DETAILED ACTION
The Action is responsive to Applicant’s Application filed July 10, 2025.
Please note claims 1-20 are pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Drawings
The drawings, filed July 10, 2025 are considered in compliance with 37 CFR 1.81 and accepted.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 5-6, 8-10, 12-13, 16-17, 19 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 5, 7-10, 12, 15-17, 19 of U.S. Patent No. 10,983,984. Although the claims at issue are not identical, they are not patentably distinct from each other because:
Instant Application 19/265151
US Patent No. 10,983,984
1. A system comprising:
one or more processors; and one or more non-transitory machine-readable storage media containing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations including:
receiving an identification of two or more media devices;
generating a device map by assigning one or more category segments to each
media device of the two or more media devices based on one or more characteristics associated with each media device;
receiving a dataset that includes viewing behavior of at least one media device of
the two or more media devices; and
modifying the device map based on the dataset, wherein modifying the device
map improves an accuracy of the one or more category segments assigned to the two or more media devices.
1. A system comprising:
one or more processors; and one or more non-transitory machine-readable storage media containing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations including:
obtain a plurality of categories assigned to groups of media player devices, wherein the plurality of categories are determined using a device mapping system, and wherein a category includes a categorization for a group of the media player devices;
determine viewing behaviors of the groups of media player devices, wherein the viewing behaviors are determined using automated content recognition by matching viewed media content viewed by the media player devices with stored media content;
determine a correlation between the plurality of categories and the viewing behaviors of the groups of media player devices; determine an accuracy score for the device mapping system using the determined correlation; and assign the accuracy score to the device mapping system, wherein the accuracy score is used to improve the device mapping system.
2. The system of claim 1, wherein the operations further include:
identifying, based on a statistical analysis of the dataset, correlations between media devices of the device map and at least one category segment; and
generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold.
3. …performing a statistical hypothesis test to determine whether the correlation between the plurality of categories and the viewing behaviors of the groups of media player devices
5. The system of claim 4, wherein the accuracy score is determined for the device mapping system based on the comparison of the result of the statistical hypothesis test to the randomness threshold.
3. The system of claim 2, wherein the correlations indicate a degree of variance in viewing behaviors among the one or more category segments.
2. The system of claim 1, wherein the correlation between the plurality of categories and the viewing behaviors of the groups of media player devices is based on a variance in viewing behaviors among the plurality of categories.
5. The system of claim 1, wherein modifying the device map is further based on a quantity of time the two or more media devices were tuned to one or more channels.
7. The system of claim 1, wherein the viewing behaviors include at least one or more of an amount of time of the groups of media player devices view one or more of a plurality of channels…
6. The system of claim 1, wherein the dataset is generated using data from an
automated content recognition system identifying media segments presented by the two or more media devices.
1. ... wherein the viewing behaviors are determined using automated content recognition by matching viewed media content viewed by the media player devices…
8. A method comprising:
receiving an identification of two or more media devices; generating a device map by assigning one or more category segments to each media device of the two or more media devices based on one or more characteristics associated with each media device;
receiving a dataset that includes viewing behavior of at least one media device of the two or more media devices; and
modifying the device map based on the dataset, wherein modifying the device
map improves an accuracy of the one or more category segments assigned to the two or more media devices.
8. A method comprising:
obtaining a plurality of categories assigned to groups of media player devices, wherein the plurality of categories are determined using a device mapping system, and wherein a category includes a categorization for a group of the media player devices;
determining viewing behaviors of the groups of media player devices, wherein the viewing behaviors are determined using automated content recognition by matching viewed media content viewed by the media player devices with stored media content;
determining a correlation between the plurality of categories and the viewing behaviors of the groups of media player devices; determining an accuracy score for the device mapping system using the determined correlation; and assigning the accuracy score to the device mapping system, wherein the accuracy score is used to improve the device mapping system.
9. The method of claim 8, further comprising:
identifying, based on a statistical analysis of the dataset, correlations between media devices of the device map and at least one category segment; and
generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold
10. …determining the accuracy score for the device mapping system includes performing a statistical hypothesis test to determine whether the correlation between the plurality of categories and the viewing behaviors of the groups of media player devices is random.
