DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on January 29, 2026, for the application with serial number 18/649,606.
Claims 1, 11, and 19 are amended.
Claims 1-20 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the rejection, below, mischaracterizes the claims. The Examiner respectfully disagrees. The rejection, below, relies on explicit language from the claims in arriving at a conclusion of ineligibility. Contrary to the Applicant’s assertions, he claims do not involve an improvement that is rooted in computer technology or any other technology. The newly amended language reciting a time-series matrix merely amounts to organizing time series data into rows and columns. Contrary to the Applicant’s assertions, making a forecast is not a technology. The claims do not involve a process that is rooted in computer technology. Modifying a service is, itself, an abstract idea.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §102/103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Ramanarayanan reference, which is cited in the obviousness rejection of the independent claims, below. The Applicant’s arguments with respect to claims 1 and 11 as being anticipated by Park are moot in light of the newly cited reference.
The Applicant additionally contends that Park does not use a nearest neighbor technique. See Remarks p. 15. In response, the Examiner points to cited ¶[0153]-[0154] of Park, which explicitly teaches that a k nearest neighbor technique is used. In the k nearest neighbors technique, the “k” represents the threshold.
The Applicant additionally submits that Lee does not teach the elements of claims 2 and 19 to which the Lee reference is mapped. See Remarks p. 17. In response, the Examiner points out that the Ramanarayanan reference is cited as having this element; making the Applicant’s arguments with regards to the independent claims moot.
The Applicant additionally traverses the application of the Yang reference to dependent claim 4. The Applicant submits that the method taught by Yan is fundamentally different that the method recited in the claims. See Remarks pp. 17-18. The Examiner respectfully disagrees. The number of iterations taught by Yang creates the data necessary for have sufficient data for forecasting which reads on the claimed subject matter. That is why combining weak classifiers, as taught by Yang, results in a strong classifier.
The rejection of the remaining claims stands or falls with the rejection of the independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to forecasting with weak data (as evidenced by exemplary independent claim 1; “cause forecasting of content based on the weak unit and the determined nearest neighbor discrete unit”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “identify discrete units;” “identify . . . a weak unit;” “determine a nearest neighbor discrete unit;” “associate historical data of the nearest neighbor discrete unit with historical data of the weak unit;” “cause forecasting of content based on the weak unit and the determined nearest neighbor discrete unit;” and “provide the forecasting to a forecast-dependent service to modify operation.” The steps are all steps for managing personal behavior related to the abstract idea of forecasting with weak data that, when considered alone and in combination, are part of the abstract idea of forecasting with weak data. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of forecasting with weak data. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes using mathematical procedures to interpolate, oversample, and balance data in data sets.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a computer readable medium in independent claim 1; a computer implemented method in independent claim 11; and a system with electronic devices in independent claim 19). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims require no more than a generic computer (a computer readable medium in independent claim 1; a computer implemented method in independent claim 11; and a system with electronic devices in independent claim 19) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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.
Claim(s) 1 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to Park (hereinafter ‘PARK’) in view of US 10176365 B1 to Ramanarayanan et al. (hereinafter ‘RAMANARAYANAN’).
Claim 1 (Currently Amended)
PARK discloses a tangible, non-transitory, computer-readable medium, comprising computer-readable instructions that, when executed by one or more processors of one or more computers (see ¶[0028]; there is provided a non-transitory computer-readable recording medium storing instructions), cause the one or more computers to: identify discrete units from a data structure associated with historical data of a plurality of content (see ¶[0152]-[0153]; no good data and okay data).
PARK does not specifically disclose, but RAMANARAYANAN discloses, wherein the data structure comprises a time-series matrix (TSM) data structure and identifying the discrete units comprises identifying the discrete units as rows of the TSM data structure having: a common video series characteristic, a common site section characteristic, or both (see col 12, ln 16-35; cluster labels for a time-series matrix with a single row vector of cluster labels comprised of frames of video imagery).
PARK further discloses identify, from the discrete units, a weak unit not having a threshold amount of historical data for forecasting (see ¶[0152]-[0153]; the imbalanced state of the data induces bias and data balancing may be needed to augment the no good data);
determine a nearest neighbor discrete unit having the threshold amount of historical data for forecasting (see ¶[0153]-[0154]; use a k nearest neighbors technique to generate similar data with a data distribution);
associate historical data of the nearest neighbor discrete unit with historical data of the weak unit (see ¶[0152]-[0154]; use a k nearest neighbors technique to generate similar data with a data distribution. Augment no good data); and
cause forecasting of content based on the weak unit and the determined nearest neighbor discrete unit (see ¶[0103] and [0157]; perform training on the machine learning models and the label for learning. Predict okay or no good quality matches).
wherein the forecasting comprises forecasting of the weak unit using the historical data of the weak unit as supplemented with the historical data of the nearest neighbor discrete unit (see again ¶[0152]-[0154]; augment the no good data. See also ¶[0103]; predict the number of defects in products); and
provide the forecasting to a forecast-dependent service to modify operation based upon the forecasting (see ¶[0056]; a machine learning-based quality analysis service. See also ¶[0010]; adjust a quality control standard for a product).
