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 . Claims 1-7, 9-16, and 18-22 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/10/2025 has been entered.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7, 9-16, and 18-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 10, and 19 recite “recognizing that the new issue is present in the service provider by applying the one or more machine learning algorithms to the service provider image in real time”. While the specification discloses that a service provider may capture and transmit images of an issue, it does not describe applying the machine learning algorithms to a service provider image, nor performing such recognition in real time. Accordingly, the claimed real-time step is not reasonably conveyed by the specification.
Additionally, claims 1, 10, and 19 recite “determining a rate of change […] by computing differences between severity ratings associated with successive images in the image-based timeline of progression of the new issue”. Although the specification discloses generating severity ratings and tracking issue progression over time, it does not describe calculating a rate of change by computing differences between successive severity ratings. Accordingly, the specific calculation recited in the claims is not supported by the disclosure.
The rest of the claims are rejected under 35 U.S.C. 112(a) by virtue of their dependency.
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 1-7, 9-16, and 18-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): is the claim to a process, machine, manufacture or composition of matter? MPEP §2106.03.
Per Step 1, claim 1 is directed to a method (i.e., process), claim 10 to an apparatus (i.e., machine), and claim 19 to a computer program product (i.e., machine or manufacture). Thus, the claims are directed to statutory categories of invention.
However, the claims are rejected under 35 U.S.C. § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP §2106.04.
The abstract idea of claims 1, 10, and 19 is (claim 1 being representative, the claims being similar in scope):
receiving at least one image;
identifying a new issue in the at least one image;
receiving metadata associated with the image including timestamp and camera-derived context information relating to the new issue identified in the at least one image;
determining a location of the new issue based on geolocation data extracted from the metadata or the context information;
identifying an existing issue corresponding to the new issue based at least in part on the existing issue being located within a predefined distance of the location of the new issue;
generating a severity rating corresponding to the new issue as a numerical output, wherein the severity rating is based on a combination of risk components including a safety component weighted based on a likelihood of the issue causing an injury, a collateral damage component weighted based on an estimated cost of potential collateral damage caused by the issue, and a degree of deterioration component that is weighted based on a degree of deterioration caused by the issue;
logging the new issue, wherein logging the new issue comprises automatically adding the new issue to an existing issue in response to the new issue corresponding to the existing issue and being located within the predefined distance of the location of the new issue;
retrieving a plurality of images of the existing issue captured over a period of time prior to identifying the new issue and associated severity ratings of the existing issue;
providing the new issue and the location of the new issue for the service provider to resolve the new issue;
receiving a service provider image;
recognizing that the new issue is present in the service provider image;
generating an image-based timeline of progression of the new issue by temporally ordering the plurality of images and rendering the ordered images as a visual progression, wherein the image-based timeline of progression of the new issue presents how the new issue changes over the period of time;
determining a rate of change the severity rating of the new issue over the period of time prior to identifying the new issue based on the associated severity ratings of the existing issue by computing differences between severity ratings associated with successive images in the image-based timeline of progression of the new issue;
predicting a future progression of the new issue as a forecast output based on the plurality of images of the image-based timeline of progression of the new issue and the rate of change of the severity rating of the new issue;
establishing a deadline to correct the new issue based on the predicted future progression of the new issue; and
based on recognizing that the new issue is present in the service provider image, providing, for real-time rendering, a[n] view of the new issue, wherein the view overlays content aligned with a physical environment and includes the image-based timeline of progression of the new issue, the rate of change of the severity rating of the new issue, the predicted future progression of the new issue, and the deadline to correct the new issue, and wherein the view of the new issue is displayed.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, managing crowd-sourced issues, which includes identifying issues, determining locations, and generating severity ratings, which are cognitive tasks that can be performed by the human mind or by hand. If a claim limitation, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to obtaining and giving information about issues within an environment to service providers to resolve, which constitutes a process that, under its BRI, covers managing personal behavior relationships, interactions between people. This is further supported by paragraphs [0002] and [0003] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: performed by a computing system comprising processing circuitry, at least one memory, and a communication interface; via the communication interface; captured by a camera of a user device and storing the at least one image in the at least one memory; by executing, on the processing circuitry, one or more identification machine learning algorithms, wherein the one or more identification machine learning algorithms are trained based on training data that includes images that are labeled with positively identified issues and associated severity ratings; by querying a database of existing issues stored in the at least one memory; to a mobile device associated with a service provider; by a camera of the mobile device; by applying the one or more machine learning algorithms to the service provider image in real time; augmented reality; digital; on a display of the mobile device.
