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 .
DETAILED ACTION
The Action is responsive to the Amendments and Remarks filed on 5/11/2026 and included in the Request for Continued Examination filed on 6/17/2026. Claims 1-18 and 21-22 are pending claims. Claims 1, 8, and 15 are written in independent form. Claims 19-20 have been cancelled.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 and 21-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Independent Claims 1, 8, and 15 contain the subject matter “when a first signature key in the first data structure matches a first signature key in the second data structure, comparing the plurality of tokens associated with the first signature key of the first data structure of the plurality of tokens associated with the first signature key of the second data structure;” which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.It is not clearly stated in the written description any step of comparing tokens of signature keys after, when, or caused by the matching of the signature keys first.It is noted that while Applicant states in the Remarks dated 5/11/2026 that the “non-limiting support may be found in at least paragraphs [0020]-[0028] and [0036] of Applicant’s originally-filed specification”, the cited paragraphs merely recite that “the various data structures may be compared to identify differences between two corresponding vector datasets. For example, with the signature key collection of the first data structure, the processor of the system may iterate through the individual signature keys of the first data structure for identical signature keys stored within the second data structure. The individual tokens of two signature keys are compared. For example, for a match, the same values for VC.sub.1, VC.sub.2, N, A.sub.T, L.sub.T, and Z.sub.T may be found. In some embodiments, the system is flexible whereby a match is found when some threshold number or percentage of tokens match between two compared signature keys. In one example, all of the tokens except for the number of vertices N may be equal. The system may indicate these two signature keys to be matches. The system may give the user the ability to set rules for what constitutes a match.” (Specification Para. [0036]). Applicant’s specification is using the tokens to compare the signature keys and then determine a match (or no match) between the signature keys, not determining that two signature keys match first, causing a comparison of the tokens associated with the two signature keys.For purposes of compact prosecution, the claim limitation is being interpreted as “
Dependent Claims 2-7, 9-14, 16-18, and 21-22 inherit the deficiencies of their parent claims and are therefore being rejected based upon the same reason(s) stated for their parent claims.
Claim Rejections - 35 USC § 103
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) 1-4 and 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Vianello (Pre-Grant Publication No. 2023/0230250) and further in view of Moustafa et al. (U.S. Pre-Grant Publication No. 2022/0126864, hereinafter referred to as Moustafa) and Burnett (U.S. Patent No. 12,242,892).
Regarding Claim 1:
Vianello teaches a method of determining changes between a first vector dataset and a second vector dataset, the method comprising:
Receiving, by a processor, the first vector dataset and the second vector dataset, wherein each of the first vector dataset and the second vector dataset comprises a plurality of features contained within a respective high-definition LiDAR map, each feature being defined by at least one vertex;
Vianello teaches comparing first and second object representations by “comparing feature vectors (e.g., extracted using the appearance change-agnostic model) and/or attribute vectors” (Para. [0109]) thereby teaching receiving a first and second vector dataset for the object representations for comparison, wherein the vector dataset comprises feature and/or attribute vectors. Vianello further teaches “the object information 20 can be from the same information provider…but can additionally and/or alternatively be from different information providers” with a modality such as LIDAR (Para. [0062]) and “each measurement can additionally and/or alternatively be depth information…, polygons, point clouds (e.g., from LIDAR…)” (Para. [0065]) thereby teaching that the feature can be defined by a vertex.
The present specification states that “High-definition maps are three-dimensional maps that include features derived from vector datasets resulting from light detection and ranging (LiDAR) scans of an environment. The LiDAR scan results in a point cloud of LiDAR points within the environment” (Para. [0001]). Therefore, Vianello teaches the features contained within a respective high-definition LiDAR map by teaching “each measurement…can additionally and/or alternatively be point clouds (e.g., from LIDAR” (Para. [0065]) because point clouds are 3-d mappings which Vianello teaches are derived “from LIDAR”.
For each feature of the plurality of features in the first vector dataset and the second vector dataset, generating a signature key corresponding to one or more geometric attributes of the feature;
Vianello teaches “the object identifier can be: a hash of the object’s characteristics (e.g., geometric characteristics, visual characteristics, etc.), generated from the object representations associated with the object version” (Para. [0102]).
Storing the signature key for each feature of the plurality of features of the first vector dataset in a first data structure;
Vianello teaches “storage (e.g., configured to store the object representations, object versions, data associated with the object versions, etc.)” (Para. [0041]) thereby teaching storing the hash of the features of the data vector and data associated with the object versions for each of the object representations or object versions.
Storing the signature key for each feature of the plurality of features of the second vector dataset in a second data structure; and
Vianello teaches “storage (e.g., configured to store the object representations, object versions, data associated with the object versions, etc.)” (Para. [0041]) thereby teaching storing the hash of the features of the data vector and data associated with the object versions for each of the object representations or object versions.
Comparing the first data structure with the second data structure;
Vianello teaches “relationships between representations (and/or respective object versions) can be determined based on rules and heuristics; example shown in Fig. 9” (Para. [0129]) where Figure 9 shows an identification/determination of segments that are common or different between each of the object versions being compared as well as if the compared segments have been unchanged/same, modified, replaced, added/created, or removed/terminated.
Vianello explicitly teaches all of the elements of the claimed invention as recited above except:
a plurality of features contained within a respective high-definition LiDAR map of an environment of a vehicle;
generating a signature key from a plurality of tokens, each of the plurality of tokens corresponding to one or more geometric attributes of the feature;
Storing the plurality of tokens and the signature key for each feature of the plurality of features of the first vector dataset in a first data structure;
Storing the plurality of tokens and the signature key for each feature of the plurality of features of the second vector dataset in a second data structure; and
Comparing the signature keys in the first data structure with the signature keys in the second data structure;
Comparing the plurality of tokens associated with the first signature key of the first data structure with the plurality of tokens associated with the first signature key of the second data structure; and
determining that the feature corresponding to the first signature key in the first data structure matches a feature corresponding to the first signature key in the second data structure when a threshold number of tokens between the first signature key in the first data structure and the first signature key in the second data structure match.
However, in the related field of endeavor of object point cloud comparison and changing environments, Moustafa teaches:
a plurality of features contained within a respective high-definition LiDAR map of an environment of a vehicle; and
Moustafa teaches determining a difference between “the data collected by the various sensors of an autonomous vehicle may be compared with data present in a relevant tile of the HD map downloaded to the autonomous vehicle. If there is a difference between the collected data and the HD map data, the delta (difference of the HD map tile and the newly collected data) may be transferred to the server hosting the HD map so that the HD map tile at that particular location may be updated” (Para. [0296]).