12. … wherein the accuracy score is determined for the device mapping system based on the comparison of the result of the statistical hypothesis test to the randomness threshold.
10. The method of claim 9, wherein the correlations indicate a degree of variance in viewing behaviors among the one or more category segments.
9. The method of claim 8, wherein the correlation between the plurality of categories and the viewing behaviors of the groups of media player devices is based on a variance in viewing behaviors among the plurality of categories
12. The method of claim 8, wherein modifying the device map is further based on a quantity of time the two or more media devices were tuned to one or more channels.
14. The method of claim 8, wherein the viewing behaviors include at least one or more of an amount of time of the groups of media player devices view one or more of a plurality of channels…
13. The method of claim 8, wherein the dataset is generated using data from an
automated content recognition system identifying media segments presented by the two or more media devices.
8. …wherein the viewing behaviors are determined using automated content recognition by matching viewed media content viewed by the media player devices with stored media conten
15. A non-transitory machine-readable storage medium containing instructions that, when executed on one or more processors, cause the one or more processors to perform operations including:
receiving an identification of two or more media devices;
generating a device map by assigning one or more category segments to each
media device of the two or more media devices based on one or more characteristics associated with each media device;
receiving a dataset that includes viewing behavior of at least one media device of
the two or more media devices; and
modifying the device map based on the dataset, wherein modifying the device
map improves an accuracy of the one or more category segments assigned to the two or more media devices.
15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions that, when executed by the one or more processors, cause the one or more processors to:
obtain a plurality of categories assigned to groups of media player devices, wherein the plurality of categories are determined using a device mapping system, and wherein a category includes a categorization for a group of the media player devices;
determine viewing behaviors of the groups of media player devices, wherein the viewing behaviors are determined using automated content recognition by matching viewed media content viewed by the media player devices with stored media content; determine a correlation between the plurality of categories and the viewing behaviors of the groups of media player devices;
determine an accuracy score for the device mapping system using the determined correlation; and assign the accuracy score to the device mapping system, wherein the accuracy score is used to improve the device mapping system.
16. The non-transitory machine-readable storage medium of claim 15, wherein the
operations further include:
identifying, based on a statistical analysis of the dataset, correlations between media devices of the device map and at least one category segment; and
generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold.
17. …wherein determining the accuracy score for the device mapping system includes performing a statistical hypothesis test to determine whether the correlation between the plurality of categories and the viewing behaviors of the groups of media player devices…
19. …wherein the accuracy score is determined for the device mapping system based on the comparison of the result of the statistical hypothesis test to the randomness threshold.
17. The non-transitory machine-readable storage medium of claim 16, wherein the
correlations indicate a degree of variance in viewing behaviors among the one or more category segments.
16. The computer-program product of claim 15, wherein the correlation between the plurality of categories and the viewing behaviors of the groups of media player devices is based on a variance in viewing behaviors among the plurality of categories
19. The non-transitory machine-readable storage medium of claim 15, wherein the
dataset is generated using data from an automated content recognition system identifying media segments presented by the two or more media devices.
