DETAILED ACTION
Claim(s) 1,2,4,6,7,8 and 9,10,11,12,13,15 and 17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Moustafa et al. (US 2022/0126864 A1) in view of DER ERFINDER WIRD et al. (DE 4121453 A1) with SEARCH machine translation further in view of Yates et al. (US 10,725,157 B1):
Claim(s) 3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Moustafa et al. (US 2022/0126864 A1) in view of DER ERFINDER WIRD et al. (DE 4121453 A1) with SEARCH machine translation further in view of Yates et al. (US 10,725,157 B1) as applied in claims 1,2,4,6,7,8 and 9,10,11,12,13,15 and 17,18,20 further in view of Andre (EP 2 469 301 A1):
Claim(s) 5 and 16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Moustafa et al. (US 2022/0126864 A1) in view of DER ERFINDER WIRD et al. (DE 4121453 A1) with SEARCH machine translation further in view of Yates et al. (US 10,725,157 B1) as applied in claims 1,2,4,6,7,8 and 9,10,11,12,13,15 and 17,18,20 further in view of Fine et al. (US 10,650,430 B2):
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“one curation engine is operable to adjust a…sampling rate” in claim 1.
“one curation engine is operable to adjust a…sampling rate” in claim 17
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof:
PNG
media_image1.png
564
802
media_image1.png
Greyscale
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
35 USC § 101 – Positive Statement
Streamlined Analysis: The claims (claim 1 representative) reflect an accuracy improvement in the technical field of sensors in view of applicant’s disclosure [0002] [0003] [00148]:
PNG
media_image2.png
152
834
media_image2.png
Greyscale
PNG
media_image3.png
343
870
media_image3.png
Greyscale
Response to Arguments
Objections
Applicant’s arguments, see remarks, page 10, filed 7/20/2026, with respect to the claim objection have been fully considered and are persuasive. The claim objection of claims 1-20 has been withdrawn.
Double Patenting Rejections
Applicant’s arguments, see remarks, pages 10,11, filed 7/20/2026, with respect to double patenting have been fully considered and are persuasive. The double patenting rejection of claims 1-20 has been withdrawn.
Rejections under 35 USC 103
Claim 1
Applicant’s arguments, see remarks, pages 11,12 , filed 7/20/2026, with respect to the rejection(s) of claim(s) 1 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 USC 103:
Claim(s) 1,2,4,6,7,8 and 9,10,11,12,13,15 and 17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Moustafa et al. (US 2022/0126864 A1) in view of DER ERFINDER WIRD et al. (DE 4121453 A1) with SEARCH machine translation further in view of Yates et al. (US 10,725,157 B1), wherein
Moustafa teaches claim 1’s “the at least one curation engine1 operable to adjust a…sampling rate” in alternative coordinate-adjective language invoking acts under 35 USC 112(f): applicant’s disclosure of LIDAR capturing more or better data; and
WIRD teaches a learning inference algorithm adjusting a threshold, via creative explicit or even routine steps:
PNG
media_image4.png
920
608
media_image4.png
Greyscale
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.
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.
Claim(s) 1,2,4,6,7,8 and 9,10,11,12,13,15 and 17,18,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Moustafa et al. (US 2022/0126864 A1) in view of DER ERFINDER WIRD et al. (DE 4121453 A1) with SEARCH machine translation further in view of Yates et al. (US 10,725,157 B1):
PNG
media_image5.png
693
438
media_image5.png
Greyscale
Re 1. (Currently Amended), Moustafa teaches A system for sensor data fusion for sensor management and utilization in autonomous transportation2 (likewise) comprising3:
at least one computer processor (“[0164] FIG. 157 is an example illustration of
a processor according to an embodiment.”: page 150 of 294) including a memory (via:
PNG
media_image6.png
841
640
media_image6.png
Greyscale
;
at least one curation engine45 (or likewise a “characteristics”- “curate”-“system”
[0216]: fig. 8:805: “Low-End Sensors AV”: classic-car-into-Batmobile),
at least one link engine (or likewise a “link”-“module 212” [0173] last S),
at least one fusion engine (or likewise “sensor fusion module 236” [0176]: fig.