Comparing the signature keys in the first data structure with the signature keys in the second data structure.
Moustafa teaches “the data collected by the various sensors of an autonomous vehicle may be compared with data present in a relevant tile of the HD map downloaded to the autonomous vehicle. If there is a difference between the collected data and the HD map data, the delta (difference of the HD map tile and the newly collected data) may be transferred to the server hosting the HD map so that the HD map tile at that particular location may be updated” (Para. [0296]).
Moustafa further teaches “the digital signature may be generated by hashing the sensor data and encrypting the hash using the private key.” (Para. [0770]) where “the network protocol…may use the public key to verify the digital signature (e.g., by unencrypting the hash using the public key and verifying the hashes match)” (Para. [0771]). Therefore, Moustafa teaches using signature keys representing the sensor data for comparing representations of the sensor data.
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Moustafa and Vianello at the time that the claimed invention was effectively filed, to have combined the detection of changes in driving environment features over time through sensor data comparison, as taught by Moustafa, with the systems and methods for detecting and comparing object attributes and measurements over time, as taught by Vianello.
One would have been motivated to make such combination because Vianello teaches “object representations for objects within and/or encompassing the geographic region 10 are extracted and analyzed from object information depicting or associated with the geographic region to determine whether the object has changed over time.” (Para. [0040]) where “The objects are preferably physical objects, but can be any other object. The objects can be: structures (e.g., built structures, such as buildings, etc.), a portion of a structure (e.g., a building component, a roof, a wall, etc.), vegetation, manmade artifacts (e.g., pavement, driveways, roads, lakes, pools, etc.), and/or be any other suitable physical object” (Para. [0035]) and Moustafa teaches “if there is any change in the environment (for example, there is a road work, accident, etc.) the HD map should be updated to reflect the change. In some implementations, data from a number of autonomous vehicles may be crowdsourced and used to update the HD map.” (Para. [0295]) and it would be obvious to a person having ordinary skill in the art that using crowdsourced vehicle data to determine whether objects have changed over time would increase the ability to track changes over time by incorporating a higher number of sensors collecting data on the environment including the object(s).
Moustafa and Vianello explicitly teach all of the elements of the claimed invention as recited above except:
generating a signature key from a plurality of tokens, each of the plurality of tokens corresponding to one or more geometric attributes of the feature;
Storing the plurality of tokens and the signature key for each feature of the plurality of features of the first vector dataset in a first data structure;
Storing the plurality of tokens and the signature key for each feature of the plurality of features of the second vector dataset in a second data structure; and
Comparing the plurality of tokens associated with the first signature key of the first data structure with the plurality of tokens associated with the first signature key of the second data structure; and
determining that the feature corresponding to the first signature key in the first data structure matches a feature corresponding to the first signature key in the second data structure when a threshold number of tokens between the first signature key in the first data structure and the first signature key in the second data structure match.
However, in the related field of endeavor of data comparison, Burnett teaches:
generating a signature key from a plurality of tokens, each of the plurality of tokens corresponding to one or more geometric attributes of the feature;
Vianello teaches “the object identifier can be: a hash of the object’s characteristics (e.g., geometric characteristics, visual characteristics, etc.), generated from the object representations associated with the object version” (Para. [0102]).
Burnett teaches converting data into “a vector comprising one or more tokens” where “the individual tokens can be extracted and inserted as elements of a comparable data structure (e.g., a vector” (Col. 170 Lines 46-53).
Therefore, Burnett in combination with Vianello teaches generating the signature key (Vianello) from one or more tokens representing elements (Burnett) such as the geometric characteristics (Vianello).
Storing the plurality of tokens and the signature key for each feature of the plurality of features of the first vector dataset in a first data structure;
Vianello teaches “storage (e.g., configured to store the object representations, object versions, data associated with the object versions, etc.)” (Para. [0041]) thereby teaching storing the hash of the features of the data vector and data associated with the object versions for each of the object representations or object versions.
Burnett teaches converting data into “a vector comprising one or more tokens” where “the individual tokens can be extracted and inserted as elements of a comparable data structure (e.g., a vector” (Col. 170 Lines 46-53).
Therefore, Burnett in combination with Vianello teaches storing the tokens (Burnett), as data associated with the object versions (Vianello), with the signature keys (Vianello).
Storing the plurality of tokens and the signature key for each feature of the plurality of features of the second vector dataset in a second data structure; and
Vianello teaches “storage (e.g., configured to store the object representations, object versions, data associated with the object versions, etc.)” (Para. [0041]) thereby teaching storing the hash of the features of the data vector and data associated with the object versions for each of the object representations or object versions.
Burnett teaches converting data into “a vector comprising one or more tokens” where “the individual tokens can be extracted and inserted as elements of a comparable data structure (e.g., a vector” (Col. 170 Lines 46-53).
Therefore, Burnett in combination with Vianello teaches storing the tokens (Burnett), as data associated with the object versions (Vianello), with the signature keys (Vianello).
Comparing the plurality of tokens associated with the first signature key of the first data structure with the plurality of tokens associated with the first signature key of the second data structure; and
Burnett teaches converting data into “a vector comprising one or more tokens” where “the individual tokens can be extracted and inserted as elements of a comparable data structure (e.g., a vector” (Col. 170 Lines 46-53).Burnett further teaches “a number of tokens in a vector that match tokens of a first pattern are counted. For example, the pattern matcher(s) 3404 can walk through a string vector, token by token, and compare each token to the corresponding token in the first pattern” where “the number of matching tokens is compared to a threshold” and “a determination is made that the vector corresponds to the first pattern in response the number of matching tokens satisfying the threshold.” (Col. 171 Line 61 – Col. 172 Line 8).
determining that the feature corresponding to the first signature key in the first data structure matches a feature corresponding to the first signature key in the second data structure when a threshold number of tokens between the first signature key in the first data structure and the first signature key in the second data structure match.
