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 .
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: “reference local path planning apparatus” in claims 1-8.
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.
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.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are 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.
In Reference to Claims 1-16
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
For example:
In Claim 1, In line 4 recites “a local semantic navigation map (LSN)” however in lines 1-2 already recites “a local semantic navigation map” however because of the recitation of “(LSN)”, it is not clear if there are two different local semantic navigation maps or they are the same map. For the purposes of treating the claim under prior art, the language is interpreted as the same however the examiner suggests amending the claim to clearly recite as such.
In Claim 1 lines 6-7 recites “extracting at least one candidate path using the extracted travel area or a robot’s location information-based global path” however since “a robot’s location information-based global path” has not been defined in the claim as to what it is therefore the scope of the claim is not clearly defined and as to what is required and how the step is being performed, and also since the claim do not recite a starting point and a step of determining a destination therefore it is not clear as to how to extract path using “the extracted travel area” since “the extracted travel area” is just an area and not a potential path that can be used to extract other paths for the robot to travel without a chosen destination. For the purposes of treating the claim under prior art, the language is interpreted as requiring merely extracting the paths from some path information or a predefined destination is already determined. The examiner suggests amending the claim to clearly recites to overcome the rejection above.
In Claim 2 lines 2-3 recites “updating the navigation map using labeled local semantic information that has not been integrated” however it is not clear as to what required by the claim since there is no step recited as to gathering “labeled local semantic information” or how to gather the information and also step of determining if the information is integrated already or not, therefore it is not clear as to what the scope of the claim is required. For the purposes of treating the claim under prior art, the language is interpret as merely requiring the updating of the map.
In Claim 9 lines 8-9 recites “extract at least one candidate path using the extracted travel area or a robot’s location information-based global path” however since “a robot’s location information-based global path” has not been defined in the claim as to what it is therefore the scope of the claim is not clearly defined and as to what is required and how the step is being performed, and also since the claim do not recite a starting point and a step of determining a destination therefore it is not clear as to how to extract path using “the extracted travel area” since “the extracted travel area” is just an area and not a potential path that can be used to extract other paths for the robot to travel without a chosen destination. For the purposes of treating the claim under prior art, the language is interpreted as requiring merely extracting the paths from some path information or a predefined destination is already determined. The examiner suggests amending the claim to clearly recites to overcome the rejection above.
In Claim 10 lines 1-2 recites “update the navigation map using labeled local semantic information that has not been integrated” however it is not clear as to what required by the claim since there is no step recited as to gathering “labeled local semantic information” or how to gather the information and also step of determining if the information is integrated already or not, therefore it is not clear as to what the scope of the claim is required. For the purposes of treating the claim under prior art, the language is interpret as merely requiring the updating of the map.
The following errors explicitly found in Claims 1, 2 and 9-10 are given way of examples only and not inclusive of all errors. Applicant should carefully review and amend all the claims to ensure all errors are corrected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
[101 Analysis Step 1]
Step 1, of the 2019 Guidance, first looks to whether the claimed invention is directed to a statutory category, namely a process, machine, manufactures, and compositions of mater.
The claim 1 is directed to a method for planning a reference local path in a local semantic navigation map (i.e. process) and claim 9 is directed to a apparatus for planning a reference local path in a local semantic navigation map (i.e. machine). Thus, claims 1 and 9 are one of four the statutory categories (Step 1: YES).
[101 Analysis Step 2A, Prong I]
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim(s) for the remainder of the 101 rejection. Claim 1 recites:
A method for planning a reference local path in a local semantic navigation map performed by a reference local path planning apparatus, the method comprising:
extracting a travel area recognized from travel information matched with location information of a robot in a local semantic navigation map (LSN) and vision information of the robot;
extracting at least one candidate path using the extracted travel area or a robot’s location information-based global path;
determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path; and
deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section of the robot.
The examiner submits that the foregoing bolded limitations(s) constitute a “mental process” because under its broadest reasonable interpretations, the claim covers performance of the limitation in the human mind. For example, “extracting …” can be comparing photo and map location to determine an area within the map, “extracting…” can be drawing paths by looking at current area or current path, “determining…” can be calculating a formula for each path and “deriving…” can be picking a path based on the calculation therefore in the context of the claim encompasses a person looking at and using the data collected to formulating a judgement. Accordingly, the claim recites at least one abstract idea.
