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 .
The instant office action having application number 18/994878, filed on January 15, 2025, has claims 1-20 pending in this application.
Claim Objections
Claim 1 and 5 are objected to because of the following informalities:
Claim 1, line 7, recites “remote sevrer”, Examiner suggest correcting the spelling to –remote server--.
Claim 5, line 7, recites “;” at the end of the claim. Examiner suggests to remove the semicolon (;) .
Appropriate correction is required.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 15, 2025 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 to 20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (an abstract idea) without reciting additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 2A, Prong One – The Claim Recites an Abstract Idea
Claim 1 recites, one or more processors (processor(s));one or more cameras (camera(s));a GPS device; an inertial measurement unit (IMU);a network interface for enabling communication between the edge device and a remote sevrer; a memory accessible to the processor(s), the memory comprising program code executable by the processor(s) to: in response to a signal from the IMU or the GPS device indicating movement of the edge device, trigger the capture of images by the camera(s); process the captured images to determine a quality metric for each of the captured images; discard images with a quality metric below a predefined image quality threshold to obtain a first refined image set; perform object detection on the first refined image set to detect one or more location markers in the images of the first refined image set; discard images not including location markers in the first refined image set to obtain a second refined image set; perform similarity analysis of the images in the second refined image set to identify clusters of similar images; select a representative image from each cluster of similar images to obtain a location representative image set; transmit the location representative image set and GPS data associated with the representative images to the remote server.
These limitations, considered at the level of abstraction appropriate for Step 2A, recite the concept of collecting information, analyzing the collected information according to selected criteria, organizing/grouping the information based on the analysis, selecting information satisfying the criteria, and communicating the selected information. More specifically, the claim obtains images and location/movement information, evaluates the images for quality and content, removes images that do not satisfy specified criteria, groups remaining images according to similarity, chooses a representative item from each group, and transmits the selected items.
The evaluation, comparison, categorization, selection, and discarding limitations fall within the “mental processes” grouping of abstract ideas because, as claimed at a functional level, they encompass observations, evaluations, judgments, and comparisons that can be conceptually performed in the human mind (for example, reviewing photographs, judging whether each photograph has acceptable quality, determining whether a photograph contains a location marker, comparing photographs for similarity, grouping similar photographs, and choosing a representative photograph from each group). The fact that the claim requires a computer to perform these operations on digital image data does not, by itself, remove the recited concept from the mental-process grouping where the claimed operations are recited functionally as evaluation, comparison, grouping, and selection.
Step 2A, Prong Two – The Abstract Idea Is Not Integrated into a Practical Application
The claim is considered as a whole to determine whether the additional elements integrate the abstract idea into a practical application. The additional elements include the one or more processors, cameras, GPS device, IMU, network interface, memory, the use of a movement signal to trigger image capture, and transmission of the selected images and associated GPS data to a remote server.
These additional elements do not, on the face of claim 1, impose a meaningful technological limitation on the abstract data-analysis concept. The cameras are used for their ordinary function of capturing images; the GPS device supplies location information; the IMU/GPS supplies movement information; the processors and memory execute the recited information-processing operations; and the network interface communicates the selected image and GPS data to a remote server. The claim does not recite a new camera structure, a new GPS or IMU architecture, a particular improved sensor-control technique, a new network protocol, or a specific image-processing algorithm that changes how the computer or imaging hardware itself operates. Rather, the physical components provide the environment and data sources in which the information-analysis and selection process is carried out.
The limitation that image capture is triggered in response to a movement signal does not, as presently claimed, amount to an improvement in the functioning of the camera, IMU, GPS device, processor, or another technology. It specifies when the otherwise conventional image-capture function is initiated. Similarly, determining a “quality metric,” applying a “predefined image quality threshold,” detecting “location markers,” performing “similarity analysis,” identifying “clusters,” and selecting a “representative image” are stated in result-oriented functional language without reciting a particular technological technique for performing those operations. The claimed filtering and selection therefore use the components as tools to implement the abstract information-analysis process rather than reciting an improvement to those tools themselves.
Nor does the final transmission of the representative images and associated GPS data transform the abstract idea into a practical application. Transmitting the results of abstract data analysis for subsequent use is an insignificant post-solution activity when it merely communicates the product of the analysis. See MPEP § 2106.05(g).
Accordingly, claim 1, considered as a whole, does not integrate the recited abstract idea into a practical application and is therefore directed to the abstract idea under Step 2A.
Step 2B – The Claim Does Not Recite Significantly More Than the Abstract Idea
Under Step 2B, the additional elements are reconsidered both individually and as an ordered combination to determine whether they amount to significantly more than the abstract idea. They do not.
