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 .
Terminal Disclaimer
The terminal disclaimer filed on 09/08/2026 has been approved on 09/08/2026.
Response to Amendment
This office action is responsive to the amendment received 09/08/2026.
In the response to the Non-Final Office Action 06/08/2026, the applicant states that claims 1, 6, 9, 11, 16, and 19 have been amended. Claims 10 and 20 have been cancelled. New claims 21 and 22 have been added. Claims 1-9, 11-19, and 21-22 are pending in current application.
Claims 1, 6, 9, 11, 16, and 19 have been amended. Claims 10 and 20 have been cancelled. New claims 21 and 22 have been added. In summary, claims 1-9, 11-19, and 21-22 are pending in current application.
Response to Arguments
Applicant's arguments filed 09/08/2026 have been fully considered but they are not persuasive.
The terminal disclaimer filed on 09/08/2026 has been approved on 09/08/2026. Therefore, the double patenting rejection is hereby withdrawn.
Regarding to claim 1, the applicant argues that none of the cited documents teaches or suggests causing a virtual assistant to present a request for the required data at a location determined based at least in part on sensor data representing the physical environment, as recited in amended independent claim 1. The arguments have been fully considered but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
What claimed is: “causing, within the augmented or mixed reality representation of the physical environment, a virtual assistant to indicate the physical object and present a request for the required data at a location relative to the physical object, wherein the location is determined based at least in part on the sensor data”.
Schmitt discloses “present a request for the required data at a location relative to the physical object”. For example, in Fig. 7 and paragraph [0033], Schmitt teaches a screen provides request for editing the insured's profile, e.g. with updated personal information; Schmitt further teaches displaying a profile including his or her personal information. In Fig. 8 and paragraph [0034], Schmitt teaches interactively adding a lost or damaged item of insured property to a claim from the insured side of the web site; Schmitt further teaches a request for selecting a list of categories of household items, i.e. physical objects. In Fig. 9 and paragraph [0034], Schmitt teaches presenting and submitting the categorized lists of damaged or lost items, i.e. physical objects; Schmitt further teaches various information are tabulated and displayed, including item number, description, quantity, unit cost, total original cost and replacement cost; Schmitt furthermore teaches the insurance claim inventory is submitted to the adjuster 10 for review, verification, adjustment and claim payment.
Lebaredian discloses “causing, within the augmented or mixed reality representation of the physical environment, a virtual assistant to indicate the object”. For example, in paragraphs [0015] and [0075], Lebaredian teaches a virtually animated and interactive agent; Lebaredian further teaches the artificial intelligence (AI) agents described herein are implemented in any number of technology spaces and within any number of applications; Lebaredian furthermore teaches the computing device 400 renders immersive augmented reality or virtual reality. In Fig. 2B and paragraph [0051], Lebaredian teaches the video conference, i.e. physical environment, and any number of users 202, e.g., users 202A-202D, may participate in the video conference; Lebaredian further teaches the AI agent 204B is a virtual assistant and is within a virtual environment 206; Lebaredian further more teaches the AI agent 204B points to the window, and speaks to the properties of the window, e.g., dimensions, materials, etc., based on some conversation, gestures, and inputs form the users 202;
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; Lebaredian suggests users 202A-202D in a video conference are mixed with AI agent 204B. In paragraph [0075], Lebaredian teaches the computing device 400 renders immersive augmented reality or virtual reality. OR is optional.
Brown discloses “causing, within the augmented or mixed reality representation of the physical environment, a virtual assistant to indicate the physical object”. For example, in paragraph [0034], Brown teaches a virtual assistant, also referred to as a virtual agent or intelligent personal assistant, may be output to a user to facilitate various functions. In paragraph [0043], Brown teaches virtual assistants are designed to simulate how a human would behave as a conversational entity using natural language processing systems. In Fig. 14 and paragraph [0192], Brown teaches capturing digital images and video of the damage 1404 of a roof, i.e. physical object;
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. In paragraph [0215], Brown teaches displaying user interface icons over a 3D generated model that represents a user's house, i.e. physical object. In Fig. 17 and paragraph [0215], Brown teaches the user interface 1702 is implemented in an Augmented Reality (AR) context by overlaying user interface icons over real-time images of an environment in which the client device 1704 is located.
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.
