Prosecution Insights
Last updated: April 19, 2026
Application No. 18/182,173

Shared Space Boundaries and Phantom Surfaces

Non-Final OA §103
Filed
Mar 10, 2023
Examiner
HAJNIK, DANIEL F
Art Unit
2616
Tech Center
2600 — Communications
Assignee
Apple Inc.
OA Round
3 (Non-Final)
78%
Grant Probability
Favorable
3-4
OA Rounds
2y 11m
To Grant
99%
With Interview

Examiner Intelligence

Grants 78% — above average
78%
Career Allow Rate
612 granted / 785 resolved
+16.0% vs TC avg
Strong +21% interview lift
Without
With
+20.9%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
10 currently pending
Career history
795
Total Applications
across all art units

Statute-Specific Performance

§101
14.6%
-25.4% vs TC avg
§103
60.0%
+20.0% vs TC avg
§102
7.7%
-32.3% vs TC avg
§112
5.5%
-34.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 785 resolved cases

Office Action

§103
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 . DETAILED ACTION 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 Jan 8, 2026 has been entered. Allowable Subject Matter Claims 3-4, 7-9, 11-12, 14-16, and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Response to Arguments Applicant's arguments filed Jan 8, 2026 have been fully considered but they are not persuasive. Applicant argues: PNG media_image1.png 296 637 media_image1.png Greyscale (lower half of page 9 in filed response). The Examiner respectfully maintains that the prior art rejections in this matter are proper because Keshavarzi in figure 1(d) shows that virtual components such as virtual chairs and virtual participants are placed into the local environment. Figure 1 and its caption indicates that standable or sittable areas are indicated using yellow and orange boundaries respectively. These boundaries are geometric shapes with properties such as lengths and sizes. Thus, dimensions of an environment available for presentation of the virtual chair components is considered. Also, please see Keshavarzi in section 3.3.2, 2nd paragraph where the bounding boxes of sittable areas are considered with respect to bounding boxes of local objects in the local room environment. In these instances, bounding boxes have dimension properties as well. Also, Keshavarzi in section 4, 2nd paragraph refers to “Figure 3 illustrates the available standable and sittable boundaries for two sample rooms processed by our system.” (emphasis added). Figure 3 in Keshavarzi shows the standable areas using the local room marked in areas with green and sittable areas using the local room marked in areas with yellow. Again, these green or yellow areas have local geometric dimensions with respect to the local room (as shown in the figure). Keshavarzi emphasizes that the virtual chairs and participants (components) should be placed in accordance with the available local standing and sitting areas (including their dimensions). For example, please see Keshavarzi in section 1, 2nd paragraph “Furthermore, the augmentation of the virtual data in the physical space must be compatible with the contextual properties of the physical space, such as a floor that is standable, a chair that is sittable, and a wall that is also a physical barrier of virtual interactions.” Keshavarzi in figure 1(d) shows the dimensions of the virtual chair objects and virtual participants fit within the dimensions of the local space. Thus, the local and virtual constraints are sized-based constraints based on using what space is available (where this includes dimensions as well) since bounding boxes are being considered. Applicant remarks: PNG media_image2.png 248 633 media_image2.png Greyscale (bottom of page 9 in filed response). The examiner respectfully maintains that this argument is moot since Valli is not relied upon for teaching these newly amended claim features from independent claim 1. 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 of this title, 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 1-2, 5-6, 10, 13, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Keshavarzi et al. (NPL Doc, “Optimization and Manipulation of Contextual Mutual Spaces for Multi-User Virtual and Augmented Reality Interaction”) in view of Valli (Pub No. US 2023/0115563 A1). As per claim 1, Keshavarzi teaches the claimed: 1. A method comprising: obtaining, at a first device, local size constraint data associated with a local environment in which the first device is located (Please see Keshavarzi in the 1st column in figures 1a and 1b where a local environment (a user’s bedroom) is shown where a first user’s AR device (HoloLens) is located: PNG media_image3.png 181 639 media_image3.png Greyscale Keshavarzi in the first column in figure 1b shows the claimed “local size constraint data” as being associated with the yellow and orange bounding boxes overlaid over the floor plan of the first user’s bedroom (the local environment). These boxes represent standing or sitting usable areas within the first user’s local environment (the first user’s bedroom). Also, please see Keshavarzi in the abstract and the first paragraph in sections 3.3.1 “Standable Spaces” and section 3.3.2 “Sittable Spaces”. The “local size constraint data” includes constraints such as “Standing spaces consist of the volume of the room in which no object located within a human user’s height range is present” or “As we mentioned before, sittable spaces normally extend the standable spaces by adding areas where humans are able to sit on. Furniture types such as sofas, chairs, and beds include sitting areas”), wherein the local size constraint data indicates geometric characteristics of a region of the local environment available for virtual component placement (The “local size constraint data” indicates geometric characteristics of a region of the local environment available for virtual chair component placement “Standing spaces consist of the volume of the room in which