Individually, the processors, memory, cameras, GPS device, IMU, and network interface perform the ordinary functions for which such components are conventionally used: processing and storing data, capturing images, providing location/movement information, and communicating data. Merely requiring generic computing and sensing components to perform the recited abstract evaluation, filtering, grouping, selection, and transmission operations does not supply an inventive concept.
As an ordered combination, the claim likewise amounts to using conventional edge-device components in their ordinary capacities to automate a sequence of information-processing steps: detect movement, acquire image data, assess the data, remove data failing criteria, recognize selected content, group similar data, select representative data, and communicate the result. The ordered combination does not recite a nonconventional arrangement of the hardware, a specific improvement in computer functionality, or a technical mechanism that changes the operation of the underlying devices. Instead, the components are invoked as tools for performing the abstract information-analysis workflow more quickly or automatically.
Therefore, the additional elements, whether considered separately or in combination, do not add significantly more than the abstract idea itself. Claim 1 is consequently ineligible under 35 U.S.C. § 101.
For the foregoing reasons, claims 1, 2, 13, 14 and 20 are rejected under 35 U.S.C. § 101 as being directed to patent-ineligible subject matter.
Claim 4 is depending on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim recites additional limitations of “wherein the location markers comprise one or more of: traffic signs, named storefronts, street signs, landmarks.”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 6 is depending on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites additional limitations of “wherein the similarity analysis is performed for images captured over a predefined time window; optionally wherein the predefined time window is any one of: 15 seconds, or 30 seconds, or 45 seconds, or 60 seconds, or 75 seconds, or 90 seconds, or 105 seconds, or 120 seconds.” , which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 8 is depending on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim recites additional limitations of “wherein the location representative image set is compressed before transmission.”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 9 wherein the location representative image set is transformed into a compressed video before transmission.
Claim 10 is depending on claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim recites additional limitations of “wherein the edge device is mounted on a helmet to capture images as the helmet wearer navigates an area”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 11 is depending on claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of claim 1. The claim recites additional limitations of “wherein the network interface comprises a cellular network radio device for transmission of signals over a cellular network.”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 12 is depending on claim 1 and includes all the limitations of claim 1. Therefore, claim 12 recites the same abstract idea of claim 1. The claim recites additional limitations of “determine presence of the edge device in a geo-fenced area based on the comprises geo-fence data and data from the GPS device; and trigger the capture of images in response to determining that the edge device is present in a geo-feanced area.”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 3 is depending on claim 2 and includes all the limitations of claim 2. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites additional limitations of “determination of a quality metric for each of the images; and processing only the images with a quality metric above a predefined quality threshold to obtain the location representative image set.” , which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 5 is depending on claim 2 and includes all the limitations of claim 2. Therefore, claim 5 recites the same abstract idea of claim 2. The claim recites additional limitations of “performing a similarity analysis of the captured images to identify clusters of similar images; selecting a representative image from each cluster of similar images; and the location representative image set is obtained based on a representative image from each cluster of similar images”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 7 is depending on claim 2 and includes all the limitations of claim 2. Therefore, claim 7 recites the same abstract idea of claim 2. The claim recites additional limitations of “wherein the edge device further comprises an inertial measurement unit (IMU) and the capturing of images is triggered on detection of motion by the IMU.”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 16 is depending on claim 13 and includes all the limitations of claim 13. Therefore, claim 16 recites the same abstract idea of claim 13. The claim recites additional limitations of “wherein the location markers comprise one or more of: traffic signs, named storefronts, street signs, landmarks.”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 18, is depending on claim 13 and includes all the limitations of claim 13. Therefore, claim 18 recites the same abstract idea of claim 13. The claim recites additional limitations of “wherein the similarity analysis is performed for images captured over a predefined time window; optionally wherein the predefined time window is any one of: 15 seconds, or 30 seconds, or 45 seconds, or 60 seconds, or 75 seconds, or 90 seconds, or 105 seconds, or 120 seconds.” , which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 15, is depending on claim 14 and includes all the limitations of claim 13. Therefore, claim 15 recites the same abstract idea of claim 14. The claim recites additional limitations of “determination of a quality metric for each of the images; and processing only the images with a quality metric above a predefined quality threshold to obtain the location representative image set” , which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 17 is depending on claim 14 and includes all the limitations of claim 14. Therefore, claim 17 recites the same abstract idea of claim 14. The claim recites additional limitations of “wherein processing the captured images comprises: performing a similarity analysis of the captured images to identify clusters of similar images; select a representative image from each cluster of similar images; and the location representative image set is obtained based on a representative image from each cluster of similar images.” , which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
Claim 19, is depending on claim 14 and includes all the limitations of claim 14. Therefore, claim 19 recites the same abstract idea of claim 14. The claim recites additional limitations of “wherein the capturing of images is triggered on a signal from an I MU detection of motion of the end device.” , which is further elaborating on the abstract idea, and therefore it does not amount to significantly more.