Brown further discloses “wherein the location is determined based at least in part on the sensor data”. For example, in paragraph [0034], Brown teaches a virtual assistant, also referred to as a virtual agent or intelligent personal assistant, may be output to a user to facilitate various functions. In Fig. 8 and paragraph [0138], Brown teaches the location of the user is determined using location-based services, global positioning systems, i.e. GPS, multilateration of radio signals between cell towers, and so forth as described above. In paragraph [0139], Brown teaches the location of the user may be approximately the same as a location of property. In paragraph [0259], Brown teaches a depth camera that detects distances to objects, surfaces, and so forth in an environment.
Claims 2-9, 11-19, and 21-22 are not allowable due to the similar reasons as discussed above.
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, 4-8, 11, 14-18, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US 20130050258 A1) in view of Schmitt (US 20090326989 A1), in view of Lebaredian (US 20210358188 A1), and further in view of Brown (US 20200143481 A1).
Regarding to claim 1 (Currently Amended), Liu discloses a system (Fig. 1; [0041]: a block diagram depicts components of an HMD device and hub computer system; Fig. 1; [0044]: the processor executes instructions stored on a processor readable storage device; Fig. 8; [0107]: a process for associating a data stream with a real-world object; display augmented reality images), comprising:
one or more processors (Fig. 1; [0044]: the processor executes instructions stored on a processor readable storage device; Fig. 1; [0046]: the hub computing system 12 includes a processor that executes instructions stored on a processor readable storage device); and
memory coupled to the one or more processors, the memory storing instructions executable by the one or more processors to perform operations (Fig. 1; [0044]: the processor executes instructions stored on a processor readable storage device; [0046]: execute instructions stored on a processor readable storage device; Fig. 8; [0107]: a process for associating a data stream with a real-world object; display augmented reality images) comprising:
receiving sensor data comprising image data representing a physical environment ([0057]: receive an augmented reality image from micro-display; Fig. 3; [0062]: receive sensory information from hub computing device 12; [0069]: a depth map of a user's living room is made; the depth map identifies walls, furniture and so forth which characterize the room; provide a 3d model of the environment; [0078]: the HMD device creates a model of the environment that the user is in and track various objects in that environment; [0080]: a depth camera captures a depth image of a scene; Fig. 1; [0101]: the hub generates the model of the environment and provides that model to all of the mobile terminals in communication with the hub; transfer that information to each of the mobile terminals);
identifying, based on the sensor data, a physical object within the physical environment (Fig. 15A; [0135]: the user 1500 wearing the HMD device 2 looks in a gaze direction 1512 toward an object 1502; other objects in the scene include a flowerpot 1504 on a table 1506; identify wall-hanging picture in a room; Fig. 16B; [0139]: the object has been identified by the user as an object which is to be associated with a data stream, e.g., based on the user gazing at the object for a period of time, making a gesture such as pointing at the object and/or providing a verbal command; [0153]: the fact that the user is gazing at the billboard is determined by recognizing the billboard in the field of view of the HMD device);
determining required data associated with the physical object ([0064]: obtain depth data, i.e. required data, for objects in a room, indicating the distance from the cameras/HMD device to the object; [0065]: images from forward-facing cameras identify people, hand gestures and other objects in a field of view of the user; [0081]: determine a physical distance, i.e. required data, from the capture device 20 to a particular location on the targets or objects in the scene; [0153]: the fact, i.e. required data, that the user is gazing at the billboard is determined by recognizing the billboard in the field of view of the HMD device);
presenting, on an electronic display, an augmented or mixed reality interface including a representation of the physical environment ([0078]: create a model of the environment that the user is in and track various objects in that environment, based on the field of view of the HMD device; [0105]: the augmented reality image includes a 3-D object; Fig. 8; [0107]: the HMD device displays augmented reality images of one or more data streams which have been associated with the real-world object; Fig. 16F; [0143]: select the augmented reality images 1632 and 1634 by the user;
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; present additional augmented reality images 1641 and 1643);
receiving, via an input device of the system, an input provided by a user responsive to the request for the required data ([0065]: identify people, hand gestures and other objects in a field of view of the user; the user makes a hand gesture; [0135]: the user 1500 wearing the HMD device 2 looks in a gaze direction 1512 toward an object 1502, such as a wall-hanging picture; Fig. 16B; [0139]: the object has been identified by the user as an object which is to be associated with a data stream, e.g., based on the user gazing at the object for a period of time, making a gesture such as pointing at the object and/or providing a verbal command; the user makes a verbal command such as next step to proceed; [0142]: the user enters a command to have different candidates displayed; [0153]: the fact that the user is gazing at the billboard could also be determined by recognizing the billboard in the field of view of the HMD device);
Liu fails to explicitly disclose:
causing, within the augmented or mixed reality representation of the physical environment, a virtual assistant to indicate the physical object and present a request for the required data at a location relative to the physical object, wherein the location is determined based at least in part on the sensor data; and
initiating an automated process on the system, based on the input and an attribute of the physical object.
