DETAILED ACTION
This is a first action on the merits. Claims 1-20 are pending. Claims dated 03/11/2026 are being examined.
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 .
Election/Restriction
Claims 10-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 03/11/2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/19/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Claim 1) A method for operating a mobile robot, the method comprising:
storing, in a memory of the mobile robot, 3D map data representing an environment;
capturing, with a camera of the mobile robot, an image of the environment;
determining, with a processor of the mobile robot, based on the 3D map data, whether the image is to be used to update the 3D map data;
transmitting, with a transceiver of the mobile robot, the image to a remote server in response to determining that the image is to be used to update the 3D map data; and
receiving, with the transceiver, updates to the 3D map data from the remote server.
101 Analysis – Step 1:
Independent claim 1 is directed to a method. Therefore, claim 1 is within at least one of the four statutory categories.
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 following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
Specifically, the limitation: determining, […], based on the 3D map data, whether the image is to be used to update the 3D map data in the context of this claim encompasses mental observation and evaluation. A person looking at existing map data and looking at new image information is capable of comparing or evaluating relevance/change and deciding (yes or no) to update the existing map. 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 idea(s) 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.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
For the following reason(s), the Examiner submits that the above identified additional elements do not integrate the above-noted abstract idea into a practical application.
Computer elements:
Regarding the additional limitations of “a processor of the mobile robot”, the processor is a generic computer component and also acts merely as a tool to perform the aforementioned abstract ideas and do not amount to significantly more than the judicial exception. See MPEP 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception.
Data gathering:
The additional limitations of “storing…” and “capturing…”, amount to mere data gathering for use in the determining step. Mere data gathering is a form of insignificant extra-solution activity. It has been held that limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea, see MPEP 2106.05.
Insignificant post-solution activity:
The additional limitations of “transmitting…” and “receiving…” amount to insignificant post-solution activity. Mere transmission of data over networks and/or mere displaying/alerting/notifying are forms of insignificant extra-solution activity.
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, that reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, 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 a 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 2019 PEG, claim 1 does 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.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field.
Regarding the computer elements:
As discussed with respect to Step 2A Prong Two, the additional elements of a processor in the claim amounts to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)). The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding the data gathering steps:
It has been determined that such limitations are conventional as they merely consist of data gathering which are recited at a high level of generality. See OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); or buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Regarding the insignificant post solution activity steps:
Examples of insignificant post-solution activities can include merely displaying a result (e.g., output) on a display device, merely communicating a message based on the result, merely recording the result in a memory storage device, and the like. Adding a final step of transmitting collected information to a process that recites an abstract idea does not add a meaningful limitation to the process. See MPEP 2106.05(d)(II) and 2106.05(g). Furthermore, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function, and as recited above the Federal Circuit has considered to be insignificant extra-solution activity, for instance the step of printing a menu that was generated through an abstract process in Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1241-42 (Fed. Cir. 2016) and the mere generic presentation of collected and analyzed data in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016). Hence the claim is not patent eligible.
Dependent claims 2-9 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, specifically only reciting additional activities that may also be reasonably performed in the human mind (claims 2-4 and 8 defining a metric to whether the image is to be used to update the 3D map data), do no more than generally link a judicial exception to a particular technological environment (claims 5-7: neural network usage), and adding additional insignificant extra-solution activity (claim 9: compression of data). The recitation of using neural networks in claims 5-7 merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exception of “determining…”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks and/or machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Also, the recitation of data compression without any particular method of an improved or novel compression, amounts to generic data processing, and Examiner submits that generic data compression is a well-known, routine, and conventional activity. As per the case law cited above, mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al. (US-20220155098-A1) and herein after will be referred to as Liu.
Regarding claim 1, Liu teaches a method for operating a mobile robot, the method comprising (FIG. 3 first vehicle; [0003] An automatic driving vehicle, also referred to as […] a wheeled mobile robot, is an intelligent vehicle that implements unmanned driving by using a computer system):
storing, in a memory of the mobile robot, 3D map data representing an environment (FIG. 3 step 202 …prestored map data; [0029] …includes three-dimensional space data);
capturing, with a camera of the mobile robot, an image of the environment (FIG. 2 step 201: obtain environmental data; [0029] environmental data collected by the data collection apparatus may be image data);
determining, with a processor of the mobile robot, based on the 3D map data, whether the image is to be used to update the 3D map data (FIG. 3 step 203: when it is determined that the environmental data does not match the map data…);
transmitting, with a transceiver of the mobile robot, the image to a remote server in response to determining that the image is to be used to update the 3D map data; and (FIG. 3 step 203: …report map update information to the cloud server)
receiving, with the transceiver, updates to the 3D map data from the remote server ([0033] …the method further includes: receiving an updated map delivered by the cloud server).
