Prosecution Insights
Last updated: April 19, 2026
Application No. 17/142,884

SYSTEMS AND METHODS FOR ADAPTING PLAYBACK DEVICE FOR CONTENT DISPLAY

Final Rejection §112
Filed
Jan 06, 2021
Examiner
CASTRO, ALFONSO
Art Unit
2421
Tech Center
2400 — Computer Networks
Assignee
Sourcepicture Inc.
OA Round
8 (Final)
50%
Grant Probability
Moderate
9-10
OA Rounds
3y 8m
To Grant
69%
With Interview

Examiner Intelligence

Grants 50% of resolved cases
50%
Career Allow Rate
218 granted / 435 resolved
-7.9% vs TC avg
Strong +19% interview lift
Without
With
+18.9%
Interview Lift
resolved cases with interview
Typical timeline
3y 8m
Avg Prosecution
38 currently pending
Career history
473
Total Applications
across all art units

Statute-Specific Performance

§101
6.5%
-33.5% vs TC avg
§103
66.4%
+26.4% vs TC avg
§102
4.0%
-36.0% vs TC avg
§112
11.0%
-29.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 435 resolved cases

Office Action

§112
DETAILED ACTION Response to Arguments Applicant's arguments, Remarks, pg. 10, filed 12/4/2025, with respect to the status of the claims are hereby acknowledged. Claims 1-8, 10-18, 20-24, 26-31 are pending. Applicant's arguments, Remarks, pg. 10-16, filed 12/4/2025, with respect to the rejection of the claims on under 35 U.S.C. 112 have been fully considered but the arguments are not persuasive. In particular, the applicant argues, inter alia, the following: Claims 1-8, 10-18, and 20-31 are rejected under 35 U.S.C. 112 as allegedly failing the written description requirement. Applicant traverses the rejection and submits that after a proper reading of the specification, the details of the previous amendments are readily apparent and all claims as presently presented are adequately described and enabled. Applicant respectfully submits that the claimed subject matter, including the most recent amendments, tracks the originally filed disclosure-including the use of exogenous data, environmental/sensor data, configuration profiles, and machine-learning-based adaptation of presentation settings. As the Office Action notes, written description is satisfied when the specification "reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date." AriadPharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en bane). The Federal Circuit has further made clear that there is no requirement that the specification recite the claimed invention in ipsissimis verbis (the exact words of the claim). See, e.g., Fujikawa v. Wattanasin, 93 F.3d 1559, 1570 (Fed. Cir. 1996) ("the exact terms need not be used"). The specification may describe the invention in words, figures, or by implication, and may rely on what a person of ordinary skill in the art ("POSIT A") would understand from the disclosure. See Capon v. Eshhar, 418 F.3d 1349, 1357-58 (Fed. Cir. 2005). Applicant submits the specification expressly teaches that the display adaptation tools may be implemented using machine learning and artificial intelligence, and that the system may learn how to adapt display settings based on multiple instances of processing exogenous data. When read together with the detailed teachings on exogenous and environmental data, a POSIT A would readily understand that the claimed machine-learning implementation is a natural, explicit embodiment of what the inventors had already described. The examiner respectfully disagrees. The applicant’s response to the rejection of the claims on under 35 U.S.C. 112 cites support for the amendments in originally filed specification paragraph [0101] which is cited in its entirety as follows: PNG media_image1.png 405 865 media_image1.png Greyscale The applicant submitted amended claims on 5/6/2025 and were amended as follows: PNG media_image2.png 779 887 media_image2.png Greyscale According to the originally filed specification as cited above, the specification states, in part, “[i]n such systems, a learning module may record and learn how to automatically adapt display settings over time and multiple instances of the system processing exogenous data to optimize a display. For example, if certain content is created, yet no exogenous data is captured or recorded, a trained display adaptation tool may be able to adapt a display showing such content based on its previously learning of similar content. Such a system may be particularly beneficial for live transmissions where frequent updates to exogenous data may be computationally expensive or not possible given limitations of the transmission.” The applicant’s Remarks pg. 11, filed 12/4/2025 further support this disclosure and states the following: PNG media_image3.png 303 776 media_image3.png Greyscale Again, applicant stated “when no exogenous data is captured.” Stated differently, the applicant’s originally filed specification does not disclose that the machine learning and artificial intelligence adaptation optimizes every single media content asset regardless of whether exogeneous data for a particular media content asset already exists. On the contrary, the applicant’s originally filed specification only supports an embodiment wherein the applicant states that “…if certain content is created, yet no exogenous data is captured or recorded, a trained display adaptation tool may be able to adapt a display showing such content based on its previously learning of similar content. Such a system may be particularly beneficial for live transmissions where frequent updates to exogenous data may be computationally expensive or not possible given limitations of the transmission.” In applicant’s amended claim forming the basis of the 35 U.S.C. 112 rejection, independent claim 1 makes clear that exogeneous data is available. For example, said claim