In same field of endeavor, Schmitt teaches:
present a request for the required data at a location relative to the physical object (Fig. 7; [0033]: a screen provides request for editing the insured's profile, e.g. with updated personal information; display a profile including his or her personal information; Fig. 8; [0034]: interactively add a lost or damaged item of insured property to a claim from the insured side of the web site; a request for selecting a list of categories of household items, i.e. physical objects; Fig. 9; [0034]: present and submit the categorized lists of damaged or lost items, i.e. physical objects; various information are tabulated and displayed, including item number, description, quantity, unit cost, total original cost and replacement cost; the insurance claim inventory is submitted to the adjuster 10 for review, verification, adjustment and claim payment);
initiating an automated process on the system, based on the input and an attribute of the physical object (Fig. 4; [0032]: generate reports of the claim settlement at 74 according to the procedures of the insurer 6; Fig. 8; Fig. 9; [0034]: office, kitchen, and garage are environment information; generate insurance claim based on item-specific information which includes environment information, such as, office, kitchen, and garage, information indicative, and damaged item name in an automated process; generate a comprehensive insurance claim inventory report based on the categorized lists of damaged or lost items, as shown in FIG. 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu to include present a request for the required data at a location relative to the physical object; initiating an automated process on the system, based on the input and an attribute of the physical object as taught by Schmitt. The motivation for doing so would have been to improve the process for both the insurers and the insureds/claimants; to generate a comprehensive insurance claim inventory report as taught by Schmitt in paragraphs [0006] and [0034].
Liu in view of Schmitt fails to explicitly disclose:
causing, within the augmented or mixed reality representation of the physical environment, a virtual assistant to indicate the physical object, wherein the location is determined based at least in part on the sensor data.
In same field of endeavor, Lebaredian teaches:
causing, within the augmented or mixed reality interface representation of the physical environment, a virtual assistant to indicate the object (or is optional; Fig. 2B; [0051]: the video conference, i.e. physical environment, includes an AI agent 204; the AI agent 204B is a virtual assistant and is within a virtual environment 206; the AI agent 204B points to the window, and speaks to the properties of the window, e.g., dimensions, materials, etc., based on some conversation, gestures, and inputs form the users 202;
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; update the CAD file corresponding to the architectural plan 208, i.e. in physical environment; [0075]: the computing device 400 renders immersive augmented reality or virtual reality).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu in view of Schmitt to include causing, within the augmented or mixed reality interface representation of the physical environment, a virtual assistant to indicate the object as taught by Lebaredian. The motivation for doing so would have been to generate a virtual representation of the location, with cloudy skies and rain falling; to make the AI agent 204B point to the window, and speak to the properties of the window, e.g., dimensions, materials, etc., based on some conversation, gestures, and inputs form the users 202 as taught by Lebaredian in Fig. 2B and paragraphs [0035] and [0051].
Liu in view of Schmitt and Lebaredian fails to explicitly disclose: the object is the physical object, wherein the location is determined based at least in part on the sensor data.
In same field of endeavor, Brown teaches the object is the physical object (Fig. 14; [0192]: capture digital images and video of the damage 1404 of a roof, i.e. physical object;
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; [0215]: display user interface icons over a 3D generated model that represents a user's house, i.e. physical object), wherein the location is determined based at least in part on the sensor data ([0034]: a virtual assistant, also referred to as a virtual agent or intelligent personal assistant, may be output to a user to facilitate various functions; Fig. 8; [0138]: the location of the user is determined using location-based services, global positioning systems, i.e. GPS, multilateration of radio signals between cell towers, and so forth as described above; [0139]: the location of the user may be approximately the same as a location of property; [0259]: a depth camera detects distances to objects, surfaces, and so forth in an environment).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu in view of Schmitt and Lebaredian to include the object is the physical object, wherein the location is determined based at least in part on the sensor data as taught by Brown. The motivation for doing so would have been to improve the loss response; to reduce processing time of the loss data 1114 when analyzing the reported loss; to automatically identify objects within the digital image 1110, such as the object recognition techniques; to improve object recognition and damage identification in digital images; to display user interface icons over a 3D generated model that represents a user's house as taught by Brown in paragraphs [0057], [0164-0165], [0199], and [0215].
