Prosecution Insights
Last updated: October 01, 2026
Application No. 18/883,896

IN-CALL EXPERIENCE ENHANCEMENT FOR ASSISTANT SYSTEMS

Non-Final OA §103
Filed
Sep 12, 2024
Priority
Oct 18, 2019 — provisional 62/923,342 +2 more
Examiner
EL-ZOOBI, MARIA
Art Unit
Tech Center
Assignee
Meta Platforms Technologies LLC
OA Round
1 (Non-Final)
79%
Grant Probability
Favorable
1-2
OA Rounds
6m
Est. Remaining
93%
With Interview

Examiner Intelligence

Grants 79% — above average
79%
Career Allowance Rate
873 granted / 1108 resolved
+18.8% vs TC avg
Moderate +14% lift
Without
With
+14.2%
Interview Lift
resolved cases with interview
Typical timeline
2y 6m
Avg Prosecution
22 currently pending
Career history
1127
Total Applications
across all art units

Statute-Specific Performance

§101
4.5%
-35.5% vs TC avg
§103
54.5%
+14.5% vs TC avg
§102
14.5%
-25.5% vs TC avg
§112
12.6%
-27.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1108 resolved cases

Office Action

§103
CTNF 18/883,896 CTNF 84111 Detailed Action Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 2, 3, 5-9, 11-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20090273659) in view of Son (US 20170084067) . Regarding claim 2, Lee teaches, a method (abstract) comprising, by one or more computing systems, establishing a video call between a plurality of client systems (abstract: mobile terminal including an input unit configured to receive an instruction from a first user to perform a video call communication with at least a second user), wherein the video call comprises a scene (Fig. 12: video call between plurality of users); invoking an assistant system during the video call (abstract: a voice recognition module configured to recognize input voice statements conducted during the video call communication between the first and second users); receiving, from a first client system of the plurality of client systems, a request to be performed by the assistant system during the video call (Paragraph 124-125, Fig. 14: using voice command user can request to write/send message to Jane), executing, by the assistant system, and providing, to one or more of the plurality of client systems, a response to the request (Fig. 14, el. 710, 740 and Paragraph 125). Lee does not teach analyzing images of the scene of the video call to identify one or more objects within the scene; wherein the request references a specific object of the one or more objects; executing, by the assistant system, the request based at least in part on the specific object as claimed. Son teaches analyzing images of the scene of the video call to identify one or more objects within the scene; wherein the request references a specific object of the one or more objects; executing, by the assistant system, the request based at least in part on the specific object (Paragraph 150: the electronic device 101 may obtain first information by analyzing the obtained image, obtain the first information which is an analysis result indicating that “a girl wears a one-piece dress” as a result of analyzing the image, image analysis result using a recognition algorithm, e.g., an image caption algorithm, based on a deep learning method or a machine learning method. Specifically, the image caption algorithm based on a deep learning method or a machine learning method may recognize an attribute of a specific object within the image and 152: second information may include information related to “girl” or “one-piece dress” in the first information indicating that “a girl wears a one-piece dress”. Specifically, the second information may include a price, a purchase detail, or a dress changing history of the “one-piece dress”, or may include information indicating that the “girl” is a “granddaughter” of the recipient. and Paragraph 184-185: voice recognition). Therefore, it would have been obvious to one with ordinary skill in the art before the filing date of the claimed invention to modify Lee with Son in order to improve the system and enhance the conference and the interaction between users. Regarding claim 3, Lee in view of Son teaches, wherein the request is a voice request made by a user of the first client system (Lee: Fig. 14, el. 710). Regarding claim 5, Lee in view of Son teaches, storing relationship data comprising a relationship between the specific object and a user of the plurality of client systems, wherein the request is executed based at least in part on the relationship data (Son: Paragraph 151: the second information may include at least one of relationship information between a sender transmitting the image and a recipient receiving the image, relationship information between the sender and the electronic device 101, or relationship information between the recipient and the electronic device 101. Further, the second information may include information related to the first information which is the image analysis result. The electronic device 101 may obtain the second information independent from the first information according to a predetermined input, or may obtain the second information through a result of learning an external environment. For example, the electronic device 101 may obtain relationship information indicating that the relationship between the electronic device 101 and the recipient is a “friend relationship”. As described above, the relationship information indicating “friend relationship” may be previously determined by a user, or may be obtained by a learning result based on interaction between a user and the electronic device 101) and Paragraph 153: the electronic device 101 may output a message 520 including the first information indicating that “a girl wears a one-piece dress” and a message 530 including the second information “granddaughter” and “Dude” corresponding to “friend relationship”, e.g. “Dude, your granddaughter puts on a one-piece dress.”