Burnett teaches converting data into “a vector comprising one or more tokens” where “the individual tokens can be extracted and inserted as elements of a comparable data structure (e.g., a vector” (Col. 170 Lines 46-53).Burnett further teaches “a number of tokens in a vector that match tokens of a first pattern are counted. For example, the pattern matcher(s) 3404 can walk through a string vector, token by token, and compare each token to the corresponding token in the first pattern” where “the number of matching tokens is compared to a threshold” and “a determination is made that the vector corresponds to the first pattern in response the number of matching tokens satisfying the threshold.” (Col. 171 Line 61 – Col. 172 Line 8).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Burnett, Moustafa, and Vianello at the time that the claimed invention was effectively filed, to have combined the vector generation using tokens obtained from raw data, as taught by Burnett, with the detection of changes in driving environment features over time through sensor data comparison, as taught by Moustafa, and the systems and methods for detecting and comparing object attributes and measurements over time, as taught by Vianello.
One would have been motivated to make such combination because Moustafa teaches “the digital signature may be generated by hashing the sensor data” (Para. [0770]) and Burnett further clarifies the details of hashing sensor data that Moustafa is silent on by teaching “machine data can be raw machine data that is generated by various components in IT environments, such as servers, sensors, routers, mobile devices, Internet of Things (IoT) devices, etc. Machine data can include system logs, network packet data, sensor data, application program data, error logs, stack traces, system performance data, etc.” (Col. 8 Lines 25-44) where “the tokenizer 6902 can take the text 6901 comprised within ingested raw machine data and extract one or more tokens 6903 or fields from the text 6901” and “the vector generator 6904 can generate a vector 6905 using the token(s) 6903. For example, the vector generator 6904 can use an algorithm, such as hashing TF or CountVectorizer, to generate the vector 6905 using the token(s) 6903.” (Col. 198 Lines 1-12).
Regarding Claim 2:
Burnett, Moustafa, and Vianello further teach:
Displaying, in a graphical user interface, at least one of the features that are present in the first vector dataset and not present in the second vector dataset and the features that are present in the second vector dataset and not present in the vector dataset.
Vianello teaches “S400 can additionally include providing the analysis to an endpoint…through an interface, report, or other medium” (Para. [0153]) where “examples of analyses that can be determined include: the relationship between a first and second object version (e.g., the next object version, a prior object version, the current object version etc.); a timeseries of changes between a first and second object version (e.g., change type, change time, building development history, parcel development history, etc.); whether the requested object version is the most recent object version for the geographic region; the most recent object version for a requested geographic region; the timeframe or duration for a requested object version; data associated with a requested object version (e.g., measurements, object representations, feature vectors, attribute values, etc.); an analysis of the timeseries that the requested object version is a part of (e.g., number of changes, types of changes, average object version duration, etc.); an analysis of the object versions associated with a requested timeframe; an analysis of the auxiliary data associated with the object version (e.g., statistical analysis, lookup, prediction, etc.); anomaly detection (e.g., a temporary structure); and/or other analyses (e.g., examples shown in FIG. 4)” (Para. [0145])
Regarding Claim 3:
Burnett, Moustafa, and Vianello further teach:
Scanning an environment using a LiDAR scanning system, and
Vianello teaches comparing first and second object representations by “comparing feature vectors (e.g., extracted using the appearance change-agnostic model) and/or attribute vectors” (Para. [0109]) thereby teaching receiving a first and second vector dataset for the object representations for comparison, wherein the vector dataset comprises feature and/or attribute vectors. Vianello further teaches “the object information 20 can be from the same information provider…but can additionally and/or alternatively be from different information providers” with a modality such as LIDAR (Para. [0062]) and “each measurement can additionally and/or alternatively be depth information…, polygons, point clouds (e.g., from LIDAR…)” (Para. [0065]) thereby teaching that the feature can be defined by a vertex.
Moustafa also teaches “Combining multiple LIDAR data scans to increase their resolution. To the best of our knowledge, achieving super-resolution with LIDAR data is an entirely new field. [0838] Combining multiple camera images captured at a given limited dynamic range, to achieve higher dynamic range. [0839] Combining multiple camera images or multiple LIDAR scans to achieve noise reduction, e.g., suppressing noise present in each individual camera image or LIDAR scan. [0840] Combining camera and LIDAR images to achieve a higher detection rate of objects present in both modalities, but with independent “noise” sources.” (Paras. [0837]-[0840]). Therefore, Moustafa also teaches scanning an environment using a LiDAR scanning system that produces “LIDAR data scans”.
creating the respective high-definition LiDAR map based on results of the scan, wherein the first vector dataset and the second vector dataset are based on the respective high-definition LiDAR map.
Vianello teaches “each measurement…can additionally and/or alternatively be point clouds (e.g., from LIDAR” (Para. [0065]) thereby teaching creating the high-definition LiDAR map based on data collected “from LIDAR”.
Vianello further teaches “comparing feature vectors (e.g., extracted using the appearance change-agnostic model) and/or attribute vectors” (Para. [0109]) where “the object information 20 can be from the same information provider…but can additionally and/or alternatively be from different information providers” with a modality such as LIDAR (Para. [0062]). Therefore, Vianello teaches the first and second vector datasets being based on object information and measurements collected from LIDAR and stored in a high-definition map such as a point cloud.
Regarding Claim 4:
Burnett, Moustafa, and Vianello further teach:
Receiving a selection of a feature that is present in the first vector dataset and not present in the second vector dataset or present in the second vector dataset and not present in the first vector dataset; and
Vianello teaches “the object representation 30 is preferably determined by an object representation model, but can alternatively be determined by another suitable model, be determined by a user, be retrieved by a database, or be otherwise determined” (Para. [0091]) thereby teaching a user selecting a feature by a user to be determined by the user in a first or second dataset for an object representation.
Adjusting vector data in at least one of the first vector dataset and the second vector dataset.
Vianello teaches “the object representation 30 is preferably determined by an object representation model, but can alternatively be determined by another suitable model, be determined by a user, be retrieved by a database, or be otherwise determined” (Para. [0091]) where an analysis can provided “to an endpoint (e.g., an endpoint on a network, customer endpoint, user endpoint, automated valuation model system, etc.) through an interface, report, or other medium” (Para. [0153]) and “when the object has changed (e.g., the relationship between the latest and new representations is indicative of change), the new representation can be stored as the latest representation for the object (and/or a new object version can be created)” (Para. [0151]). Therefore, Vianello teaches a user that can modify or adjust the object representation with features determined by a user.
Regarding Claim 8:
Some of the limitations herein are similar to some or all of the limitations of Claim 1.
Burnett, Moustafa, and Vianello further teach:
A processor (Vianello – Para. [0159]); and
A memory storing instructions that, when executed by the processor, cause the apparatus to perform steps (Vianello – Para. [0159]).