Regarding to claim 4 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein determining the required data associated with the physical object (same as rejected in claim 1) comprises:
based on identifying the physical object, retrieving, from a database, metadata associated with the physical object (Brown; [0047]: locations of shelters and time; [0087]: current location of the property; [0165]: the image processing component 1014 verifies when the digital image 1110 was taken, e.g., from metadata associated with the digital image; [0188]: the image processing component 1014 identifies a time stamp associated with the digital images from metadata received with the digital images; [0241]: retrieve the policy data for the users that have property from a database for analysis and/or processing; [0243]: retrieve data from a database); and
determining, based on the sensor data and the retrieved metadata, the required data associated with the physical object (Brown; [0074]: determine different rooms in a building, and what items are included in different rooms; [0193]: the reporting verification component 1030 verifies that the damage 1404 documented in digital images and video submitted by the user 1402 coordinates with the damage as documented by the drone 1408, including but not limited to comparing time stamps, comparing extent of the damage, comparing locations of the damage, and so on).
Same motivation of claim 1 is applied here.
Regarding to claim 5 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein:
identifying the physical object comprises detecting the physical object as a damaged object (Schmitt; Fig. 9; [0034]: determine and submit the categorized lists of damaged or lost items; submit lost property claims; [0035]: adjust a property loss claim; identify the damaged or lost items); and
determining the required data associated with the physical object comprises determining a request based on damage detected to the physical object (Schmitt; Fig. 8; Fig. 9; [0034]: office, kitchen, and garage are environment information; generate insurance claim based on item-specific information which includes environment information, such as, office, kitchen, and garage, information indicative, and damaged item name; generate a comprehensive insurance claim inventory report based on the categorized lists of damaged or lost items, as shown in FIG. 9; [0035]: adjust a property loss claim; identify the damaged or lost items).
Same motivation of claim 1 is applied here.
Regarding to claim 6 (Currently Amended), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein the input comprises an indication of a gesture or utterance and the operations (Liu; [0127]: the user provides the voice command "select" or "yes" to determine and select a currently highlighted augmented reality image; Fig. 16B; [0139]: the object has been identified by the user as an object which is to be associated with a data stream, e.g., based on the user gazing at the object for a period of time, making a gesture such as pointing at the object and/or providing a verbal command; Fig. 16F; [0143]: the user selects two of the three initial candidates, namely the augmented reality images 1632 and 1634, using a voice command or gesture) further comprising:
confirming an identity of the object based on the indication of the gesture or utterance (Liu; [0065]: identify people, hand gestures and other objects in a field of view of the user; the user makes a hand gesture; Fig. 3; [0111]: detect a user hand gesture; detect a user voice command; [0127]: the user provides the voice command "select" or "yes" to determine and select a currently highlighted augmented reality image; Fig. 16F; [0143]: the user selects two of the three initial candidates, namely the augmented reality images 1632 and 1634, using a voice command or gesture).
Regarding to claim 7 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein the sensor data represents the physical environment at a first time and the operations (Liu; [0057]: receive an augmented reality image from micro display; Fig. 3; [0062]: receive sensory information from hub computing device 12; [0069]: a depth map of a user's living room is made; the depth map identifies walls, furniture and so forth which characterize the room; provide a 3d model of the environment; [0078]: the HMD device creates a model of the environment that the user is in and track various objects in that environment; Fig. 1; [0101]: the hub generates the model of the environment and provides that model to all of the mobile terminals in communication with the hub; transfer that information to each of the mobile terminals) further comprising:
receiving second sensor data representing the physical environment at a second time prior to the first time (Liu; [0047]: the surrounding space are captured, analyzed, and tracked to perform one or more controls or actions; Fig. 2; [0051]: the visible light video camera 113; one visible light video camera 113 captures video; Fig. 5; [0079]: capture device 20 captures video with depth information in different times; [0105]: render the augmented reality image is in synchronism with content displayed on a video display screen; [0152]: the user's head has another orientation); and
presenting, on the electronic display, based on the second sensor data, a representation of the physical object at the second time (Liu; [0054]: video images; [0105]: rendering the augmented reality image is in synchronism with content displayed on a video display screen; [0105]: the augmented reality image includes a 3-D object).
