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 .
This final rejection is in response to the amendment filed on: 03/30/2026.
Claim(s) 1, 3, 4, 8, 10, 15, and 17-20 remain rejected under 35 U.S.C. 103 as being unpatentable over Eldar et al (US Application: US 2021/0064057, published: Mar. 4, 2021, filed: Nov. 12, 2020), in view of Golding et al (US Application: US 20170314954, published: Nov. 2, 2017, filed: May 2, 2016), in view of Padegimaite et al (US Application: US 2021/0404833, published: Dec 30, 2021, filed: Nov. 2, 2018).
Claim(s) 7 and 14 remain rejected under 35 U.S.C. 103 as being unpatentable over Eldar et al (US Application: US 2021/0064057, published: Mar. 4, 2021, filed: Nov. 12, 2020), Golding et al (US Application: US 20170314954, published: Nov. 2, 2017, filed: May 2, 2016), in view of Padegimaite et al (US Application: US 2021/0404833, published: Dec 30, 2021, filed: Nov. 2, 2018), in view of Masuda et al (US Application: US 2020/0003572, published: Jan. 2, 2020, filed: Jan. 23, 2017).
Claim(s) 21 remains rejected under 35 U.S.C. 103 as being unpatentable over Eldar et al (US Application: US 2021/0064057, published: Mar. 4, 2021, filed: Nov. 12, 2020), Golding et al (US Application: US 20170314954, published: Nov. 2, 2017, filed: May 2, 2016), in view of Padegimaite et al (US Application: US 2021/0404833, published: Dec 30, 2021, filed: Nov. 2, 2018) in view of Arnicar et al (US Patent: 11566912, issued: Jan. 31, 2023, filed: Jun. 18, 2020).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 4, 8, 10, 15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldar et al (US Application: US 2021/0064057, published: Mar. 4, 2021, filed: Nov. 12, 2020), in view of Golding et al (US Application: US 20170314954, published: Nov. 2, 2017, filed: May 2, 2016), in view of Padegimaite et al (US Application: US 2021/0404833, published: Dec 30, 2021, filed: Nov. 2, 2018).
With regards to claim 1. Eldar et al teaches a method (Fig. 1: a processor and memory implemented method) for generating a direction identifying model, comprising:
acquiring direction-targeted road test data corresponding to a target road, a guide arrow sign corresponding to the target road (paragraph 0232: sensor data is collected/acquired of surroundings of a vehicle traveling on a road are interpreted as the claimed ‘road test data’. As explained in paragraphs 0194, 0223, 0224 road features such as a road sign(s) (see fig. 10, ref 1025 and 1030) associated with the road are collected/acquired.); and
acquiring an accessible road-direction corresponding to the target road from a preset knowledge graph based on the guide arrow sign corresponding to the target road, wherein the preset knowledge graph is a network presenting relationships between a plurality of entities, the preset knowledge graph comprising the plurality of entities and the relationships, and the plurality of entities comprising the guide arrow sign and the accessible road-direction (paragraphs 0218, 0220, 0223, 0224, 0235-0237: in a sparse map (interpreted as a knowledge graph), there includes a plurality of data that includes spatially relating landmarks (which include signs/arrow-signs) to their respective road segments having trajectories (interpreted as accessible road-direction(s) of corresponding target road(s));
training a model … by: using the direction-targeted road test data and the guide arrow sign … as input of the … model, wherein the direction-targeted road test data comprises … guide arrow sign used for the guiding, warning, regulating, or instructing traffic, a turning angle of an intersection of the target road, and an instruction outputted by a highlighting signal light located on the target road and using the accessible road-direction as desired output to obtain a trained direction (paragraphs 0218, 0223, 0224, 0384-0387, 0473, 0474, 0481-0484, and 0514-0515 : a map model is trained/generated by using road feature data obtained from the vehicle sensors to correlate arrow signs and accessible road directions to obtain an accessible road direction for vehicle navigation. More specifically, the data acquired for training/updating the model also include the state of a traffic light issuing an instruction to light/display/highlight a turn-arrow and user driver action/feedback for navigational actions (the examiner interprets any navigational turn-action in a specific direction is an ‘angle’)) made by the user for the vehicle with respect to a traffic light state data collected).