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.
Claims 1-20 are rejected under 35 USC 103(a) as being unpatentable over Walker et al. (US 20150146026 A1) (hereinafter Walker) in view of Zhou et al. (A Utility Model for Photo Selection in Mobile Crowdsensing) (hereinafter Zhou).
As per claims 1, 2, 13, 14 and 20, Walker discloses one or more processors (processor(s)) [a processor, paragraph 43];one or more cameras (camera(s)) [an imaging device (e.g., a camera) to take a picture, paragraph 22];a GPS device [a GPS device, paragraph 81]; an inertial measurement unit (IMU) [a rating may include a measurement of the quality of an image, paragraph 61];a network interface for enabling communication between the edge device and a remote sevrer [communication may take place over the Internet through a Web site maintained by a remote server, paragraph 91]. However Walker does not disclose a memory accessible to the processor(s), the memory comprising program code executable by the processor(s) to: in response to a signal from the IMU or the GPS device indicating movement of the edge device, trigger the capture of images by the camera(s); process the captured images to determine a quality metric for each of the captured images; discard images with a quality metric below a predefined image quality threshold to obtain a first refined image set; perform object detection on the first refined image set to detect one or more location markers in the images of the first refined image set; discard images not including location markers in the first refined image set to obtain a second refined image set; perform similarity analysis of the images in the second refined image set to identify clusters of similar images; select a representative image from each cluster of similar images to obtain a location representative image set; transmit the location representative image set and GPS data associated with the representative images to the remote server. On the other hand, Zhou discloses a memory accessible to the processor(s), the memory comprising program code executable by the processor(s) to [4GB memory, section 6.1]: in response to a signal from the IMU or the GPS device indicating movement of the edge device, trigger the capture of images by the camera(s) [the built-in camera applications on their own mobile devices are used to collect photos for PoIs in the area, Section 3.1]; process the captured images to determine a quality metric for each of the captured images [View quality quantifies the view acceptance level of the photos in selection I. Generally, a photo is determined to have a good view quality if it does not provide a blocked (the PoI is blocked by an obstacle, e.g., the 4th photo in Fig. 2) or blurred view or shooting at a wrong direction (no object is captured, e.g., the 8th photo in Fig. 2). Then the view quality of a selection I can be defined, section 3.2]; discard images with a quality metric below a predefined image quality threshold to obtain a first refined image set [Fig. 2, the crowd-contributed photos together capture a group of PoIs with varied quality on the views (e.g., clear view, blocked view). To this end, in order to satisfy the requester’s expectation in understanding the area, the selection process should attain both photo coverage to cover as many PoIs and view quality to provide useful summations, section 3.1]; perform object detection on the first refined image set to detect one or more location markers in the images of the first refined image set [step in Fig. 2, the crowd-contributed photos together capture a group of PoIs with varied quality on the views (e.g., clear view, blocked view). To this end, in order to satisfy the requester’s expectation in understanding the area, the selection process should attain both photo coverage to cover as many PoIs and view quality to provide useful summations (i.e., certain coverage). The server can access the photos and in some cases, the PoI number information, during photo selection, which can facilitate both a basic selection strategy and a PoI number-aware selection
strategy, section 3.1]; discard images not including location markers in the first refined image set to obtain a second refined image set [discard images not including location markers in the first refined image set to obtain a second refined image set (part 1: "exploiting valuable context information (i.e., the Pol number information) to facilitate representative selection with better coverage"; part 2.1: "quantifies a photo's coverage as the Pol aspects it captures and selects photos that captures the most aspects", FIG. 2 indicates photos of "blocked view", "wrong direction", i.e., without location markers, are not selected): perform similarity analysis of the images in the second refined image set to identify clusters of similar images (para. 4.2: "The similarity between two photos can be calculated]; perform similarity analysis of the images in the second refined image set to identify clusters of similar images [A pair of useful photos of one PoI are likely to show high similarity on content due to the intersection of
their covered aspects, while photos with low view quality (i.e., blocked, blurred, or wrong direction) are supposed to have their own defects, thus distinct on their content. We denote the visual similarity between a pair of photos as their content influence on each other., section 4.2]; select a representative image from each cluster of similar images to obtain a location representative image set [define the graph similarity measure used in PAPS6. Generally, photos of the same PoI usually present
smaller geographical distances and higher matching levels on visual than photos of different PoIs. Hence, we novelly combine spatial similarity and visual similarity together and formulate the graph similarity between photos, section 5.2]; transmit the location representative image set and GPS data associated with the representative images to the remote server [FIG. 2, delivering a proper photo subset I" from "MCS server" to "Requester"; part 3.1: "Each photo Pi has a location tag Lis which denotes where it is captured", para. 5.2: "the selected photos from different clusters are combined into one set and returned to the requester]. It would have been obvious to one of ordinary skill in the art before the effective filing date to determines the rating of acquired image of Walker to include the to measure photo merits of coverage and quality by exploiting photos’ spatial distribution and visual representativeness thought by Zhou. The modification would use capturing and managing high quality images easily and reliably, while minimizing or managing the danger of running out of memory in camera for cellular phone.