Regarding to claim 8 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein identifying the physical object (same as rejected in claim 1) comprises:
determining an object identifier associated with the physical object, wherein determining the required data is based at least in part on the object identifier (Liu; [0067]: the location is learned from an identifier; [0069]: a depth map of an environment of the user is saved and used as an identifier of the location; [0110]: the RFID tag of an object responds by transmitting an identifier; [0128]: the record identifies the one or more selected data streams such as by a URL or other web address, file name, or other data source identifier; Fig. 13B; [0132]: the record 1310 includes an object and/or object class identifier 1312, one or more visual characteristics 1314, one or more types of location data 1316, privacy settings 1318 and one or more data streams 1320 which are associated with the object or object class).
Regarding to claim 11 (Currently Amended), Liu discloses a method (Fig. 1; [0041]: a block diagram depicts components of an HMD device and hub computer system; Fig. 1; [0044]: the processor executes instructions stored on a processor readable storage device; Fig. 8; [0107]: a process for associating a data stream with a real-world object; display augmented reality images) comprising:
a mobile computing device (Fig. 1; [0101]: the hub generates the model of the environment and provides that model to all of the mobile terminals in communication with the hub; transfer that information to each of the mobile terminals);
the rest claim limitations are similar to claim limitations recited in claim 1. Therefore, same rational used to reject claim 1 is also used to reject claim 11.
Regarding to claim 14 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11,
The rest claim limitations are similar to claim limitations recited in claim 4. Therefore, same rational used to reject claim 4 is also used to reject claim 14.
Regarding to claim 15 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11, wherein:
The rest claim limitations are similar to claim limitations recited in claim 5. Therefore, same rational used to reject claim 5 is also used to reject claim 15.
Regarding to claim 16 (Currently Amended), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11,
The rest claim limitations are similar to claim limitations recited in claim 6. Therefore, same rational used to reject claim 6 is also used to reject claim 16.
Regarding to claim 17 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11,
The rest claim limitations are similar to claim limitations recited in claim 7. Therefore, same rational used to reject claim 7 is also used to reject claim 17.
Regarding to claim 18 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11,
The rest claim limitations are similar to claim limitations recited in claim 8. Therefore, same rational used to reject claim 8 is also used to reject claim 18.
Regarding to claim 21 (New), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein the sensor data further comprises pupil data indicating a gaze direction of the user (Liu; Fig. 9B; [0115]: determine a user is looking at a particular real-world object based on the gaze direction, and optionally, the focal distance; [0116]: estimate the location of the gaze based on the glint), and wherein the location is determined based at least in part on the gaze direction (Liu; [0117]: the gaze direction determined by the eye tracking camera can determine the direction in which the user is looking; [0128]: determine if the user is in a location of the object and looking in a direction of the object based on gaze direction; Fig. 14; [0133]: when a user gazes at, and focuses on, an object, a focal distance can be determined which is a distance from the eye to a gaze location of the object).
Regarding to claim 22 (New), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11,
The rest claim limitations are similar to claim limitations recited in claim 21. Therefore, same rational used to reject claim 21 is also used to reject claim 22.
Claims 2-3, 9, 12-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US 20130050258 A1) in view of Schmitt (US 20090326989 A1), in view of Lebaredian (US 20210358188 A1), in view of Brown (US 20200143481 A1), and further in view of Flick (US 10137984 B1).
Regarding to claim 2 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein identifying the physical object (same as rejected in claim 1) comprises:
providing the sensor data to a trained image recognition machine learning engine (Lebaredian; [0027]: train machine learning and/or deep learning models using custom data; [0087]: a machine learning model(s) is trained by calculating weight parameters; the trained or deployed machine learning models corresponding to one or more neural networks receive input to infer or predict information; [0088]: image recognition; allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services); and
Liu in view of Schmitt, Lebaredian and Brown fails to explicitly disclose:
identifying the physical object based on an output of the trained image recognition machine learning engine.