However Eldar et al does not expressly teach training an initial deep neural network model to obtain a trained direction identifying model by: using the direction targeted test data and the guide arrow sign as input of the initial deep neural network, wherein the direction-targeted road test data comprises user feedback data for the accessible road-direction of the target road, user feedback data for the guide arrow sign comprising a printed sign on the target road, ; using the accessible road-direction as a desired output of the initial deep neural network model, to obtain a trained direction identifying model, inputting acquired guide arrow sign and direction-targeted road test data corresponding to the to-be-predicted road into the trained direction identifying model to output an accessible road-direction corresponding to the to-be-predicted road, wherein the accessible road direction corresponding to the to-be-predicted road indicates a traveling direction of a traffic participant on the to-be-predicted road at a next moment; and displaying the guide arrow sign and the accessible road-direction corresponding to the to-be-predicted road on an interface of a navigation application of an electronic device for navigation.
Yet Golding et al teaches a … model to obtain a trained direction identifying model by: using the direction targeted test data and the guide arrow sign as input …, wherein the direction-targeted road test data comprises user feedback data for the accessible road-direction of the target road, user feedback data for the guide arrow sign comprising a printed sign on the target road (paragraphs 0051, 0054, 0055, 0056, 0057, 0076: a navigation model obtains feedback about a visual landmark/sign/prominent-object (selected based on usefulness) for whether the user was able to successfully access the target/next-road, and also about whether a user sees a next landmark/sign/prominent-object. Some visual landmarks also include printed sign/lane markings/arrow(s) on the road ), and a turning angle of an intersection of the target road (paragraph 0057: the navigation model also obtains feedback of driver activity with regards to a driver’s turning activity at an intersection to the targeted road (the examiner interprets any turn in a specific direction is an ‘angle’) and takes in right or left turning angle activity at an intersection such as sudden braking data or a ‘miss’ data belonging to the turn/turning-angle); using the accessible road-direction as a desired output of the … model, to obtain a trained direction identifying model, inputting acquired …sign and direction-targeted road test data corresponding to the to-be-predicted road into the trained direction identifying model to output an accessible road-direction corresponding to the to-be-predicted road, wherein the accessible road direction corresponding to the to-be-predicted road indicates a traveling direction of a traffic participant on the to-be-predicted road at a next moment (Fig 3, paragraphs 0051, 0054, 0056, 0067, 0070: all data collected from feedback updates to a next /new iteration of the navigation model (which provides output of next maneuver (accessible road-direction)); and displaying the … sign and the accessible road-direction corresponding to the to-be-predicted road on an interface of a navigation application of an electronic device for navigation (paragraphs 0047 and 0051: directions are provided to the user/driver can be displayed and include the landmark/sign/prominent-object, along with the accessible road direction to a target road).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Eldar et al’s ability to train a model to reference road test data and guide arrow sign data (a type of landmark/sign/prominent-object), such that the model’s functionality could further include obtaining data for the accessible road-direction and the sign/landmark/prominent-object and obtaining turning angle data in order to revise to an updated model to output the accessible road direction to a display, (along with the sign/landmark/prominent-object), as taught by Golding et al. The combination would have allowed Eldar et al to helped guide a driver along a navigation route based on real-time imagery of the user’s vantage point in a vehicle … to augment step-by-step navigation directions for the navigation route (Golding et al, paragraph 0018).
However although the combination teaches a model trained by using multiple input context parameter data (the direction -targeted road test data and the guide arrow sign) as input … to obtain a trained direction identifying model, the combination does not expressly teach ‘training an initial deep neural network model to obtain a trained direction identifying model’. Additionally, although the combination teaches using an output of the model (desired output being the accessible road-direction), the combination does not expressly teach output is ‘… of the initial deep neural network model’.