recites “receiving from the presentation device presentation device information and a request for a configuration profile associated with a content asset; retrieving provider presentation settings according to the presentation device information and the content asset, retrieving exogenous data relating to capture and production of the content asset; receiving environmental data from the presentation device, wherein the environmental data includes sensory information relating to the environment in which the presentation device is located; inputting the provider presentation settings, the exogenous data, and environmental data to a machine learning model trained on a corpus of historical presentation settings and exogenous data.” (Emphasis ours). The applicant’s independent claim 1 recites an invention wherein presentation provider presentation settings is utilized to retrieve (existing) exogenous data for the requested content asset and the exogenous data is then utilized to present the requested content asset (i.e., generating presentation instructions according to the output, the presentation instructions adapted to configure the presentation device to display the content asset according to the provider presentation settings, exogenous data and environmental data). The applicant’s original disclosure does not reasonably convey that the inventor had possession of the subject matter of the amendment at the time of the filing of the application because the applicant’s specification does not support nor appear to contemplate where existing/available exogenous data for a content asset is then optimized, instead, the applicant’s originally filed specification makes clear that “a learning module may record and learn how to automatically adapt display settings over time and multiple instances of the system processing exogenous data to optimize a display. For example, if certain content is created, yet no exogenous data is captured or recorded, a trained display adaptation tool may be able to adapt a display showing such content based on its previously learning of similar content. Such a system may be particularly beneficial for live transmissions where frequent updates to exogenous data may be computationally expensive or not possible given limitations of the transmission” as discussed in the applicant’s originally filed specification. All things considered, the applicant’s arguments are not persuasive. Accordingly, Examiner submits that independent claims 1, 16, 17, 18 and 30-31 are not in a condition for allowance over the cited references. Examiner further submits the pending dependent claims, by at least their dependence on a rejected base independent claims are also patentable. Claim Rejections - 35 USC § 112 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. Claim 1, 16-18 and 30-31 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 claim(s) contains 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. With respect to amended limitations submitted on 5/6/2025 and recited in representative independent claim 1 (and similarly recited in independent claims 16, 17, 18, and 30-31 reciting the representative limitations “inputting the provider presentation settings, the exogenous data, and environmental data to a machine learning model trained on a corpus of historical presentation settings and exogenous data; adapting the provider presentation settings according to an output of the machine learning model; generating presentation instructions according to the output, the presentation instructions adapted to configure the presentation device to display the content asset according to the provider presentation settings, exogenous data and environmental data; generating a configuration profile including the provider presentation settings and the presentation instructions; and transmitting the configuration profile to the presentation device” in relation to “retrieving exogenous data relating to capture and production of the content asset; receiving environmental data from the presentation device, wherein the environmental data includes sensory information relating to the environment in which the presentation device is located,”) when submitted, the applicant did not point out where the new (or amended) claim is supported, nor does there appear to be a written description of said representative claim limitation in the application as filed. The claims depending on independent claims 1, 16-18, and 30-31 (i.e., 2-8, 10-15 and 20-24, 26-29) are similarly rejected as being dependent on a rejected claim. Therefore, the claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph. See MPEP §2163.04 , e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a “simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.”); see also MPEP §§ 714.02 and 2163.06 (“Applicant should ... specifically point out the support for any amendments made to the disclosure.”); and MPEP § 2163.04 states “If applicant amends the claims and points out where and/or how the originally filed disclosure supports the amendment(s), and the examiner finds that the disclosure does not reasonably convey that the inventor had possession of the subject matter of the amendment at the time of the filing of the application, the examiner has the initial burden of presenting evidence or reasoning to explain why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims.”). In particular, the newly amended limitations recite, inter alia, “inputting the provider presentation settings, the exogenous data, and environmental data to a machine learning model trained on a corpus of historical presentation settings and exogenous data; adapting the provider presentation