DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-21 are pending in this application.
Claims 1-4, 6-7, 10-11, 15-19 and 20 are presented as currently amended claims.
Claims 3, 5, 8-9, and 12-14 are presented as original claims.
Claim 21 is newly presented.
No claims are cancelled.
Examiner's Note
Examiner has cited particular paragraphs / columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicants’ definition which is not specifically set forth in the claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 21 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 21 recites a “a complicated driving environment” without definition.
The term “complicated” is a relative term which renders the claim indefinite. The term “complicated” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. While the specification discloses that certain complicated driving environments may be confusing to the driver “or may behave in a manner that is risky and increases the potential of a dangerous accident (or collision)” (Applicant’s Specification: ¶ 021) the specification does not define what a complicated driving environment is or provide a standard for ascertaining the requisite degree at which a driving situation becomes complicated. While the recited limitations are provided the broadest reasonable interpretation in light of the specification, the scope of the claim is rendered indefinite. For the purposes of the prior art rejection below this term has been interpreted as an area where there is an increased risk of a collision.
No further claims are rejected due to dependency on claim 21. Correction or clarification is required.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-10 and 19-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite obtaining, identifying, generating, and transmitting . This judicial exception is not integrated into a practical application or significantly more because the implementation is a generic application of an abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Subject Matter Eligibility Analysis of representative claim 1 (see MPEP 2106.03):
Step 1: As a method, the claim is directed to a statutory category.
Step 2A: Prong 1: Claim 1 is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 is directed to:
obtaining a plurality of contextual features of a driving environment based on knowledge related to the driving environment obtained using sensor data collected by a first vehicle in the driving environment; identifying a plurality of nodes of a vehicular knowledge network based on the plurality of contextual features, wherein each of the plurality of nodes comprises node knowledge associated with a respective subset of the plurality of contextual features; generating merged knowledge by combining the first knowledge with the node knowledge of at least one of the plurality of nodes; and transmitting the merged knowledge to a second vehicle, wherein the second vehicle performs a vehicular operation based on the merged knowledge.
These limitations recites a concept that falls into the “mental process” group of abstract ideas. Using obtained contextual information around a vehicle to identify nodes of knowledge such that the respective subsets of node knowledge could be merged could be done in the human mind or with the aid of paper (see MPEP 2106.04(a)(2)(III). An akin example would be a driver driving passed a sign reducing the speed limit then later seeing orange cones and merging the information into a finding that a construction zone is beginning.
Step 2A: Prong 2: The Applicant does recite the additional elements including (1) obtaining a plurality of contextual features of a driving environment based on knowledge related to the driving environment obtained using sensor data collected by a first vehicle in the driving environment and (2) “transmitting the merged knowledge to a second vehicle, wherein the second vehicle performs a vehicular operation based on the merged knowledge” are claimed as additional elements in conjunction with the abstract idea. However the additional elements are (1) insignificant pre- or post-solution activity and (2) the additional limitation is claimed generally at an “apply it” level and is well-known in the art and therefore this does not integrate the judicial exception into a practical application.
The Applicant has recited a claim wherein (1) obtaining a plurality of contextual features of a driving environment based on knowledge related to the driving environment obtained using sensor data collected by a first vehicle in the driving environment and (2) “transmitting the merged knowledge to a second vehicle, wherein the second vehicle performs a vehicular operation based on the merged knowledge” are claimed as additional elements in conjunction with the abstract idea.
Here, (1) obtaining the data from sensors and (2) transmitting the results of a mental process are insignificant pre- and post-solution activity because “the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).” MPEP § 2106.05(g). (1) Gathering data and (2) outputting data from the mental activity of merging knowledge is a required step that does not meaningful limit the judicial exception. (1) Collecting data for knowledge-based operations would be required in any implementation of the claimed abstract idea. And, (2) given its broadest reasonable interpretation, “performing a vehicular operation based on the merged knowledge” could include further merging knowledge or display of results at the second this vehicle. Therefore, under the broadest reasonable interpretation, the second additional element could (a) be additional mental process or (b) additional insignificant post-solution activity. Consequently both additional elements fail to integrate the abstract idea into a practical application.
Further, both (1) obtaining a plurality of contextual features of a driving environment based on knowledge related to the driving environment obtained using sensor data collected by a first vehicle in the driving environment and (2) “transmitting the merged knowledge to a second vehicle, wherein the second vehicle performs a vehicular operation based on the merged knowledge” are claimed generally at an “apply it” level of generality. (1) The claimed sensor data gathering does not specify particular sensors or even vehicle specific sensors. (2) Data transmission and could be facilitated over any number of generalized technologies including WiFi, 5G, or 802.11(p) (V2V communication). The additional elements are claimed generally and do not recite “improvements in the functioning of a computer or an improvement to any other technology (MPEP § 2106.04(d)(1)) and therefore do not integrate the judicial exception into a practical application. Rather, the additional elements are claimed in such a way as to be generally linking to a particular technological environment without integration into a practical application. Similarly, “performing a vehicular operation based on the merged knowledge” is claimed at an apply it level without a specific positive control step. Therefore, neither of the additional elements integrate the abstract idea into a practical application.