In same field of endeavor, Flick teaches:
identifying the physical object based on an output of the trained image recognition machine learning engine (col. 18, lines 10-20: train a machine learning program; determine and estimate damages to property and personal articles using a trained machine program; generate a proposed insurance claim based upon the estimate amount of damages; col. 20, lines 15-25: machine learning program is trained to determine or estimate damages to property and/or personal articles; col. 21, lines 1-10: a machine learning program is trained to identify abnormal conditions; col. 33, lines 10-20: train a machine learning program to identify damaged home features or personal articles).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu in view of Schmitt, Lebaredian and Brown to include identifying the physical object based on an output of the trained image recognition machine learning engine as taught by Flick. The motivation for doing so would have been to determine and estimate damages to property and personal articles using a trained machine program; to generate a proposed insurance claim based upon the estimate amount of damages as taught by Flick in col. 18, lines 10-20, col, 20, lines 15-25, and col. 21, lines 1-10.
Regarding to claim 3 (Original), Liu in view of Schmitt, Lebaredian, Brown, and Flick discloses the system of claim 2, wherein the image recognition machine learning engine is trained to automatically recognize damaged objects (Flick; col. 18, lines 10-20: a machine learning program is trained to automatically determine or estimate damages to property and personal articles; generate a proposed insurance claim based upon the estimate amount of damages; col. 20, lines 15-25: machine learning program is trained to determine or estimate damages to property and/or personal articles; col. 21, lines 1-10: a machine learning program is trained to identify and recognize abnormal conditions; col. 33, lines 10-20: Flick teaches training a machine learning program to automatically identify and recognize damaged home features or personal articles).
Same motivation of claim 2 is applied here.
Regarding to claim 9 (Currently Amended), Liu in view of Schmitt, Lebaredian and Brown discloses the system of claim 1, wherein the sensor data represents the physical environment at a first time and the operations (Liu; [0057]: receive an augmented reality image from micro display; Fig. 3; [0062]: receive sensory information from hub computing device 12; [0069]: a depth map of a user's living room is made; the depth map identifies walls, furniture and so forth which characterize the room; provide a 3d model of the environment; [0078]: the HMD device creates a model of the environment that the user is in and track various objects in that environment; Fig. 1; [0101]: the hub generates the model of the environment and provides that model to all of the mobile terminals in communication with the hub; transfer that information to each of the mobile terminals) further comprising:
Liu in view of Schmitt, Lebaredian and Brown fails to explicitly disclose:
receiving second sensor data representing the physical environment at a second time prior to the first time; and
automatically determining damage to the physical object, based on comparing the sensor data and the second sensor data.
In same field of endeavor, Flick teaches:
receiving second sensor data representing the physical environment at a second time prior to the first time (Flick; col. 18, lines 1-10: collect and receive sensor data; the sensor data is associated with the impacted area within the property before, during, and/or after the insurance-related event; col. 18, lines 15-25: the sensor data is associated with the interior of the property and personal articles before an insurance-related event); and
automatically determining damage to the physical object, based on comparing the sensor data and the second sensor data (Flick; col. 18, lines 10-20: a machine learning program is trained to automatically determine or estimate damages to property and personal articles; generate a proposed insurance claim based upon the estimate amount of damages; col. 21, lines 1-10: Flick teaches a machine learning program is trained to automatically identify abnormal conditions; col. 33, lines 10-20: Flick teaches training a machine learning program to automatically identify damaged home features or personal articles).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Liu in view of Schmitt, Lebaredian and Brown to include receiving second sensor data representing the physical environment at a second time prior to the first time; and automatically determining damage to the physical object, based on comparing the sensor data and the second sensor data as taught by Flick. The motivation for doing so would have been to determine and estimate damages to property and personal articles using a trained machine program; generate a proposed insurance claim based upon the estimate amount of damages as taught by Flick in col. 18, lines 10-20, col, 20, lines 15-25, and col. 21, lines 1-10.
Regarding to claim 12 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11,
The rest claim limitations are similar to claim limitations recited in claim 2. Therefore, same rational used to reject claim 2 is also used to reject claim 12.
Regarding to claim 13 (Original), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 12,
The rest claim limitations are similar to claim limitations recited in claim 3. Therefore, same rational used to reject claim 3 is also used to reject claim 13.
Regarding to claim 19 (Currently Amended), Liu in view of Schmitt, Lebaredian and Brown discloses the method of claim 11,
The rest claim limitations are similar to claim limitations recited in claim 9. Therefore, same rational used to reject 9 is also used to reject claim 19.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hai Tao Sun whose telephone number is (571)272-5630. The examiner can normally be reached 9:00AM-6:00PM.
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, Daniel Hajnik can be reached at 5712727642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAI TAO SUN/Primary Examiner, Art Unit 2616
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