Yet Padegimaite et al teaches using multiple input context parameter data as input to … ‘training an initial deep neural network model to obtain a trained … model’. Additionally teaches the desired output (from the training using a plurality of context data) is ‘… of the initial deep neural network model’ (paragraphs 0062, 0064, 0068: multiple context parameter data is taken as input into a machine learning model to obtain a trained model, and further multiple context data can be taken as input as feedback to update the trained model. The trained model provides an output based upon the multiple context data).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Eldar et al and Golding et al’s ability to train a model by using multiple input context parameter data (the direction -targeted road test data and the guide arrow sign) as input in order to obtain a trained direction identifying model (which produces as desired output the accessible road-direction), such that an initial deep neural network is used to take in the multiple input context parameter data, as taught by Padegimaite et al. The combination would have allowed Eldar et al and Golding et al to have generated navigation instructions utilizing machine learning techniques to generate a machine learning model based on user past experiences (Padegimaite et al, paragraph 0005).
With regards to claim 3. The method according to claim 1, Eldar et al, Golding et al, and Padegimaite et al teaches wherein the method further comprises: establishing the preset knowledge graph by using the guide arrow sign and the accessible road-direction as entities, and using a relationship between the guide arrow sign and the accessible road-direction as a relationship between the entities of the preset knowledge graph (Eldar et al, paragraphs 0218, 0220, 0223, 0224, 0235-0237, 0384: the sparse map includes spatial relationships between directional arrow signs (landmarks) and direction/trajectory of travel).
With regards to claim 4. The method according to claim 1, Eldar et al, Golding et al, and Padegimaite et al teaches wherein the direction- targeted road test data comprises at least a road type of the target road (Eldar et al, paragraph 0235: a type of road having one or a type of road having multiple trajectories/directions can be part of the sensed road test data).
With regards to claim 8, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches an apparatus for generating a direction identifying model, comprising: at least one processor; and a memory storing instructions, wherein the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising: acquiring direction-targeted road test data corresponding to a target road and a guide arrow sign corresponding to the target road; acquiring an accessible road-direction corresponding to the target road from a preset knowledge graph based on the guide arrow sign corresponding to the target road, wherein the preset knowledge graph is a network presenting relationships between a plurality of entities, the preset knowledge graph comprising the plurality of entities and the relationships, and the plurality of entities comprising the guide arrow sign and the accessible road-direction; training an initial deep neural network model to obtain a trained direction identifying model by: using the direction-targeted road test data and the guide arrow sign as an input of the initial deep neural network model, wherein the direction-targeted road test data comprises user feedback data for the accessible road-direction of the target road, user feedback data for the guide arrow sign used for guiding, warning, regulating, or instructing traffic, , a turning angle of an intersection of the target road, and an instruction outputted by a highlighting signal light located on the target road, using the accessible road-direction as a desired output of the initial deep neural network model, to obtain the trained direction identifying model; inputting acquired guide arrow sign and direction-targeted road test data corresponding to a to-be-predicted road into the trained direction identifying model to output an accessible road-direction corresponding to the to-be-predicted road, wherein the accessible road-direction corresponding to the to-be-predicted road indicates a traveling direction of a traffic participant on the to-be-predicted road at a next moment; and displaying the guide arrow sign and the accessible road-direction corresponding to the to-be-predicted road on an interface of a navigation application of an electronic device for navigation, , as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
With regards to claim 10. The apparatus according to claim 9, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches wherein the operations further comprise: establishing the preset knowledge graph by using the guide arrow sign and the accessible road-direction as entities, and using a relationship between the guide arrow sign and the accessible road-direction as a relationship between the entities of the preset knowledge graph, as similarly explained in the rejection of claim 3, and is rejected under similar rationale.