If post-solution communication are not "incidental to the primary process or product [or] merely a nominal or tangential addition to the claim" (MPEP § 2106.05(g) they may integrate the claim into a practical application; however, this is not the case in the instant application.
Step 2B: The claim does not recites an element or combination of elements that is unconventional or significantly more than its individual elements. “[A]n ‘inventive concept’ is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. (MPEP § 2106.05 citing Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, at 72-73)). “Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself” (MPEP § 2106.05). The claim recites the additional elements including:
The Applicant has recited a claim wherein (1) obtaining a plurality of contextual features of a driving environment based on knowledge related to the driving environment obtained using sensor data collected by a first vehicle in the driving environment and (2) “transmitting the merged knowledge to a second vehicle, wherein the second vehicle performs a vehicular operation based on the merged knowledge” are claimed as additional elements in conjunction with the abstract idea. However, the limitations are recited such that the Applicant is merely adding well understood and conventional steps in the art on how to apply the judicial exception.
The first additional element, (1) sensor-based data gathering is commonly applied to vehicles, is well-understood and conventional in the art, and is merely claimed at an “apply it” level of detail. (2) The second additional element does not claim, recite or detail behavior beyond general description of standard data transmission because it is claimed at an “apply it” level of detail that does not meet the test for “significantly more” (MPEP § 2106.05(I)(A) (see MPEP § 2106.05(f))). Similarly, “performing a vehicular operation based on the merged knowledge” is claimed at an apply it level without a positive control step, so this limitation also fails to meet the test for “significantly more” Accordingly, these additional elements do not integrate the abstract idea into significantly more than abstract idea but rather would monopolize the abstract idea.
Finally, as discussed with respect to Step 2A: Prong 2, (1) data gathering from sensors and (2) transmitting the results of a mental process is insignificant post-solution activity because “the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).” MPEP § 2106.05(g). Outputting data from the mental activity of merging knowledge is a required step that does not meaningful limit the judicial exception. Also as discussed supra, given its broadest reasonable interpretation, “performing a vehicular operation based on the merged knowledge” could include merging knowledge or displaying the results at the second this vehicle. The first instance would be additional mental process while the second would still be insignificant post-solution activity, so this limitation also fails to integrate the abstract idea into significantly more.
Regarding the further claims:
Claim 2 does not cure the deficiencies of claim 1 because claim 2 is still drawn to the “mental process” group of abstract ideas as it merely further claims the abstract idea by further limiting merging knowledge and does not include an extra-solution activity or additional elements.
Claim 3 does not cure the deficiencies of claim 1 because claim 3 is still drawn to the “mental process” group of abstract ideas as it merely further claims the abstract idea by further limiting contextual features applied and does not include an extra-solution activity or additional elements.
Claim 4 does not cure the deficiencies of claim 1 because claim 4 is still drawn to the “mental process” group of abstract ideas as it merely further claims the abstract idea by further limiting the type of data analysis and does not include an extra-solution activity or additional elements. Time series analysis is broadly claimed and could be interpreted as simply the order of events which is withing the mental capability of human observer.
Claim 5 does not cure the deficiencies of claim 1 because claim 5 is still drawn to the “mental process” group of abstract ideas as it merely further claims the abstract idea by further limiting vehicular knowledge network and does not include an extra-solution activity or additional elements..
Claim 6 does not cure the deficiencies of claim 1 because claim 6 is still drawn to the “mental process” group of abstract ideas as it merely further defines the abstract idea by limiting identifying a plurality of nodes and does not include an extra-solution activity or additional elements.
Claims 7 and 8 do not cure the deficiencies of their dependent claims because the claims are still drawn to the “mental process” group of abstract ideas as it merely further limits “generating the merged knowledge by combining the first knowledge with the node knowledge” aspect of the abstract idea and does not include an extra-solution activity or additional elements.
Claim 9 does not cure the deficiencies of claim 1 because claim 9 is still drawn to the “mental process” group of abstract ideas as it merely further describes the abstract idea and does not include an extra-solution activity or additional elements.
Claim 10 does not cure the deficiencies of claim 9 because claim 10 is still drawn to the “mental process” group of abstract ideas as it merely further limits the “knowledge refinement criteria” aspect of the mental process and does not include an extra solution activity or instructions on how to apply the abstract idea.
The previous rejection of claim 11 under 35 U.S.C. § 101 rejection is withdrawn. Applicant’s amendments which includes “execute an autonomous or semi-autonomous vehicle maneuver based on the merged knowledge” recites the additional element of positive control of an autonomous or semi-autonomous vehicle. Therefore the claimed invention is directed to an improvement in autonomous or semi-autonomous vehicle, not to an abstract idea.
