Prosecution Insights
Last updated: October 27, 2026
Application No. 18/913,402

SYSTEMS AND METHODS FOR MANAGING DOWNLOADS WITHIN A LOCAL NETWORK

Final Rejection §103
Filed
Oct 11, 2024
Priority
Mar 21, 2022 — provisional 63/322,137 +1 more
Examiner
SURVILLO, OLEG
Art Unit
2457
Tech Center
2400 — Computer Networks
Assignee
DISH Network LLC
OA Round
2 (Final)
73%
Grant Probability
Favorable
3-4
OA Rounds
2y 2m
Est. Remaining
99%
With Interview

Examiner Intelligence

Grants 73% — above average
73%
Career Allowance Rate
425 granted / 585 resolved
+14.6% vs TC avg
Strong +29% interview lift
Without
With
+28.9%
Interview Lift
resolved cases with interview
Typical timeline
4y 3m
Avg Prosecution
19 currently pending
Career history
606
Total Applications
across all art units

Statute-Specific Performance

§101
11.6%
-28.4% vs TC avg
§103
48.4%
+8.4% vs TC avg
§102
13.1%
-26.9% vs TC avg
§112
23.8%
-16.2% vs TC avg
Black line = Tech Center average estimate • Based on career data from 585 resolved cases

Office Action

§103
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 . Response to Amendment Claims 1-20 are pending in the application. Claims 1, 8, and 15 are currently amended. No claims have been canceled. No new claims are currently added. Response to Arguments With regard to Applicant’s remarks dated July 31, 2026: Regarding the rejection of claims 8-9, 11, 13, 15-17 and 19-20 under 35 U.S.C. 102(a)(2) and the rejection of claims 1-7, 10, 12, 14, and 18 under 35 U.S.C. 103, Applicant’s amendment and arguments have been fully considered. Applicants argue at page 8 of the Remarks that “neither of these references appear to describe that a prediction of whether a computing device will download data is made based on a version of at least one of the software applications used by that device, as claimed in amended claim 1.” While Examiner agrees that Alfano and Litvin fail to recite “a version of at least one of the software application”, the claims fail to explain how a version of a software is utilized in predicting whether a device will download the data. Examiner maintains that it is inherent for a software application to have a version and notoriously old and well known for user equipment devices to have software applications installed on each device. Therefore, without an algorithm that explains how a version is utilized as a factor in determining (predicting) whether the user equipment will download the data, Applicant’s amendment recites only what is notoriously old and well known, as demonstrated by the newly found reference to Qiao. As to any arguments not specifically addressed, they are the same as those 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 1-3, 5-6, 8-11, 13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alfano et al. (US Patent 11,076,016 B1) in view of Qiao (US 2017/0155741 A1). As to claim 1, Alfano teaches a system (Fig. 1B) comprising: at least one processor (Fig. 4, col. 16 lines 28-37); and at least one memory coupled to the at least one processor, the memory having computer- executable instructions stored thereon (Fig. 4, col. 16 lines 28-37, col. 17 lines 5-20) that, when executed by the at least one processor, cause the system to perform: identify one or more devices connected to a local network [obtain information from network elements that can form a direct connection] (col. 8 lines 20-34 and 44-51), wherein each device of the one or more devices uses one or more software applications [it is notoriously old and well known for User Equipment devices to have software applications installed on each device]; at a first time: detect that a first device of the one or more devices is downloading data while connected to the local network [UE102a is downloading content from the content provider] (Fig. 1A col. 1 lines 46-55, col. 2 lines 41-57); predict whether a second device of the one or more devices connected to the local network is to download the data at a time after the first time [content cache manager determines whether to cache the content on the first UE 102a based on a characteristic of the content which includes a trend score associated with the content and the content is frequently downloaded/accessed] (col. 4 lines 4-36); and cause the data to be stored based on a prediction that the second device is to download the data [caching content at the first UE 102a] (col. 6 lines 1-24); and at a second time after the first time: detect that the second device requires the data [receiving a content request from the second UE 102b] (col. 7 line 54 to col. 8 line 19); and cause the stored data to be transmitted to the second device [transmitting the cached content data to the second UE 102b by a direct short-range wireless connection] (col. 8 lines 35-51). Alfano fails to expressly teach that prediction of whether a second device of the one or more devices connected to the local network is to download the data at a time after the first time is based on a version of at least one or more software applications used by the second device. While Alfano teaches several parameters for the determining whether to cache the content data, the one or more software applications of the second device are not being considered when determining whether to cache the data. Qiao is directed