no object located within a human user’s height range is present” or “As we mentioned before, sittable spaces normally extend the standable spaces by adding areas where humans are able to sit on. Furniture types such as sofas, chairs, and beds include sitting areas” Figure 1d shows the virtual chair component placement in the local environment. Figure 3 shows that the available sitting and standing areas are based upon geometric space. These geometric spaces have dimensions as well, e.g. length and width with respect to the surrounding local room); obtaining, at the first device, remote size constraint data associated with a remote environment in which a second device is located, wherein the remote size constraint data indicates geometric characteristics of a region of the remote environment available for virtual component placement, and wherein the first device and the second device are active in a multi-user communication session (Please see Keshavarzi in the 2nd column in figures 1a and 1b: PNG media_image4.png 193 647 media_image4.png Greyscale In Keshavarzi in the 2nd column in figures 1a and 1b, a remote environment is shown as a second user’s bedroom where a first user’s AR device (HoloLens) is located. This second user is located in a different room from the first user. Thus, the second user is remotely located from the first user. The second column in figure 1b shows the claimed “remote size constraint data” as being associated with the yellow and orange bounding boxes overlaid over the second user’s bedroom. These boxes represent standing or sitting usable areas within the second user’s bedroom (the remote environment). Also, please see the abstract and the first paragraph in sections 3.3.1 “Standable Spaces” and section 3.3.2 “Sittable Spaces”. The “remote size constraint data” includes constraints such as “Standing spaces consist of the volume of the room in which no object located within a human user’s height range is present” or “As we mentioned before, sittable spaces normally extend the standable spaces by adding areas where humans are able to sit on. Furniture types such as sofas, chairs, and beds include sitting areas”. The claimed “remote size constraint data” is received at the first device when a common collaborative environment is created that includes both the first and second user sitting a table (please see the 1st column in figure 1d of Keshavarzi). PNG media_image5.png 196 650 media_image5.png Greyscale Remote size constraint data is received at the first device because this data is used to create the virtual representation of the second user appearing within the first user’s local environment, e.g. the second user’s appears around the area of the second user’s chair where they are sitting down in the remote environment. In this instance, the “remote size constraint data” includes the area in which the second user is sitting (shown as a virtual representation) within the first user’s local environment. Also, in the 1st column in figure 1d, the first device (first user’s device) and the second device (second user’s device) are active in a multi-user communication session when these two users are actively communicating with each other in real-time. Also, please see the end of the abstract which refers to “Results show the proposed algorithm can effectively discover optimal shareable space for multiuser virtual interaction and hence facilitate remote spatial computing communication in various collaborative workflows”); determining a usable geometry based on the local size constraint data and the remote size constraint data (This is shown in Keshavarzi in figure 1b in the first and second columns respectively. In particular, the yellow and orange bounding boxes determine the usable geometry based on the local size constraint data and the remote size constraint data. The local size constraint data and the remote size constraint data includes standing areas or sittable areas within the local or remote environments for the first and second users respectively), wherein the usable geometry delineates a region which fits in the local environment and the remote environment (This is shown in Keshavarzi in the first column in figure 1d where the usable geometry defines (delineates using bounding boxes) common sitting regions which fits in the local environment and the remote environment for collaborative meetings and communication) presenting a representation of the virtual components of the multi-user communication session within the usable geometry in the local environment (This is also shown in Keshavarzi in the first column in figure 1d where the representation of the virtual participants and virtual chairs (virtual components) of the multi-user communication session is presented graphically to the first user within the usable geometry (valid standing areas and sittable areas in the first and second user’s bedrooms). In figure 1d on the left side, the available standing or sitting area where virtual participants in virtual chairs are placed in first user’s bedroom is an example of usable geometry in the local environment). Keshavarzi alone does not explicitly teach the remaining claim limitations relating to the claimed “using a common set of dimensions”. However, Keshavarzi in combination with Valli teaches the claimed “using a common set of dimensions”. As mentioned above, Keshavarzi in the first column in figure 1d shows a virtual collaborate meeting that includes usable geometry and local and remote size constraints to define standable and sittable areas within the local environment and remote environment. Keshavarzi is silent about a common set of dimensions being used for their multi-user collaborate virtual meeting. Valli provides additional evidence that this technical feature was known in the art, e.g. please see Valli in figure 2 and in paragraph [0046]: “A virtual meeting