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-4, 6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Liu, in view of Wang et al. (US-20190258737-A1) and herein after will be referred to as Wang.
Regarding claim 2, Liu teaches the method according to claim 1.
Liu does not explicitly teach the determining whether the image is to be used to update the 3D map data further comprising: determining, based on the 3D map data, a metric that quantifies an amount of new information about the environment that is in the image; and determining that the image is to be used to update the 3D map based on a comparison of the metric with a threshold value.
However, Wang teaches the determining whether the image is to be used to update the 3D map data (FIG. 3 voxels 134) further comprising:
determining, based on the 3D map data, a metric that quantifies an amount of new information about the environment that is in the image; and ([0036] For example, as sensor datasets are accumulated over time, the process 100 may include determining whether a voxel is occupied by an object at first time based at least in part on the sensor dataset at time T1 122, and thereafter, determine whether the voxel is occupied at additional times such as at a second time T2 124 through a time TN 126; [0061] In some examples, one or more counters may increment or decrement based on whether the voxels are associated with data, and thresholds may be used to mark the unoccupied voxels as “free space” and occupied voxels as “not free space,” for example, as described with respect to FIG. 5 – – Examiner interprets the counter summing up how many times the voxels were occupied in total is a metric that quantifies an amount of new information about the environment that is in the image)
determining that the image is to be used to update the 3D map based on a comparison of the metric with a threshold value ([0038] For example, if the number of instances is greater than a threshold number of instances (e.g., five or more), the voxel may be determined to be occupied. Alternatively, if the number of counted instances is less than the threshold number of instances, the process 100 may include indicating that the region of the environment associated with the voxel is not occupied. In some examples, this may include not adding the object associated with the voxel to a map being created (or updated), or removing the object (e.g., clearing the voxels) from an existing map).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the determining whether the image is to be used to update the 3D map data as taught in Liu, as modified, to incorporate the teachings of Wang to include wherein the 3D map data includes a plurality of voxels, each voxel having an occupancy score that quantifies how occupied a corresponding portion of the environment is by obstacles, the determining the metric further comprising: identifying a subset of voxels from the plurality of voxels that are within a field of view of the image; and the determining the metric based on the occupancy scores of the subset of voxels, with a reasonable expectation of success “to improve the functioning of a computing device by providing a framework for efficiently omitting undesired or extraneous data from maps” (Wang [0021] and supported by [0069]).
Regarding claim 3, Liu, as modified, teaches the method according to claim 2.
Liu does not explicitly teach wherein the 3D map data includes a plurality of voxels, each voxel having an occupancy score that quantifies how occupied a corresponding portion of the environment is by obstacles, the determining the metric further comprising: identifying a subset of voxels from the plurality of voxels that are within a field of view of the image; and the determining the metric based on the occupancy scores of the subset of voxels.
However, Wang teaches wherein the 3D map data includes a plurality of voxels (FIG. 3 voxels 134),
each voxel having an occupancy score that quantifies how occupied a corresponding portion of the environment is by obstacles ([0036] For example, as sensor datasets are accumulated over time, the process 100 may include determining whether a voxel is occupied by an object at first time based at least in part on the sensor dataset at time T1 122, and thereafter, determine whether the voxel is occupied at additional times such as at a second time T2 124 through a time TN 126),
the determining the metric further comprising: identifying a subset of voxels from the plurality of voxels that are within a field of view of the image; and ([0038] In some examples, the process 100 may also include updating, based at least in part on the comparison, a map including the voxel space to identify occupancy of the one or more voxels by an object; [0039] …the process 100 may include ray casting operations to determine whether voxels occupied at a first time are not occupied at a second time; [0061] For example, as an object moves within the voxel space over time, the voxels occupied by a dynamic object may capture data over time. In some examples, the ray casting component 318 may be configured to analyze the path of a ray associated with sensor datasets (e.g., LIDAR data), for example, to determine that voxels through which the ray travels should be cleared. In this example manner, the ray casting component 318 may be configured to determine that voxels occupied at a first time are not occupied at one or more subsequent times, which may be provided to the various components, for example, and to determine that objects are dynamic objects. In some examples, one or more counters may increment or decrement based on whether the voxels are associated with data, and thresholds may be used to mark the unoccupied voxels as “free space” and occupied voxels as “not free space,” for example, as described with respect to FIG. 5.)