PARK discloses quality data based machine learning that augments a data set with no good data using a k nearest neighbors technique, including classifying data using a classification model (see ¶[0115]-[0116]). RAMANARAYANAN discloses performance scoring using time series features for classification and labeling (see col 10, ln 42-61 and col 12, ln 16-35). It would have been obvious for one of ordinary skill in the art to use a time series matrix for labeling and classification as taught by RAMANARAYANAN in the system executing the method of PARK with the motivation to classify data for quality control.
Claim 11
PARK discloses a computer-implemented method (see ¶[0178]; the device may be a programmable computer), comprising: identifying discrete units from a data structure associated with historical data of a plurality of content (see ¶[0152]-[0153]; no good data and okay data).
PARK does not specifically disclose, but RAMANARAYANAN discloses, wherein the data structure comprises a time-series matrix (TSM) data structure and identifying the discrete units comprises identifying the discrete units as rows of the TSM data structure having: a common video series characteristic, a common site section characteristic, or both (see col 12, ln 16-35; cluster labels for a time-series matrix with a single row vector of cluster labels comprised of frames of video imagery).
PARK further discloses, identifying, from the discrete units, a weak unit not having a threshold amount of historical data for forecasting (see ¶[0152]-[0153]; the imbalanced state of the data induces bias and data balancing may be needed to augment the no good data);
determining a nearest neighbor discrete unit having the threshold amount of historical data for forecasting (see ¶[0153]-[0154]; use a k nearest neighbors technique to generate similar data with a data distribution);
associating historical data of the nearest neighbor discrete unit with historical data of the weak unit (see ¶[0152]-[0154]; use a k nearest neighbors technique to generate similar data with a data distribution. Augment no good data):
causing forecasting of content based on the weak unit and the determined nearest neighbor discrete unit (see ¶[0103] and [0157]; perform training on the machine learning models and the label for learning. Predict okay or no good quality matches).
wherein the forecasting comprises forecasting of the weak unit using the historical data of the weak unit as supplemented with the historical data of the nearest neighbor discrete unit (see again ¶[0152]-[0154]; augment the no good data. See also ¶[0103]; predict the number of defects in products); and
providing the forecasting to a forecast-dependent service to modify operation based upon the forecasting (see ¶[0056]; a machine learning-based quality analysis service. See also ¶[0010]; adjust a quality control standard for a product).
PARK discloses quality data based machine learning that augments a data set with no good data using a k nearest neighbors technique, including classifying data using a classification model (see ¶[0115]-[0116]). RAMANARAYANAN discloses performance scoring using time series features for classification and labeling (see col 10, ln 42-61 and col 12, ln 16-35). It would have been obvious for one of ordinary skill in the art to use a time series matrix for labeling and classification as taught by RAMANARAYANAN in the system executing the method of PARK with the motivation to classify data for quality control.
Claim(s) 2 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK and US 10176365 B1 to RAMANARAYANAN et al. as applied to claim 1 above, and further in view of US 20210097372 A1 to Lee et al. (hereinafter ‘LEE’).
Claim 2 (Original)
PARK discloses the tangible, non-transitory, computer-readable medium as set forth in claim 1.
PARK does not specifically disclose, but LEE discloses, wherein the data structure comprises a time-series matrix (TSM) data structure and the tangible, non-transitory, computer-readable medium comprises computer-readable instructions that, when executed by the one or more processors of the one or more computers, cause the one or more computers to: identify the discrete units as rows of the TSM data structure having a common video series characteristic and a common site section characteristic (see ¶[0022]; data matrices that may be co-clustered by the combiner CI-GAN model 200A may include, for example, user behavioral data as the row data and user content viewing data as the column data, video data as the row data and video-caption data as the column data, biological genetic data as the row data and biological condition data as the column data, movie data as the row data and music data as the column data, image data as the row data and audible data as the column data, or other similar data existing in two disparate data dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). LEE discloses data co-clustering to create clusters of data matrices with similar characteristics to create data groups (see ¶[0019]). It would have been obvious to include the video data clusters as taught by LEE in the system executing the method of PARK with the motivation to classify data for analysis.
Claim 19 (Currently Amended)
PARK discloses a system, comprising: a forecasting service, hosted by a first electronic device (see ¶[0178]; the device may be a programmable computer).