[101 Analysis Step 2A, Prong II]
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for planning a reference local path in a local semantic navigation map performed by a reference local path planning apparatus, the method comprising:
extracting a travel area recognized from travel information matched with location information of a robot in a local semantic navigation map (LSN) and vision information of the robot;
extracting at least one candidate path using the extracted travel area or a robot’s location information-based global path;
determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path; and
deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section of the robot.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract into a practical applications.
Regarding the additional limitations of “performed by a reference local path planning apparatus” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processing circuitry of a computer system) to perform the process. The “a reference local path planning apparatus” (which invokes 112(f)) , “robot” are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component and known robot.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical filed, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
[101 Analysis Step 2B]
Regarding Step 2B of the Revised Guidance, representative independent claims 1 and 9 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processing circuitry of a computer system to perform the steps amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claims are not patent eligible. The examiner suggest amending the claims to include step of controlling the robot based on the “deriving a candidate path” in order to overcome the rejection.
Dependent claims 2-8 and 10-16 do not recite any further limitations that cause the claims to be directed towards statutory subject matter. The claims merely recite: abstract idea. Each of the further limitations expound upon the abstract ideas and do not recite additional elements integrating the abstract ideas into a practical application or additional elements that are not well-understood, routine or conventional. Therefore, dependent claims 2-8 and 10-16 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claims 1-16 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
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.
Claim(s) 1-16 (as best understood) are rejected under 35 U.S.C. 103 as being unpatentable over US Patent No. 11,474,530 B1 to Boyraz et. al. (Boyraz) in view of Pub No. US 2022/0097725 A1 to Corte et. al. (Corte).
In Reference to Claim 1
Boyraz teaches (except for the bolded and italic recitations below):
A method for planning a reference local path in a local semantic navigation map performed by a reference local path planning apparatus (252), the method comprising:
extracting a travel area recognized from travel information matched with location information of a robot in a local semantic navigation map (LSN) and vision information of the robot (see at least Boyraz Figs. 1-7 and column 4 lines 4-30, “In accordance with some embodiments of the present disclosure, the autonomous ground vehicle 150 is programmed or otherwise configured to generate semantic maps of a region in which the autonomous ground vehicle 150 operates based on imaging data captured using the imaging device 162 or any other imaging devices provided aboard the autonomous ground vehicle 150 (not shown). For example, in some embodiments, one or more computer processors, devices or systems provided aboard the autonomous ground vehicle 150 (not shown) may be configured to process images captured using the imaging device 162, and to detect, recognize or identify one or more ground surfaces within a vicinity of the autonomous ground vehicle based on such images. In some embodiments, the autonomous ground vehicle 150 may semantically segment such images, such as by assigning labels to one or more pixels or sets of pixels depicted within the images. The labels may indicate whether such pixels depict certain types or categories of ground surfaces, e.g., not only sidewalks but also roads, driveways, lawns or other surfaces. Alternatively, or additionally, such labels may indicate whether such surfaces are formed from one or more specific materials, or whether one or more of the surfaces are passable or desirable for travel thereon by the autonomous ground vehicle 150, or whether one or more of the surfaces are impassable or undesirable for travel thereon by the autonomous ground vehicle 150”);
extracting at least one candidate path using the extracted travel area or a robot’s location information-based global path (see at least Boyraz Figs. 1-7 and column 8 lines 43-67 and column 9 lines 1-3 “Moreover, the autonomous ground vehicles of the present disclosure may include any number of sensors such as position sensors (e.g., GPS receivers, or cellular transceivers configured to triangulate positions based on signals received from multiple cellular transmitters), imaging sensors (e.g., digital cameras or other imaging devices) or other sensors, including but not limited to speedometers, inclinometers, compasses, altimeters, gyroscopes or scanners. The autonomous ground vehicles of the present disclosure may also include communications equipment (e.g., wired or wireless means for communication such as components or systems operating Wireless