obtained” in line 3, “techniques may be employed” in line 7, and “at least a portion of the machine learning model may be updated” in lines 8-9 can be implied. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 6 is 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 6 lines 1-3, the phrase “a path traveled by an autonomous vehicle is different from a projected path traveled by the autonomous vehicle” does not make sense, as it is unclear how a vehicle can travel both “a path” and “a projected path” that are different from each other at the same time. Therefore, the claim is indefinite. For the purposes of examination, Examiner is interpreting the claim language to mean “a path traveled by an autonomous vehicle is different from a projected path to be traveled by the autonomous vehicle.”
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US11573569B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the exact language and subject matter of the pending claims is recited in the associated claims of the issued patent.
Pending Application 18/513240
U.S. Patent No. US 11573569B2
Claim 1, Claim 7
Claim 1
A method comprising: autonomously driving a path generated by a first autonomous vehicle model; receiving a statistical accuracy associated with the first autonomous vehicle model based on comparison of the path generated by the first autonomous vehicle model and a second path followed by an autonomous vehicle; comparing the statistical accuracy to a threshold; based on the comparison, determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect; updating the first autonomous vehicle model to a second autonomous vehicle model based on the determination that the first autonomous vehicle model is statistically incorrect; autonomously driving a third path generated by the second autonomous vehicle model; and wherein determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect includes determining that the statistical accuracy associated with the first autonomous vehicle model is less than a threshold.
Claim 2
Claim 2
The method of claim 1, further comprising: receiving at least one update for the first autonomous vehicle model; applying the at least one update to the first autonomous vehicle model; and generating the second autonomous vehicle model based on the at least one update.
Claim 3
Claim 3
The method of claim 2, wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model.
Claim 4
Claim 4
The method of claim 1, wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations.
Claim 5
Claim 5
The method of claim 4, wherein a course correction includes determining that an input associated with a manual override was received.
Claim 6
Claim 6
The method of claim 4, wherein a course deviation includes determining that the path followed by a path to be traveled by the autonomous vehicle is different from a projected the generated path to be traveled by the autonomous vehicle.
Claim 8
Claim 7
The method of claim 1, further comprising: providing the second autonomous vehicle model to the autonomous vehicle.
Claim 9
Claim 8
The method of claim 7, further comprising: providing the second autonomous vehicle model to a second autonomous vehicle.
Claim 10
Claim 9
The method of claim 1, wherein the second autonomous vehicle model is generated at the autonomous vehicle.
Claim 11
Claim 10
A system comprising: a memory; a processor in communication with the memory and with
an autonomous vehicle autonomously driving a path generated by a first autonomous vehicle model, wherein the processor executes instructions stored in the memory, which cause the processor to execute a method, the method comprising: from the autonomous vehicle, receiving a statistical accuracy
associated with the first autonomous vehicle
model, based on comparison of the path generated by the first autonomous vehicle model and a second path followed by the autonomous vehicle; comparing the statistical accuracy to a threshold; based on the comparison, determining that the statistical
accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect; updating the first autonomous vehicle model to a second autonomous vehicle model based on the determination that the first autonomous vehicle model is statistically incorrect; sending the second autonomous vehicle model to the autonomous vehicle, wherein the autonomous vehicle autonomously drives a third path generated by the second autonomous vehicle model; and wherein determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect includes determining that the statistical accuracy associated with the first autonomous vehicle model is less than a threshold.
Claim 12
Claim 11
The system of claim 10, wherein the method includes: receiving at least one update for the first autonomous vehicle model; applying the at least one update to the first autonomous vehicle model; and generating the second autonomous vehicle model based on the at least one update.
Claim 13
Claim 12
The system of claim 11, wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model.
Claim 14
Claim 13
The system of claim 10, wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations.
Claim 15
Claim 14
The system of claim 13, wherein a course correction includes determining that an input associated with a manual override was received.
Claim 16
Claim 15
A non-transitory computer readable medium having stored thereon instructions, which when executed by a processor cause the processor to execute a method, the method comprising: from an autonomous vehicle autonomously driving a path generated by a first autonomous vehicle model, receiving a statistical accuracy associated with the first autonomous vehicle model, based on comparison of the path generated by the first autonomous vehicle model and a second path followed by the autonomous vehicle; comparing the statistical accuracy to a threshold; based on the comparison, determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect; updating the first autonomous vehicle model to a second autonomous vehicle model based on the determination that the first autonomous vehicle model is statistically incorrect; sending the second autonomous vehicle model to the autonomous vehicle, wherein the autonomous vehicle autonomously drives a third path generated by the second autonomous vehicle model; and wherein determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect includes determining that the statistical accuracy associated with the first autonomous vehicle model is less than a threshold.