layout may be formed by placing captured meeting spaces in a spatial relation. The captured spaces may be mapped to a common coordinate system, e.g., one relative to the real world, by a cascaded matrix operation performing rotation, scaling, and translation. In particular, any user position in a captured sub-space, e.g. space 210, 220, 230 can be mapped to the coordinates of any other sub-space. Correspondingly, all viewpoints and viewing directions between participants are known, as indicated by arrows 250, 251, 252, 253, 254, 255 representing lines-of-sight in a global coordinate system between the users.” In this instance, the virtual meeting where a plurality of captured spaces from different users may be mapped to a common coordinate system. This “common coordinate system” in Valli corresponds to the claimed “using a common set of dimensions”. This is because the common coordinate system in Valli helps define a common set of dimensions for all participants and their usable spaces in the virtual collaborative meeting. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a common set of dimensions as taught by Valli with the system of Keshavarzi. This helps ensure consistent spatial size and appearance for each of the participants collaborating together within the virtual meeting. For example, a common set of dimensions may help ensure that the dimensions for each location have a common size scale when these are displayed together as a single virtual meeting. This ensures that each virtual representation of each participant has a consistent body size and that some virtual representations of participants do not appear to be much larger or smaller than other participants within the virtual meeting. This helps ensure that the virtual meeting has proportional sizes that mimics how people would be sized in a real-world meeting. As per claim 2, Keshavarzi teaches the claimed: 2. The method of claim 1, wherein components of the multi user communication session are presented within the usable geometry (Keshavarzi teaches this feature in the first column in figure 1d where components relating to each participant and their body (e.g. their arms, legs, or top of their head) of the multi-user communication session are presented within the usable geometry (they are presented within their respective available standing areas or sittable areas from their respective locations)). As per claim 5, Keshavarzi teaches the claimed: 5. The method of claim 1, further comprising generating the local size constraint data based on one or more images of the local environment (In section 6, 2nd paragraph “In addition, we scan the space using a Matterport camera, and perform the semantic segmentation step using Matterport classifications to locate the bounding boxes of all the furniture located in the room. We then feed the bounding box data to our algorithm for mutual boundary search. The implementation outputs spatial coordinates for standable and sittable areas which are automatically updated in the Unity Game Engine to be rendered in the Hololenses” In this instance, by using the camera to scan the space, images of the local environment are obtained. These images are used to generate local size constraints relating to valid standing or sittable areas within the room). As per claim 6, Keshavarzi teaches the claimed: 6. The method of claim 1, further comprising generating the local size constraint data based on user input (According to the first paragraph in section 3.3.1 “Standable Spaces” and section 3.3.2 “Sittable Spaces” the local size constraint data is based upon the layout of the room including its furniture and object placement within that room. Thus, the local size constraints relating to available standing areas or sittable areas is based on user input of where the user places those items in the room. For example, based upon where the user places their sittable areas such as their chair and bed in their bedroom environment (including for a first user in a local environment)). As per claim 10, Keshavarzi teaches the claimed: 10. The method of claim 1, further comprising displaying a representation of the usable geometry within the local environment (Please see figure 6 and its caption which recites: “Figure 6: Screenshots from HoloLens illustrating the identified mutual boundaries as augmented overlays for three rooms: A) kitchen; B) conference room; C) robotic laboratory. Blue color indicates mutual boundaries, green color indicates standable spaces and red color indicates non-standable spaces”). As per claim 13, Keshavarzi teaches the claimed: 13. The method of claim 1, wherein the usable geometry comprises an area or volume (Figure 1b in the 1st and 2nd columns shows that the usable geometry comprises an area or volume defined by orange and yellow bounding boxes. Also, the first paragraph in sections 3.3.1 “Standable Spaces” and section 3.3.2 “Sittable Spaces”, defines the usable geometry comprises an area or volume in which the user can stand or sit within their space or environment). As per claims 17 and 18, these claims are similar in scope to limitations recited in claims 1 and 2, respectively, and thus are rejected under the same rationale. The system of Keshavarzi would have to have some type of non-transitory computer readable medium and processor present in order to function and run on a computer as described by the reference. As per claim 20, this claim is similar in scope to limitations recited in claims 1 and 17, and thus is rejected under the same rationale. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL F HAJNIK whose telephone number is (571) 272-7642. The examiner can normally be reached Mon-Fri 8am-5pm. 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. 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. /DANIEL F HAJNIK/Supervisory Patent Examiner, Art Unit 2616
Read full office action