to providing service data (abstract). In particular, Qiao teaches determining whether to cache the data based on a version of at least one software application used by the second device [in response to an application update request by a client 100, storing the latest version of the application in the local cache in anticipation of a subsequent application update request of another client device 100, thereby increasing the speed of responding to the application update request by the another client 100] (par. [0025]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method and system of Alfano by having the downloading being based on a version of at least one or more software applications used by the second device, in order to increase the speed of responding to the application update request by the another client (par. [0025] in Qiao). As to claim 2, Alfano teaches that identifying the one or more devices includes obtaining device information that comprises one or more of a device type and a device version for each device of the one or more devices [information regarding the UE 102 making the request for content] (col 3 line 62 to col. 4 line 3). As to claim 3, Alfano teaches in view of Qiao teaches that the data comprises an update for one or more software applications used the first device (par. [0025] in Qiao). As to claim 5, Alfano teaches that the computer-executable instructions, when executed by the at least one processor, further cause the system to: cause the data not to be stored based on a prediction that the second device is not to download the data [caching is being performed selectively, so not all of the requested content by the UE 102a is cached] (col. 4 lines 37-50). As to claim 6, Alfano teaches that the computer-executable instructions, to detect that the first device is downloading data, further cause the system to: examine one or more packets being transmitted to the first device [packet processing] (col. 15 lines 38-46). As to claim 8, Alfano teaches a method in a download management system connected to a local network (Fig. 1B), the method comprising: at a first time: detecting that a first device is connected to a local network and is downloading data [obtain information from network elements that can form a direct connection and receiving a content request from UE 102a to download a content] (col. 8 lines 20-34 and 44-51, Fig. 1A col. 1 lines 46-55, col. 2 lines 41-57); determining that a second device connected to the local network is predicted to download the data at a time after the first time [content cache manager determines whether to cache the content on the first UE 102a based on a characteristic of the content which includes a trend score associated with the content and the content is frequently downloaded/accessed] (col. 4 lines 4-36); and causing the data to be stored based on a determination that the second device is predicted to download the data [caching content at the first UE 102a] (col. 6 lines 1-24); and at a second time after the first time: detecting that the second device is ready to download the data [receiving a content request from the second UE 102b] (col. 7 line 54 to col. 8 line 19); and causing the stored data to be transmitted to the second device [transmitting the cached content data to the second UE 102b by a direct short-range wireless connection] (col. 8 lines 35-51). Alfano fails to expressly teach that determining is based on a version of at least one software application used by the second device. Qiao is directed to providing service data (abstract). In particular, Qiao teaches determining whether to cache the data based on a version of at least one software application used by the second device [in response to an application update request by a client 100, storing the latest version of the application in the local cache in anticipation of a subsequent application update request of another client device 100, thereby increasing the speed of responding to the application update request by the another client 100] (par. [0025]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method and system of Alfano by having the determining being based on a version of at least one software application used by the second device, in order to increase the speed of responding to the application update request by the another client (par. [0025] in Qiao). As to claim 9, Alfano teaches that that the second device is predicted to download the data based on one or more of a device type and a device version [information regarding the UE 102 making the request for content] (col 3 line 62 to col. 4 line 3). As to claim 10, Alfano in view of Qiao teaches that the data comprises an update for the second device (par. [0025] in Qiao). As to claim 11, Alfano teaches that the data comprises streamed media (col. 3 lines 10-20). As to claim 13, Alfano teaches that detecting that the first device is downloading data further comprises examining one or more packets being transmitted to the first device [packet processing] (col. 15 lines 38-46). As to claim 15, Alfano teaches a method in a first computing device connected to a local network, the method comprising: receiving an indication that data is to be downloaded to a first computing device from a source outside of the local