Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is in response to the applicant's communication filed on 02/17/2026. In virtue of this communication, claims 1-20 filed on 02/17/2026 are currently pending in the instant application.
Claims 1, 8, and 15 have been amended. The support for the amendments can be found in ¶[0024] and ¶[0057] of the specification.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/17/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new grounds of rejection necessitated by the amendments filed 02/17/2026.
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.
Claim(s) 1, 3-8, 10-15, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sugimoto et al. (US 2023/0069920), further in view of Christian (US 2017/0171234).
As per claim 1 , A method comprising: “receiving one or more cues from one or more frames of video of media being presented by a display device, wherein the one or more cues includes a set of features derived from the one or more frames of video; receiving a new cue from one or more subsequent frames of video being presented by the display device, wherein the new cue is received after the one or more cues;”(Sugimoto, ¶[0006] discloses an obtainer that obtains first content associated with a first time and second content associated with a second time, the second time preceding the first time by a predetermined amount of time; a first determiner that, by applying first processing for determining a type of content to each of the first content and the second content, obtains first type information indicating a type of the first content and second type information indicating a type of the second content; ¶[0017-0018],¶[0022],¶[0049], figures 3 and 6.)
“generating, using a trained machine-learning model and the one or more cues, a first prediction of a first content type represented by the one or more frames of video; generating, using the trained machine-learning model and the new cue, a second prediction of a second content type represented by the one or more frames of video;”(Sugimoto, ¶[0061] discloses determining the type of the content using a recognition model constructed using machine learning (processing using what is known as Artificial Intelligence (AI)) is an example of the processing performed by determiner 12. Determiner 12 holds a recognition model constructed through appropriate machine learning, and takes, as a determination result, type information of the content obtained by obtainer 11, the type information being output when the content is input to the recognition model. ¶[0062] The recognition model is a recognition model for recognizing the type of the content. The recognition model is a recognition model constructed in advance through machine learning by using supervisory data containing at least one combination of a single item of content and the type of that single item of content. The recognition model is, for example, a neural network model, and more specifically, is a convolutional neural network model (CNN). When the recognition model is a convolutional neural network model, the recognition model is constructed by determining coefficients (weights) of a filter in a convolutional layer based on features such as images, sounds, or the like contained in the content through machine learning based on the supervisory data.¶ [0064] Calculator 14 is a functional unit that calculates confidence level information of the first type information using the first type information and the second type information. Calculator 14 obtains the first type information of the target content from determiner 12, and obtains the second type information of the reference content from storage 13. Calculator 14 then calculates the confidence level information of the first type information using the first type information and the second type information. Here, the confidence level information is an indicator of how reliable the first type information calculated by calculator 14 is as information indicating the type of the content obtained by obtainer 11. The confidence level being high or low may be expressed as “high confidence level” and “low confidence level”. ¶[0079]. )
“determining, based on the first prediction and the second prediction, a probability that the first content type does not match the second content type;”(Sugimoto, ¶[0067] discloses with respect to determiner 12, the first type information may include a first probability, which is a probability of the target content being classified as a predetermined type. The second type information may include a second probability, which is a probability of the reference content being classified as the predetermined type. In this case, calculator 14 may calculate the confidence level information so as to include an average value of the first probability and the second probability as the confidence level. Note that when a plurality of items of the reference content are present, the “second probability” in the foregoing is a plurality of second probabilities including the second probability for respective ones of the plurality of items of reference content. ¶[0079] The output type information may be (1) information that specifies which type the content is, among the predetermined plurality of types, or (2) information including the confidence level, which is the probability of the content being classified as each of the predetermined plurality of types. Then see ¶[0092-0094])
“ and executing a function of the display device based on the probability that the first content type does not match the second content type.”(Sugimoto, ¶[0097] discloses determiner 12 executes processing of determining the type of the target content obtained by obtainer 11 in step S102. As a result of the determination processing, determiner 12 provides, to calculator 14, the type information including the confidence level for each of the plurality of types related to the target content. Determiner 12 furthermore stores the stated type information in storage 13.) It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to combine the various embodiments of reference Sugimoto, wherein the combination would allow for the system of the claim to include machine learning models and pixel value detection in completing the steps to determine the type of the content by recognition model and features of the content. One skilled in the art would have been motivated to modify Sugimoto in this manner in order to utilize different type of contents in video frames and switching between the contents. Therefore, one of ordinary skill in the art, would be capable to have combined the elements as claimed by known methods, and that in combination, each element merely performs the same function as it does separately.