Regarding Claim 9:
All of the limitations herein are similar to some or all of the limitations of Claim 2.
Regarding Claim 10:
All of the limitations herein are similar to some or all of the limitations of Claim 3.
Regarding Claim 11:
All of the limitations herein are similar to some or all of the limitations of Claim 4.
Claim(s) 5-6, 12-13, 15-18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Vianello, Moustafa, and Burnett, and further in view of Hougen (U.S. Pre-Grant Publication No. 2005/0273212).
Regarding Claim 5:
Burnett, Moustafa, and Vianello further teach:
Wherein the plurality of tokens associated with each signature key comprises at least one determined value.
Burnett teaches converting data into “a vector comprising one or more tokens” where “the individual tokens can be extracted and inserted as elements of a comparable data structure (e.g., a vector” (Col. 170 Lines 46-53) and “a number of tokens in a vector that match tokens of a first pattern are counted. For example, the pattern matcher(s) 3404 can walk through a string vector, token by token, and compare each token to the corresponding token in the first pattern” where “the number of matching tokens is compared to a threshold” and “a determination is made that the vector corresponds to the first pattern in response the number of matching tokens satisfying the threshold.” (Col. 171 Line 61 – Col. 172 Line 8).
Vianello teaches “the information determined for different object versions preferably has the same information modality (e.g., RGB, LIDAR, stereovision, radar, sonar, text, audio, etc.), but can additionally and/or alternatively have different information modalities” (para. [0062]) where “Each measurement…can additionally and/or alternatively be depth information (e.g., digital elevation model (DEM), digital surface model (DSM), digital terrain model (DTM), etc.), polygons, point clouds (e.g., from LIDAR, stereoscopic analyses, correlated images sampled from different poses, etc.), radar, sonar, virtual models, audio, video, and/or any other suitable measurement.” (Para.[0065]).Burnett in combination with Vianello teaches a signature key/vector comprising one or more tokens extracted and inserted as elements (Burnett) where the extracted elements are information/values determined from different information modalities such as “RGB, LIDAR, stereovision, radar, sonar, text, audio, etc.” (Vianello)
Burnett, Moustafa and Vianello explicitly teaches all of the elements of the claimed invention as recited above except:
each signature key comprises at least one azimuth value that is greater than or equal to zero and at least one length value that is greater than or equal to zero.
However, in the related field of endeavor of object classification for a vehicle, Hougen teaches:
each signature key comprises at least one azimuth value that is greater than or equal to zero and at least one length value that is greater than or equal to zero.
Vianello teaches “the information determined for different object versions preferably has the same information modality (e.g., RGB, LIDAR, stereovision, radar, sonar, text, audio, etc.), but can additionally and/or alternatively have different information modalities” (para. [0062]) where “Each measurement…can additionally and/or alternatively be depth information (e.g., digital elevation model (DEM), digital surface model (DSM), digital terrain model (DTM), etc.), polygons, point clouds (e.g., from LIDAR, stereoscopic analyses, correlated images sampled from different poses, etc.), radar, sonar, virtual models, audio, video, and/or any other suitable measurement.” (Para.[0065]).
Hougen teaches “Radar provides derived measurements such as range, range rate, azimuth angle, elevation, and approximate size of an object, as well as other information: known in the art” (Para. [0034]). Hougen further teaches “the object detection signals are utilized to compute derived measurements such as object relative range, azimuth angle, velocity, and bearing information, as well as other object information known in the art” (Para. [0035]). Since azimuth angles can only be positive, negative, or zero, and relative ranges can only be positive, negative, or zero, Hougen teaches at least one azimuth value as being is greater than or equal to zero when it is positive and at least one length value as being greater than or equal to zero when it is positive.
Therefore, Vianello teaches determining measurements/values from different sources from same or different information modalities (RGB, LIDAR, stereovision, radar, sonar, text, audio, etc.) and Hougen is teaching that these determined measurements/values can further include “derived measurements” provided from a radar “such as range, range rate, azimuth angle, elevation, and approximate size of an object, as well as other information: known in the art” (Para. [0034]).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Hougen, Burnett, Moustafa, and Vianello at the time that the claimed invention was effectively filed, to have combined the inclusion of azimuth angles of objects as taught by Hougen with the vector generation using tokens obtained from raw data, as taught by Burnett, the detection of changes in driving environment features over time through sensor data comparison, as taught by Moustafa, and the systems and methods for detecting and comparing object attributes and measurements over time, as taught by Vianello.
One would have been motivated to make such combination because while Vianello teaches using RGB imaging data, depth information, and LiDAR data to collect object information (Figure 2), Hougen provides additional attributes/measurements to be collected and used for comparison, such as “radar provides derived measurements such as range, range rate, azimuth angle, elevation, and approximate size of an object, as well as other information: known in the art.” (Para. [0034]), and it would be obvious to a person having ordinary skill in the art that further attributes/measurements for comparison would improve the ability to determine similarities and differences between objects at particular points in time.
Regarding Claim 6:
Hougen, Burnett, Moustafa, and Vianello further teach:
Wherein the plurality of tokens associated with each signature key comprises a first vertex, a second vertex, a number of vertices, a total azimuth value, a total length value, and a total elevation value.
Burnett teaches converting data into “a vector comprising one or more tokens” where “the individual tokens can be extracted and inserted as elements of a comparable data structure (e.g., a vector” (Col. 170 Lines 46-53) thereby teaching the vector/key comprising a plurality of tokens (more than one tokens) representing extracted and inserted elements.
Hougen teaches extracted elements including “a small rectangular region” that include different objects (Paras. [0060]-[0061]) where “once a sum-array has been generated and, for example, is in rectangular form, the sum of the values in the sum-array can be calculated from the values associated with the four corners of the rectangle” (Para. [0077]) thereby teaching a first and a second vertex, and a number of vertices as features.
Hougen further teaches other extracted elements as including “radar provides derived measurements such as range, range rate, azimuth angle, elevation, and approximate size of an object, as well as other information: known in the art” (Para. [0034]) thereby teaching a total azimuth value, a total length value, and a total elevation value.
Regarding Claim 12:
All of the limitations herein are similar to some or all of the limitations of Claim 5.
Regarding Claim 13:
All of the limitations herein are similar to some or all of the limitations of Claim 6.
Regarding Claim 15:
Some of the limitations herein are similar to some or all of the limitations of Claims 1 and 6.