2:236), and
at least one validation engine (or likewise a “validating autonomous vehicle
sensor data”-“server” [0296] 4th S: figs. 25,26,27, via:
PNG
media_image7.png
725
1077
media_image7.png
Greyscale
PNG
media_image8.png
1213
861
media_image8.png
Greyscale
PNG
media_image9.png
655
1072
media_image9.png
Greyscale
PNG
media_image10.png
1244
725
media_image10.png
Greyscale
PNG
media_image11.png
1154
680
media_image11.png
Greyscale
;
at least one first distance (LIDAR- substitute) sensor (or likewise “The
autonomous vehicle 12700 includes sensors 12702, 12703, 12704, 12705, 12706, 12707, 12708…to calculate the distance from all surrounding vehicles (substituting the depth information that the LIDAR currently provides” [0820] 3rd & 6th Ss: figs. 127A-127B) operable to capture a first distance measurement (or likewise “depth information”-“distance” [0820] 5th S) from a vehicle to at least one object (or likewise a “bicycle” [0809] last S: fig. 1:120); and
at least one second distance (LIDAR-substitute) sensor (fig. 127A,B) operable to
capture a second distance measurement (or likewise said “depth information”-“distance” [0820] 5th S) from the vehicle to the at least one object (via:
PNG
media_image12.png
658
995
media_image12.png
Greyscale
;
wherein the at least one computer processor is operable to analyze6 (“analyzed”
[0210] 1st S) the first distance measurement and the second distance measurement (or likewise “the data7 collected by two sensors” [0228] 1st S);
wherein
the at least one curation engine8 (fig. 2:210: “Autonomous Driving System” comprising said “curated”-“in-vehicle computing system”-“data” [0216] 1st S) is operable to curate the first distance measurement and the second distance measurement (resulting in “curated”- (measurements-)“data”9 [0216] 1st S),
the at least one link engine10 (or likewise any software symbol flowchart piece in fig. 26) is operable to link (or likewise joining data via fig. 26:2612: “update11 HD map”) the first distance measurement and the second distance measurement (to map data via fig. 26:
PNG
media_image13.png
1207
725
media_image13.png
Greyscale
, and
the at least one validation engine is operable to validate the first distance measurement and the second distance measurement (fig. 25: “Data12 Verification”);
wherein at least one curation engine is operable to adjust13 (or likewise “adjust control” [0166] 3rd S) an 14 sampling rate to gather more or sufficient data (or likewise “[0780] In one embodiment, a sampling rate of a sensor may be tuned to the sensitivity of the sensor for a given weather condition. For example, the sampling rate for a sensor that is found to produce useful data when a particular weather condition is present may be sampled more frequently than a sensor that produces unusable data during the weather condition.”);
wherein the at least one link engine is operable15 to16 link (via update) the first
distance (data) measurement17 and the second distance (data) measurement18 exceeding19 a mathematical threshold (via said fig. 26:2610: “Delta > threshold?”: “Yes”, via:
PNG
media_image13.png
1207
725
media_image13.png
Greyscale
);
wherein the at least one curation engine is operable to curate (as the output of
fig. 8:845) the first distance measurement by (classification-“sensor data 1310” [0234] 2nd S:fig. 13, below) categorizing (via “the classification model” [0234] 2nd S: fig. 54:5406: “CLASSIFICATION MODEL”) the first distance measurement (via “leveraged”- LIDAR-substitute Fig. 127A,B - “sensors” [0215] 6th S: fig. 8:825: “Hi-End Sensor Laden AV”) into at least one first distance (common-attribute) property (class-group: fig. 55:5510) or at least one first distance sub-property (via:
PNG
media_image15.png
1558
1130
media_image15.png
Greyscale
PNG
media_image16.png
1710
1168
media_image16.png
Greyscale
);
wherein the at least one first (“Camera…LIDAR” [0599] 2nd S: LIDAR-sensors
substitute, fig. 127A:12702) distance (attribute) property20 (comprised by “ the current data point21 as well as previous data points”, [0599] penult S for classification into said groups 1315,1320, 1325, 1330) or the at least one first distance sub-property includes22 (i.e., to be like/alike to or likewise a “data point”-“series”23) at least one first (T=0) data point (or likewise said previous alike data-property points “collected at time T… processed before the next data generated is recorded at time T+1” [0599] 2nd S) of the vehicle (via alike substitute sensor LIDAR datapoints being input in fig. 10:
PNG
media_image17.png
1350
1100
media_image17.png
Greyscale
);
wherein the at least one curation engine is operable to curate (as the output of
fig. 8:845, shown above) the second distance measurement (or likewise “The autonomous vehicle 12700 includes sensors 12702, 12703, 12704, 12705, 12706, 12707, 12708…to calculate the distance from all surrounding vehicles (substituting the depth information that the LIDAR currently provides” [0820] 3rd & 6th Ss: figs. 127A-127B) by categorizing (via fig. 13, shown above) the second distance measurement into at least one second distance property (groups 1315,1320,1325,1330 via said fig. 13, above in said real-time classification) or at least one second distance sub-property;
wherein the at least one second (time T=2 relative to time T=1) distance
(LIDAR-substitute-sensor) property (comprised by said datapoints to be classified via fig. 10:1040: detailed view: fig. 13) or the at least one second distance sub-property includes (i.e., to be like/alike) the at least one first (T=1) data point (via said “as well as previous data points” [0599] penult S, collected at T=1,T=2) of the vehicle or at least one second data point of the vehicle24;
wherein the at least one fusion engine (or likewise comprised by fig. 124B:
“Sensor Fusion Algorithm”: “(such as algorithms based on, e.g., Kalman
filters) may combine data from multiple sensors using equal weights” [0777] penult S) is operable to fuse the first distance measurement and the second distance measurement (represented in fig. 124B: “LIDAR” (photodetector-sensor-substituted: Figs. 127A,B, above) via:
PNG
media_image18.png
698
990
media_image18.png
Greyscale
);
wherein the at least one fusion engine creates at least one new data set
(or likewise “to generate a fusion-context dictionary 12110 in accordance with certain embodiments” [0788] “to fuse new sets of data from sensors more effectively” [0790] last S, via:
PNG
media_image19.png
525
1152
media_image19.png
Greyscale
PNG
media_image20.png
669
967
media_image20.png
Greyscale
PNG
media_image21.png
1291
1191
media_image21.png
Greyscale
);
wherein at least one inference engine (or “detector 4910 may include a training
module and an inference module” [0417] 2nd S: fig. 2:254: “Inference Engine” or likewise fig. 2:236: “Sensor Fusion”: fig. 123) is operable to determine internal sensor25 damage (or likewise “damaged/ disabled sensors” [0214]) for the at least one first distance sensor or the at least one second distance sensor based2627 the at least one new data set, thereby28 creating at least one inference (via:
PNG
media_image22.png
711
1155
media_image22.png
Greyscale
);
wherein the at least one validation engine is operable to validate the at least one inference29 based on a validation threshold30 (or likewise “if the output is comparable” [0282] 3rd S), wherein the validation threshold31 is dynamically adjustable; and
wherein the at least one computer processor (or likewise fig. 2:220: “Drive
Controls”) is operable to instruct the vehicle to brake (or likewise “an autonomous driving stack of a vehicle 105 may be coupled with drive controls 220 to affect how the vehicle is driven, including steering controls (e.g., 260), accelerator/throttle controls (e.g., 262), braking controls (e.g., 264), signaling controls (e.g., 266), among other examples.” [0183] 1st S: fig. 124A: braking “Action:”) based32 on the at least one new data set (represented in fig. 124A: new-data-set-derived braking “Action: Sensor weights”:
PNG
media_image23.png
1339
956
media_image23.png
Greyscale
PNG
media_image24.png
1032
1181
media_image24.png
Greyscale
Moustafa does not teach the difference of claim 1 of:
(wherein the at least one validation engine is operable to validate the at least one)33 inference34 (based on a validation threshold35), (wherein the validation) threshold36 is dynamically adjustable; (and)… 37
determine internal (sensor38 damage)39…40 based4142.