the determining the metric based on the occupancy scores of the subset of voxels ([0038] For example, if the number of instances is greater than a threshold number of instances (e.g., five or more), the voxel may be determined to be occupied. Alternatively, if the number of counted instances is less than the threshold number of instances, the process 100 may include indicating that the region of the environment associated with the voxel is not occupied. In some examples, this may include not adding the object associated with the voxel to a map being created (or updated), or removing the object (e.g., clearing the voxels) from an existing map; [0069] In some examples, the ray casting process 400 may be configured to clear the voxels 422, 424, 426, 428, and 430 at, for example, a subsequent time, as described herein. In some such examples, the techniques described herein may be used to update a state of the voxel space over time to reduce an amount of data to be maintained at an instant in time, as well as to improve operations to detect and segment dynamic objects in a voxel space 418. This may facilitate a relative reduction in computer processing and/or memory use).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the determining whether the image is to be used to update the 3D map data as taught in Liu, as modified, to incorporate the teachings of Wang to include wherein the 3D map data includes a plurality of voxels, each voxel having an occupancy score that quantifies how occupied a corresponding portion of the environment is by obstacles, the determining the metric further comprising: identifying a subset of voxels from the plurality of voxels that are within a field of view of the image; and the determining the metric based on the occupancy scores of the subset of voxels, with a reasonable expectation of success “to improve the functioning of a computing device by providing a framework for efficiently omitting undesired or extraneous data from maps” (Wang [0021] and supported by [0069]).
Regarding claim 4, Liu, as modified, teaches the method according to claim 3.
Liu, as modified, does not explicitly teach the determining the metric further comprising: determining the metric as a sum of the occupancy scores of the subset of voxels.
However, Wang teaches the determining the metric further comprising: determining the metric as a sum of the occupancy scores of the subset of voxels ([0038] For example, if the number of instances is greater than a threshold number of instances (e.g., five or more), the voxel may be determined to be occupied. Alternatively, if the number of counted instances is less than the threshold number of instances, the process 100 may include indicating that the region of the environment associated with the voxel is not occupied. In some examples, this may include not adding the object associated with the voxel to a map being created (or updated), or removing the object (e.g., clearing the voxels) from an existing map; [0071] A “free space” or “not free space” determination may be made, for example, by observing aggregated data over time for the observed voxels, which may incorporate various algorithms or machine learned models. Incrementing or decrementing a counter associated with voxels is merely one example mechanism to accomplish the “free space” or “not free space” determination. – Examiner interprets summing up how many times the voxels were occupied in total is a sum of the occupancy scores).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the determining whether the image is to be used to update the 3D map data as taught in Liu, as modified, to incorporate the teachings of Wang to include the determining the metric further comprising: determining the metric as a sum of the occupancy scores of the subset of voxels, with a reasonable expectation of success “to improve the functioning of a computing device by providing a framework for efficiently omitting undesired or extraneous data from maps” (Wang [0021] and supported by [0069]).
Regarding claim 6, Liu, as modified, teaches the method according to claim 2.
Liu, as modified, does not explicitly teach the determining the metric further comprising: determining a semantic representation of the image using a neural network model; and determining the metric based on the semantic representation.
However, Wang teaches the determining the metric further comprising: determining a semantic representation of the image using a neural network model; and (FIG. 1 segment dataset 128 and 140; [0047] In addition, in some examples, at least portions of sensor dataset may be segmented, for example, by a machine learning network including a sensor data segmentation network using a sensor data segmentation model configured to segment the sensor data. The segmented sensor data associated with detected objects may be used to classify the objects, for example, as either being dynamic or static, and dynamic objects may be omitted from the map. This may permit omission from the map of objects such as parked vehicles, which may be difficult to remove from the map using only the example process 100 describe with respect to FIG. 1. In some examples, based at least in part of the segmented sensor data associated with the objects, semantics associated with the objects may be added to the map, for example, as described above with respect to example process 200 with respect to FIG. 2.)
determining the metric based on the semantic representation (FIG. 1 update counters and update map 144 and 146).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the determining whether the image is to be used to update the 3D map data as taught in Liu, as modified, to incorporate the teachings of Wang to include the determining the metric further comprising: determining a semantic representation of the image using a neural network model; and determining the metric based on the semantic representation, with a reasonable expectation of success to classify objects (Wang [0047]) and “may improve various processes, such as localization” (Wang [0019]).
Regarding claim 8, Liu, as modified, teaches the method according to claim 2.
Liu, as modified, does not explicitly teach the determining whether the image is to be used to update the 3D map data further comprising: determining that the image is to be used to update the 3D map in response to the metric exceeding a threshold value.