Hougen, Burnett, Moustafa, and Vianello further teach:
A first vertex and a second vertex defining a bounding box,
Hougen teaches features including “a small rectangular region” that include different objects (Paras. [0060]-[0061]) where “once a sum-array has been generated and, for example, is in rectangular form, the sum of the values in the sum-array can be calculated from the values associated with the four corners of the rectangle” (Para. [0077]) thereby teaching a first and a second vertex as upper and lower corners of a bounding box/rectangle surrounding features of an object.
Wherein the total azimuth value comprises a summation of an azimuth between successive vertices of the feature;
Hougen teaches “the attributed data includes a set of sum-arrays based on a set of features” (Para. [0039]) where “A sum-array may be generated and used to represent the squared values, which eases and decreases the amount of time in the calculation of the denominator of such normalized feature values. Feature values may refer to the sizes, shapes, colors, and edge characteristics of an object, as well as other feature values known in the art.” (Para. [0041]). Hougen further “Radar provides derived measurements such as range, range rate, azimuth angle, elevation, and approximate size of an object, as well as other information: known in the art” (Para. [0034]) thereby teaching the azimuth as a feature.
Wherein the total length value comprises a summation of a length of segments between successive vertices of the feature; and
Hougen teaches “the attributed data includes a set of sum-arrays based on a set of features” (Para. [0039]) where “A sum-array may be generated and used to represent the squared values, which eases and decreases the amount of time in the calculation of the denominator of such normalized feature values. Feature values may refer to the sizes, shapes, colors, and edge characteristics of an object, as well as other feature values known in the art.” (Para. [0041]). Hougen further “Radar provides derived measurements such as range, range rate, azimuth angle, elevation, and approximate size of an object, as well as other information: known in the art” (Para. [0034]) thereby teaching the range/length as a feature.
The total elevation value comprises a summation of an elevation for each vertex;
Hougen teaches “the attributed data includes a set of sum-arrays based on a set of features” (Para. [0039]) where “A sum-array may be generated and used to represent the squared values, which eases and decreases the amount of time in the calculation of the denominator of such normalized feature values. Feature values may refer to the sizes, shapes, colors, and edge characteristics of an object, as well as other feature values known in the art.” (Para. [0041]). Hougen further “Radar provides derived measurements such as range, range rate, azimuth angle, elevation, and approximate size of an object, as well as other information: known in the art” (Para. [0034]) thereby teaching elevation as a feature.
Regarding Claim 16:
All of the limitations herein are similar to some or all of the limitations of Claim 2.
Regarding Claim 17:
All of the limitations herein are similar to some or all of the limitations of Claim 3.
Regarding Claim 18:
All of the limitations herein are similar to some or all of the limitations of Claim 4.
Regarding Claim 21:
All of the limitations herein are similar to some or all of the limitations of Claim 5.
Claim(s) 7, 14, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Vianello, Moustafa, Burnett, and Hougen, and further in view of Khasanova et al. (U.S. Pre-Grant Publication No. 2022/0294757, hereinafter referred to as Khasanova).
Regarding Claim 7:
Hougen, Burnett, Moustafa and Vianello explicitly teaches all of the elements of the claimed invention as recited above except:
Wherein each signature key is a summation of the plurality of tokens associated therewith.
However, in the related field of endeavor of representing data in vector representations, Khasanova teaches:
Wherein each signature key is a summation of the plurality of tokens associated therewith.
Khasanova teaches creating “vector representations…by generating a weighted summation of the embeddings of all of the tokens” (Para. [0018]).
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Khasanova, Hougen, Burnett, Moustafa, and Vianello at the time that the claimed invention was effectively filed, to have combined the embeddings, as taught by Khasanova, with the inclusion of azimuth angles of objects, as taught by Hougen, the vector generation using tokens obtained from raw data, as taught by Burnett, the detection of changes in driving environment features over time through sensor data comparison, as taught by Moustafa, and the systems and methods for detecting and comparing object attributes and measurements over time, as taught by Vianello.
One would have been motivated to make such combination because Khasanova teaches “the DNS request embeddings, which encode semantics of the DNS requests, potentially allow identification of new types of suspicious activities in the network and/or improvement of the speed of analysis” (Para. [0022]).
Regarding Claim 14:
All of the limitations herein are similar to some or all of the limitations of Claim 7.
Regarding Claim 22:
All of the limitations herein are similar to some or all of the limitations of Claim 7.
Response to Amendment
Applicant’s Amendments, filed on 5/11/2026 and included in the Request for Continued Examination filed on 6/17/2026, are acknowledged.
Response to Arguments
In light of the Amendments and Remarks filed on 5/11/2026 and further review of the Application’s specification and amended claims, the 101 rejection of claims 1-18 and 21-22 for being directed to an abstract idea without significantly more has been withdrawn. In particular, upon further time for consideration and analysis, and based on Applicant’s reference to “the MPEP's clear instruction in 2106.04(a)(2) that ‘a claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept’ and corresponding examples that are based on, but do not recite, math any more or less than those claims at issue are here.”, it was determined that the limitation “for each feature of the plurality of features in the first vector dataset and the second vector dataset, generating a signature key from a plurality of tokens, each of the plurality of tokens corresponding to one or more geometric attributes of the feature” was not understood as reciting a mathematical concept and the limitation “determining that the feature corresponding to the first signature key in the first data structure matches the feature corresponding to the first signature key in the second data structure when a threshold number of tokens between the first signature key in the first data structure and the first signature key in the second data structure match”, was understood as integrating the claims into a practical application.
On pages 11-12 of the Remarks filed on 5/11/2026, Applicant argues that “Claims 1, 8, and 15 have each been amended as set forth herein to more explicitly recite, inter alia, the two-step process described in the specification that is used to identify differences between first and second datasets” where “a signature key is generated for each feature contained within each dataset based on geometric aspects of that feature. When the datasets are compared, features having matching signature keys are first identified. For those features having matching signature keys between datasets, the associated geometric aspects of those features are then compared. When the number of matching geometric attributes, or tokens, exceeds a threshold, it may then be concluded that the features match one another.” and “respectfully submits that such a scheme is not reflected in any of the cited references, alone or in combination. Particularly, none of the references teach or suggest the generation of signature keys, hashes, or otherwise in a manner that may give rise to matching signature keys between datasets for a given feature at a stage where it is uncertain whether a given feature is even present in subsequent datasets. Likewise, none of the references consider and/or address how to distinguish between features having signature keys which might match in this way.”.Applicant’s argument is not convincing at least because an uncertainty of given features being present in subsequent datasets does not appear to be recited in the claims let alone directly stated in the specification.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Portail et al. (U.S. Patent No. 12,100,159) teaches determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.The reference further teaches “a hash of the object's characteristics (e.g., geometric characteristics, visual characteristics, etc.), generated from the object representations associated with the object version” and “determining whether the object representations represent the same object version can include comparing feature vectors (e.g., extracted using the appearance change-agnostic model) and/or attribute vectors. In this variant, the object representations can be considered to represent the same object version when the vectors match (e.g., exactly or above a threshold similarity). However, the feature vectors can be otherwise compared.”
