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
The information disclosure statement (IDS) submitted on 09/25/2024 has been considered and is in compliance with the provisions of 37 CFR 1.97.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 9-14, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fidler et al. (US 2025/0131700; hereinafter Fidler).
Regarding Claim 1:
Fidler discloses a system, comprising: a memory configured to store computer-executable instructions (Fidler, Para. [0030], Fidler discloses a memory configured the instructions); and
a processor configured to execute the computer-executable instructions (Fidler, Para. [0030], Fidler discloses a processor to execute the instructions) to:
obtain observation data associated with each observation of a plurality of observations, wherein the plurality of observations is associated with an object (Fidler, Para. [0099], Fidler discloses obtaining map data of the surrounding environment, including expected objects such street signs, traffic lights, and the like);
obtain ground truth data associated with the object, wherein the ground truth data indicates one of a presence of the object in map data of a map database, or an absence of the object in map data of the map database (Fidler, Para. 0105-0109], Fidler discloses a plurality of sensors for recognizing the surrounding environment, including objects and obstacles near the host vehicle);
classify each observation of the plurality of observations as one observation feature of a plurality of observation features, based on the observation data of a corresponding observation of the plurality of observations and the ground truth data (Fidler, Para. [0082], Fidler discloses classifying the object recognition result based on the observed data);
determine a generalized probability distribution for the plurality of observations, based on the classification of each observation of the plurality of observations, wherein the generalized probability distribution includes a set of probability values for a set of observation features of the plurality of observation features (Fidler, Para. [0082], Fidler discloses determining a conditional probability distribution based on the object recognition result with the probability including a set of weights for each detection result);
determine a confidence score for the plurality of observations based on the generalized probability distribution (Fidler, Para. [0138], Fidler discloses determining a confidence score for each object detection);
determine updated status data of the object, based on the ground truth data and the confidence score (Fidler, Para. [0138], Fidler discloses determining a true positive detection and false positive detection based on the confidence score); and
update the map database based on the updated status data of the object (Fidler, Para. [0195], Fidler discloses updating the map information based on at least the confidence score results).
Regarding Claim 2:
Fidler discloses the system of claim 1.
Fidler further discloses wherein to classify each observation of the plurality of observations, the processor is configured to: compare the observation data associated with each observation of the plurality of observations with the ground truth data to obtain a corresponding result (Fidler, Para. [0138], Fidler discloses correlating the vehicle sensor data with the received data to determine the confidence score and subsequent true or false positive detection results); and
generate, based on the corresponding result, a classification matrix for each corresponding observation of the plurality of observations, wherein each element of the classification matrix corresponds to an observation feature of the plurality of observation features (Fidler, Para. [0034-0042], Fidler discloses generating a Hadamard product of two matrices which each element of the matrices being representing a set of points of the observed features).
Regarding Claim 3:
Fidler discloses the system of claim 2.
Fidler further discloses wherein to determine the generalized probability distribution, the processor is configured to: determine an individual probability distribution for each observation of the plurality of observations, based on the classification matrix of a corresponding observation of the plurality of observations (Fidler, Para. [0039-0041], [0139], Fidler discloses the process for determining the individual probabilities based on the generated matrices); and
generate the generalized probability distribution, based on the individual probability distribution of each observation of the plurality of observations (Fidler, Para. [0082], Fidler discloses determining a conditional probability distribution based on the object recognition result with the probability including a set of weights for each detection result).
Regarding Claim 9:
Fidler discloses the system of claim 1.
Fidler further discloses wherein the confidence score corresponds to a maximum probability value among the set of probability values (Fidler, Para. [0139], Fidler discloses the confidence score having a minimum threshold before the map is updated with the new information).
Regarding Claim 10:
Fidler discloses the system of claim 2.
Fidler further discloses wherein the observation data associated with a first observation of the plurality of observations includes at least one of: object type information of the object, location information of the object, identity information of the object, time-stamp data of the first observation, or metadata associated with the first observation (Fidler, Para. [0139], Fidler discloses observation data included are at least 3D location information of the object).
Regarding Claim 11:
Fidler discloses the system of claim 10.
Fidler further discloses wherein the metadata associated with the first observation includes at least one of environmental data associated with the first observation or occlusion data associated with the first observation (Fidler, Para. [0107], Fidler discloses metadata may be used for object detection and avoidance), and
wherein the processor is configured to classify the first observation as a first observation feature of the plurality of observation features, based on the metadata associated with the first observation (Fidler, Para. [0082], Fidler discloses classifying the object recognition result based on the observed data).
Regarding Claim 12:
The claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise.
Regarding Claim 13:
The claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise.
Regarding Claim 14:
The claim recites analogous limitations to claim 3 above, and is therefore rejected on the same premise.
Regarding Claim 20:
The claim recites analogous limitations to claim 1 above, and is therefore rejected on the same premise.