15. …wherein the viewing behaviors are determined using automated content recognition by matching viewed media content viewed by the media player devices with stored media content;
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 8, 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hsu et al. (US Pub. No. 2016/0125471)
Regarding claim 1, Hsu teaches a system comprising:
‘one or more processors’ (¶0089)
‘one or more non-transitory machine-readable storage media containing instructions that, when executed on the one or more processors (¶0099), cause the one or more processors to perform operations including:
receiving an identification of two or more media devices’ as receiving device data records associated with various user devices (¶0027, 33)
‘generating a device map by assigning one or more category segments to each media device of the two or more media devices based on one or more characteristics associated with each media device’ as generating a device map and associating categories to each device based on user data associated with each device (¶0034, 39)
‘receiving a dataset that includes viewing behavior of at least one media device of the two or more media devices’ as tracking user behavior across the different devices to capture user viewing behavior (¶0039)
‘modifying the device map based on the dataset, wherein modifying the device map improves an accuracy of the one or more category segments assigned to the two or more media devices’ as synchronizing categories user devices based on received user data records (¶0027) to improve the effectiveness of assigned categories (¶0026)
Regarding claim 8., Hsu teaches a method comprising:
receiving an identification of two or more media devices’ as receiving device data records associated with various user devices (¶0027, 33)
‘generating a device map by assigning one or more category segments to each media device of the two or more media devices based on one or more characteristics associated with each media device’ as generating a device map and associating categories to each device based on user data associated with each device (¶0034, 39)
‘receiving a dataset that includes viewing behavior of at least one media device of the two or more media devices’ as tracking user behavior across the different devices to capture user viewing behavior (¶0039)
‘modifying the device map based on the dataset, wherein modifying the device map improves an accuracy of the one or more category segments assigned to the two or more media devices’ as synchronizing categories user devices based on received user data records (¶0027) to improve the effectiveness of assigned categories (¶0026)
Regarding claim 15, Hsu teaches a non-transitory machine-readable storage medium containing instructions that, when executed on one or more processors, cause the one or more processors to perform operations including:
receiving an identification of two or more media devices’ as receiving device data records associated with various user devices (¶0027, 33)
‘generating a device map by assigning one or more category segments to each media device of the two or more media devices based on one or more characteristics associated with each media device’ as generating a device map and associating categories to each device based on user data associated with each device (¶0034, 39)
‘receiving a dataset that includes viewing behavior of at least one media device of the two or more media devices’ as tracking user behavior across the different devices to capture user viewing behavior (¶0039)
‘modifying the device map based on the dataset, wherein modifying the device map improves an accuracy of the one or more category segments assigned to the two or more media devices’ as synchronizing categories user devices based on received user data records (¶0027) to improve the effectiveness of assigned categories (¶0026)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-4, 9-11, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsu et al. (US Pub. No. 2016/0125471) further in view of Wu et al. (US Pub. No. 2019/0205761)
Regarding claim 2, Hsu teaches wherein the operations further include:
‘identifying, based on a statistical analysis of the dataset, correlations between media devices of the device map and at least one category segment’ as using the user data to categorize the user and user device (¶0034)
Hsu fails to explicitly teach:
‘generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold’
Wu teaches:
‘generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold’ as generating confidence scores that determine accuracy for a category correlation and modifying in response to the score being less than a threshold (¶0089-91)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Wu’s would have allowed Hsu’s to execute dynamic categorization (¶0003)
Regarding claim 3, Wu teaches ‘wherein the correlations indicate a degree of variance in viewing behaviors among the one or more category segments’ as calculating the degree of variance for each category (¶0195)
Regarding claim 4, Wu teaches ‘wherein the statistical analysis includes executing an f-test, and wherein the f-test indicates whether there is a high amount of viewing behavior variance between category segments or a low amount of viewing behavior variance between category segments’ as an indication of high amounts or low amounts of variance between categories (¶0195)
Regarding claim 9, Hsu teaches wherein the operations further include:
‘identifying, based on a statistical analysis of the dataset, correlations between media devices of the device map and at least one category segment’ as using the user data to categorize the user and user device (¶0034)
Hsu fails to explicitly teach:
‘generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold’
Wu teaches:
‘generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold’ as generating confidence scores that determine accuracy for a category correlation and modifying in response to the score being less than a threshold (¶0089-91)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Wu’s would have allowed Hsu’s to execute dynamic categorization (¶0003)