Claim 17
Claim 16
The non-transitory computer readable medium of claim 15, wherein the method includes: receiving at least one update for the first autonomous vehicle model; applying the at least one update to the first autonomous vehicle model; and generating the second autonomous vehicle model based on the at least one update.
Claim 18
Claim 17
The non-transitory computer readable medium of claim 16, wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model.
Claim 19
Claim 18
The non-transitory computer readable medium of claim 15, wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations.
Claim 20
Claim 19
The non-transitory computer readable medium of claim 18, wherein a course correction includes determining that an input associated with a manual override was received.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 7-13, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (US 2018/0349782 A1) in view of Boesen (US 2018/0060555 A1).
Regarding claim 1, Zheng teaches “A method comprising:
receiving a [condition] associated with a first autonomous vehicle model (Fig. 1 shows autonomous vehicles 110-1,…,110-N receiving vehicle models; Par. [0009] teaches sensor data acquired continuously by sensors deployed on the vehicle is first received, where the sensor data provides information about surrounding of the vehicle, and one or more items surrounding the autonomous driving vehicle are tracked, based on some models, from the sensor data acquired by one or more of a first type of the plurality of types of sensors. Some of the items are labeled, automatically on-the-fly, via at least one of cross modality validation and cross temporal validation of the one or more items);
determining that the [condition] associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is incorrect (Par. [0074] teaches events of interest are processed and selected, then used to update local and/or global models, where the model adaptation may be scheduled based on, e.g., some fixed time intervals, some criterion such as when the events of interest selected have accumulated to a pre-determined volume, or when some events of interest selected suggest an error that needs to be corrected (i.e., model is incorrect)); and
updating the first autonomous vehicle model to a second autonomous vehicle model based on the determination that the first autonomous vehicle model is incorrect (Par. [0009] teaches at least one of the labeled items is sent to a model update center, and the model update information is derived based on the at least one of the labeled items, and the at least one model is updated in accordance with the model update information (implying that a first model is updated to a different second model); Par. [0008] teaches based on the received labeled data items, at least some of the models are updated and model update information is generated).”
However, Zheng does not explicitly teach receiving a “statistical accuracy” associated with the first autonomous vehicle model and determining the “statistical accuracy” indicates the first autonomous vehicle model is “statistically” incorrect.
From the same field of endeavor, Boesen teaches receiving a “statistical accuracy” associated with the first autonomous vehicle model and determining the “statistical accuracy” indicates the first autonomous vehicle model is “statistically” incorrect (Par. [0060] teaches biometric readings or other user input is analyzed for accuracy and statistical significance where, for example, the biometric readings are compared against default, baseline, or standard biometric data, values, or readings associated with the biometric profiles for the user to ensure accuracy. User input received for verification purposes can be compared against pre-established or trained data, and the devices can also perform biasing or error correction as needed to endure the sensor measurements are accurate).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teaching of Zheng to incorporate the teachings of Boesen with a reasonable expectation of success to have the method taught by Zheng include receiving statistical accuracy associated with the model and determining the statistical accuracy indicates the model is statistically incorrect as taught by Boesen.
The motivation for doing so would be to ensure sensor measurements are accurate (Boesen, Par. [0060]).
Regarding claim 11 and claim 16, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng and Boesen based on the exemplary analysis of the method claim 1 above as the limitations of system claim 11 and non-transitory computer readable medium claim 16 are commensurate in scope to the limitations of rejected method claim 1.
Regarding claim 2, the combination of Zheng and Boesen teaches all the limitations of claim 1 above, and further teaches “receiving at least one update for the first autonomous vehicle model (Zheng; Par. [0009] teaches model update information is received from the model update center); applying the at least one update to the first autonomous vehicle driving model (Zheng; Par. [0009] teaches the at least one model is updated in accordance with the model update information); and generating the second autonomous vehicle model based on the at least one update (Zheng; Par. [0009] teaches the at least one model is updated in accordance with the model update information (implying that a first model is updated to a different second model)).”
Regarding claim 12 and claim 17, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng and Boesen based on the exemplary analysis of the method claim 2 above as the limitations of system claim 12 and non-transitory computer readable medium claim 17 are commensurate in scope to the limitations of rejected method claim 2.
Regarding claim 3, the combination of Zheng and Boesen teaches all the limitations of claim 2 above, and further teaches “wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model (Zheng, Par. [0009] teaches at least one of the labeled items is sent to a model update center, and the model update information is derived based on the at least one of the labeled items; Par. [0008] teaches based on the received labeled data items, at least some of the models are updated and model update information is generated).”
Regarding claim 13 and claim 18, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng and Boesen based on the exemplary analysis of the method claim 3 above as the limitations of system claim 13 and non-transitory computer readable medium claim 18 are commensurate in scope to the limitations of rejected method claim 3.