Claim 10 recites the following additional elements: An apparatus comprising processing circuitry, at least one memory including computer program code, and a communication interface, the at least one memory and the computer program code configured to, with the processing circuitry, cause the apparatus to at least; via the communication interface; captured by a camera of a user device and storing the at least one image in the at least one memory; by executing, on the processing circuitry, one or more identification machine learning algorithms, wherein the one or more identification machine learning algorithms are trained based on training data that includes images that are labeled with positively identified issues and associated severity ratings; camera-derived; a database stored in a memory; to a mobile device associated with a service provider; by a camera of the mobile device; by applying the one or more machine learning algorithms to the service provider image in real time; augmented reality; digital; on a display of the mobile device.
Claim 19 recites the following additional elements: A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code portions stored therein, the computer-executable program code portions comprising program code instructions configured to; via the communication interface; captured by a camera of a user device and storing the at least one image in the at least one memory; by executing, on the processing circuitry, one or more identification machine learning algorithms, wherein the one or more identification machine learning algorithms are trained based on training data that includes images that are labeled with positively identified issues and associated severity ratings; camera-derived; a database stored in a memory; to a mobile device associated with a service provider; by a camera of the mobile device; by applying the one or more machine learning algorithms to the service provider image in real time; augmented reality; digital; on a display of the mobile device.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs [0032] to [0035] of applicant’s specification as filed. Further, the combination of these elements is nothing more than a generic computing system.
Examiner interprets with one or more machine learning algorithms; wherein the one or more identification machine learning algorithms are trained based on training data that includes images that are labeled with positively identified issues and associated severity ratings; by applying the one or more machine learning algorithms to the service provider image in real time described in paragraph [0051] of applicant’s specification as filed as an additional element. MPEP 2106.05(f) is explicit that simply using other machinery as a tool also amounts to no more than merely applying the abstract idea to a computer, especially when claimed in a solution-oriented manner:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
[…]
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
In this case, with one or more machine learning algorithms; wherein the new issue is identified based on a machine learning algorithm trained to recognize a plurality of issues; by applying the one or more machine learning algorithms to the service provider image in real time is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP 2106.05(f).
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Regarding claims 2-4, ,6-7, 9, 11-13, 15-16, 18, and 20-22, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Claims 5 and 14 further narrows the abstract idea with additional steps and/or description, in addition to including additional elements: image capture device. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Accordingly, claims 1-7, 9-16, and 18-22 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
No Prior Art Applied to Claims 1-7, 9-16, and 18-22
Claims 1, 10, and 19
There is no prior art applied to claims 1, 10, and 19 because the cited prior art fails to disclose or suggest the complete feature set recited in the claims. Storey, considered the closest art, discloses:
(Claim 1) A method for managing crowd-sourced issues performed by a computing system comprising processing circuitry, at least one memory, and a communication interface, comprising {an example method for processing an indication of an equipment issue from a client to generate an action indication accordingly (paragraph 0085). (Operating environment 400) may be implemented via personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics such as smart phones, network PCs, minicomputers, mainframe computers, distributed computing environments. (paragraph 0121); Operating environment 400 typically may include at least one processing unit 402 and memory (paragraph 0122).}
(Claim 10) An apparatus comprising processing circuitry, at least one memory including computer program code, and a communication interface, the at least one memory and the computer program code configured to, with the processing circuitry, cause the apparatus to at least {(Operating environment 400) may be implemented via personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics such as smart phones, network PCs, minicomputers, mainframe computers, distributed computing environments. (paragraph 0121); Operating environment 400 typically may include at least one processing unit 402 and memory (paragraph 0122).}