WIRD teach the difference of claim 1 of:
(wherein the at least one validation engine is operable to validate the at least one)43 inference44 (or likewise “inaccurate”-”inference results”, pg. 4, last txt blk) (based on a validation threshold45), (wherein the validation) threshold46 is dynamically adjustable (or likewise “adjusting a Threshold”, pg. 6, last text blk); (and)… 47
determine internal (sensor48 damage)49…50 based5152.
Since Moustafa teaches an “error…in the training data” “During inference” with multiple solutions such as “assign a catchall label…adding a specific anomaly class” via [0607]:
[0607] During inference, test data 8514 (which in some embodiments may include information gathered or derived from one or more sensors of an autonomous vehicle) is provided to the baseline model 8504 and the SRU model 8502. If the error representing the difference between the outputs of the models is relatively high as calculated by error calculator 8512, then the system 8500 determines a class for the object was not included in the training data and an anomaly is detected. For example, during inference, the system may use anomaly detector 8510 to determine whether the error for the test data is greater than the anomaly threshold 8506. In one example, if the error is greater than the anomaly threshold 8506, an anomaly class may be assigned to the object.
[0608] In various embodiments, the anomaly detector 8510 may assign a catchall label of unknown classes to the object. In another embodiment, the anomaly detector 8510 may assign a specific anomaly class to the object. In various embodiments, the anomaly detector may assign various anomaly classes to various objects. For example, a first anomaly class may be assigned to each of a first plurality of objects having similar characteristics, a second anomaly class may be assigned to each of a second plurality of objects having similar characteristics, and so on. In some embodiments, a set of objects may be classified as a catchall (e.g., default) anomaly class, but once the system 8500 recognizes similar objects as having similar characteristics, a new anomaly class may be created for such objects.
one of skill in the art could or would have done is refer to others for a solution to the training error and thus make Moustafa’s be as WIRD’s seeing in the change, WIRD, pg. 27, 3rd txt blk:
First, the operator designates an inference whose threshold value is to be changed from a list of conclusions displayed on the screen of the display device using a mouse or the like. Next, the designated threshold threshold value is entered by a volume change, a mouse, and so on. Since the operator can thus enter the threshold directly whenever required, an improvement in the accuracy of the final results of the conclusion is expected.
via “explicit…creative…or even routine steps” (MPEP 2143 2143 Examples of Basic Requirements of a Prima Facie Case of Obviousness [R-01.2024],3rd para) A,B,C:
A) create a “threshold revision” (WIRD, pg. 31, 4th txt blk) program:
(14) threshold-changing unit
The threshold changing unit 35 is for changing a threshold value included in the error information based on the error information supplied by the error information input unit 34 . There are two cases where a threshold is changed. These are the following:
1) A case where a possibility value is equal to or greateras the corresponding threshold, the corresponding oneBut the conclusion does not really apply; in this case, theThreshold low and must be raised. 2) A case where a possibility value is smaller than the corresponding oneThreshold is, but the corresponding inference is inReality is true; in this case, the threshold is high andmust be lowered.
Such a changed threshold and the inference concerning this threshold are stored in the threshold storage unit 32 by being tasked thereon by data transfer, a memory or a file. That is, the threshold value stored in the threshold storage unit 32 is revised. This threshold revision is preferably continued until the error information no longer occurs.
An example of a threshold revision algorithm will now be explained.
The threshold changing unit 35 changes a threshold using a certain function f (t, v) in a case where a certain conclusion and its possibility value are input as error information. We have the following equation:
t '= f (t, v) (25)
in which
t = threshold of conclusion,
t '= threshold of change after change,
v = possibility value of the conclusion.
The following can be considered as the function f (t, v): in the case of point 1 above):
t '= MIN (t + Δt, 1.0) (26)
in which
t = 0.3 (0.5v-t1.0)
0.1 (0.3v-t <0.5)
0.05 (0.1v-t <0.3)
0.01 (0 <vt <0.1)
and MIN represents the process of selecting the smaller one.
In the case of item 2 above:
t '= MIN (t-Δt, 0) (27)
where Δt has the same meaning as above and MAX the Represents process of selecting the larger one.
If the measures in equations (26) and (27) apply to the Conclusions c₁ and c₂ are applied, the following results:
Conclusion c₁: t '= 0.5 + 0.05 = 0.55 (28)
End c₂: t '= 0.7-0.05 = 0.65 (29)
It goes without saying that the algorithm is for change the threshold does not match that of the above example is limited.
PNG
media_image4.png
920
608
media_image4.png
Greyscale
B) install the threshold program into Moustafa’s fig. 2:206:
PNG
media_image25.png
1774
922
media_image25.png
Greyscale
B) run Moustafa’s classification error program of fig. 86 obtaining classification differences:
B1) obtain centroid dot for a bicycle and pedestrian (malfunctiong) sensor data;
B2) subtract data values to find any major (malfunctioning) sensor data value differences, such a wheel of a bicycle:
PNG
media_image26.png
1194
991
media_image26.png
Greyscale
B3) input the (misclassed bicycle wheel) sensor results (difference) of fig. 86:8604: “DIFFERENCE” into the Threshold Revision Program as “Input Error/loss function”:
PNG
media_image27.png
1749
1021
media_image27.png
Greyscale
C) redeploy machine learning model with adjusted thresholds of the Threshold Program and see what happens (I foresee: correct sorting of bicycle/pedestrian or otherwise perform “sensor…malfunctioning” (Moustafa [0128] 5th S) troubleshooting:
First, the operator designates an inference (i.e., a pedestrian or bicycle classification) whose threshold value is to be changed from a list of conclusions displayed on the screen of the display device using a mouse or the like. Next, the designated threshold threshold value is entered by a volume change, a mouse, and so on. Since the operator can thus enter the threshold directly whenever required, an improvement in the accuracy of the final (classification) results of the conclusion is expected).