Kristensen et al. (U.S. Pre-Grant Publication No. 2021/0286923) teaches a sensor model may be learned to predict virtual sensor data for a given scene configuration. For example, a sensor model may include a deep neural network that supports generative learning—such as a generative adversarial network (GAN). The sensor model may accept an encoded representation of a scene configuration as an input using any number of data structures and/or channels (e.g., concatenated vectors, matrices, tensors, images, etc.), and may output virtual sensor data. Real-world data and/or virtual data may be collected and used to derive training data, which may be used to train the sensor model to predict virtual sensor data for a given scene configuration. As such, one or more sensor models may be used as virtual sensors in any of a variety of applications, such as in a simulated environment to test features and/or functionality of one or more autonomous or semi-autonomous driving software stacks.The reference further teaches “real-world data and/or virtual data may be collected from RADAR and/or LIDAR sensor(s) and used to encode the existence of reflections and values for the reflections such as bearing, azimuth, elevation, range (e.g., time of beam flight), intensity, Doppler velocity, RADAR cross section (RCS), reflectivity, signal-to-noise ratio, some combination thereof, and/or the like” (Para. [0028]).
Xie et al. (U.S. Pre-Grant Publication No. 2023/0384448) teaches a LiDAR system that improves target object distance detection by mitigating internal reflections and glare artifacts. The system includes a laser source, an optical module, a pixel circuit, a time-to-digital converter (TDC), a memory device, and a processor module. The processor module uses two data arrays to determine two vectors and utilizes a similarity function, such as the Pearson correlation coefficient, to identify and remove artifact peaks from the data. These artifact peaks may be associated with glare, semi-transparent objects, or internal reflections within lens elements.
Dal Mutto et al. (U.S. Pre-Grant Publication No. 2019/0096135) teaches a system for visual inspection includes: a scanning system configured to capture images of an object and to compute a three-dimensional (3-D) model of the object based on the captured images; an inspection system configured to: compute a descriptor of the object based on the 3-D model of the object; retrieve metadata corresponding to the object based on the descriptor; and compute a plurality of inspection results based on the retrieved metadata and the 3-D model of the object; and a display device system including: a display; a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: generate overlay data from the inspection results; and show the overlay data on the display, the overlay data being aligned with a view of the object through the display.
Kirchner (U.S. Pre-Grant Publication No. 2020/0050901) teaches identifying a defined object (e.g., hazard): a sensor detecting and defining a digital representation of an object; a processor (connected to the sensor) which executes two techniques to identify a signature of the defined object; a memory (connected to the processor) storing reference data relating to two signatures derived, respectively, by the two techniques; responsive to the processor receiving the digital representation from the sensor, the processor executes the two techniques, each technique assessing the digital representation to identify any signature candidate defined by the object, derive feature data from each identified signature candidate, compare the feature data to the reference data, and derive a likelihood value of the signature candidate corresponding with the respective signature; combining likelihood values to derive a composite likelihood value and thus determine whether the object in the digital representation is the defined object.The reference further teaches “a comparator, typically being a classifier or a finding algorithm, to compare each derived feature vector (a) with the feature variance distribution defined by the reference data” (Para. [0106]).
Baxley et al. (U.S. Pre-Grant Publication No. 2016/0127931) teaches determining a physical position of a radio transmitter. A physical model for electromagnetic signal propagation within the electromagnetic environment may be established. Radio frequency signal power levels associated with the radio transmitter may be received from one or more radio frequency sensors. Parameters associated with the physical model may be estimated for one or more test locations within the electromagnetic environment. An error metric between the received radio frequency signal power levels and the physical model may be computed for the one or more test locations. Bounds on the parameters associated with the physical model may be established to prune away physically impossible solutions. The parameters associated with the physical model may be optimized across the one or more test locations to establish a preferred location estimate for the radio transmitter.The reference further teaches “The device classification module 370 can further perform anomaly analysis, which compares the features associated with each data throughput feature vector into an aggregate metric.” (Para. [0128]).
Kilaru (U.S. Pre-Grant Publication No. 2024/0194068) teaches one or more of determining a vehicle in a group of vehicles that has an unobstructed view of an area ahead of the group, collecting data from sensors on the vehicle, sending the data to one or more other vehicles in the group of vehicles, and providing the unobstructed view of the area ahead of the group on a display of the one or more other vehicles.The reference further teaches “individually comparing each feature from the new image to this database to find a candidate that may match features based on a Euclidean distance of their feature vectors.” (Para. [0074]).
Bergen (U.S. Pre-Grant Publication No. 2021/0073571) teaches point clouds of objects are compared and matched using logical arrays based on the point clouds. The point clouds are azimuth aligned and translation aligned. The point clouds are converted into logical arrays for ease of processing. Then the logical arrays are compared (e.g. using the AND function and counting matches between the two logical arrays). The comparison is done at various quantization levels to determine which quantization level is likely to give the best object comparison result. Then the object comparison is made. More than two objects may be compared and the best match found.The reference further teaches “Azimuth aligning may be performed by finding a center of gravity of each point cloud, generating weighted sums of each point cloud by determining the number of points along various angular directions from the center of gravity, generating a characteristic signature of each point cloud based upon its weighted sums, cross correlating the two characteristic signatures and choosing the angular alignment between the point clouds” (Para. [0015]).a
Singh et al. (U.S. Pre-Grant Publication No. 2024/0127596) teaches a perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and feature maps corresponding to the received images. The perception system may generate scene dependent radar-based object queries. The perception system may use the generated scene dependent radar-based object queries and scene independent object queries to generate one or more bounding boxes for objects in the vehicle scene.The reference further teaches “the radar detection stage 507 may determine positions of objects based on certain ranges and azimuth angles of radar points of the returned radio waves that satisfy the signature condition” (Para. [0117]).