Regarding claim 10, Wu teaches ‘wherein the correlations indicate a degree of variance in viewing behaviors among the one or more category segments’ as calculating the degree of variance for each category (¶0195)
Regarding claim 11, Wu teaches ‘wherein the statistical analysis includes executing an f-test, and wherein the f-test indicates whether there is a high amount of viewing behavior variance between category segments or a low amount of viewing behavior variance between category segments’ as an indication of high amounts or low amounts of variance between categories (¶0195)
Regarding claim 16, Hsu teaches wherein the operations further include:
‘identifying, based on a statistical analysis of the dataset, correlations between media devices of the device map and at least one category segment’ as using the user data to categorize the user and user device (¶0034)
Hsu fails to explicitly teach:
‘generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold’
Wu teaches:
‘generating an accuracy score for the device map based on the correlations, wherein the device map is modified in response to the accuracy score being less than a threshold’ as generating confidence scores that determine accuracy for a category correlation and modifying in response to the score being less than a threshold (¶0089-91)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Wu’s would have allowed Hsu’s to execute dynamic categorization (¶0003)
Regarding claim 17, Wu teaches ‘wherein the correlations indicate a degree of variance in viewing behaviors among the one or more category segments’ as calculating the degree of variance for each category (¶0195)
Regarding claim 18, Wu teaches ‘wherein the statistical analysis includes executing an f-test, and wherein the f-test indicates whether there is a high amount of viewing behavior variance between category segments or a low amount of viewing behavior variance between category segments’ as an indication of high amounts or low amounts of variance between categories (¶0195)
Claim(s) 5-6, 12-13, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsu et al. (US Pub. No. 2016/0125471) further in view of Soon-Shiong et al. (US Pub. No. 2015/0281756)
Regarding claim 5, Hsu fails to explicitly teach ‘wherein modifying the device map is further based on a quantity of time the two or more media devices were tuned to one or more channels’
Soon-Shiong teaches ‘wherein modifying the device map is further based on a quantity of time the two or more media devices were tuned to one or more channels’ as monitoring duration of time spent viewing content (¶0039)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Wu’s would have allowed Hsu’s to improve display of relevant content to a user (¶0011)
Regarding claim 6, Soon-Shiong teaches ‘the dataset is generated using data from an automated content recognition system identifying media segments presented by the two or more media devices’ as utilizing methods of automatic content recognition (¶0037)
Regarding claim 12, Hsu fails to explicitly teach ‘wherein modifying the device map is further based on a quantity of time the two or more media devices were tuned to one or more channels’
Soon-Shiong teaches ‘wherein modifying the device map is further based on a quantity of time the two or more media devices were tuned to one or more channels’ as monitoring duration of time spent viewing content (¶0039)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Wu’s would have allowed Hsu’s to improve display of relevant content to a user (¶0011)
Regarding claim 13, Soon-Shiong teaches ‘the dataset is generated using data from an automated content recognition system identifying media segments presented by the two or more media devices’ as utilizing methods of automatic content recognition (¶0037)
Regarding claim 19, Soon-Shiong teaches ‘the dataset is generated using data from an automated content recognition system identifying media segments presented by the two or more media devices’ as utilizing methods of automatic content recognition (¶0037)
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hsu et al. (US Pub. No. 2016/0125471) further in view of Guan et al. (US Pat. No. 9,514,248)
Regarding claim 7, Hsu fails to explicitly teach ‘where modifying the device map includes modifying one or more one or more operations of a device mapping system that generated the device map’
Guan teaches ‘where modifying the device map includes modifying one or more one or more operations of a device mapping system that generated the device map’ as using a remapping module to modify generation of a map (Col. 18, Lines 44-67)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Guan’s better organize a user across multiple devices (Col. 1, Lines 23-43)
Regarding claim 14, Hsu fails to explicitly teach ‘where modifying the device map includes modifying one or more one or more operations of a device mapping system that generated the device map’
Guan teaches ‘where modifying the device map includes modifying one or more one or more operations of a device mapping system that generated the device map’ as using a remapping module to modify generation of a map (Col. 18, Lines 44-67)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Guan’s better organize a user across multiple devices (Col. 1, Lines 23-43)
Regarding claim 20, Hsu fails to explicitly teach ‘where modifying the device map includes modifying one or more one or more operations of a device mapping system that generated the device map’
Guan teaches ‘where modifying the device map includes modifying one or more one or more operations of a device mapping system that generated the device map’ as using a remapping module to modify generation of a map (Col. 18, Lines 44-67)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Guan’s better organize a user across multiple devices (Col. 1, Lines 23-43)
Examiner’s Note
Examiner has cited particular columns/paragraphs and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Conclusion
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/VAN H OBERLY/Primary Examiner, Art Unit 2166
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