Moustafa of the combination of Moustafa-WIRD does not teach the remaining difference of claim 1 of:
determine internal (sensor53 damage)54…55 based5657.
Yates teach the difference of claim 1 of:
determine (or likewise “an “analysis component 318”, c.20,ll. 55-60) internal (or likewise voltage58 “V1”, c.20,ll. 55-60--in an electric circuit (fig. 3:busbar)--) (sensor59 damage)60 (“for the pixel (e.g., “Static Pixel,” “Non-Functioning Pixel,” “Incorrect Timing or Synchronization of Pixel,” etc.) and/or disable the pixel such that the pixel is excluded from subsequent distance measurement sequences during normal operation “, c.21,ll.1-5)…61 (“distance measurements”, c. 21, 5-10) based626364 (or likewise “sets of values V1,V2, and VBKG”, c.20, 55-60, i.e., voltage-value sets).
Since Moustafa of the combination of Moustafa-WIRD teaches sensor damage, one of skill in the art of sensor damage can make Moustafa’s of the combination of Moustafa-WIRD be as Yates’ (“a short between pixels 414”, Yates: c.24,ll. 55-60:
PNG
media_image28.png
685
1046
media_image28.png
Greyscale
) seeing in the change an:
“analysis component 318 can instruct user interface component 316 to generate a diagnostic fault65 or error message for the pixel (e.g., “Static Pixel,” “Non-Functioning Pixel,” “Incorrect Timing or Synchronization of Pixel,” etc.) and/or disable the pixel such that the pixel is excluded from subsequent distance measurement sequences during normal operation. In some embodiments, analysis component may also instruct control output component 314 to initiate a safety action in response to determining that a number of faulty pixels exceeds a maximum allowable number of faulty pixels.”, Yates, c.20, last line to c. 21, line 10:
PNG
media_image29.png
1240
1948
media_image29.png
Greyscale
PNG
media_image29.png
1240
1948
media_image29.png
Greyscale
Re 2. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 1, wherein the at least one first distance property 6667 a timestamp (“timestamp” [0441] 3rd to last S) 68 and thus is struck-out).
Re 4. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 1, wherein the at least one curation engine is operable to use artificial intelligence (or likewise “machine learning69 232” [0174] 4th S) to automatically categorize the first distance measurement (or likewise “classification of the sensor data” [0234] penult S: fig. 13:1305: “ML Based Scene Classification”) into the at least one first distance property 70 in part on historical accuracy (or likewise a “recently” “updated”71 “classifier” [0473] 1st S) of previous categorizations (or likewise “used” “classifiers” [0473] 1st S) by an artificial intelligence engine (The strike-thru text is not limiting under the broadest reasonable interpretation in view of MPEP 2143.0372 and thus is struck-out).
Re 6. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 1, wherein the at least one computer processor is located on or in a machine73 (or likewise “vehicles, including automobiles”[0809] , last S: fig. 2:202: “Processors”), an edge device, at least one server, and/or a cloud.
Re 7. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 1, wherein the at least one first distance sensor 74 and thus is struck-out).
Re 8. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 1, wherein the at least one first distance property rd S) between the vehicle and the at least one object 7576 and thus is struck-out).
Claim 9 is rejected like claim 1:
Re 9. (Currently Amended), Moustafa of the combination of Moustafa-WIRD-Yates teaches A method for sensor data fusion for sensor management and utilization in autonomous transportation comprising:
providing at least one computer processor including a memory;
providing at least one curation engine, at least one link engine, at least one fusion engine, and at
least one validation engine;
at least one first distance sensor capturing a first distance measurement from a vehicle to at least
one object;
at least one second distance sensor capturing a second distance measurement from the vehicle
to the at least one object;
analyzing by the at least one computer processor the first distance measurement and the second
distance measurement;
curating by the at least one curation engine the first distance measurement and the second
distance measurement, linking by the at least one link engine the first distance measurement and the second distance measurement, and validating by the at least one validation engine the first distance measurement and the second distance measurement;
adjusting by the at least one curation engine an environmental sampling rate;
linking by the at least one link engine the first distance measurement and the second distance
measurement exceeding a mathematical threshold;
curating by the at least one curation engine the first distance measurement by categorizing the
first distance measurement into at least one first distance property or at least one first distance sub-property;
wherein the at least one first distance property or the at least one first distance sub- property
includes at least one first data point of the vehicle;
curating by the at least one curation engine the second distance measurement by categorizing
the second distance measurement into at least one second distance property or at least one second distance sub-property;
wherein the at least one second distance property or the at least one second distance sub-
property includes the at least one first data point of the vehicle or at least one second data point of the vehicle;
fusing by the at least one fusion engine the first distance measurement and the second distance
measurement;
creating by the at least one fusion engine at least one new data set;
determining by at least one inference engine77 internal78 sensor damage for the at least one first
distance sensor or the at least one second distance sensor based the at least one new data set thereby creating at least one inference; and
instructing by the at least one computer processor the vehicle to brake based on the at least one
new data set;
wherein the at least one validation engine is operable to validate the at least one inference based on a validation threshold, wherein the validation threshold is dynamically adjustable.