Prosecution Timeline

Mar 10, 2023
Application Filed
Apr 14, 2025
Non-Final Rejection — §103
Jul 17, 2025
Response Filed
Oct 07, 2025
Final Rejection — §103
Jan 08, 2026
Request for Continued Examination
Jan 23, 2026
Response after Non-Final Action
Mar 19, 2026
Non-Final Rejection — §103 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12586259
IMAGE GENERATION USING A TEXT AND IMAGE CONDITIONED MACHINE LEARNING MODEL
2y 5m to grant Granted Mar 24, 2026
Patent 12573138
ENDOSCOPIC EXAMINATION SUPPORT APPARATUS, ENDOSCOPIC EXAMINATION SUPPORT METHOD, AND RECORDING MEDIUM
2y 5m to grant Granted Mar 10, 2026
Patent 12573118
ATOMIC STREAMING OF VIRTUAL OBJECTS
2y 5m to grant Granted Mar 10, 2026
Patent 12541903
AVATAR GENERATION APPARATUS, AVATAR GENERATION METHOD, AND PROGRAM
2y 5m to grant Granted Feb 03, 2026
Patent 12430819
SYSTEMS AND METHODS FOR ENHANCING COLOR ACCURACY OF FACE CHARTS
2y 5m to grant Granted Sep 30, 2025
Study what changed to get past this examiner. Based on 5 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

3-4
Expected OA Rounds
78%
Grant Probability
99%
With Interview (+20.9%)
2y 11m
Median Time to Grant
High
PTA Risk
Based on 785 resolved cases by this examiner. Grant probability derived from career allow rate.

Sign in with your work email

Enter your email to receive a magic link. No password needed.

Personal email addresses (Gmail, Yahoo, etc.) are not accepted.

Free tier: 3 strategy analyses per month