Regarding claim 7, the combination of Zheng and Boesen teaches all the limitations of claim 1 above, and further teaches “wherein determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect includes determining that the statistical accuracy associated with the first autonomous vehicle model is less than a threshold (Boesen, Par. [0060] teaches biometric readings or other user input is analyzed for accuracy and statistical significance where, for example, the biometric readings are compared against default, baseline, or standard biometric data, values, or readings associated with the biometric profiles for the user to ensure accuracy. User input received for verification purposes can be compared against pre-established or trained data, and the devices can also perform biasing or error correction as needed to endure the sensor measurements are accurate; This ensures the design incentive of determining and maintaining accuracy of the device, thus one or ordinary skill in the art, in view of the design incentive, could have implemented the claimed variation of determining the statistical accuracy of the first vehicle model is statistically incorrect when it is less than a threshold and the variation would have been predictable to one or ordinary skill in the art).”
Regarding claim 8, the combination of Zheng and Boesen teaches all the limitations of claim 1 above, and further teaches “providing the second autonomous vehicle model to an autonomous vehicle (Zheng, Par. [0008] teaches generated model update information is distributed to (i.e. provided to) the autonomous driving vehicles).”
Regarding claim 9, the combination of Zheng and Boesen teaches all the limitations of claim 8 above, and further teaches “providing the second autonomous vehicle model to a second autonomous vehicle (Zheng, Par. [0008] teaches generated model update information is distributed to (i.e. provided to) the autonomous driving vehicles (i.e., a first and a second autonomous vehicle).”
Regarding claim 10, the combination of Zheng and Boesen teaches all the limitations of claim 1 above, and further teaches “wherein the second autonomous vehicle driving model is generated at an autonomous vehicle (Zheng, Par. [0009] teaches when model update information is received from the model update center, the at least one model is updated).”
Claims 4-6, 14-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (US 2018/0349782 A1) in view of Boesen (US 2018/0060555 A1) and further in view of Della Penna (US 2019/0220011 A1).
Regarding claim 4, the combination of Zheng and Boesen teaches all the limitations of claim 1 above, however the combination of Zheng and Boesen does not explicitly teach “wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations.”
From the same field of endeavor, Della Penna teaches “wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations (Par. [0029] teaches event recorder 156 detects that applied vehicular drive parameter (i.e., human input) is being applied to alter course into lane 113 and identifies the conflicting courses of action as an event, and event-adaptive computing platform 109 evaluates a subset of signals representing event data, including comparing the subset of signals against one or more patterns of similar event data to identify modifications to autonomy controller 150).”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to modify the teachings of the combination of Zheng and Boesen to incorporate the teachings of Della Penna with a reasonable expectation of success to base the statistical accuracy associated with the first autonomous vehicle model taught by the combination of Zheng and Boesen using a quantity of course corrections or deviations as taught by Della Penna.
The motivation for doing so would be to determine an optimal subset of actions or rules autonomy controller may implement in similar subsequent situations (Della Penna, Par. [0029]).
Regarding claim 14 and claim 19, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng, Boesen, and Della Penna based on the exemplary analysis of the method claim 4 above as the limitations of system claim 14 and non-transitory computer readable medium claim 19 are commensurate in scope to the limitations of rejected method claim 4.
Regarding claim 5, the combination of Zheng, Boesen, and Della Penna teaches all the limitations of claim 4 above, and further teaches “wherein a course correction includes determining that an input associated with a manual override was received (Della Penna, Par. [0029] teaches event recorder 156 detects that applied vehicular drive parameter (i.e., human input) is being applied to alter course into lane 113 and identifies the conflicting courses of action as an event).”
Regarding claim 15 and claim 20, the limitations of this system claim and this non-transitory computer readable medium claim, respectively, are rejected using the combination of cited references Zheng, Boesen, and Della Penna based on the exemplary analysis of the method claim 5 above as the limitations of system claim 15 and non-transitory computer readable medium claim 20 are commensurate in scope to the limitations of rejected method claim 5.
Regarding claim 6, the combination of Zheng, Boesen, and Della Penna teaches all the limitations of claim 4 above, and further teaches “wherein a course deviation includes determining that a path traveled by an autonomous vehicle is different from a projected path traveled by the autonomous vehicle (Della Penna, Par. [0025] teaches an event of interest is an instance during which human input overrides autonomy controller 150 or autonomous operation to deviate from one or more trajectories computed by autonomy controller 150).”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE M FITZHARRIS whose telephone number is (469)295-9147. The examiner can normally be reached 7:30 am - 6:00 pm M-Th.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CHRISTIAN CHACE can be reached at (571)272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.M.F./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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