(Claim 19) A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code portions stored therein, the computer-executable program code portions comprising program code instructions configured to {Operating environment 400 may include at least some form of computer readable media. The computer readable media may be any available media that can be accessed by processing unit 402 or other devices comprising the operating environment. (paragraph 0123); The different aspects described herein may be employed using software, hardware, or a combination of software and hardware to implement and perform the systems and methods disclosed herein (paragraph 0126).}
receiving, via the communication interface, at least one image captured by a camera of a user device and storing the at least one image in the at least one memory {A customer or technician may use a client application to capture one or more images or videos for on-premises equipment. The captured image data is analyzed for various purposes, including troubleshooting equipment issues and may be stored in a memory (paragraphs 0019, 0026, 0050).}
identifying a new issue in the at least one image by executing, on the processing circuitry, one or more identification machine learning algorithms, wherein the one or more identification machine learning algorithms are trained based on training data that includes images that are labeled with positively identified issues and associated severity ratings {Captured image data is processed by trained machine learning models (e.g., convolutional neural networks) to classify telecommunications equipment and detect issues (paragraphs 0026-0027, 0056-0057). The system generates predicted issues categorized by severity (e.g., minor, major, critical) (paragraph 0034).}
receiving metadata associated with the image including timestamp and camera-derived context information relating to the new issue identified in the at least one image {Image data and associated metadata are analyzed using machine learning models to identify equipment and determine configuration or operational states. Upon identifying an issue, additional information, such as log data related to the equipment, is accessed and analyzed to assist with troubleshooting (paragraph 0019).}
determining a location of the new issue based on geolocation data extracted from the metadata or the camera-derived context information {A location associated with image data using geolocation information extracted from metadata may be determined. (paragraphs 0019, 0023, 0042, 0070)}
identifying an existing issue corresponding to the new issue based at least in part on the existing issue being located within a predefined distance of the location of the new issue by querying a database of existing issues stored in the at least one memory {The system supports accessing stored records and using location metadata and device information to identify issues associated with equipment, including previously known issues. (paragraphs 0021, 0033-0034, 0051)}
logging the new issue in the database stored in the at least one memory, wherein logging the new issue in the database comprises automatically adding the new issue to an existing issue in response to the new issue corresponding to the existing issue {Once an issue is identified through image data analysis, the system updates or creates an inventory record in a database. If a conflict or duplicate is detected, the system may automatically resolve it by updating the existing record or generating a new one (paragraphs 0029, 0058, 0110).}
retrieving, by the processing circuitry, a plurality of images of the existing issue captured over a period of time prior to identifying the new issue and associated severity ratings of the existing issue from the database stored in the at least one memory {Image data and metadata is received from clients and is stored and reused to train or retrain models (paragraph 0027). Such image data is tagged and added to a training data store (paragraph 0059).}
providing, via the communication interface, the new issue and the location of the new issue to a mobile device associated with a service provider for the service provider to resolve the new issue {The client application communicates data to the server, which analyzes the data and “generates a predicted issue and one or more actions”, and the “client application 120 receives and displays the results of the analysis accordingly.” Additionally, actions may include generating service tickets or dispatching a technician (paragraphs 0035 and 0042).}
receiving, via the communication interface, a service provider image captured by a camera of the mobile device {[C]omprise image data with one or more highlighted regions and an instruction to the user to capture additional image data associated therewith. The user may be the service technician, and the indication may assist the service technician in capturing the additional image data…; [C]lient application 120 provides instructions for a user of client device 104 to use image capture device 122 to capture image data associated with equipment 106. The client device may be operated remotely (e.g., by a service technician). Also, image data may be captured during a service visit (paragraphs 0028, 0042-0043, 0075-0079).}
recognizing that the new issue is present in the service provider image by applying the one or more machine learning algorithms to the service provider image in real time {The client application receives and displays the results of server-side analysis of collected data, which includes image data captured by the mobile device. The equipment classification engine processes this image data using a machine learning model. The machine learning model analyzes images captured during a service visit (paragraphs 0026, 0042, 0056, 0075, 0079).}