Duan et al. (U.S. Pre-Grant Publication No. 2024/0196124) teaches a microphone array and a processor. The microphone array detects first sound signal and the second sound signal. Each of the first and second sound signals has a particular frequency band. Each of the first and second sound signals is originated from a particular sound source. The processor receives the first and second sound signals. The processor amplifies the first sound signals, each with a different amplification order. The processor disregards the second sound signal, where the second sound signal includes interference noise signals. The processor determines that the first sound signals indicate that a vehicle is within a threshold distance from an autonomous vehicle and traveling in a direction toward the autonomous vehicle. In response, the processor instructs the autonomous vehicle to perform a minimal risk condition operation. The minimal risk condition operation includes pulling over or stopping the autonomous vehicle.The reference further teaches “The control device 550 may compare the feature vector with each vector associated with the sound data samples included in the training dataset. The control device 550 may determine a Euclidean distance between the determined feature vector and each feature vector associated with the sound sample data included in the training dataset” (Para. [0109]).
Banerjee et al. (U.S. Pre-Grant Publication No. 2019/0082103) teaches receiving a plurality of images from a first camera with a first field of view and a second plurality of images from a second camera with a second field of view. An overlapping region exists between the first field of view and the second field of view. The method also includes predicting a disparity of a moving object present in a first image of the first plurality of images. The moving object is not present in a corresponding second image of the second plurality of images. The method further includes determining warp vectors based on the predicted disparity. The method additionally includes combining an image from the first plurality of images with an image from the second plurality of images based on the determined warp vectors.The reference further teaches “Detected features may be compared between the rectangular images to determine vectors (e.g., distances) between the features. The vectors may be indicate and/or may be utilized to determine 1110 the disparity (and/or depth)” (Para. [0135]).
Ishigami et al. (U.S. Pre-Grant Publication No. 2024/0219582) teaches “the reference further teaches in Step S1026, the angular velocity sensor correcting means 146 corrects a 0-point (also referred to as a bias) of the angular velocity sensor 144 based on a difference between an azimuth obtained by summing momentary yaw angles and a subject vehicle azimuth corrected by the hybrid positioning means 148, using a subject vehicle azimuth at a given time during the traveling of the subject vehicle as an initial value. The angular velocity sensor correcting means 146 can perform this correction using the correction method described in, for example, Japanese Patent No. 3321096 and Japanese Patent No. 3727489. After Step S1026, the processes return to Step S1001 in FIG. 11.” (Para. [0198]). And the use of LiDAR (Para. [0254]).
Kakeda et al. (U.S. Pre-Grant Publication No. 2022/0161794) teaches a vehicle control device includes: a recognition part recognizing a first road marking line on a first side of a lane traveled by a vehicle; an obtaining part obtaining information on a second road marking line on the first side of the lane from map information; and a support part that executes a support processing for preventing the vehicle from deviating from a road marking line when a probability that the vehicle deviates from the road marking line is greater than or equal to a predetermined degree. When a degree of matching between a first position of the first road marking line and a second position of the second road marking line is less than or equal to a first threshold value, the support part suppresses execution of the support processing for preventing the vehicle from deviating from a road marking line on the first side.The reference further teaches “in the equation (1), the support part 146 divides the total of the azimuth differences (Σw[i]Δθ[i]) for each corresponding sampling point by the number of corresponding sampling points (Σw[i]) to calculate the average difference (Δθave). The azimuth difference for each corresponding sampling point may be weighted based on the position of the sampling point. For example, the azimuth differences of the sampling points near the vehicle M may be given a heavier weight than the azimuth differences of the sampling points far from the vehicle M.” (Para. [0096]).
Kalita et al. (U.S. Pre-Grant Publication No. 2023/0108406) teaches a method for detecting, localizing, reporting, and displaying potholes on a road, in a system comprising a sensing device mounted on a vehicle, a cartography display, and a control unit, the sensing device comprising at least a radar device, the method comprising scanning with the sensing device an area of interest in front of and ahead the vehicle, the area of interest including at least a surface of a road traveled by the vehicle, the sensing device outputting a data flow; identifying first candidate potholes formed on the road surface; further processing the data flow to find out first confirmed potholes among the first candidates potholes; allocating a geolocation to each of the first confirmed potholes; and displaying, on the cartography display, first potholes with their localization superimposed on the map.The reference further teaches “the angular scan domain is symmetrical with regard to the longitudinal axis (left/right symmetry). There may be a proportional link 660 between azimuth and elevation, wherein the total azimuthal range is a function of a elevation (negative), as illustrated at FIG. 6.” (Para. [0104]).
League et al. (U.S. Patent No. 5,587,929) teaches tracking objects first receives returns from objects in the field of view of a detector. The sensor generates a current frame of datapoints, where an object in the field of view can be represented by multiple datapoints. The datapoints are converted into global coordinates and mapped into a next frame of datapoints and generated at a next sample time to cream a new current frame of datapoints. This new current frame of datapoints is processed to form a list of objects with location and power information that includes information from one or more previous frames. This mapping and processing to form the list of objects allows the system to detect weak signal targets in a ground cluttered environment and minimizes the occurrence of false alarms.The reference further teaches “In one embodiment of blob coloring the label (described as a 1 or 2 in the above description) that is used for an object is used as the actual software pointer (computer memory position of the information) to a structure of information about the blob. This information can include the near right range and azimuth of the blob, the far left range and azimuth of the blob, the centroid, the sum of the power of the datapoints in the blob, the number of points in the blob, and the maximum power of a datapoint in the blob. By using this technique, the list of blobs and their relevant information is completed. This list of blob pointers is searched to determine when to slow down or stop the vehicle.” (Col. 12 Lines 28-39).
Unnikrishnan et al. (U.S. Pre-Grant Publication No. 2020/0218913) teaches techniques for determining a motion state of a target object. In an aspect, an on-board computer of an ego vehicle detects the target object in one or more images, determines one or more first attributes of the target object based on measurements of the one or more images, determines one or more second attributes of the target object based on measurements of a map of a roadway on which the target object is travelling, and determines the motion state of the target object based on the one or more first attributes and the one or more second attributes of the target object.The reference further teaches “this cost is computed as a weighted sum of the difference in azimuth, and difference in range of the camera and radar detections. The height of the camera 2D bounding box detection is used as a proxy for its range.” (Para. [0087]).