Fidelity, Bluetooth, near-field communications or cellular technologies or protocols), along with one or more power modules (e.g., batteries), which may be rechargeable, refuelable or replaceable in nature. Information or data obtained or determined by such sensors or such communications equipment may be utilized in manually or automatically controlling an autonomous ground vehicle, e.g., in causing the autonomous ground vehicle to travel along one or more paths or routes, to search for alternate paths or routes, or to avoid expected or unexpected hazards encountered by the autonomous ground vehicle while traveling along such paths or routes. The autonomous ground vehicles of the present disclosure may further include any number of computer components (e.g., processors, data stores, transceivers or input/output devices) for performing any of the tasks or executing any of the functions described herein”);
determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path (see at least Boyraz Figs. 1-7 and column 33 lines 30-41, “Moreover, those of ordinary skill in the pertinent arts will recognize that one or more of the computer systems disclosed herein may be configured to execute one or more optimal path or route algorithms (e.g., shortest path or shortest route algorithms) for providing guidance to or instructing one or more vehicle systems or couriers to travel to a selected area. A path or route may be identified as “optimal” on any objective or subjective basis. For example, an optimal path or route may have a lowest financial cost, a shortest time, or any other beneficial or preferential ranking or status according to one or more objective or subjective metrics”); and
deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section of the robot (see at least Boyraz Figs. 1-7 and column 26 lines 37-67 and column 27 lines 1-5, “According to some embodiments, autonomous ground vehicles of the present disclosure, such as the autonomous ground vehicle 450 of FIG. 4, may be configured for travel throughout an area or environment, such as travel on traditional transportation and non-traditional transportation infrastructure provided within the area or environment. The autonomous ground vehicle 450 may select a path or route based on geospatial data regarding physical features in an area or environment, including but not limited to topographical data regarding the composition and surface features within the area or environment, as well as elevations of points within the area or environment, which may be identified or determined from satellite or ground-based imagery, GIS data, or any other information or data obtained from any other source. Similarly, where an area or environment includes transportation infrastructure such as roads, sidewalks, crosswalks, bicycle or walking paths, bridges or trails, and also non-traditional transportation infrastructure, e.g., parks, fields, forests, lots, clearings or other spaces, the autonomous ground vehicle 450 may be configured for travel thereon along one or more paths or routes, which may be selected or defined based on attributes of the transportation infrastructure and other physical features within the area or environment, as well as attributes of prior travel through the area or environment. Information or data regarding prior travel that may be used to select or define a path or route within an area or environment includes, but is not limited to, one or more time stamps (e.g., times and positions of autonomous ground vehicles within the given area at such times), elapsed times between departures and arrivals, net speeds, courses, angles of orientation (e.g., a yaw, a pitch or a roll), levels of traffic congestion, sizes or dimensions of any payloads carried, environmental conditions or any other information or data that describes or represents instances of prior travel by autonomous ground vehicles within the area or environment”).
Boyraz does not explicitly teaches (bolded and italic recitations above) as to determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section. However, it is known in the art before the effective filing date of the claimed invention to determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section. For example, Corte teaches to determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section. Corte further teaches that preforming such steps provide improvement in efficiency of services and safety (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boyraz to include the steps of determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section as taught by Corte in order to provide improvement in efficiency of services and safety.
In Reference to Claim 2
The method of claim 1 (see rejection to claim 1 above), further comprising: updating the navigation map using labeled local semantic information that has not been integrated (see at least Boyraz Figs. 1-7 and column 5 lines 4-12, “The autonomous ground vehicle 150 may utilize semantic maps in selecting courses and/or speeds for performing a mission, such as a delivery of an item to a destination, and such semantic maps may be updated or new semantic maps may be generated as images are captured by the imaging device 162, or at regular intervals of time, e.g., once every second or fraction of a second, or after a regular number of images have been captured, e.g., once every ten images, fifteen images, or twenty images”).