As per claim 3, Zhou discloses wherein processing the captured images comprises: determination of a quality metric for each of the images; and processing only the images with a quality metric above a predefined quality threshold to obtain the location representative image set [the target area comprehensively and clearly, the server-to-requester photo selection should attain photo coverage by capturing as many Points of Interest (PoIs) in the area and attain view quality with clear and accurate views, intro section].
As per claims 4 and 16, Walker discloses wherein the location markers comprise one or more of: traffic signs, named storefronts, street signs, landmarks [images of Alice may be considered to be more valuable/higher quality than images of buildings or traffic lights, paragraph 283].
As per claim 5, Zhou discloses wherein processing the captured images comprises: performing a similarity analysis of the captured images to identify clusters of similar images; selecting a representative image from each cluster of similar images; and the location representative image set is obtained based on a representative image from each cluster of similar images [A pair of useful photos of one PoI are likely to show high similarity on content due to the intersection of
their covered aspects, while photos with low view quality (i.e., blocked, blurred, or wrong direction) are supposed to have their own defects, thus distinct on their content. We denote the visual similarity between a pair of photos as their content influence on each other., section 4.2].
As per claims 6 and 18, Walker discloses wherein the similarity analysis is performed for images captured over a predefined time window; optionally wherein the predefined time window is any one of: 15 seconds, or 30 seconds, or 45 seconds, or 60 seconds, or 75 seconds, or 90 seconds, or 105 seconds, or 120 seconds [Some examples of meta-data that may be associated with an image include: a time, a date, a location, one or more subjects of an image, one or more settings of an imaging device, paragraph 62].
As per claims 7 and 19, Walker discloses wherein the edge device further comprises an inertial measurement unit (IMU) and the capturing of images is triggered on detection of motion by the IMU [a rating may include a measurement of the quality of an image, paragraph 61].
As per claim 8, Zhou discloses wherein the location representative image set is compressed before transmission [Then only those photos corresponding to the selected features need to be uploaded and transmitted to the requester, section 7].
As per claim 9, Zhou discloses wherein the location representative image set is transformed into a compressed video before transmission [Fig. 11].
As per claim 10, Walker discloses wherein the edge device is mounted on a helmet to capture images as the helmet wearer navigates an area [the edge detect, paragraph 293].
As per claim 11, Walker discloses wherein the network interface comprises a cellular network radio device for transmission of signals over a cellular network [the camera 210 may include or be connected to a cellular telephone with wireless communication capabilities (e.g., a cellular telephone on a 2.5G or 3G wireless network). Using the cellular telephone, the camera 210 may transmit one or more images to a server, which may store the images, paragraph 84].
As per claim 12, Zhou discloses determine presence of the edge device in a geo-fenced area based on the comprises geo-fence data and data from the GPS device; and trigger the capture of images in response to determining that the edge device is present in a geo-feanced area [the built-in camera applications on their own mobile devices are used to collect photos for PoIs in the area, Section 3.1].
As per claim 15, Zhou discloses wherein processing the captured images comprises: determination of a quality metric for each of the images; and processing only the images with a quality metric above a predefined quality threshold to obtain the location representative image set [View quality quantifies the view acceptance level of the photos in selection I. Generally, a photo is determined to have a good view quality if it does not provide a blocked (the PoI is blocked by an obstacle, e.g., the 4th photo in Fig. 2) or blurred view or shooting at a wrong direction (no object is captured, e.g., the 8th photo in Fig. 2). Then the view quality of a selection I can be defined, section 3.2].
As per claim 17, Zhou discloses wherein processing the captured images comprises: performing a similarity analysis of the captured images to identify clusters of similar images; select a representative image from each cluster of similar images; and the location representative image set is obtained based on a representative image from each cluster of similar images [A pair of useful photos of one PoI are likely to show high similarity on content due to the intersection of their covered aspects, while photos with low view quality (i.e., blocked, blurred, or wrong direction) are supposed to have their own defects, thus distinct on their content. We denote the visual similarity between a pair of photos as their content influence on each other., section 4.2].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOSHA ARJOMANDI whose telephone number is (571)272-9784. The examiner can normally be reached 8:00am to 4:30pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at 571-272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
NOOSHA ARJOMANDI
Primary Examiner
Art Unit 2166
August 7, 2026
/NOOSHA ARJOMANDI/Primary Examiner, Art Unit 2166
Prosecution Insights