Claim 10 is rejected/interpreted like claim 7:
Re 10. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The method of claim 9, wherein the at least one first distance sensor and/or the at least one second distance sensor is operable to include a Light Detection and Ranging (LiDAR) sensor, a radar sensor, an ultrasonic sensor, a visible spectrum camera, a Global Positioning System (GPS) sensor, an infrared sensor, and/or a depth camera.
Claim 11 is rejected/interpreted like claim 4:
Re 11. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The method of claim 9, further comprising categorizing via the at least one curation engine using artificial intelligence the at least one first distance property and/or the at least one first distance sub-property and the at least one second distance property and/or the at least one second distance sub-property based in part on historical accuracy of previous categorizations by the at least one curation engine.
Re 12. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The method of claim 9, further comprising curating the first distance measurement and the second distance measurement in real-time (“in real time” [0224] last S).
Claim 13 is rejected/interpreted like claim 2:
Re 13. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The method of claim 9, wherein the at least one first distance property
Re 15. (Original),. Moustafa of the combination of Moustafa-WIRD-Yates teaches The method of claim 9, wherein the at least one first distance property79 time” [0392], last S, preventing the presence, existence, or occurrence of time: The strike-thru text is not limiting under the broadest reasonable interpretation in view of MPEP 2143.0380 and thus is struck-out).
Claim 17 is rejected like claims 1 and 9:
Re 17. (Currently Amended), Moustafa of the combination of Moustafa-WIRD-Yates teaches A system for sensor data fusion for sensor management and utilization in autonomous transportation comprising:
at least one computer processor including a memory;
at least one curation engine, at least one link engine, at least one fusion engine,
and at least one validation engine; and
at least two sensors, each of the at least two sensors operable to measure a first
distance from a vehicle to at least one object and a second distance from the vehicle to the at least one object;
wherein the at least one computer processor is operable to analyze the first
distance and the second distance;
wherein the at least one curation engine is operable to curate the first distance
and the second distance, the at least one link engine is operable to link the first distance and the second distance, and the at least one validation engine is operable to validate the first distance and the second distance;
wherein the at least one curation engine is operable to adjust an environmental sampling
rate;
wherein the at least one link engine is operable to link the first distance
measurement and the second distance measurement exceeding a mathematical threshold;
wherein the at least one curation engine is operable to curate the first distance by
categorizing the first distance into at least one first distance property or at least one first distance sub-property;
wherein the at least one first distance property or the at least one first distance
sub- property includes at least one first data point of the vehicle;
wherein the at least one curation engine is operable to curate the second
distance by categorizing the second distance into at least one second distance property or at least one second distance sub-property;
wherein the at least one second distance property or the at least one second
distance sub-property includes the at least one additional data point of the vehicle or at least one second data point of the vehicle;
wherein the at least one fusion engine is operable to fuse the first distance and
the second distance;
wherein the at least one fusion engine creates at least one new data set;
wherein at least one inference engine is operable to determine internal sensor
damage for the at least two sensors based the at least one new data set thereby creating at least one inference;
wherein the at least one validation engine is operable to validate the at least one
inference based on a validation threshold, wherein the validation threshold is dynamically adjustable, and
wherein the at least one computer processor is operable to instruct the vehicle to
brake based on the at least one new data set.
Claim 18 is rejected/interpreted like claims 2 and 13:
Re 18. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 17, wherein the at least one curation engine is further operable to curate the first distance and the second distance based in part on a timestamp 81 and thus is struck-out).
Claim 20 is rejected/interpreted like claims 4 and 11
Re 20. (Original), Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 17, wherein the at least one curation engine is operable to use artificial intelligence to automatically categorize the first distance into the at least one first distance property and/or the at least one first distance sub-property and the second distance into the at least one second distance property and/or the at least one second distance sub-property based in part on historical accuracy of previous categorizations by an artificial intelligence engine.
Claim(s) 3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Moustafa et al. (US 2022/0126864 A1) in view of DER ERFINDER WIRD et al. (DE 4121453 A1) with SEARCH machine translation further in view of Yates et al. (US 10,725,157 B1) as applied in claims 1,2,4,6,7,8 and 9,10,11,12,13,15 and 17,18,20 further in view of Andre (EP 2 469 301 A1):
PNG
media_image30.png
693
531
media_image30.png
Greyscale
Re 3., Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 2, wherein the at least one curation engine is further operable to curate (as mapped to figure 8 in the rejection of claim 1 and thus curating by “an in-vehicle computing system” : fig. 117: “11708”, [0216] 1st S & [0771] 1st S) the first distance measurement (represented in fig. 117 as 11702: “Sensor Data (Object List) with Digital Signature”: fig. 10:1005: “Object list”) based82 in part on the timestamp 83 and thus is struck-out; mapping curation from rejection claim 1: curated old-fashion-car-into-Batmobile:
PNG
media_image15.png
1558
1130
media_image15.png
Greyscale
PNG
media_image31.png
1096
1125
media_image31.png
Greyscale
PNG
media_image32.png
687
1080
media_image32.png
Greyscale
).
Moustafa of the combination of Moustafa-WIRD-Yates does not teach the difference of claim 3 of:
(measurement) based in part on (the timestamp).
Andre teach the difference of claim 3 of:
(measurement)84 (or likewise “correlation”85 [0150] 1st S) based in part on (“the same timestamp” [0150], 1st S) (the timestamp).
Since Moustafa of the combination of Moustafa-WIRD-Yates teaches 3D sensors, one of skill in the art of 3D sensors can make Moustafa’s of the combination of Moustafa-WIRD-Yates be as Andre’s seeing the change “ an optimization of the pulse launching parameters in view of generating an electric signal representing the illuminated scene with maximum resolution”, Andre [0082].
Claim 14 is rejected/interpreted like claim 3:
Re 14., Moustafa of the combination of Moustafa-WIRD-Yates-Andre teaches The method of claim 13, further comprising curating the first distance measurement and the second distance measurement based in part on the timestamp and/or measurement over the common period of time.