However Sugimoto does not explicitly disclose the following which would have been obvious in view of Christian forms similar filed of endeavor “wherein the trained machine-learning model generates the first prediction based on a rolling baseline from the one or more cues” (Christian, ¶[0016] discloses the packets involved in data communication are clustered, using an unsupervised machine learning procedure that may use any machine learning algorithms, preferably k-means clustering. ¶[0018] discloses the baseline continues to automatically evolve as more data keeps getting analyzed. As such the system “learns” or calibrates its baseline, and thus adapts with time. ¶[0058] discloses as the operation of network 108 in system 100 continues, existing baseline of data 130 as determined by hypercube 180 continues to evolve. This evolution or “rolling” of baseline 120 allows the instant invention to automatically learn from data and calibrate itself. ¶[0060] Employing the dynamic or rolling baseline technology of the instant invention taught herein, a system may continuously and automatically evolve or calibrate its definitions of a threat and normal data.)
“wherein the trained machine-learning model generates the second prediction based in part on a distance between the new cue and the rolling baseline”(Christian, ¶[0017] discloses Subsequent packets are then compared against this baseline by scoring/weighting them to determine their distance from the centroid. [0018] If this distance is far enough, that constitutes an anomaly or deviance for the packet. If the score of the packet sufficiently matches any existing signatures/data-sets, an appropriate alert is generated for the admin/user (second decision or prediction).¶[0052] discloses k-means clustering is a well-known technique whereby an objective function J minimizes the within-cluster sum of squares i.e. sum of distance functions of each observation or packet in the cluster, to the center of the cluster.)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Christian technique of network monitoring using rolling baseline into Sugimoto technique to provide the known and expected uses and benefits of Christian technique over obtaining video content technique of Sugimoto. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Christian to Sugimoto in order to improve security of network infrastructure and detecting anomaly. (Refer to Christian ¶[0002].)
Claim 8 and 15 have been analyzed and are rejected for the reasons indicated in claim 1 above.
As per claim 3, in view of claim 1, Sugimoto as modified by Christian discloses “wherein receiving the first cue from one or more frames of video of media being displayed by the display device includes: identifying one or more sets of pixels from a frame of video of the one or more frames of video; and extracting one or more features corresponding to pixel values from each of the one or more sets of pixels.”(Sugimoto, ¶[0147] discloses calculator 14A obtains a degree of similarity of the color (pixel value), position, spatial frequency of the color (pixel value) (i.e., the frequency when the pixel value is taken as a wave on the spatial axis), luminance, or saturation of the image, between the target content and the reference content, as analyzed by analyzer 27. The confidence level may be increased when the obtained degree of similarity is at least a predetermined value. Further ¶[0148-0150].)
Claim 10 and 17 have been analyzed and are rejected for the reasons indicated in claim 3 above.
As per claim 4, in view of claim 1, Sugimoto as modified by Christian discloses “wherein the machine-learning model is an ensemble model derived from two or more machine-learning models.”(Sugimoto, ¶[0062] discloses The recognition model is a recognition model for recognizing the type of the content. The recognition model is a recognition model constructed in advance through machine learning by using supervisory data containing at least one combination of a single item of content and the type of that single item of content. The recognition model is, for example, a neural network model, and more specifically, is a convolutional neural network model (CNN). When the recognition model is a convolutional neural network model, the recognition model is constructed by determining coefficients (weights) of a filter in a convolutional layer based on features such as images, sounds, or the like contained in the content through machine learning based on the supervisory data. ¶[0144])
Claim 11 and 18 have been analyzed and are rejected for the reasons indicated in claim 4 above.