The previous rejection of claims 12-18 under 35 U.S.C. § 101 rejection is also withdrawn. The additional limitations do not claim a further abstract idea subject to analysis beyond the positive control step claimed in claim 11.
Claim 19 is rejected for reasons parallelling claim 1. The addition of establishing a communication path is post-solution extra solution activity and does not integrate the abstract idea into a practical application or significantly more.
Claim 20 does not cure the deficiencies of claim 19 because claim 20 is still drawn to the “mental process” group of abstract ideas as it merely further describes the abstract idea and does not include an extra-solution activity or additional elements.
Claim 21 does not cure the deficiencies of claim 1 because claim 21 is still drawn to the “mental process” group of abstract ideas as it merely further describes the abstract idea and does not include an extra-solution activity or additional elements.
Therefore, the claims do not amount to significantly more than the abstract idea and have been rejected under 35 USC 101.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. § 102 and § 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1-3, 5-8, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim et al. (US 20200256681 A1).
Regarding claim 1, Kim discloses a method comprising:
obtaining a plurality of contextual features of a driving environment based on knowledge related to the driving environment obtained using sensor data collected by a first vehicle in the driving environment; (Kim: ¶ 392; dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle. For example, a sign, a traffic light, a vehicle involved with an accident, and the like may be set as dynamic objects. The dynamic object includes at least one of an identification number of an object, a kind of an object, a size of an object, a shape of an object, and location information (e.g., latitude, longitude, altitude) of an object.) identifying a plurality of nodes of a vehicular knowledge network based on the plurality of contextual features, wherein each of the nodes comprises a computing entity or device that stores node knowledge associated with a respective subset of the plurality of contextual features; (Kim: ¶ 035; one or more nodes include a first node for a first section and a second node for a second section, and wherein the processor is configured to based on the vehicle being located in the first section, match the first path with the second path using the first node and based on the vehicle being located in the second section, match the first path with the second path using the second node.) (Kim: ¶ 037; the map providing device provides the map data through a cache memory. Since an amount of the map data is drastically reduced, a lifespan of the cache memory can be extended.) generating merged knowledge associated with the driving environment by combining the first knowledge with the node knowledge of at least one of the plurality of nodes; and (Kim: ¶¶ 390-392; device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon) . . . a first map having . . . a plurality of dynamic objects sensed by at least one electric component. Here, the dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle.) (Kim: ¶ 346; processor 870 may merge the acquired location information of the vehicle and the received location information of the another vehicle into the received map information) transmitting the merged knowledge to a second vehicle, wherein the second vehicle performs a vehicular operation based on the merged knowledge. (Kim: ¶¶ 375-376; generate a merged map . . .[t]hen, the processor 870 may transmit the redefined V2X message to the another vehicle)
Regarding claim 2, as detailed above, Kim teaches the invention as detailed with respect to claim 1. Kim further teaches:
wherein generating merged knowledge comprises: generating merged knowledge by combining the first knowledge with the node knowledge of each of the plurality of nodes. (Kim: ¶¶ 390-392; device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon) . . . a first map having . . . a plurality of dynamic objects sensed by at least one electric component. Here, the dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle.) (Kim: ¶ 346; processor 870 may merge the acquired location information of the vehicle and the received location information of the another vehicle into the received map information)
Regarding claim 3, as detailed above, Kim discloses the invention as detailed with respect to claim 1. Kim further discloses:
wherein the plurality of contextual features comprises at least one of static properties (Kim: ¶ 398; second map provides location information (x2, y2, z2) for the same traffic light, which information has to be used is a matter.) and dynamic properties obtained from the driving environment. (Kim: ¶¶ 390-392; device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon) . . . a first map having . . . a plurality of dynamic objects sensed by at least one electric component. Here, the dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle.)
Regarding claim 5, as detailed above, Kim discloses the invention as detailed with respect to claim 1. Kim further discloses:
wherein the plurality of nodes of the vehicular knowledge network collectively comprise the plurality of contextual features. (Kim: ¶ 390; map providing device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon), and provides the merged map to the electric components) (Kim: Fig. 012)
Regarding claim 6, as detailed above, Kim discloses the invention as detailed with respect to claim 1. Kim further discloses:
wherein identifying a plurality of nodes of a vehicular knowledge network comprises: identifying a communication path through the vehicular knowledge network that traverses the plurality of nodes. (Kim: ¶ 0452-453; The processor 870 searches for one or more nodes to match the first path with the second path (S1730), and matches the first path with the second path using the detected nodes (S1750). The processor 870 searches for one or more nodes to match the first path with the second path, and controls the memory to store the one or more nodes.)