Foreign Publication ID GB 2612878 A published May 17, 2023 teaches the steps of obtaining at least two light detection and ranging (LiDAR) point clouds 602, processing the at least two LiDAR point clouds using at least one classifier network 604, obtaining at least one output dataset from the at least one classifier network 606, determining that the at least two LiDAR point clouds are misaligned based on the at least one output dataset 608, and performing a first action based on determining that the at least two LiDAR point clouds are misaligned 610. The at least one classifier network may comprise at least one of a pillar-based network (see figure 5b) or a kernel point convolution-based network (see figure 5c). The first action may comprise labelling the at least two LiDAR point clouds as misaligned, and/or updating a locality of a map based on labelling the at least two LiDAR point clouds as misaligned.
Silberman et al. (U.S. Patent No. 11,556,638) teaches generating event-specific handling instructions for accelerating a threat mitigation of a cybersecurity event includes identifying a cybersecurity event; generating a cybersecurity event digest based on the cybersecurity event, computing a cybersecurity hashing-based signature of the cybersecurity event based on the cybersecurity event digest; searching, based on the distinct cybersecurity hashing-based signature of the cybersecurity event, an n-dimensional space comprising a plurality of historical cybersecurity event hashing-based signatures; returning one or more historical cybersecurity events or historical cybersecurity alerts homogeneous to the cybersecurity event based on the search; deriving one or more cybersecurity event-specific handling actions for the cybersecurity event based on identifying a threat handling action corresponding to each of the one or more historical cybersecurity events or historical cybersecurity alerts homogeneous to the cybersecurity event; and executing one or more cybersecurity threat mitigation actions to resolve or mitigate the cybersecurity event.The reference further teaches “assessing the hashing-based signature of the target cybersecurity event against each of a subset of the plurality of historical cybersecurity event hashing-based signatures having a same or similar number of tokens.” (Claim 3).
Cheng et al. (U.S. Pre-Grant Publication No. 2014/0280082) teaches “The received 402 query may then be tokenized 404, which may include generating a vector or array of tokens in the query. The method by which tokens are identified may be as described above with respect to the tokenization module 304. The product records 114 of the product database 112 may then be analyzed to identify 406 a plurality of product records 114 at least one of the tokens of the query. In some embodiments, only those product records 114 matching all or a threshold amount or percentage of the tokens are identified 406 as matching.” (Para. [0038]).
Heisele et al. (U.S. Pre-Grant publication No. 2007/0179918) teaches object recognition techniques are disclosed that provide both accuracy and speed. One embodiment of the present invention is an identification system. The system is capable of locating objects in images by searching for local features of an object. The system can operate in real-time. The system is trained from a set of images of an object or objects. The system computes interest points in the training images, and then extracts local image features (tokens) around these interest points. The set of tokens from the training images is then used to build a hierarchical model structure. During identification/detection, the system, computes interest points from incoming target images. The system matches tokens around these interest points with the tokens in the hierarchical model. Each successfully matched image token votes for an object hypothesis at a certain scale, location, and orientation in the target image. Object hypotheses that receive insufficient votes are rejected.The reference further teaches “each successfully matched image token votes for an object hypothesis at a certain scale, location, and orientation in the target image. The hypothesis verification module 420 is programmed or otherwise configured to determine if a token match threshold is satisfied. In one particular embodiment, a valid hypothesis is obtained if a minimum number of tokens matched and voted for the same object hypothesis” (Para. [0058]).
Meyer et al. (U.S. Pre-Grant Publication No. 2025/0085416) teaches computing a point cloud based upon data output by several radar sensors in a distributed radar system. The radar sensors generate tensors based upon echo signals detected by the radar sensors. Values are extracted from the tensors and a sequence of tokens is created, where the sequence of tokens includes the values extracted from the tensors. The sequence of tokens is provided as input to a transformer model, which outputs a point cloud based upon the sequence of tokens.The reference further teaches “The generated tensors may also include other values, for example, such as values for range-velocity bins, values for range-azimuth bins, values for range-cross-track angle bins, values for Doppler-velocity bins, etc. “ (Para. [0020]), “The radar system 118 and/or the radar sensors 120 may also be configured to compute measurements of other variables based upon the detected echo signals reflected from the environment, such as velocity, angular, and/or azimuth data.” (Para. [0029]), and “At 212, sequences of tokens are generated based upon the radar data contained in the tensor bins. As discussed above, the sequences of tokens may be indicative of values contained in each tensor bin of each radar tensor.” (Para. [0062]).
Foreign Publication CN118018206A teaches an identity verification method based on a time stamp and a dynamic token, which adopts a data processing and analysis algorithm to carry out hash coding and association analysis on a secret key and the time stamp in the process of generating a first dynamic token so as to generate a corresponding dynamic token. Thus, a dynamic token which is related to the key and has uniqueness can be generated according to the user key and the timestamp, so that the security of the user is improved.The reference further teaches “the corrected feature vector and the fused hash code vector may be fused to obtain the corrected fused hash code vector by, for example: inputting the correction feature vector and the fusion hash code vector; different fusion methods may be used, such as vector concatenation, weighted summation, element-by-element multiplication, etc. More specifically, vector connection: connecting the corrected feature vector and the fusion hash code vector along a feature dimension to obtain the corrected fusion hash code vector; weighted summation: and carrying out weighted summation on the correction characteristic vector and the fusion hash code vector to obtain the corrected fusion hash code vector.” (Page 4 Paragraph 6).
Bruening et al. (U.S. Patent No. 8,065,739) teaches detecting policy violations in information content containing data in a character-based language is described. In one embodiment, the method includes identifying a policy for protecting source data having a tabular format. The source data contains one or more data fragments in the character-based language. The method further includes receiving information content having at least a portion in the character-based language, and determining whether any part of the information content, including the portion in the character-based language, violates the policy.The reference further teaches “generating a first set of tokens from the first portion of the information content using the abstract data structure comprises: finding a token candidate in the first portion of the information content using a token delimiter indicator from a corresponding token identified in the abstract data structure; generating a signature of the token candidate; comparing the signature of the token candidate with a signature of the corresponding token from the abstract data structure; and adding the token candidate to the first set of tokens if the signature of the token candidate matches the signature of the corresponding token from the abstract data structure.” (Claim 6).
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/ROBERT F MAY/Examiner, Art Unit 2154 7/11/2026
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
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