With regards to claim 15, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches a non-transitory computer readable storage medium storing computer instructions, wherein the computer instructions are used for causing a computer to execute operations comprising: acquiring direction-targeted road test data corresponding to a target road and a guide arrow sign corresponding to the target road; acquiring an accessible road-direction corresponding to the target road from a preset knowledge graph based on the guide arrow sign corresponding to the target road, wherein the preset knowledge graph is a network presenting relationships between a plurality of entities, the preset knowledge graph comprising the plurality of entities and the relationships, and the plurality of entities comprising the guide arrow sign and the accessible road-direction; training an initial deep neural network model to obtain a trained direction identifying model by using the direction-targeted road test data and the guide arrow sign as an input of the initial deep neural network model, wherein the direction-targeted road test data comprises user feedback data for the accessible road-direction of the target road, user feedback data for the guide arrow sign used for guiding, warning, regulating or instructing traffic, a turning angle of an intersection of the target road, and an instruction outputted by a highlighting signal light located on the target road and using the accessible road-direction as a desired output of the initial deep neural network model to obtain the trained direction identifying model; inputting acquired guide arrow sign and direction-targeted road test data corresponding to a to-be-predicted road into the trained direction identifying model to output an accessible road- direction corresponding to the to-be-predicted road, wherein the accessible road-direction corresponding to the to-be-predicted road indicates a traveling direction of a traffic participant on the to-be-predicted road at a next moment; and displaying the guide arrow sign and the accessible road-direction corresponding to the to- be-predicted road on an interface of a navigation application of an electronic device for navigation, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
With regards to claim 17. The non-transitory computer readable storage medium according to claim 16, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches wherein the operations further comprise: establishing the preset knowledge graph by using the guide arrow sign and the accessible road-direction as entities, and using a relationship between the guide arrow sign and the accessible road-direction as a relationship between the entities of the preset knowledge graph, as similarly explained in the rejection of claim 3, and is rejected under similar rationale.
With regards to claim 18. The non-transitory computer readable storage medium according to claim 15, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches wherein the direction-targeted road test data comprises a road type of the target road, as similarly explained in the rejection of claim 4, and is rejected under similar rationale.
With regards to claim 19, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches a roadside device, comprising the apparatus according to claim 8, as similarly explained in the rejection of claim 8, and is rejected under similar rationale.
With regards to claim 20, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches a cloud control platform, comprising the apparatus according to claim 8, as similarly explained in the rejection of claim 8, and is rejected under similar rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldar et al (US Application: US 2021/0064057, published: Mar. 4, 2021, filed: Nov. 12, 2020), Golding et al (US Application: US 20170314954, published: Nov. 2, 2017, filed: May 2, 2016), in view of Padegimaite et al (US Application: US 2021/0404833, published: Dec 30, 2021, filed: Nov. 2, 2018), in view of Masuda et al (US Application: US 2020/0003572, published: Jan. 2, 2020, filed: Jan. 23, 2017).
With regards to claim 7. The method according to claim 1, the combination of Eldar et al, Golding et al, and Padegimaite et al teaches wherein the method further comprises: … the to-be-predicted road , … the guide arrow sign, and the accessible road direction corresponding to the to-be-predicted road …, as similarly explained in the rejection of claim 5 (Eldar et al, paragraphs 0218, 0223, 0224, and 0384-0387: a map model is trained/generated by using road feature data obtained from the vehicle sensors and is used to correlate arrow signs and accessible road directions to obtain an accessible road direction for vehicle navigation), and is rejected under similar rationale.
However the combination does not expressly teach storing, in the preset knowledge graph, an ID of the to- be-predicted road as an external key, and the guide arrow sign and the accessible road-direction corresponding to the to-be-predicted road as an attribute content.
Yet Masuda et al teaches storing, in [a] preset knowledge graph, an ID of the to- be-predicted road as an external key, and [marked traffic feature/attribute] and the accessible road-direction corresponding to the to-be-predicted road as an [feature]/attribute content (paragraph 0007, Fig. 4, Fig. 5: in a graphed/table structure, a to be predicted road can be interpreted as an adjacent node, and the adjacent node includes an external key (adjacent node ID) to obtain a potential accessible/predicted-road. The table structure further includes knowledge traversal to access lane feature(s)/attribute that correspond to selection of the road segment along a route).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Eldar et al, Golding et al, Padegimaite et al ’s ability to reference marked traffic guidance (the guide arrow sign), the to be predicted road and the to be predicted road using/referencing data (a knowledge graph), such that the referenced data can is further modified to be accessed as an organized retrieval structure based upon identifiers/keys, as taught by Masuda et al. The combination would have allowed Eldar et al, Golding et al, and Padegimaite et al to have implemented an efficient way to perform route searching for subsequent vehicle guidance (Masuda, paragraph 0005).