Regarding claim 7, as detailed above, Kim discloses the invention as detailed with respect to claim 6. Kim further discloses:
wherein generating the merged knowledge by combining the first knowledge with the node knowledge (Kim: ¶ 391; map providing device 800 may receive a first map having accuracy of several meters from a first server. As illustrated in FIG. 13A, the first map may be referred to as ‘dynamic map’ in that it includes a plurality of dynamic objects sensed by at least one electric component.)of each of the plurality of nodes (Kim: ¶ 393; map providing device 800 may receive a second map with accuracy of several centimeters (cm) from a second server. As illustrated in FIG. 13B, the second map may be referred to as ‘highly detailed (HD) map’ in that it has specific information in a lane unit included in a road.)comprises: successively combining the first knowledge with each node of the plurality of nodes (Kim: ¶ 390; map providing device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon), and provides the merged map to the electric components.) while traversing the communication path through the vehicular knowledge network. (Kim: ¶ 394; The map providing device 800 may receive a third map having accuracy of several tens of meters (m) from a third server.)
Regarding claim 8, as detailed above, Kim discloses the invention as detailed with respect to claim 1. Kim further discloses:
wherein generating the merged knowledge by combining the first knowledge with the node knowledge of each of the plurality of nodes comprises: aggregating the first knowledge with node knowledge of a first node of the plurality of nodes to generate first intermediate-merged knowledge; and aggregating the first intermediate-merged knowledge with node knowledge of a second node of the plurality of nodes to generate second intermediate-merged knowledge. (Kim: ¶ 380; may calculate a relative location (network) between the vehicle and the another vehicle using the location information of the another vehicle received from the another vehicle through V2X communication. Then, the calculated relative location information may be aligned in the lane unit on the highly detailed MAP received from the external server (eHorizon).)
Regarding claim 21, as detailed above, Kim teaches the invention as detailed with respect to claim 1. Kim teaches:
wherein the driving environment has previously been identified as a complicated driving environment.; (Kim: ¶ 028; driving information satisfies the preset condition in the first state based on at least one of a determination that, in the manual driving state, a turn signal is turned on, a detection, in the manual driving state, of an object with a potential for collision that is higher than a reference value)
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20200256681 A1) as applied to claims 1 and 11 respective above, and further in view of Takeyasu (US 20230386340 A1).
Regarding claim 4, as detailed above, Kim teaches the invention as detailed with respect to claim 1. Kim does not explicitly teach:
further comprising: executing time series analysis on the sensor data to derive the plurality of contextual features; however, Takeyasu does teach:
►wherein inferring the plurality of contextual features comprises: executing time series analysis on the sensor data to derive the plurality of contextual features. (Takeyasu: ¶ 076; estimation time determination unit 151 is a function unit to determine a time range and a time interval to generate a traffic condition map.) (Takeyasu: ¶ 009; calculate a peripheral object distribution indicating an object existence range where there is a possibility for each object included in a peripheral object group constituted of at least one object existing around a target moving object to exist in an estimation time range,) (Takeyasu: ¶ 215; traffic condition map generation unit 153 generates a traffic condition map in a target time range by merging existence probability maps corresponding to each peripheral object obtained)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Takeyasu with the teachings of Kim because doing so would result in the predicable benefit of "making it possible to consider, when a delay in driving instruction from a driving assistance device to a vehicle occurs, [a] change in a traffic condition that may occur during the delay" (Takeyasu: ¶ 008).
Regarding claim 13, as detailed above, Kim teaches the invention as detailed with respect to claim 11. Kim does not explicitly teach:
wherein deriving the plurality of contextual features comprises: executing time series analysis on the sensor data to derive the plurality of contextual features; however, Takeyasu does teach:
wherein deriving the plurality of contextual features comprises: executing time series analysis on the sensor data to derive the plurality of contextual features. (Takeyasu: ¶ 076; estimation time determination unit 151 is a function unit to determine a time range and a time interval to generate a traffic condition map.). (Takeyasu: ¶ 009; calculate a peripheral object distribution indicating an object existence range where there is a possibility for each object included in a peripheral object group constituted of at least one object existing around a target moving object to exist in an estimation time range,) (Takeyasu: ¶ 215; traffic condition map generation unit 153 generates a traffic condition map in a target time range by merging existence probability maps corresponding to each peripheral object obtained)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Takeyasu with the teachings of Kim because doing so would result in the predicable benefit of "making it possible to consider, when a delay in driving instruction from a driving assistance device to a vehicle occurs, [a] change in a traffic condition that may occur during the delay" (Takeyasu: ¶ 008).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20200256681 A1) as applied to claims 1 above, and further in view of Micks et al. (US 20220026919 A1).
Regarding claim 9, as detailed above, Kim teaches the invention as detailed with respect to claim 1. Kim does not explicitly teach:
further comprising: detecting a knowledge refinement criteria, wherein identifying the plurality of nodes of the vehicular knowledge network is based on detecting the knowledge refinement criteria; however, Micks does teach:
further comprising: detecting a knowledge refinement criteria, wherein identifying the plurality of nodes of the vehicular knowledge network is based on detecting the knowledge refinement criteria. (Micks: ¶ 053; vehicle further comprises one or more of a camera and a LIDAR system; the method further comprises determining that one or more of the camera and the LIDAR system are not providing usable data or are damaged; and determining the location of the vehicle based on the perception information from a radar system is performed in response to determining that the camera or LIDAR system are not providing usable data or are damaged.) (Micks: Clm. 001; merging the drive history data with the sensor data and the supplemental data to generate merged data)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Micks with the teachings of Kim because doing so would result in the predicable benefit of "useful data [being] acquired in even very adverse weather conditions." (Micks: ¶ 011).