However, Storey does not disclose or suggest “generating a severity rating corresponding to the new issue with the one or more identification machine learning algorithms as a numerical output of the one or more identification machine learning algorithms executed by the processing circuitry, wherein the severity rating is based on a combination of risk components including a safety component weighted based on a likelihood of the issue causing an injury, a collateral damage component weighted based on an estimated cost of potential collateral damage caused by the issue, and a degree of deterioration component that is weighted based on a degree of deterioration caused by the issue; being located within the predefined distance of the location of the new issue; generating an image-based timeline of progression of the new issue by temporally ordering the plurality of images and rendering the ordered images as a visual progression, wherein the image-based timeline of progression of the new issue presents how the new issue changes over the period of time; determining a rate of change the severity rating of the new issue over the period of time prior to identifying the new issue based on the associated severity ratings of the existing issue by computing differences between severity ratings associated with successive images in the image-based timeline of progression of the new issue; predicting a future progression of the new issue as a forecast output generated by the one or more identification machine learning algorithms based on the plurality of images of the image-based timeline of progression of the new issue and the rate of change of the severity rating of the new issue with the one or more identification machine learning algorithms; establishing a deadline to correct the new issue based on the predicted future progression of the new issue; based on recognizing that the new issue is present in the service provider image, providing, for real-time rendering, an augmented reality view of the new issue to the mobile device via the communication interface, wherein the augmented reality view overlays digital content aligned with a physical environment captured by the camera and includes the image-based timeline of progression of the new issue, the rate of change of the severity rating of the new issue, the predicted future progression of the new issue, and the deadline to correct the new issue, and wherein the augmented reality view of the new issue is displayed on a display of the mobile device.”
Examiner also considered the following additional references:
Bargoti (US 20230052727), which teaches: A computer can operate, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.
Palla (US 20180321997), which teaches: A problem with a computing system is detected, a root cause is identified and a solution is also identified. Diagnostic data is obtained and an issue signature is generated that maps the issue to failed components and product functionality.
Zass (US 20200413011), which teaches: Systems, methods and non-transitory computer readable media for controlling image acquisition robots in construction sites are provided. For example, a first image corresponding to a first point in time and a second image corresponding to a second point in time may be obtained. The first image and the second image may be analyzed to determine whether a change occurred in a particular area of a construction site between the first point in time and the second point in time. It may be determined whether a higher quality image of the particular area of the construction site is needed. An image acquisition robot may be controlled based on the determination of whether a change occurred in the particular area of the construction site and the determination of whether a higher quality image is needed.
Cella (US 20200133257), which teaches: Methods and systems for detecting operating characteristics of an industrial machine in which the systems include at least one data capture device configured to capture raw data of a point of interest of the industrial machine and a computer vision system. The computer vision system can generate one or more image data sets using the raw data captured, identify one or more values corresponding to a portion of the industrial machine within the point of interest represented by the one or more image data sets, compare the one or more values to corresponding predicted values, generate a variance data set based on the comparison of the one or more values and the corresponding predicted values, detect an operating characteristic of the industrial machine based on the variance data, and generate data indicating the detection of the operating characteristic.
However, none of the references disclose or suggest “generating a severity rating corresponding to the new issue with the one or more identification machine learning algorithms as a numerical output of the one or more identification machine learning algorithms executed by the processing circuitry, wherein the severity rating is based on a combination of risk components including a safety component weighted based on a likelihood of the issue causing an injury, a collateral damage component weighted based on an estimated cost of potential collateral damage caused by the issue, and a degree of deterioration component that is weighted based on a degree of deterioration caused by the issue”.
Accordingly, there is no prior art applied to claims 1, 10, and 19. The rest of the claims are rejected by virtue of their dependency.
Response to Arguments
Applicant's arguments filed on January 5, 2026 have been fully considered
but they are not persuasive.