Claim(s) 5 and 16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over IDS cited Moustafa et al. (US 2022/0126864 A1) in view of DER ERFINDER WIRD et al. (DE 4121453 A1) with SEARCH machine translation further in view of Yates et al. (US 10,725,157 B1) as applied in claims 1,2,4,6,7,8 and 9,10,11,12,13,15 and 17,18,20 further in view of Fine et al. (US 10,650,430 B2):
PNG
media_image33.png
693
531
media_image33.png
Greyscale
Re 5., Moustafa of the combination of Moustafa-WIRD-Yates teaches The system of claim 1, wherein86 the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties (or “similar properties” [0479] last S) 87 and thus is struck-out).
Moustafa of the combination of Moustafa-WIRD-Yates does not teach the difference of claim 5 of:
heterogeneous, partially heterogeneous, or homogeneous.
Fine teach the difference of claim 5 of:
heterogeneous (or “heterogeneous digital content data88”, c.2,ll.10-15), partially heterogeneous, or homogeneous (properties).
Since Moustafa of the combination of Moustafa-WIRD-Yates teaches data and curation and queries (“queries…on matters such as shortest/fastest route navigation to a destination, closest ‘coffee’ shop, offer movies recommendations, or where to go for the upcoming anniversary celebration, etc. may be implemented through an example recommender system (e.g., utilizing passengers' profile and preference information”, Moustafa [0208] last S), one of skill in the art of data and curation can make Moustafa’s of the combination of Moustafa-WIRD-Yates be as Fine’s seeing in the change that “The Shop search engine applies specialized knowledge about a topic and the topic's set of connected topics, to finding and presenting to the user the best and most relevant products about a topic.”, Fine, c.15,ll.1-5, such as the best and most relevant navigation, coffee shop, movie, celebration place, etc..
Claim 16 is rejected/interpreted like claim 5:
Re 16., Moustafa of the combination of Moustafa-WIRD-Yates-Fine teaches The method of claim 9, further comprising curating heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.
Claim 19 is rejected/interpreted like claim 5:
Re 19., Moustafa of the combination of Moustafa-WIRD-Yates-Fine teaches The system of claim 17, wherein the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties and/or sub-properties.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
IDS cited Low et al. (US 2025/0139948 A1)
Low teaches data curation 13 and validation 18 in fig. 2 via [0105]:
PNG
media_image34.png
952
968
media_image34.png
Greyscale
“[0105] At 15, the curated datasets 14 are processed to set up training, testing, and validation datasets.”
as the closest to the claimed “the at least one curation engine is operable to curate the first distance measurement…the at least one validation engine is operable to validate the first distance measurement” of claim 1.
Lloyd et al. (US 2022/0076160 A1)
Lloyd teaches “verified...Threshold(s) 34……verification…output…inference(s) 42” via [0056] and fig. 1:
PNG
media_image35.png
682
973
media_image35.png
Greyscale
[0056] Threshold(s) 34 can include values, ranges, or other data that can be used in a verification process. As shown in more detail below with respect to the discussion of FIG. 9, each output of a machine learning model can be verified prior to utilization within the storage device. Threshold(s) 34 can be utilized in the verification process to compare against the output data (i.e. inference(s) 42) generated by the machine learning models. The storage device may be configured to utilize multiple threshold(s) 34 either together in a single evaluation, or in a series of successive verification steps. Threshold(s) 34 may also be statically set during manufacture and/or dynamically created and adjusted based on newly received data.
as the closest to the claimed “validate the at least one inference based on a validation threshold” of claim 1.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1 engine: a means by which something is achieved, accomplished, or furthered. (Dictionary.com)
2 The preamble gives meaning to the remainder (e.g., “wherein” clauses) of claim 1.
3 BROAD CLAIM LANGUAGE: -ing (of “comprising” or any “-ing” word in claims 1-20): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted)., wherein so is defined: likewise or correspondingly; also; too. (Dictionary.com)
4 engine: Computers. a piece or collection of software that drives a later process (used in combination, as in ). (Dictionary.com)
5 engine: computing any software designed to perform a basic function (CollinsDictionary.com)
6 BROAD CLAIM LANGUAGE: analyze: to examine carefully and in detail so as to identify causes, key factors, possible results, etc. (Dictionary.com)
7 data: a series of observations, measurements, or facts; information (Dictionary.com)
8 engine: engine: Computers. a piece or collection of software that drives a later process (used in combination, as in ). (Dictionary.com)
9 data: a series of observations, measurements, or facts; information (Dictionary.com)
10 engine: Computers. a piece or collection of software that drives a later process (used in combination, as in ). (Dictionary.com)
11 update: to bring (a book, figures, or the like) up to date as by adding new information or making corrections. (Dictionary.com)
12 data: a series of observations, measurements, or facts; information (Dictionary.com)
13 The claimed “wherein at least one curation engine is operable to adjust” invokes 35 USC 112(f) to include one more additional required act (“to gather”) from applicant’s disclosure:
PNG
media_image14.png
591
863
media_image14.png
Greyscale
14The crossed text “does not limit” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 2nd para: As a general matter, the grammar (e.g., coordinate adjectives: “environmental sampling” each an independent trait of “rate”) and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language (e.g., coordinate adjectives: “environmental sampling”) that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
15 operable ADJECTIVE: capable of being put into use, operation, or practice, wherein use is defined: the act of employing, using, or putting into service.(Dictionary.com)
16 to: (used as the ordinary sign or accompaniment of the infinitive, as in expressing motion, direction, or purpose, in ordinary uses with a substantive object.), wherein infinitive is defined: in English, the simple or basic form of a verb, with no endings to indicate the verb’s subject or tense, such as come, take, eat, be: used after auxiliary verbs or preceded by the word to, and sometimes functioning as a noun, such as He must be, I want to eat, To love is to understand, wherein tense is defined: the time, as past, present, or future, expressed by such a category. (Dictionary.com)
17 BROAD CLAIM LANGUAGE: extent, size, etc., ascertained by measuring. (Dictionary.com)