In Reference to Claim 3
The method of claim 1 (see rejection to claim 1 above), wherein the extracting of the candidate path includes: extracting the candidate path while maintaining tendency of the global path close to local coordinates of the robot (see at least Boyraz Figs. 1-7 and column 28 lines 43-67, column 29 lines 1-7 “As is shown in FIG. 5D, a semantic map 50 may be generated based on outputs received from the machine learning tool. The semantic map 50 identifies surfaces within a vicinity of the autonomous ground vehicle 550 with one or more express identifiers or labels, such as grass, sidewalk, roadway, or others, and may depict the surfaces with one or more discrete colors or textures accordingly. Additionally, as is also shown in FIG. 5D, the autonomous ground vehicle 550 selects a point (x, y, z).sub.GOAL on the semantic map 50 that is associated with one or more goals for the autonomous ground vehicle 550, and determines a velocity V (e.g., a course and a speed) for travel from a point (x, y, z).sub.AGV corresponding to a location of the autonomous ground vehicle 550 to the point (x, y, z).sub.GOAL. For example, such goals may include not only traveling to the location associated with the point (x, y, z).sub.GOAL, or to another location, but also safely maintaining a roadway or another surface represented within the semantic map 50 at a sufficient distance from the autonomous ground vehicle 550, or on a selected side of the autonomous ground vehicle 550, or along or near a centerline of a sidewalk or other surface upon which the autonomous ground vehicle 550 travels, e.g., on a line or series of midpoints between boundaries on a left side and a right side of the sidewalk or the other surface depicted within the semantic map 50, or any other goals. While traveling to the location associated with the point (x, y, z).sub.GOAL, the autonomous ground vehicle 550 may rely upon the semantic map 50 in selecting or modifying one or more aspects of the velocity V, e.g., to remain on passable or desirable ground surfaces, or to avoid impassable or undesirable ground surfaces, or other obstacles, and continuing to travel in furtherance of one or more goals”).
In Reference to Claim 4
The method of claim 1 (see rejection to claim 1 above), wherein the extracting of the candidate path includes: extracting the respective candidate paths for both edges and a central portion from the extracted travel area (see at least Boyraz Figs. 1-7 and column 7 lines 23-33, “Semantic maps that are generated based on semantically segmented images in accordance with the present disclosure may have any dimensions or shapes. For example, a semantic map may have real or scaled dimensions on the order of five meters, ten meters, fifteen meters, twenty meters or more, as well as shapes such as circles, ellipses, rectangles other than squares, cones or sectors, or any others. Additionally, a semantic map may consider or include a location of the autonomous ground vehicle at a geometric center of the semantic map, or in any other location within or outside of the semantic map”).
In Reference to Claim 5
The method of claim 1 (see rejection to claim 1 above), wherein the extracting of the candidate path includes: extracting the candidate path from the robot when the extracted candidate path has the number of used points smaller than a preset threshold number or has a distance from the robot greater than a preset threshold distance (see at least Boyraz Figs. 1-7 and column 28 lines 43-67, column 29 lines 1-7 “As is shown in FIG. 5D, a semantic map 50 may be generated based on outputs received from the machine learning tool. The semantic map 50 identifies surfaces within a vicinity of the autonomous ground vehicle 550 with one or more express identifiers or labels, such as grass, sidewalk, roadway, or others, and may depict the surfaces with one or more discrete colors or textures accordingly. Additionally, as is also shown in FIG. 5D, the autonomous ground vehicle 550 selects a point (x, y, z).sub.GOAL on the semantic map 50 that is associated with one or more goals for the autonomous ground vehicle 550, and determines a velocity V (e.g., a course and a speed) for travel from a point (x, y, z).sub.AGV corresponding to a location of the autonomous ground vehicle 550 to the point (x, y, z).sub.GOAL. For example, such goals may include not only traveling to the location associated with the point (x, y, z).sub.GOAL, or to another location, but also safely maintaining a roadway or another surface represented within the semantic map 50 at a sufficient distance from the autonomous ground vehicle 550, or on a selected side of the autonomous ground vehicle 550, or along or near a centerline of a sidewalk or other surface upon which the autonomous ground vehicle 550 travels, e.g., on a line or series of midpoints between boundaries on a left side and a right side of the sidewalk or the other surface depicted within the semantic map 50, or any other goals. While traveling to the location associated with the point (x, y, z).sub.GOAL, the autonomous ground vehicle 550 may rely upon the semantic map 50 in selecting or modifying one or more aspects of the velocity V, e.g., to remain on passable or desirable ground surfaces, or to avoid impassable or undesirable ground surfaces, or other obstacles, and continuing to travel in furtherance of one or more goals”).