With regards to claim 14. The apparatus according to claim 8, Eldar et al, Golding et al, Padegimaite et al, and Masuda et al wherein the operations further comprise: storing, in a preset knowledge graph, an ID of the to- be-predicted road as an external key, and the guide arrow sign and the accessible road-direction corresponding to the to-be-predicted road as an attribute content, as similarly explained in the rejection of claim 7, and is rejected under similar rationale.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eldar et al (US Application: US 2021/0064057, published: Mar. 4, 2021, filed: Nov. 12, 2020), Golding et al (US Application: US 20170314954, published: Nov. 2, 2017, filed: May 2, 2016), in view of Padegimaite et al (US Application: US 2021/0404833, published: Dec 30, 2021, filed: Nov. 2, 2018) in view of Arnicar et al (US Patent: 11566912, issued: Jan. 31, 2023, filed: Jun. 18, 2020).
With regards to claim 21, which depends on claim 1, Eldar et al, Golding et al, and Padegimaite et al teaches the target road, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly teach wherein a road type of the target road is a special lane, the special lane being a lane whose accessible direction is variable in different time periods.
Yet Arnicar et al teaches wherein a road type of the target road is a special lane, the special lane being a lane whose accessible direction is variable in different time periods (column 2, lines 5-30: a route that is planned can take into account a road type such as HOV or time of day restricted lanes or passing restrictions with respect to directions).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Eldar et al, Golding et al, Padegimaite et al’s ability to take into account road data to make road direction decisions/determinations, such that the road data would have taken into account features such as road types that are variable with for different time periods, as taught by Arnicar et al. The combination would have allowed Eldar et al, Golding et al and Padegimaite et al to have reduced challenges encountered for determining a route for an autonomous vehicle to reach its destination (Arnicar, column 1, lines 20-22).
Response to Arguments
Applicant's arguments filed 03/30/2026 have been fully considered but they are not persuasive.
With respect to claim 1, the applicant argues Eldar’s solution does not “acquir[e] an accessible road-direction corresponding to the target road from a preset knowledge graph” [since] “Eldar’s solution determines the intended direction of travel for a roadway by analyzing the captured image of the directional arrows”. However, the examiner respectfully points out that this argument is not persuasive since Eldar references a sparse map that contains spatial relationship content correlating landmarks (including arrow-signs) and trajectories (direction of travel) for specific road segments (target roads) (see paragraphs 0218, 0220, 0223, 0224, 0235-0237 of Eldar)).
The applicant further argues that Eldar’s sparse map stores discrete physical parameters such as location identifiers and directional indicators of arrows … rather than associated relationships between entities such as ‘guide arrow sign’ and ‘accessible road direction’”. However this argument is not persuasive since as explained above and paragraphs 0218, 0220, 0223, 0224, 0235-0237, Eldar teaches a sparse map (interpreted as the claimed knowledge graph), includes a plurality of data that includes spatially relating landmarks (which include signs/arrow-signs) to their respective road segments having trajectories (interpreted as accessible road-direction(s) of corresponding target road(s)).
With regards to claims 8 and 15, the applicant argues they are allowable for reasons presented by the applicant for claim 1. However this argument is not persuasive since claim 1 has been shown/explained to be rejected above.
With regards to dependent claims 3-4, 7, 10-11, 14 and 17-21, the applicant argues they are allowable due to their dependency upon one of the independent claims 1, 8, and 15. However this argument is not persuasive since claims 1, 8 and 15 have been shown/explained to be rejected above.
Conclusion
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.
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/WILSON W TSUI/Primary Examiner, Art Unit 2172
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