Regarding claim 10, as detailed above, Kim in view of Micks teaches the invention as detailed with respect to claim 9. Micks further teaches:
wherein the knowledge refinement criteria comprises at least one of a performance degradation in the first knowledge and an amount of sensor data used to generate the first knowledge being less than a threshold amount. (Micks: ¶ 053; vehicle further comprises one or more of a camera and a LIDAR system; the method further comprises determining that one or more of the camera and the LIDAR system are not providing usable data or are damaged; and determining the location of the vehicle based on the perception information from a radar system is performed in response to determining that the camera or LIDAR system are not providing usable data or are damaged.) (Micks: Clm. 001; merging the drive history data with the sensor data and the supplemental data to generate merged data)
Claims 11-12, 14-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (US 20200256681 A1) in view of Cunningham (US 20200043342 A1).
Regarding claim 11, Kim teaches a vehicle comprising:
memory storing instructions; and one or more processors communicably coupled (Kim: ¶ 016; processor . . . vehicle) to the memory and configured to execute the instructions to: (Kim: ¶ 037; memory) derive a plurality of contextual features of a driving environment based on sensor data collected by a first vehicle in the driving environment, the plurality of contextual features are associated with first knowledge related to the driving environment; (Kim: ¶ 392; dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle. For example, a sign, a traffic light, a vehicle involved with an accident, and the like may be set as dynamic objects. The dynamic object includes at least one of an identification number of an object, a kind of an object, a size of an object, a shape of an object, and location information (e.g., latitude, longitude, altitude) of an object.) identify a plurality of nodes of a vehicular knowledge network based on the plurality of contextual features, wherein each of the plurality of nodes comprises a computing entity or device that stores node knowledge associated with a respective subset of the plurality of contextual features; (Kim: ¶ 035; one or more nodes include a first node for a first section and a second node for a second section, and wherein the processor is configured to based on the vehicle being located in the first section, match the first path with the second path using the first node and based on the vehicle being located in the second section, match the first path with the second path using the second node.) (Kim: ¶ 037; the map providing device provides the map data through a cache memory. Since an amount of the map data is drastically reduced, a lifespan of the cache memory can be extended.) . . receive merged knowledge associated with the driving environment based on combining the first knowledge with the knowledge of each of the plurality of nodes via traversal of the communication path; and (Kim: ¶ 342; module 830 may receive an ADAS MAP [Examiner note: containing previously merged and uploaded data] from the external server.) execute an autonomous or semi-autonomous vehicle maneuver based on the merged knowledge (Kim: ¶ 379; can control the vehicle using the ADAS MAP (map information, highly detailed MAP) in which the relative location between the vehicle and the another vehicle is aligned in the lane unit)
Kim does not explicitly teach: establish a communication path through the vehicular knowledge network based on the plurality of contextual features,; however, Cunningham does teach:
establish a communication path through the vehicular knowledge network based on the plurality of contextual features, (Cunningham: ¶ 056; communication circuit 194 in intermediate vehicle 115 may be used to relay V2V messages between host vehicle 105 and one or more target vehicles 110. In some embodiments, host vehicle 105 communicates with target vehicle 110 by way of intermediate vehicle 115 when the distance between host vehicle 105 and target vehicle 110 is greater than the range of their communication capabilities. For example, based on sensor information, controller 182 in an intermediate vehicle 115 may determine that the distance between host vehicle 105 and target vehicle 110 (i.e., separation distance 120A) exceeds the distance range for DSRC communication).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Cunningham with the teachings of Kim because doing so would result in the predicable benefit of allowing relaying a message beyond the range of the current vehicle (Cunningham: ¶ 034).
Regarding claim 12, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 11. Kim further teaches:
wherein the plurality of contextual features comprises at least one of static properties and dynamic properties obtained from the driving environment. (Kim: ¶ 398; second map provides location information (x2, y2, z2) for the same traffic light, which information has to be used is a matter.) ◄ (Kim: ¶¶ 390-392; device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon) . . . a first map having . . . a plurality of dynamic objects sensed by at least one electric component. Here, the dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle.)