In Reference to Claim 6
The method of claim 1 (see rejection to claim 1 above), wherein the determining of the reliability of each candidate path includes: weighting the reliability cost for each of the extracted at least one candidate path based on the number of points (nodes) used to extract each of the at least one candidate path (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111, 123-124).
In Reference to Claim 7
The method of claim 1 (see rejection to claim 1 above), wherein the determining of the reliability of each candidate path includes: weighting (score) the reliability cost for each of the extracted at least one candidate path based on a distance from the robot (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111, 123-124).
In Reference to Claim 8
The method of claim 1 (see rejection to claim 1 above), further comprising: deriving the reference local path using the location information of the robot and the global path when the determined reliability of the at least one candidate path is smaller than a preset threshold (total cost) (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111, 123-124).
In Reference to Claim 9
Boyraz teaches (except for the bolded and italic recitations below):
An apparatus for planning a reference local path in a local semantic navigation map, the apparatus comprising:
a memory (254) configured to store one or more programs; and
a processor (252) configured to execute the one or more stored programs, wherein the processor (252) is configured to:
extract a travel area recognized from travel information matched with location information of a robot in the local semantic navigation map and vision information of the robot (see at least Boyraz Figs. 1-7 and column 4 lines 4-30, “In accordance with some embodiments of the present disclosure, the autonomous ground vehicle 150 is programmed or otherwise configured to generate semantic maps of a region in which the autonomous ground vehicle 150 operates based on imaging data captured using the imaging device 162 or any other imaging devices provided aboard the autonomous ground vehicle 150 (not shown). For example, in some embodiments, one or more computer processors, devices or systems provided aboard the autonomous ground vehicle 150 (not shown) may be configured to process images captured using the imaging device 162, and to detect, recognize or identify one or more ground surfaces within a vicinity of the autonomous ground vehicle based on such images. In some embodiments, the autonomous ground vehicle 150 may semantically segment such images, such as by assigning labels to one or more pixels or sets of pixels depicted within the images. The labels may indicate whether such pixels depict certain types or categories of ground surfaces, e.g., not only sidewalks but also roads, driveways, lawns or other surfaces. Alternatively, or additionally, such labels may indicate whether such surfaces are formed from one or more specific materials, or whether one or more of the surfaces are passable or desirable for travel thereon by the autonomous ground vehicle 150, or whether one or more of the surfaces are impassable or undesirable for travel thereon by the autonomous ground vehicle 150”);
extract at least one candidate path using the extracted travel area or a robot’s location information-based global path (see at least Boyraz Figs. 1-7 and column 8 lines 43-67 and column 9 lines 1-3 “Moreover, the autonomous ground vehicles of the present disclosure may include any number of sensors such as position sensors (e.g., GPS receivers, or cellular transceivers configured to triangulate positions based on signals received from multiple cellular transmitters), imaging sensors (e.g., digital cameras or other imaging devices) or other sensors, including but not limited to speedometers, inclinometers, compasses, altimeters, gyroscopes or scanners. The autonomous ground vehicles of the present disclosure may also include communications equipment (e.g., wired or wireless means for communication such as components or systems operating Wireless Fidelity, Bluetooth, near-field communications or cellular technologies or protocols), along with one or more power modules (e.g., batteries), which may be rechargeable, refuelable or replaceable in nature. Information or data obtained or determined by such sensors or such communications equipment may be utilized in manually or automatically controlling an autonomous ground vehicle, e.g., in causing the autonomous ground vehicle to travel along one or more paths or routes, to search for alternate paths or routes, or to avoid expected or unexpected hazards encountered by the autonomous ground vehicle while traveling along such paths or routes. The autonomous ground vehicles of the present disclosure may further include any number of computer components (e.g., processors, data stores, transceivers or input/output devices) for performing any of the tasks or executing any of the functions described herein”);
determine reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path (see at least Boyraz Figs. 1-7 and column 33 lines 30-41, “Moreover, those of ordinary skill in the pertinent arts will recognize that one or more of the computer systems disclosed herein may be configured to execute one or more optimal path or route algorithms (e.g., shortest path or shortest route algorithms) for providing guidance to or instructing one or more vehicle systems or couriers to travel to a selected area. A path or route may be identified as “optimal” on any objective or subjective basis. For example, an optimal path or route may have a lowest financial cost, a shortest time, or any other beneficial or preferential ranking or status according to one or more objective or subjective metrics”); and