Allowable Subject Matter
Claim 4 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The claim recites limitations which the examiner has found to overcome the cited art above, and is also not disclosed by the pertinent art shown below. Such limitations which overcome the cited and pertinent art include “the classification matrix of a first observation of the plurality of observations includes a first column corresponding to a set of positive observation features of the plurality of observation features and a second column corresponding to a set of negative observation features of the plurality of observation features”…” normalize the classification matrix of the first observation to obtain a normalized classification matrix” and “generate the individual probability distribution of the first observation based on one of the first column of the normalized classification matrix or the second column of the normalized classification matrix”. While the cited art of Fidler discloses the use of matrices for determining true or false positive detection results, Fidler does not use the matrices in the same way as disclosed in the current application.
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim 5 is dependent on claim 4 which has been discussed above.
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The claim recites limitations which the examiner has found to overcome the cited art above, and is also not disclosed by the pertinent art shown below. Such limitations which overcome the cited and pertinent art include “determine a Hadamard product of the individual probability distribution of each observation of the pair of observations”, “determine a dot product of the individual probability distribution of each observation of the pair of observations”, and “generate the generalized probability distribution based on the Hadamard product and the dot product”. While the cited art of Fidler discloses the use of a Hadamard product between two matrices, this does not appear to be the same use of a Hadamard product as shown in the current application.
Claim 7 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The claim recites limitations which the examiner has found to overcome the cited art above, and is also not disclosed by the pertinent art shown below. Such limitations which overcome the cited and pertinent art include “determine a Hadamard product of the individual probability distribution of each observation of the plurality of observations” and “generate the generalized probability distribution based on an all-ones column matrix and the determined Hadamard product”. While the cited art of Fidler discloses the use of a Hadamard product between two matrices, this does not appear to be the same use of a Hadamard product as shown in the current application.
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The claim recites limitations which the examiner has found to overcome the cited art above, and is also not disclosed by the pertinent art shown below. Such limitations which overcome the cited and pertinent art include “a first observation associated with a first-time instance and a second observation associated with a second-time instance subsequent to the first-time instance”, “generate a first classification matrix for the first observation, based on the corresponding result of the first observation”, and “generate a second classification matrix for the second observation based on the corresponding result of the first observation and the corresponding result of the second observation”. Fidler does not appear to generate a second classification matrix for observation data which a second time-instance compared to the first observation data.
Furthermore claims 15-19 are found to include allowable subject matter analogous to claim 4-8, respectfully, as shown above, and would therefore be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alkhateeb et al. (US 2026/0074771) – discloses a system and method for object classification using, at least, machine learning models. While Alkhateeb does disclose the use of matrices, probability distributions, and Hadamard products for updating the confidence levels of a map, the effective filing date falls after the effective date of the current application.
Franci Rodon et al. (USP 12,553,739) – discloses techniques for receiving sensor data from a first and second vehicle, and updating the map data based on the confirmed received environmental data. However no use of the Hadamard product is found to be disclosed.
Jeong et al. (US 2025/0297866) – discloses techniques and method for receiving map data and updating the map data based on newly collected environmental data. While Jeong discloses similar limitations for updating the map based on the confidence levels of the collected data, Jeong uses techniques involving polygonal shapes of the 3D environment and not matrices.
Alam et al. (US 2019/0139403) – discloses systems and methods for determining differences between a crowdsourced map of an environment and a real-time volumetric map of the environment. While Alam performs similar operations such as determining confidence values for new objects within the environment and updating the map accordingly, Alam does not perform these operations using matrices or a Hadamard product as disclosed in the claims above.
Lin et al. (Hadamard Matrix Guided Online Hashing) – discloses using Hadamard Matrix for improving classification methods. However there is no discussion of updating a map based on object classification or ground truth data.
Liu et al. (Uncertainty and Confidence in Land Cover Classification using a Hybrid Classifier Approach) – discloses techniques for improving confidence when mapping land cover regions. While this paper does disclose reducing uncertainty for mapped areas, there is no disclosure involving matrices and confidence determination for objects.
Steele et al. (Estimation and Mapping of Misclassification Probabilities for Thematic Land Cover Maps) – discloses discussion of the misclassification of land cover maps, and various techniques for reducing errors and improving classification. However, there is no disclosure involving matrices and confidence determination for objects.
Chen et al. (An automated approach for updating land cover maps based on integrated change detection and classification methods) – discloses updating of land cover maps from the remotely sensed data and discusses a variety of techniques for improving the uncertainty of these land cover maps. However, there is no disclosure involving matrices and confidence determination for objects.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZACHARY JOSEPH WALLACE whose telephone number is (469)295-9087. The examiner can normally be reached 7:00 am - 5:00 pm, Monday - Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached at (571) 270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Z.J.W./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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