As per claim 5, in view of claim 1, Sugimoto as modified by Christian discloses “wherein executing a function of the display device includes: facilitating a transmission to a server that includes an identification of the first content type and a duration of time over which the first content type is presented by the display device.”(Sugimoto,¶[0097-0098], ¶[0157] discloses For example, when the specifying information output the previous time indicated the default type, if type information having a high confidence level and indicating the sports type and the music type is obtained from determiner 12 and calculator 14, outputter 15 transitions to the music type. Similarly, when the specifying information output the previous time indicated the default type, if type information having a high confidence level and indicating the talkshow type is obtained, the type transitions to the talkshow type. When the specifying information output the previous time indicated the default type, if the confidence level obtained from calculator 14 is relatively low, the type is kept as the default type.¶[0158] Additionally, when the specifying information output the previous time indicated the sports type, if type information having a high confidence level and indicating the music type is obtained from determiner 12 and calculator 14, outputter 15 transitions to the music type. Similarly, when the specifying information output the previous time indicated the sports type, if type information having a high confidence level and indicating the talkshow type is obtained from determiner 12 and calculator 14, or if the confidence level obtained from calculator 14 is relatively low, the type transitions to the default type. When the specifying information output the previous time indicated the sports type, if type information having a high confidence level and indicating the sports type is obtained from determiner 12 and calculator 14, the type is kept as the sports type.¶[0221-0223])
Claim 12 and 19 have been analyzed and are rejected for the reasons indicated in claim 5 above.
As per claim 6, in view of claim 1, Sugimoto as modified by Christian discloses “wherein generating the first prediction of the first content type represented by the one or more frames of video includes: identifying the media corresponding to the first content type.”(Sugimoto, ¶[0067] discloses For example, with respect to determiner 12, the first type information may include a first probability, which is a probability of the target content being classified as a predetermined type. The second type information may include a second probability, which is a probability of the reference content being classified as the predetermined type. In this case, calculator 14 may calculate the confidence level information so as to include an average value of the first probability and the second probability as the confidence level. Note that when a plurality of items of the reference content are present, the “second probability” in the foregoing is a plurality of second probabilities including the second probability for respective ones of the plurality of items of reference content. ¶[0079], ¶[0084-0086])
As per claim 7, in view of claim 1, Sugimoto as modified by Christian discloses further comprising: “modifying the video display or audio settings of the display device, based on the first prediction and the first content type, to improve presentation of media corresponding to the first content type.”(Sugimoto, ¶[0053] discloses estimation device 10 changes an acoustic effect of speaker 5 included in television receiver 1 by controlling speaker 5 based on the type obtained as the estimation result. When, for example, the type of the content is estimated to be “sports”, estimation device 10 performs the control to make the spread of the sound relatively broad and produce an effect that the viewer feels enveloped by the sound. When the type of the content is estimated to be “music”, estimation device 10 performs the control to make the spread of the source relatively broad and produce an effect that vocalists' voices are emphasized. When the type of the content is estimated to be “talkshow”, estimation device 10 performs the control to produce an effect that makes it easier for the viewer to heat- the voice of the speaker.)
Claim 14 and 20 have been analyzed and are rejected for the reasons indicated in claim 7 above.
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sugimoto et al. (US 2023/0069920), in view of Christian (US 2017/0171234), further in view of Choi et al. (US 2020/0304883).
As per claim 2, in view of claim 1, Sugimoto as modified by Christian does not explicitly disclose the following which would have been obvious in view of Choi from similar field of endeavor “wherein one of the first and second content types corresponds to a video game.”(Choi, ¶[0027] discloses the determining of the type of the content may include determining the content as a game. )
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Choi technique of Executing content into Sugimoto as modified by Christian technique to provide the known and expected uses and benefits of Choi technique over obtaining video content technique of Sugimoto as modified by Christian. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Choi to Sugimoto as modified by Christian in order to provide better control on image quality considering use convenience. (Refer to Choi paragraph [0003].)
Claim 9 and 16 have been analyzed and are rejected for the reasons indicated in claim 2 above.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAGHAYEGH AZIMA whose telephone number is (571)272-1459. The examiner can normally be reached Monday-Friday, 9:30-6:30.
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/SHAGHAYEGH AZIMA/
Examiner, Art Unit 2671