However, Wang teaches the determining whether the image is to be used to update the 3D map data further comprising: determining that the image is to be used to update the 3D map in response to the metric exceeding a threshold value ([0038] For example, if the number of instances is greater than a threshold number of instances (e.g., five or more), the voxel may be determined to be occupied. Alternatively, if the number of counted instances is less than the threshold number of instances, the process 100 may include indicating that the region of the environment associated with the voxel is not occupied. In some examples, this may include not adding the object associated with the voxel to a map being created (or updated), or removing the object (e.g., clearing the voxels) from an existing map).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the determining whether the image is to be used to update the 3D map data as taught in Liu, as modified, to incorporate the teachings of Wang to include the determining whether the image is to be used to update the 3D map data further comprising: determining that the image is to be used to update the 3D map in response to the metric exceeding a threshold value, with a reasonable expectation of success “to improve the functioning of a computing device by providing a framework for efficiently omitting undesired or extraneous data from maps” (Wang [0021] and supported by [0069]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, in view of Wang, in further view of Mahadavi et al. (US-20260098740-A1) and herein after will be referred to as Mahadavi.
Regarding claim 5, Liu, as modified, teaches the method according to claim 3.
Liu, as modified, does not explicitly teach wherein the occupancy score of each voxel in the plurality of voxels is determined by the remote server using a neural radiance field representation of the environment.
However, Mahadavi teaches wherein the occupancy score of each voxel in the plurality of voxels is determined by the remote server using a neural radiance field representation of the environment ([0041] The AI model may divide the ego's surroundings into different voxels and then determine an occupancy status for each voxel. Accordingly, using the methods discussed herein, the system may generate a map of the ego's surroundings. Using the voxel data (e.g., coordinates of each voxel) and the corresponding occupancy status, the AI model (or sometimes another model using the data predicted by the AI model) may generate a map of the ego's surroundings; [0144] In some embodiments, the analytics server may use a neural radiance field (NeRF) technique to recreate a rendering of the ego's surroundings (step 290). In some embodiments, the analytics server may generate a map that indicates various surfaces surrounding the ego using the image data captured. The map may correspond to the predicted surfaces and their predicted attributes. In a non-limiting example, the analytics server may use a multi-view 3D reconstruction protocol to visualize each voxel and its surface status/attribute. A non-limiting example of the map or the surface map is presented in FIGS. 4A-C (e.g., a simulation 400)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Liu, as modified, to incorporate the teachings of Mahadavi to include wherein the occupancy score of each voxel in the plurality of voxels is determined by the remote server using a neural radiance field representation of the environment, with a reasonable expectation of success since doing so would have achieved the benefit of enabling training, and “providing more consistent results” (Mahadavi [0142]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, in view of Wang, in further view of Tang et al. (US-20230358563-A1) and herein after will be referred to as Tang.
Regarding claim 7, Liu, as modified, teaches the method according to claim 6.
Liu, as modified, does not explicitly teach wherein the neural network model is a contrastive language-image pre-training model.
However, Tang teaches wherein the neural network model is a contrastive language-image pre-training model ([0005] … update a map generator generating the map data using the loss. The contrastive loss of an example embodiment is determined using a contrastive language image pre-training (CLIP) loss function).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the neural network taught in Liu, as modified, to substitute Tang’s contrastive language-image pre-training model because it has been held that the substitution of one known element for another would have been obvious if the substitution yielded predictable results to one of ordinary skill in the art at the time of the invention. In this case, the substitution of a stereo camera for the RGB-D camera would have had the predictable result of updating a map. Additionally, doing so would have achieved the benefit of “reducing processing requirements and capitalizing on image analysis models and algorithms” (Tang [0077]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Liu, in view of Bailly et al. (US-20210004363-A1) and herein after will be referred to as Bailly.
Regarding claim 9, Liu teaches the method according to claim 1.
Liu does not explicitly teach further comprising: compressing, with the processor, the image prior to transmitting the image to the remote server.
However, Bailly teaches further comprising: compressing, with the processor, the image prior to transmitting the image to the remote server ([0108] …the vehicle computing system 120 may be configured to use data compression techniques to store and transfer map data thereby reducing storage and transmission costs).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify transmitting the image to the remote server as taught in Liu to incorporate the teachings of Bailly to include further comprising: compressing, with the processor, the image prior to transmitting the image to the remote server, with a reasonable expectation of success since doing so would have achieved the benefit of “reducing storage and transmission costs” (Bailly [0108]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20250198798A1 – Orendovici alternatively teaches the determining whether the image is to be used to update the 3D map data further comprising: determining, based on the 3D map data, a metric that quantifies an amount of new information about the environment that is in the image; and ([0043] As described herein, a metric associated with a portion of the map may include […] any other metric that measures how well the data represents the area of the environment that corresponds to the portion of the map)
determining that the image is to be used to update the 3D map based on a comparison of the metric with a threshold value ([0043] For instance, the system(s) may determine that a portion of the map is ready to be updated based at least on a metric value associated with the portion of the map satisfying a threshold).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVIN SEOL whose telephone number is (571) 272-6488. The examiner can normally be reached on Monday-Friday 9:00 a.m. to 5:00 p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached on (571) 270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVIN SEOL/Examiner, Art Unit 3662
Prosecution Insights