Regarding claim 14, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 11. Kim further teaches:
wherein the plurality of nodes of the vehicular knowledge network collectively comprise the plurality of contextual features. (Kim: ¶ 390; map providing device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon), and provides the merged map to the electric components) (Kim: Fig. 012)
Regarding claim 15, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 11. Cunningham further teaches:
wherein establishing the communication path through the vehicular knowledge network comprises: identifying the plurality of nodes based on the plurality of contextual features. (Cunningham: ¶ 056; communication circuit 194 in intermediate vehicle 115 may be used to relay V2V messages between host vehicle 105 and one or more target vehicles 110. In some embodiments, host vehicle 105 communicates with target vehicle 110 by way of intermediate vehicle 115 when the distance between host vehicle 105 and target vehicle 110 is greater than the range of their communication capabilities. For example, based on sensor information, controller 182 in an intermediate vehicle 115 may determine that the distance between host vehicle 105 and target vehicle 110 (i.e., separation distance 120A) exceeds the distance range for DSRC communication) (Cunningham: ¶ 009; determining a communication blockage between the first vehicle and the second vehicle; and detecting a signal from the first vehicle, wherein the signal is determined to be unstable by the third vehicle.)
Regarding claim 16, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 11. Kim further teaches:
wherein the one or more processors are further configured to execute the instructions to: cause the vehicle to: transmit the first knowledge to a first node of the plurality of nodes based on at least one of the plurality of contextual features. (Kim: ¶ 369; another vehicle to which the V2X message is transmitted may be another vehicle existing within a predetermined distance from the vehicle 100. The predetermined distance may be determined by an available distance of the V2X module or the setting of a user. When a number of other vehicles to which the V2X message is transmitted is plural, the processor 870 may acquire location information of the another vehicle)
Regarding claim 17, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 11. Cunningham further teaches:
wherein the one or more processors are further configured to execute the instructions to: cause the vehicle to: detect a knowledge refinement criteria, wherein establishing the communication path through the vehicular knowledge network is based on detecting the knowledge refinement criteria. (Cunningham: ¶ 056; communication circuit 194 in intermediate vehicle 115 may be used to relay V2V messages between host vehicle 105 and one or more target vehicles 110. In some embodiments, host vehicle 105 communicates with target vehicle 110 by way of intermediate vehicle 115 when the distance between host vehicle 105 and target vehicle 110 is greater than the range of their communication capabilities. For example, based on sensor information, controller 182 in an intermediate vehicle 115 may determine that the distance between host vehicle 105 and target vehicle 110 (i.e., separation distance 120A) exceeds the distance range for DSRC communication) (Cunningham: ¶ 009; determining a communication blockage between the first vehicle and the second vehicle; and detecting a signal from the first vehicle, wherein the signal is determined to be unstable by the third vehicle.)
Regarding claim 19, Kim teaches a system comprising:
memory storing instructions; and one or more processors communicably (Kim: ¶ 016; processor . . . vehicle) coupled to the memory and configured to cause the system to: execute the instructions to: (Kim: ¶ 037; memory) receive, from a vehicle, a plurality of contextual features associated with knowledge related to a driving environment based on sensor data of the vehicle collected from the driving environment; (Kim: ¶ 392; dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle. For example, a sign, a traffic light, a vehicle involved with an accident, and the like may be set as dynamic objects. The dynamic object includes at least one of an identification number of an object, a kind of an object, a size of an object, a shape of an object, and location information (e.g., latitude, longitude, altitude) of an object.)
Kim does not explicitly teach: execute cycle detection to identify a plurality of nodes of a vehicular knowledge network based on the plurality of contextual features, wherein each of the plurality of nodes comprises a computing entity or device that stores node knowledge associated with at least a contextual feature of the plurality of contextual features, and wherein the plurality of nodes collectivelystore node knowledge associated with the plurality of contextual features; and; however, Cunningham does teach:
execute cycle detection to identify plurality of nodes of a vehicular knowledge network based on the plurality of contextual features, wherein each of the plurality of nodes comprises a computing entity or device that stores node knowledge associated with at least a contextual feature of the plurality of contextual features, and wherein the plurality of nodes collectivelystore node knowledge associated with the plurality of contextual features; and (Cunningham: ¶ 056; communication circuit 194 in intermediate vehicle 115 may be used to relay V2V messages between host vehicle 105 and one or more target vehicles 110. In some embodiments, host vehicle 105 communicates with target vehicle 110 by way of intermediate vehicle 115 when the distance between host vehicle 105 and target vehicle 110 is greater than the range of their communication capabilities. For example, based on sensor information, controller 182 in an intermediate vehicle 115 may determine that the distance between host vehicle 105 and target vehicle 110 (i.e., separation distance 120A) exceeds the distance range for DSRC communication) (Cunningham: ¶ 009; determining a communication blockage between the first vehicle and the second vehicle; and detecting a signal from the first vehicle, wherein the signal is determined to be unstable by the third vehicle.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Cunningham with the teachings of Kim because doing so would result in the predicable benefit of allowing relaying a message beyond the range of the current vehicle (Cunningham: ¶ 034).
Regarding claim 20, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 19. Kim further teaches:
wherein the one or more processors are further configured to execute the instructions to cause the system to: execute knowledge creation based on raw data obtained from one or more connected vehicles, the knowledge creation comprising generating node knowledge and deriving a context associated with the knowledge. (Kim: ¶¶ 390-392; device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon) . . . a first map having . . . a plurality of dynamic objects sensed by at least one electric component. Here, the dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle.) (Kim: ¶ 346; processor 870 may merge the acquired location information of the vehicle and the received location information of the another vehicle into the received map information)
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Cunningham as applied to claim 11 above, and further in view of Takeyasu (US 20230386340 A1).