. The electronic device 101 may generate the message 530 using an attribute of the second information. Specifically, the electronic device 101 may dispose a word “Dude” in a front portion of a sentence in that an attribute of the word “Dude” based on the relationship information is an appellation. Further, the electronic device 101 may replace “girl” in the first information with “granddaughter”, in that an attribute of the word “granddaughter” is a word capable of replacing “girl”. In the meantime, the electronic device 101 may additionally convert “wears” into “puts on” based on the relationship information between the electronic device 101 and the recipient, e.g., the relationship information of “friend relationship”. Accordingly, the electronic device 101 may output the message 530 indicating that “Dude, a granddaughter puts on a one-piece dress”. The outputted message 530 may include both the first information indicating “puts on a one-piece dress” and the second information indicating “Dude” and “granddaughter”. As described above, the electronic device 101 may output a message including the image analysis result and the additional information) and Paragraph 180: A result of inferring the relationship information may be created into a database and stored in a memory accessible by the electronic device 101). Regarding claim 6, Lee in view of Son teaches, wherein access to the assistant system is persistently maintained during the video call (Lee: Abstract: voice recognition module configured to recognize input voice statements conducted during the video call communication between the first and second users). Regarding claim 7, Lee in view of Son teaches, wherein the response to the request is provided while maintaining the video call between the plurality of client systems (Lee: Abstract: voice recognition module configured to recognize input voice statements conducted during the video call communication between the first and second users). Regarding claim 8, see claim 2 rejections. Regarding claim 9, see claim 3 rejections. Regarding claim 11, see claim 5 rejections. Regarding claim 12, see claim 6 rejections. Regarding claim 13, see claim 7 rejections. Regarding claim 14, see claim 2 rejections (regarding client system (see Paragraph 129: smart phone) comprising: one or more processors; a non-transitory computer-readable media; a camera configured to capture a video (smart phone performing video call Fig. 1, el 181: processor, 121 cameras, 160: memory). Regarding claim 15, Lee in view of Son teaches, wherein at least a portion of the assistant system is remotely connected to the client system via the network (Paragraph 33, 35). Regarding claim 16, Lee in view of Son teaches, wherein the video is part of a video call with at least one other client system connected to the client system via the network (Fig. 11 and 12). Regarding claim 17, Lee in view of Son teaches, wherein access to the assistant system is persistently maintained during the video call (Lee abstract). Regarding claim 18, Lee in view of Son teaches, wherein the response to the voice request is received while maintaining the video call (Lee: abstract). Regarding claim 20, see claim 5 rejections . 07-21-aia AIA Claim s 4, 10, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lee (US 20090273659) in view of Son (US 20170084067) in view of Riza (US 20190353887) . Regarding claim 4, Lee in view of Son teaches, executing the request (see claim 1 rejection). Lee in view of Son does not teach wherein executing the request comprises adjusting a camera associated with the video call based at least in part on a location of the specific object within the scene. Riza teaches adjusting a camera associated with the video call based at least in part on a location of the specific object within the scene (Paragraph 192: the smart camera in general, and specifically the CAOS line scan camera, can be used for the tracking of moving objects in a HDR scene, as a smaller amount of pixels in the scene cover the moving object and its track). Therefore, it would have been obvious to one with ordinary skill in the art before the filing date of the claimed invention to modify Lee with Son in with Riza order to improve the system and enhance the conference and the interaction between users and allow the user better view of what he/she interested in. Regarding claim 10, see claim 4 rejections. Regarding claim 19, see claim 4 rejections . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Examiner found another art that teaches the limitations of Claim 2 that primary prior art reference Lee fails to disclose: Zia (US 20200051338) also teaches analyzing images of the scene of the video call to identify one or more objects within the scene; wherein the request references a specific object of the one or more objects; executing, by the assistant system, the request based at least in part on the specific object (Paragraph 25: a collaborative platform via which the end-user can communicate in real-time (e.g., via voice or video call) with a room designer. For instance, while simultaneously sharing an image (or, images) and corresponding AR metadata of a scene, as captured by one or more cameras of the end-user's mobile computing device, the end-user can communicate various product preferences to the room designer. Furthermore, using computer vision and object recognition analysis, the shared image (or, images) received from the end-user's mobile computing device is analyzed to identify objects (and their attributes) present in the image. Accordingly, the information extracted from analyzing an image is used, in combination with explicit and/or inferred end-user preference information, to query one or more databases of products to quickly and efficiently identify products that may be both complementary to those products identified in the image, and suiting the preferences and tastes of the end-user. As such, by interacting with the room design application, the room designer can select and position products within images, as presented on his or her client computing device, such that the positioned products will also appear in a live AR scene as rendered by the end-user's AR-capable mobile computing device. The end-user benefits by having the expertise of the room designer in both selecting appropriate products, and positioning or designing the layout and look of those products in the end-user's room, to coordinate with the existing look of the room). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA EL-ZOOBI whose telephone number is (571)270-3434. The examiner can normally be reached Monday-Friday 7-4. 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, Carolyn Edward can be reached at (571)270-7136. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MARIA EL-ZOOBI/ Primary Examiner, Art Unit 2692 /CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692 Application/Control Number: 18/883,896 Page 2 Art Unit: 2692 Application/Control Number: 18/883,896 Page 3 Art Unit: 2692 Application/Control Number: 18/883,896 Page 4 Art Unit: 2692 Application/Control Number: 18/883,896 Page 5 Art Unit: 2692 Application/Control Number: 18/883,896 Page 6 Art Unit: 2692 Application/Control Number: 18/883,896 Page 7 Art Unit: 2692 Application/Control Number: 18/883,896 Page 8 Art Unit: 2692
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Prosecution Timeline

Sep 12, 2024
Application Filed
Jun 02, 2026
Non-Final Rejection mailed — §103 (current)

Precedent Cases

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Prosecution Projections

1-2
Expected OA Rounds
79%
Grant Probability
93%
With Interview (+14.2%)
2y 6m (~6m remaining)
Median Time to Grant
Low
PTA Risk
Based on 1108 resolved cases by this examiner. Grant probability derived from career allowance rate.

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