Rejections under 35 U.S.C. §101
Applicant has conflated the abstract idea, considered at Step 2A Prong One, with the additional elements, considered at Step 2A Prong Two and Step 2B. Here, examiner identified the following steps as part of the abstract idea (claim 1 being representative): receiving at least one image; identifying a new issue in the at least one image; receiving metadata associated with the image including timestamp and camera-derived context information relating to the new issue identified in the at least one image; determining a location of the new issue based on geolocation data extracted from the metadata or the context information; identifying an existing issue corresponding to the new issue based at least in part on the existing issue being located within a predefined distance of the location of the new issue; generating a severity rating corresponding to the new issue as a numerical output, wherein the severity rating is based on a combination of risk components including a safety component weighted based on a likelihood of the issue causing an injury, a collateral damage component weighted based on an estimated cost of potential collateral damage caused by the issue, and a degree of deterioration component that is weighted based on a degree of deterioration caused by the issue; logging the new issue, wherein logging the new issue comprises automatically adding the new issue to an existing issue in response to the new issue corresponding to the existing issue and being located within the predefined distance of the location of the new issue; retrieving a plurality of images of the existing issue captured over a period of time prior to identifying the new issue and associated severity ratings of the existing issue; providing the new issue and the location of the new issue for the service provider to resolve the new issue; receiving a service provider image; recognizing that the new issue is present in the service provider image; generating an image-based timeline of progression of the new issue by temporally ordering the plurality of images and rendering the ordered images as a visual progression, wherein the image-based timeline of progression of the new issue presents how the new issue changes over the period of time; determining a rate of change the severity rating of the new issue over the period of time prior to identifying the new issue based on the associated severity ratings of the existing issue by computing differences between severity ratings associated with successive images in the image-based timeline of progression of the new issue; predicting a future progression of the new issue as a forecast output based on the plurality of images of the image-based timeline of progression of the new issue and the rate of change of the severity rating of the new issue; establishing a deadline to correct the new issue based on the predicted future progression of the new issue; and based on recognizing that the new issue is present in the service provider image, providing, for real-time rendering, a[n] view of the new issue, wherein the view overlays content aligned with a physical environment and includes the image-based timeline of progression of the new issue, the rate of change of the severity rating of the new issue, the predicted future progression of the new issue, and the deadline to correct the new issue, and wherein the view of the new issue is displayed. The “performed by a computing system comprising processing circuitry, at least one memory, and a communication interface; via the communication interface; captured by a camera of a user device and storing the at least one image in the at least one memory; by executing, on the processing circuitry, one or more identification machine learning algorithms, wherein the one or more identification machine learning algorithms are trained based on training data that includes images that are labeled with positively identified issues and associated severity ratings; by querying a database of existing issues stored in the at least one memory; to a mobile device associated with a service provider; by a camera of the mobile device; by applying the one or more machine learning algorithms to the service provider image in real time; augmented reality; digital; on a display of the mobile device” are considered additional elements, which are merely facilitating the tasks of said abstract idea. MPEP 2106.05(f) is clear that this generic recitation does not integrate the abstract idea into practical application and/or add significantly more. This interpretation holds whether the additional elements are viewed alone or in combination, where the combination of elements is nothing more than a network-enabled computing system. (Examiner relied on MPEP 2106.05(f) in the eligibility analysis, as noted above).
Rejections under 35 U.S.C. §103
Arguments are moot under 35 USC §103 because there is no prior art applied to claims 1-7, 9-16, and 18-22. Accordingly, Examiner directs Applicant’s attention to the analysis above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20210073962, which teaches: A method for detecting damage and determining a type of damage and a degree or severity of damage to a component of a transmission. The method includes providing a first image of a portion of the component in a reference state and detecting a further image of the portion of the component after intended use. The further image of the portion of the component is detected in an installed state of the component. The method further includes determining, by comparing the further image with the first image, that the component is damaged, determining, by matching the further image against data of a damage database, a type of damage, and outputting the determined type of damage and a degree or severity of damage. The first image, the further image, and the data of the damage database are photographs.
US 20210225038, which teaches: Orientation data for image data of an object may be determined. The orientation information may identify camera location and orientation for image data with respect to an object model represented the object at a point in time. A change to the object between different points in time may be identified by identifying a difference in image data associated with different points in time. The change may be presented in a visual representation of the object model in a user interface displayed on a display screen.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS F MONTALVO whose telephone number is (703)756-5863. The examiner can normally be reached Monday - Friday 8:00AM - 5:30PM; First Fridays OOO.
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/C.F.M./ Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629