Regarding claim 13, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 11. Kim does not explicitly teach:
wherein deriving the plurality of contextual features comprises: executing time series analysis on the sensor data to derive the plurality of contextual features; however, Takeyasu does teach:
wherein deriving the plurality of contextual features comprises: executing time series analysis on the sensor data to derive the plurality of contextual features. (Takeyasu: ¶ 076; estimation time determination unit 151 is a function unit to determine a time range and a time interval to generate a traffic condition map.) (Takeyasu: ¶ 008). (Takeyasu: ¶ 009; calculate a peripheral object distribution indicating an object existence range where there is a possibility for each object included in a peripheral object group constituted of at least one object existing around a target moving object to exist in an estimation time range,) (Takeyasu: ¶ 215; traffic condition map generation unit 153 generates a traffic condition map in a target time range by merging existence probability maps corresponding to each peripheral object obtained)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Takeyasu with the teachings of Kim because doing so would result in the predicable benefit of "making it possible to consider, when a delay in driving instruction from a driving assistance device to a vehicle occurs, [a] change in a traffic condition that may occur during the delay" (Takeyasu: ¶ 008).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Cunningham as applied to claim 17 above, and further in view of Micks et al. (US 20220026919 A1).
Regarding claim 18, as detailed above, Kim in view of Cunningham teaches the invention as detailed with respect to claim 17. Kim does not explicitly teach:
wherein the knowledge refinement criteria comprises at least one of a performance degradation in the first knowledge and an amount of sensor data used to generate the first knowledge being less than a threshold amount; however, Micks does teach:
wherein the knowledge refinement criteria comprises at least one of a performance degradation in the first knowledge and an amount of sensor data used to generate the first knowledge being less than a threshold amount. (Micks: ¶ 053; vehicle further comprises one or more of a camera and a LIDAR system; the method further comprises determining that one or more of the camera and the LIDAR system are not providing usable data or are damaged; and determining the location of the vehicle based on the perception information from a radar system is performed in response to determining that the camera or LIDAR system are not providing usable data or are damaged.) (Micks: Clm. 001; merging the drive history data with the sensor data and the supplemental data to generate merged data)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Micks with the teachings of Kim because doing so would result in the predicable benefit of "useful data [being] acquired in even very adverse weather conditions." (Micks: ¶ 011).
Response to Arguments
Applicant's remarks filed Dec. 31, 2025 have been fully considered.
Applicant’s argument and amendments with respect to the previous applied 35 U.S.C. § 101 rejection of claims 11-18 is persuasive and the rejection is hereby withdrawn. The amended claims provide a direct positive control step that integrates the judicial exception into a practical application.
Applicant’s argument and amendments with respect to the previous applied 35 U.S.C. § 101 rejection of claims 1-10 and 19-21 is not persuasive. The amended claims recites “performs a vehicle operation” limitation which does not integrate the judicial exception into a practical application because the broadest reasonable interpretation of the claimed “vehicle operation” could be additional judicial exception operations such as merging vehicle information. Narrowing of the transmission process or the vehicular operation may integrate the judicial exception into a practical application.
Applicant’s arguments with respect to the previous applied 35 U.S.C. § 102 or § 103 rejection of claims 1-21 have been fully considered but they are not persuasive.
Applicant argues that Kim fails to claimed invention because
Kim's simple road "maps" are not analogous to the recited "contextual" "knowledge" about a specific "driving environment." As described in the present application, “'knowledge' is conceptually a state of understanding that is obtained through experience and analysis of collected information, such as raw data. A function of vehicular knowledge networking, as disclosed herein, is the capability to transform data, such as raw data collected from vehicle sensors, into searchable knowledge, and subsequently to create and distribute this knowledge with various life cycles and relevance... For instance, [] raw data 254 from [] vehicle can be processed to ascertain meaningful information 252 about the vehicle, such as 'the vehicle was moving at a speed mph at time t and at position p.' (Applicant’s Arguments filed Dec. 31, 2025, pg. 11).
The maps that Kim teaches are not static representations of only roadways but rather are “map[s] having . . . a plurality of dynamic objects sensed by at least one electric component. Here, the dynamic object refers to an object which is sensed by electric components such as a camera, a LiDAR, a radar, etc., disposed in the vehicle [including] a vehicle involved with an accident, and the like may be set as dynamic objects. “ (Kim: ¶¶ 391-392) In other words, Kim’s maps include real-time contextual knowledge that is meaningful to a moving vehicle, such as trying to avoid an accident site.