18 BROAD CLAIM LANGUAGE: extent, size, etc., ascertained by measuring. (Dictionary.com)
19 “exceeding” is participle, wherein participle is defined: Grammar. a form (“exceeding”) derived from a verb (“exceed”), used in English as an adjective (i.e., an adjective modifying a noun: “engine” and/or “measurement”) to express participation in the action (“is operable to link”) or state of the verb (“exceed”), or combined with an auxiliary verb to construct any of various tenses, as in a burning candle, a known fact, I am freezing, She has gone: a participle does not specify person or number, but may have a subject or object, show tense, etc., wherein action (“is operable to link”) is defined: the process or state of acting (“is operable to link”) or of being active (“is operable to link”). (Dictionary.com)
20 property: an essential or distinctive attribute or quality of a thing. (Dictionary.com)
21 data point: a single fact or piece of information; a datum, where fact is defined: that which actually exists or is the case; reality or truth, wherein truth is defined: the state or character of being true, wherein character is defined: one such feature or trait; characteristic, wherein characteristic is defined: a distinguishing feature or quality, wherein quality is defined: an essential or distinctive characteristic, property, or attribute.(Dictionary.com)
22 BROAD CLAIM LANGUAGE: include: to contain, as a whole does parts or any part or element, wherein contain is defined: to be equal to., wherein equal is defined: like or alike in quantity, degree, value, etc.; of the same rank, ability, merit, etc.. (Dictionary.com)
23 series: a group or a number of related or similar things, events, etc., arranged or occurring in temporal, spatial, or other order or succession; sequence, wherein similar is defined: having a likeness or resemblance, especially in a general way.(Dictionary.com)
24 These two “wherein” limitations correspond to the previous two “wherein” limitations with the difference being “second distance”.
25 “internal” of “internal sensor” is a cumulative adjective
26 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“damage”) that is logically the object of a verb (“determine”), also used in certain compound tenses and passive forms of the verb in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
27 CLAIM SCOPE: Is the claimed “based” referring to “new data set”-“damage” as the grammatical OBJECT of “based”? or “new data set”-“sensor” as the grammatical OBJECT of “based”? I interpret the claimed “sensor” (of “at least one first distance sensor or the at least one second distance sensor”) as the grammatical OBJECT of “based”.
28 BROAD CLAIM LANGUAGE/LONG RANGING CLAIM SCOPE: thereby ADVERB: by that; by means of that, wherein that is defined: (used to indicate a person, thing, idea, state, event, time, remark, etc., as pointed out or present, mentioned before, supposed to be understood, or by way of emphasis), where SCOPE is defined: Linguistics, Logic. the range of words or elements of an expression (claim 1) over which a modifier (e.g., patent examiner) or operator (e.g., me) has control. (Dictionary.com)
29 “inference” being the grammatical object of “validate”
30 BROAD CLAIM LANGUAGE: threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too. (Dictionary.com)
31 “threshold” being the grammatical object of “dynamically adjustable”
32 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“processor” & “vehicle”) that is logically the object of a verb (“is” & “instruct” & brake” of “is operable to instruct the vehicle to brake”), also used in certain compound tenses and passive forms of the verb in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
33 (italics) represent claim limitations already taught
34 “inference” being the grammatical object of “validate”
35 BROAD CLAIM LANGUAGE: threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too. (Dictionary.com)
36 “threshold” being the grammatical object of “dynamically adjustable”
37 ellipses (…) represent claim limitations already taught
38 “internal” of “internal sensor” is a cumulative adjective (internal-sensor)
39 (italics) represent claim limitations already taught
40 ellipses (…) represent claim limitations already taught
41 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“damage”) that is logically the object of a verb (“determine”), also used in certain compound tenses and passive forms of the verb (“base”) in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
42 CLAIM SCOPE: Is the claimed “based” referring to “new data set”-“damage” as the grammatical OBJECT of “based”? or referring to “new data set”-“sensor” as the grammatical OBJECT of “based”? I interpret the claimed “sensor” (of “at least one first distance sensor or the at least one second distance sensor”) as the grammatical OBJECT of “based”.
43 (italics) represent claim limitations already taught
44 “inference” being the grammatical object of “validate”
45 BROAD CLAIM LANGUAGE: threshold: a level or point at which something would happen, would cease to happen, or would take effect, become true, etc, wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too. (Dictionary.com)
46 “threshold” being the grammatical object of “dynamically adjustable”
47 ellipses (…) represent claim limitations already taught
48 “internal” of “internal sensor” is a cumulative adjective (internal-sensor)
49 (italics) represent claim limitations already taught
50 ellipses (…) represent claim limitations already taught
51 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“damage”) that is logically the object of a verb (“determine”), also used in certain compound tenses and passive forms of the verb (“base”) in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
52 CLAIM SCOPE: Is the claimed “based” referring to “new data set”-“damage” as the grammatical OBJECT of “based”? or referring to “new data set”-“sensor” as the grammatical OBJECT of “based”? I interpret the claimed “sensor” (of “at least one first distance sensor or the at least one second distance sensor”) as the grammatical OBJECT of “based”.
53 “internal” of “internal sensor” is a cumulative adjective (internal-sensor)
54 (italics) represent claim limitations already taught
55 ellipses (…) represent claim limitations already taught
56 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“damage”) that is logically the object of a verb (“determine”), also used in certain compound tenses and passive forms of the verb (“base”) in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
57 CLAIM SCOPE: Is the claimed “based” referring to “new data set”-“damage” as the grammatical OBJECT of “based”? or referring to “new data set”-“sensor” as the grammatical OBJECT of “based”? I interpret the claimed “sensor” (of “at least one first distance sensor or the at least one second distance sensor”) as the grammatical OBJECT of “based”.