settings according to an output of the machine learning model; generating presentation instructions according to the output, the presentation instructions adapted to configure the presentation device to display the content asset according to the provider presentation settings, exogenous data and environmental data; generating a configuration profile including the provider presentation settings and the presentation instructions; and transmitting the configuration profile to the presentation device” in relation to “retrieving exogenous data relating to capture and production of the content asset; receiving environmental data from the presentation device, wherein the environmental data includes sensory information relating to the environment in which the presentation device is located.” In reviewing the applicant’s originally filed specification, the disclosure states the following: [0101] According to one or more aspects of the present disclosure, a set of display adaptation tools may be built and adapted using machine learning and artificial intelligence. In such systems, a learning module may record and learn how to automatically adapt display settings over time and multiple instances of the system processing exogenous data to optimize a display. For example, if certain content is created, yet no exogenous data is captured or recorded, a trained display adaptation tool may be able to adapt a display showing such content based on its previously learning of similar content. Such a system may be particularly beneficial for live transmissions where frequent updates to exogenous data may be computationally expensive or not possible given limitations of the transmission. The newly amended limitations are broader in scope what is recited in the originally filed specification, however, the examiner requested that the applicant clearly identify where the originally filed specification supports the newly amended limitations before making a decision on whether the newly amended limitations rise to the level of new matter. For example, the applicant’s specification states “[f]or example, if certain content is created, yet no exogenous data is captured or recorded, a trained display adaptation tool may be able to adapt a display showing such content based on its previously learning of similar content. Such a system may be particularly beneficial for live transmissions where frequent updates to exogenous data may be computationally expensive or not possible given limitations of the transmission…,” however, the newly amended limitations are not in line with the disclosure because the newly amended limitations recite the requirement “retrieving exogenous data relating to capture and production of the content asset; receiving environmental data from the presentation device, wherein the environmental data includes sensory information relating to the environment in which the presentation device is located.” Therefore, the new claims provide a substantive change to the claims which require a different interpretation of the claims but the applicant has not clearly pointed out where and/or how the originally filed disclosure supports the amendment(s). No prior art rejection is made under the current interpretation of the claims. Correction is required. Prior art made of record but not relied upon - Navin; Ashwin et al. US 20190327526 A1 relevant to utilizing training data to control a presentation device wherein the prior art discloses - In various embodiments, one or more machine learning or artificial intelligence systems may be incorporated into the systems and methods described herein in order to adjust and refine certain recommended settings. The machine learning systems may be utilized in ACR, for example by incorporating object recognition, facial recognition or video fingerprints or audio fingerprints, or may be utilized in determining and recommending settings for adjustments to user devices. For example, the machine learning systems may evaluate different settings over different content types of associated a range of particular settings or adjustments associated with different content types. Additionally, the machine learning systems may incorporate user feedback to tune or adjust trained models to provide enhanced user experiences. 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 ALFONSO CASTRO whose telephone number is (571)270-3950. The examiner can normally be reached on Monday to Friday from 10am to 6pm. 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, Nathan Flynn can be reached. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ALFONSO CASTRO/Primary Examiner, Art Unit 2421
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Prosecution Timeline

Jan 06, 2021
Application Filed
Jul 03, 2021
Non-Final Rejection — §112
Jan 10, 2022
Response Filed
Apr 23, 2022
Final Rejection — §112
Oct 28, 2022
Request for Continued Examination
Nov 02, 2022
Response after Non-Final Action
Nov 05, 2022
Non-Final Rejection — §112
May 05, 2023
Response Filed
Jun 17, 2023
Final Rejection — §112
Dec 22, 2023
Request for Continued Examination
Dec 28, 2023
Response after Non-Final Action
Dec 30, 2023
Non-Final Rejection — §112
Jul 03, 2024
Response Filed
Nov 01, 2024
Final Rejection — §112
May 06, 2025
Request for Continued Examination
May 13, 2025
Response after Non-Final Action
May 31, 2025
Non-Final Rejection — §112
Dec 04, 2025
Response Filed
Dec 27, 2025
Final Rejection — §112 (current)

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Prosecution Projections

9-10
Expected OA Rounds
50%
Grant Probability
69%
With Interview (+18.9%)
3y 8m
Median Time to Grant
High
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