Further, Kim does teach “a fact, a belief extracted by analyzing patterns in information.” At ¶¶ 305-320 Kim teaches that each layer of the map shared contains differing information and that the fourth layer in particular, or the map in general “may include road-related information that is transformed in real time as it goes from the first layer to the fourth layer, similarly to the LDM data” (Kim: ¶ 320). Kim further teaches that a second vehicle may merge data with a first vehicle and share the transformed map with a third vehicle. Kim: ¶ 346. A person of ordinary skill in the art would recognize merging map information would at a minimum require deduplication of the data between the first and second vehicles, thus transforming or processing the data.
Applicant further argues that
. . .information 252 can be subjected to analysis 251 in order to derive knowledge 250. It is knowledge 250 that is the most contextual-rich in comparison to data 254 and information 252... As alluded to above, knowledge 250 can be considered as a fact, a belief extracted by analyzing patterns in information 252. For instance, knowledge 250 can be created through analysis 251 of multiple instances of information 252 and it is a fact or a belief that represents the hidden relationship among the information 252. Again, continuing with the example, knowledge 250 can be inferred insight surrounding the vehicle, where knowledge 250 identifies that sequential conflicts happen the most in an area X (related to vehicle's location P), thus X is a risky zone. The creation of knowledge 250 may require computationally hungry algorithms and a set of information 252 and/or data 254 that are potentially from multiple sources (e.g., vehicles)." Present application, [0023]. (Applicant’s Arguments filed Dec. 31, 2025, pg. 16).
With respect to Applicant's argument that applied prior art Kim fail to teach or suggest, in particular, that “a fact or a belief that represents a hidden relationship”, it is noted that the features upon which applicant relies are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Independent claim 1 explicitly recites “generating merged knowledge” and does not limit the claimed invention to hidden knowledge or conflict zones. The broadest reasonable interpretation of “merged knowledge” could encompass hidden relationships or computationally hungry processes, but it would also encompass deduplication of data as Kim suggests when teaching that the “map providing device 800 merges a plurality of maps received from a plurality of servers into one map (or eHorizon), and provides the merged map to the electric components.” (Kim: ¶ 390)
Applicant also argues:
the Office Action does not analogize Kim's merging of "maps" from different servers to this limitation. Instead, the Office Action cites to portions from Kim that describe "nodes" of a map. See e.g., Office Action, page 11 and Kim, [0035]. But these "nodes" in Kim are not the "servers" from which Kim obtains the "maps" that are merged. Instead, Kim's nodes are mere map elements. Thus, the Office Action's application of Kim for this limitation is improper. (Applicant’s Arguments filed Dec. 31, 2025, pg. 13).
Kim teaches equal sharing of maps between a plurality of vehicles where each vehicle adds information it has obtain to the received synthesized map. In other words, vehicle one may populate a map with data, then pass it to vehicle two, who further populates the map with data before passing it to vehicle three. Consequently each vehicle is acting as both a data node and a server to multiple other vehicles within transmission range.
Applicant also argues:
For example, in rejecting dependent claims 6 and 7 that recite (as now amended), inter alia, "identifying the plurality of nodes" by "identifying a communication path through the vehicular knowledge network that traverses the plurality of nodes," the Office Action cites to portions of Kim that describe controlling a vehicle along a geographic route. See e.g., Office Action, page 13 and Kim, [0379]. This analogy is also improper as controlling a vehicle along a geographic route is not analogous to "traver[sing]" "a communication path through [a] vehicular knowledge network."(Applicant’s Arguments filed Dec. 31, 2025, pg. 13).
Because Kim teaches that nodes are vehicles acting as servers finding a geographical path and a communication path through the node system occurs at the same time. For example when Kim teaches that the “processor 870 searches for one or more nodes to match the first path with the second path, and controls the memory to store the one or more nodes” (Kim: ¶ 0452); it is describing finding a nodes along a desired geographical path or in a geographical area. However, when Kim later teaches that the “processor 870 searches for one or more nodes to match the first path with the second path, and controls the memory to store the one or more nodes” (Kim: ¶ 453); it is describing downloading data from the communication nodes found along those geographical paths in order to gain data on the route the vehicle is planning to traverse, thus teaching “identifying a communication path through the vehicular knowledge network that traverses the plurality of nodes.” (Instant Application: Clm. 6).
Similarly with the instant application claim 7, when Kim describes downloading real-time changeable data from nodes (Kim: ¶ 391) that are later merged into a single map (Kim: ¶ 390) used for navigation (Kim: ¶ 393) and later passing that information to other vehicles traversing similar geographical paths (Kim: ¶ 394), Kim is teaching “successively combining the first knowledge with each node of the plurality of nodes while traversing the communication path through the vehicular knowledge network” (Instant Application: Clm. 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Huang et al. (US 20240062533 A1) which discloses a visual enhancement method and a system based on fusion of spatially aligned features of multiple networked vehicles.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 CHARLES PALL whose telephone number is (571)272-5280. The examiner can normally be reached on M-F 9:30 - 18:30.
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, Angela Ortiz can be reached on 571-272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.P./ Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
Prosecution Insights