derive a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and set the derived reference local path as a target section of the robot (see at least Boyraz Figs. 1-7 and column 26 lines 37-67 and column 27 lines 1-5, “According to some embodiments, autonomous ground vehicles of the present disclosure, such as the autonomous ground vehicle 450 of FIG. 4, may be configured for travel throughout an area or environment, such as travel on traditional transportation and non-traditional transportation infrastructure provided within the area or environment. The autonomous ground vehicle 450 may select a path or route based on geospatial data regarding physical features in an area or environment, including but not limited to topographical data regarding the composition and surface features within the area or environment, as well as elevations of points within the area or environment, which may be identified or determined from satellite or ground-based imagery, GIS data, or any other information or data obtained from any other source. Similarly, where an area or environment includes transportation infrastructure such as roads, sidewalks, crosswalks, bicycle or walking paths, bridges or trails, and also non-traditional transportation infrastructure, e.g., parks, fields, forests, lots, clearings or other spaces, the autonomous ground vehicle 450 may be configured for travel thereon along one or more paths or routes, which may be selected or defined based on attributes of the transportation infrastructure and other physical features within the area or environment, as well as attributes of prior travel through the area or environment. Information or data regarding prior travel that may be used to select or define a path or route within an area or environment includes, but is not limited to, one or more time stamps (e.g., times and positions of autonomous ground vehicles within the given area at such times), elapsed times between departures and arrivals, net speeds, courses, angles of orientation (e.g., a yaw, a pitch or a roll), levels of traffic congestion, sizes or dimensions of any payloads carried, environmental conditions or any other information or data that describes or represents instances of prior travel by autonomous ground vehicles within the area or environment”).
Boyraz does not explicitly teaches (bolded and italic recitations above) as to determine reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and derive a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section. However, it is known in the art before the effective filing date of the claimed invention to determine reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and derive a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section. For example, Corte teaches to determine reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and derive a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section. Corte further teaches that preforming such steps provide improvement in efficiency of services and safety (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Boyraz to include the steps of determining reliability of each candidate path by calculating a reliability cost of each of the extracted at least one candidate path and deriving a candidate path with the highest reliability among the determined at least one candidate path as the reference local path and setting the derived reference local path as a target section as taught by Corte in order to provide improvement in efficiency of services and safety.
In Reference to Claim 10
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to update the navigation map using labeled local semantic information that has not been integrated (see at least Boyraz Figs. 1-7 and column 5 lines 4-12, “The autonomous ground vehicle 150 may utilize semantic maps in selecting courses and/or speeds for performing a mission, such as a delivery of an item to a destination, and such semantic maps may be updated or new semantic maps may be generated as images are captured by the imaging device 162, or at regular intervals of time, e.g., once every second or fraction of a second, or after a regular number of images have been captured, e.g., once every ten images, fifteen images, or twenty images”).
In Reference to Claim 11
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to extract the candidate path while maintaining tendency of the global path close to local coordinates of the robot (see at least Boyraz Figs. 1-7 and column 28 lines 43-67, column 29 lines 1-7 “As is shown in FIG. 5D, a semantic map 50 may be generated based on outputs received from the machine learning tool. The semantic map 50 identifies surfaces within a vicinity of the autonomous ground vehicle 550 with one or more express identifiers or labels, such as grass, sidewalk, roadway, or others, and may depict the surfaces with one or more discrete colors or textures accordingly. Additionally, as is also shown in FIG. 5D, the autonomous ground vehicle 550 selects a point (x, y, z).sub.GOAL on the semantic map 50 that is associated with one or more goals for the autonomous ground vehicle 550, and determines a velocity V (e.g., a course and a speed) for travel from a point (x, y, z).sub.AGV corresponding to a location of the autonomous ground vehicle 550 to the point (x, y, z).sub.GOAL. For example, such goals may include not only traveling to the location associated with the point (x, y, z).sub.GOAL, or to another location, but also safely maintaining a roadway or another surface represented within the semantic map 50 at a sufficient distance from the autonomous ground vehicle 550, or on a selected side of the autonomous ground vehicle 550, or along or near a centerline of a sidewalk or other surface upon which the autonomous ground vehicle 550 travels, e.g., on a line or series of midpoints between boundaries on a left side and a right side of the sidewalk or the other surface depicted within the semantic map 50, or any other goals. While traveling to the location associated with the point (x, y, z).sub.GOAL, the autonomous ground vehicle 550 may rely upon the semantic map 50 in selecting or modifying one or more aspects of the velocity V, e.g., to remain on passable or desirable ground surfaces, or to avoid impassable or undesirable ground surfaces, or other obstacles, and continuing to travel in furtherance of one or more goals”).