58 voltage: A measure of the difference in electric potential between two points in space, a material, or an electric circuit, expressed in volts. (Dictionary.com)
59 “internal” of “internal sensor” is a cumulative adjective (internal-sensor)
60 (italics) represent claim limitations already taught
61 ellipses (…) represent claim limitations already taught
62 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“damage”) that is logically the object of a verb (“determine”), also used in certain compound tenses and passive forms of the verb (“base”) in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
63 CLAIM SCOPE: Is the claimed “based” referring to “new data set”-“damage” as the grammatical OBJECT of “based”? or referring to “new data set”-“sensor” as the grammatical OBJECT of “based”? I interpret the claimed “sensor” (of “at least one first distance sensor or the at least one second distance sensor”) as the grammatical OBJECT of “based”.
64 This part of the claimed is objected as discussed in Claim Objections section for missing “on” of the claimed “based”:-- based on--.
65 fault: electronics a defect in a circuit, component, or line, such as a short circuit, wherein short circuit is defined: a faulty or accidental connection between two points of different potential in an electric circuit, bypassing the load and establishing a path of low resistance through which an excessive current can flow. It can cause damage to the components if the circuit is not protected by a fuse (Dictionary.com)
66 and: (used to connect alternatives) (Dictionary.com)
67 The verb “includes” grammatically refers to a singular object (e.g., “at least one first distance property”)
68 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and/or” “and” “or”) that suggests (“and/or” “and” & “or” by definition suggests alternatives) or makes a feature (“the at least one first distance sub-property “; “the at least one second distance property”; “ the at least one second distance sub-property”; and “a measurement over a common period of time”) or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art.--
69 machine learning: a branch of artificial intelligence in which a computer generates rules underlying or based on raw data that has been fed into it (Dictionary.com)
70 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“measurement”) that is logically the object of a verb (“to use artificial intelligence to automatically categorize”), also used in certain compound tenses and passive forms of the verb (“base”) in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
71 updated: Computers. to incorporate new or more accurate information in (a database, program, procedure, etc.). (Dictionary.com)
72 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and/or” & “and”) that suggests (“and/or” & “and” by definition suggests alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art.--
73 machine: Older Use. an automobile or airplane (Dictionary.com)
74 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and/or” “and”) that suggests (“and” & “or” by definition suggests alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art.--
75 and: (used to connect alternatives) (Dictionary.com)
76 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and/or” “and”) that suggests (“and” & “or” by definition suggests alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art.--
77 “engine” is interpreted as a noun
78 “internal” is interpreted as a cumulative adjective modifying “sensor” modifying the noun “damage”
79 or: (used to connect words, phrases, or clauses representing alternatives), wherein alternative is defined: a choice limited to one of two or more possibilities, as of things, propositions, or courses of action, the selection of which precludes any other possibility, wherein preclude is defined: to prevent the presence, existence, or occurrence of; make impossible (Dictionary.com)
80 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and/or” “and”) that suggests (“and” & “or” by definition suggests alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art.--
81 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and/or” “and”) that suggests (“and” & “or” by definition suggests alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art.--
82 BROAD CLAIM LANGIUAGE: based: the simple past tense and past participle of base, wherein past participle is defined: a participial form of verbs (“base”) used to modify a noun (“measurement”) that is logically the object of a verb (“curate”), also used in certain compound tenses and passive forms of the verb (“base”) in English and other languages, wherein base (USED WITHOUT OBJECT) is defined: to have a basis; be based (usually followed by on or upon ), wherein based (USED WITH OBJECT) is defined: to place or establish on a base or basis; ground; found (usually followed by on or upon ), wherein basis is defined: a basic fact, amount, standard, etc., used in making computations, reaching conclusions, or the like. (Dictionary.com)
83 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and” & “and/or”) that suggests (“and” & :and/or” by definition suggests alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art.--
84 (italics) represent claim limitations already taught
85 correlation: Statistics. the degree to which two or more attributes or measurements on the same group of elements show a tendency to vary together. (Dictionary.com)
86 This “wherein” clause -- wherein the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties-- is not limiting under the broadest reasonable interpretation since this wherein clause does not give "meaning and purpose to the manipulative steps" as indicated in claim 1’s preamble: “for sensor data fusion for sensor management and utilization in autonomous transportation” in view of of
MPEP 2111.04 "Adapted to," "Adapted for," "Wherein," "Whereby," and Contingent Clauses [R-10.2019]
I. "ADAPTED TO," "ADAPTED FOR," "WHEREIN," and "WHEREBY"
Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are:
(A) "adapted to" or "adapted for" clauses;
(B) "wherein" clauses (“wherein the at least one curation engine is operable to curate heterogeneous, partially heterogeneous, or homogeneous properties”); and
(C) "whereby" clauses.
The determination of whether each of these clauses is a limitation in a claim depends on the specific facts of the case. See, e.g., Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps").
87 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, last two sentences: --Language (“and/or” & “and”) that suggests (“and/or” & “and” by definition suggests alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives (“heterogeneous, partially heterogeneous, or homogeneous”), the prior art teaches the element if one of the alternatives is taught by the prior art.--
88 data: (usually used with a singular verb) information in digital format, as encoded text or numbers, or multimedia images, audio, or video, wherein information is defined: knowledge communicated or received concerning a particular fact or circumstance; news, wherein fact is defined: that which actually exists or is the case; reality or truth, wherein truth is defined: the state or character of being true,
wherein character is defined: the aggregate of features and traits that form the individual nature of some person or thing, wherein feature is defined: a prominent or conspicuous part or characteristic, wherein characteristic is defined: a distinguishing feature or quality, wherein quality is defined: an essential or distinctive characteristic, property, or attribute (Dictionry.com)
Prosecution Insights