network [receiving a content request from the second UE 102b] (col. 7 line 54 to col. 8 line 19, Fig. 1B and 1C); receiving an indication from a download management system that the data was already downloaded by a second computing device that is connected to the local network, the data having been stored in response to a prediction that the first computing device is to download the data at a later time [content cache manager provides the connection configuration information necessary to obtain requested content via the direct connection between the requesting UE 102b and providing UE 102a that has the requested content already cached] (col. 10 lines 24-49); obtaining the data that has already been downloaded from at least one computing device connected to the network [UE 102b obtains content from UE 102a using direct connection] (Fig. 1C, col. 10 lines 50-61). Alfano fails to expressly teach that determining is based on a version of at least one software application used by the second device. Alfano fails to expressly teach that downloading is based on a version of at least one software application used by the second device. Qiao is directed to providing service data (abstract). In particular, Qiao teaches determining whether to cache the data based on a version of at least one software application used by the second device [in response to an application update request by a client 100, storing the latest version of the application in the local cache in anticipation of a subsequent application update request of another client device 100, thereby increasing the speed of responding to the application update request by the another client 100] (par. [0025]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method and system of Alfano by having the downloading being based on a version of at least one software application used by the second device, in order to increase the speed of responding to the application update request by the another client (par. [0025] in Qiao). As to claim 16, Alfano teaches that the data is obtained from the second computing device [content is obtained from the UE 102a that has the content cached] (col. 10 lines 50-61, Fig. 1C). As to claim 17, Alfano teaches that the data is obtained from the download management system [data is obtained via edge connection] (col. 9 lines 12-22). As to claim 18, Alfano in view of Qiao teaches that the data comprises an update for the first device and the second device (par. [0025] in Qiao). As to claim 19, Alfano teaches that the data comprises streamed media (col. 3 lines 10-20). As to claim 20, Alfano teaches that obtaining the data that has already been downloaded from at least one computing device within the network comprises: receiving, via the download management system [content cache manager 110], an indication of a third computing device on which the data was stored, wherein the second computing device transmitted the data to the third computing device [Alfano anticipates any number of UEs 102-N in Figure 2, where the content cache manager provides the connection configuration information necessary to obtain requested content via the direct connection between the requesting UE 102 and providing UE 102-N that has the requested content already cached] (col. 10 lines 24-49); and transmitting a request to the third computing device to transmit the data to the first computing device (col. 10 lines 50-61). Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Alfano et al. in view of Qiao and in further view of Latvin et al. (US Patent 6,721,814 B1). As to claim 4, Alfano in view of Qiao teaches all the elements except that the data comprises streamed media. Latvin is directed to data caching (abstract). In particular, Latvin teaches that the data comprises streamed media (col. 10 lines 28-31 in Latvin). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and system of Alfano by having the data comprise streamed media, in order to more intelligently caching data that is most likely to be used by the one or more applications of the requesting device (col. 1 lines 9-24 in Latvin). Claims 7, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Alfano et al. in view of Qiao and in further view of Kang (US 2023/0363024 A1). As to claim 7, Alfano in view of Qiao fails to teach obtaining a determination of whether the first device is downloading at least one selected type of data by applying at least one packet transmitted to the first device to a machine learning model trained to determine whether a device is downloading at least one selected type of data based on at least one or more packets transmitted to a device. Kang teaches obtaining a determination of whether the first device is downloading at least one selected type of data by applying at least one packet transmitted to the first device to a machine learning model trained to determine whether a device is downloading at least one selected type of data based on at least one or more packets transmitted to a device [determining a type of the target transmission data such as type of to-be-downloaded data] (par. [0057]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and system of Alfano in view of Qiao by obtaining a determination of whether the first device is downloading at least one selected type of