In Reference to Claim 12
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to extract the respective candidate paths for both edges and a central portion from the extracted travel area (see at least Boyraz Figs. 1-7 and column 7 lines 23-33, “Semantic maps that are generated based on semantically segmented images in accordance with the present disclosure may have any dimensions or shapes. For example, a semantic map may have real or scaled dimensions on the order of five meters, ten meters, fifteen meters, twenty meters or more, as well as shapes such as circles, ellipses, rectangles other than squares, cones or sectors, or any others. Additionally, a semantic map may consider or include a location of the autonomous ground vehicle at a geometric center of the semantic map, or in any other location within or outside of the semantic map”).
In Reference to Claim 13
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to extract the candidate path from the robot when the extracted candidate path has the number of used points smaller than a preset threshold number or has a distance from the robot greater than a preset threshold distance (see at least Boyraz Figs. 1-7 and column 28 lines 43-67, column 29 lines 1-7 “As is shown in FIG. 5D, a semantic map 50 may be generated based on outputs received from the machine learning tool. The semantic map 50 identifies surfaces within a vicinity of the autonomous ground vehicle 550 with one or more express identifiers or labels, such as grass, sidewalk, roadway, or others, and may depict the surfaces with one or more discrete colors or textures accordingly. Additionally, as is also shown in FIG. 5D, the autonomous ground vehicle 550 selects a point (x, y, z).sub.GOAL on the semantic map 50 that is associated with one or more goals for the autonomous ground vehicle 550, and determines a velocity V (e.g., a course and a speed) for travel from a point (x, y, z).sub.AGV corresponding to a location of the autonomous ground vehicle 550 to the point (x, y, z).sub.GOAL. For example, such goals may include not only traveling to the location associated with the point (x, y, z).sub.GOAL, or to another location, but also safely maintaining a roadway or another surface represented within the semantic map 50 at a sufficient distance from the autonomous ground vehicle 550, or on a selected side of the autonomous ground vehicle 550, or along or near a centerline of a sidewalk or other surface upon which the autonomous ground vehicle 550 travels, e.g., on a line or series of midpoints between boundaries on a left side and a right side of the sidewalk or the other surface depicted within the semantic map 50, or any other goals. While traveling to the location associated with the point (x, y, z).sub.GOAL, the autonomous ground vehicle 550 may rely upon the semantic map 50 in selecting or modifying one or more aspects of the velocity V, e.g., to remain on passable or desirable ground surfaces, or to avoid impassable or undesirable ground surfaces, or other obstacles, and continuing to travel in furtherance of one or more goals”).
In Reference to Claim 14
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to weight the reliability cost for each of the extracted at least one candidate path based on the number of points used to extract each of the at least one candidate path (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111, 123-124).
In Reference to Claim 15
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to weight the reliability cost for each of the extracted at least one candidate path based on a distance from the robot (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111, 123-124).
In Reference to Claim 16
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to derive the reference local path using the location information of the robot and the global path when the determined reliability of the at least one candidate path is smaller than a preset threshold (see at least Corte Figs. 1-16 and paragraphs 32, 52, 94-111, 123-124).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub No. US 2021/0197377 A1 to Gaschler (Gaschler) teaches to determine the path and alternative path of the robot.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON DONGPA LEE whose telephone number is (571)270-3525. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm.
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/BRANDON D LEE/Primary Examiner, Art Unit 3662 July 31, 2026