data by applying at least one packet transmitted to the first device to a machine learning model trained to determine whether a device is downloading at least one selected type of data based on at least one or more packets transmitted to a device in order to make a better determination of which content files are more beneficial to be included in the proxy cache for future downloads by other devices. As to claim 12, Alfano teaches determining that the second device is predicted to download the data further comprises: receiving data comprising: an indication of a device type of the first device [information regarding the UE 102a making the request for content] (col 3 line 62 to col. 4 line 3), an indication of a device type of the second device [information regarding the UE 102b making the request for content] (col 3 line 62 to col. 4 line 3), an indication of information describing a type of the data downloaded by the first device (col. 4 lines 10-19), or an indication of information describing one or more software applications used by both the first device and the second device [not required because of the “or” condition]. Alfano in view of Qiao fails to teach obtaining a determination of whether the second device is predicted to download the data by applying the received data to a machine learning model trained to predict whether a device is predicted to download data based on at least an indication of one or more device types, an indication of one or more types of data, or an indication of one or more shared applications. Kang teaches obtaining a determination of whether the second device is predicted to download the data by applying the received data to a machine learning model trained to predict whether a device is predicted to download data based on at least an indication of one or more device types, an indication of one or more types of data, or an indication of one or more shared applications [determining a type of the target transmission data such as type of to-be-downloaded data] (par. [0057]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and system of Alfano in view of Qiao by obtaining a determination of whether the second device is predicted to download the data by applying the received data to a machine learning model trained to predict whether a device is predicted to download data based on at least an indication of one or more device types, an indication of one or more types of data, or an indication of one or more shared applications in order to make a better determination of which content files are more beneficial to be included in the proxy cache for future downloads by other devices. As to claim 14, Alfano in view of Qiao teaches all the elements except detecting that the first device is downloading data further comprises: obtaining a prediction of whether the first device is downloading at least one selected type of data by applying at least one packet transmitted to the first device to a machine learning model trained to predict whether a device is downloading data based on at least one or more packets transmitted to a device. Kang teaches obtaining a prediction of whether the first device is downloading at least one selected type of data by applying at least one packet transmitted to the first device to a machine learning model trained to predict whether a device is downloading at least one selected type of data based on at least one or more packets transmitted to a device [determining a type of the target transmission data such as type of to-be-downloaded data] (par. [0057]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method and system of Alfano in view of Qiao by obtaining a prediction of whether the first device is downloading at least one selected type of data by applying at least one packet transmitted to the first device to a machine learning model trained to predict whether a device is downloading at least one selected type of data based on at least one or more packets transmitted to a device in order to make a better determination of which content files are more beneficial to be included in the proxy cache for future downloads by other devices. Conclusion Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 OLEG SURVILLO whose telephone number is (571)272-9691. The examiner can normally be reached 9:00am - 5:00pm. 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, Ario Etienne can be reached at 571-272-4001. 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. /OLEG SURVILLO/Primary Examiner, Art Unit 2457
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Prosecution Timeline

Oct 11, 2024
Application Filed
Mar 27, 2026
Applicant Interview (Telephonic)
Apr 01, 2026
Non-Final Rejection mailed — §103
Jul 16, 2026
Interview Requested
Jul 28, 2026
Applicant Interview (Telephonic)
Jul 28, 2026
Examiner Interview Summary
Jul 31, 2026
Response Filed
Oct 02, 2026
Final Rejection mailed — §103 (current)

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Prosecution Projections

3-4
Expected OA Rounds
73%
Grant Probability
99%
With Interview (+28.9%)
4y 3m (~2y 2m remaining)
Median Time to Grant
Moderate
PTA Risk
Based on 585 resolved cases by this examiner. Grant probability derived from career allowance rate.

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