PARK does not specifically disclose, but LEE discloses, configured to: receive historical data associated with a discrete unit of content having one or more particular video series characteristics and one or more particular site section characteristics (see ¶[0022]; data matrices that may be co-clustered by the combiner CI-GAN model 200A may include, for example, user behavioral data as the row data and user content viewing data as the column data, video data as the row data and video-caption data as the column data, biological genetic data as the row data and biological condition data as the column data, movie data as the row data and music data as the column data, image data as the row data and audible data as the column data, or other similar data existing in two disparate data dimensions).
The combination of PARK and LEE does not specifically disclose, but RAMANARAYANAN discloses, wherein the historical data is received from a time-series matrix (TSM) data structure having rows with: a common video series characteristic, a common site section characteristic, or both (see col 12, ln 16-35; cluster labels for a time-series matrix with a single row vector of cluster labels comprised of frames of video imagery).
PARK does not specifically disclose, but LEE discloses, forecast viewership for the content using the historical data associated with the discrete unit (see ¶[0035]; improve programming personalization/recommendation, ad-targeting (e.g., displaying relevant ads to particular users), content demand prediction (e.g., predicting what content particular users want to watch).
PARK further discloses wherein the forecasting comprises forecasting of the discrete unit using the historical data of the discrete unit as supplemented with historical data of a nearest neighbor discrete unit (see ¶[0152]-[0154]; augment the no good data. See also ¶[0103]; predict the number of defects in products);
a nearest neighbor identification service, hosted by a second electronic device, configured to: identify the discrete unit as a weak unit not having a threshold amount of historical data for forecasting (see ¶[0152]-[0153]; the imbalanced state of the data induces bias and data balancing may be needed to augment the no good data);
determine a nearest neighbor discrete unit having the threshold amount of historical data for forecasting (see ¶[0153]-[0154]; use a k nearest neighbors technique to generate similar data with a data distribution); and
associate the historical data of the nearest neighbor discrete unit with the discrete unit to cause the forecasting service to forecast the viewership of the content based upon the nearest neighbor discrete unit and the discrete unit (see ¶[0103] and [0157]; perform training on the machine learning models and the label for learning. Predict okay or no good quality matches).
and a forecast-dependent service configured to receive the forecasting and modify operation based upon the forecasting (see ¶[0056]; a machine learning-based quality analysis service. See also ¶[0010]; adjust a quality control standard for a product).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). LEE discloses data co-clustering to create clusters of data matrices with similar characteristics to create data groups (see ¶[0019]). It would have been obvious to include the video data clusters as taught by LEE in the system executing the method of PARK with the motivation to classify data for analysis.
PARK discloses quality data based machine learning that augments a data set with no good data using a k nearest neighbors technique, including classifying data using a classification model (see ¶[0115]-[0116]). RAMANARAYANAN discloses performance scoring using time series features for classification and labeling (see col 10, ln 42-61 and col 12, ln 16-35). It would have been obvious for one of ordinary skill in the art to use a time series matrix for labeling and classification as taught by RAMANARAYANAN in the system executing the method of PARK with the motivation to classify data for quality control.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al. and US 10176365 B1 to RAMANARAYANAN et al. as applied to claims 1 and 2 above, and further in view of CN 113657489 A to Yang et al. (hereinafter ‘YANG’).
Claim 4 (Original)
The combination of PARK, LEE, and RAMANARAYANAN discloses the tangible, non-transitory, computer-readable medium as set forth in claim 2.
The combination of PARK, LEE, and RAMANARAYANAN does not specifically disclose, but YANG discloses comprising computer-readable instructions that, when executed by the one or more processors of the one or more computers, cause the one or more computers to identify the nearest neighbor discrete unit for the weak unit, by: identifying strong discrete units from the identified discrete units having the threshold amount of historical data for forecasting (see abstract; combine all weak classifiers to obtain a strong classifier. Examiner Note: the combination of weak classifiers constitutes a strong classifier. The reference teaches that classifiers are weak/strong according to the iterations);
identifying from the strong discrete units, a subset of matching strong discrete units that match either one or more video series characteristics or one or more site section characteristics of the weak unit (see again abstract; update training of samples based on comprehensive similarity determined from similarity theory and Euclidean distance);
determining a preferred match from the subset of matching strong discrete units (see again abstract; update the weight of the training samples according to the classification result of the weak classifier. Examiner Note: the weightings set preferences); and
selecting the preferred match as the nearest neighbor discrete unit for the weak unit (see again abstract; combine the weak classifiers to obtain a strong classifier. Update weights using a k-nearest neighbors algorithm).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0155]). YANG discloses balancing data for classification by using KNN classification and Euclidean distance to combine weak classifiers into strong classifiers. It would have been obvious for one of ordinary skill in the art at the time of invention to create strong classifiers from weak classifiers as taught by YANG in the system executing the method of PARK with the motivation to balance data from classification.
Claim(s) 3 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al. and US 10176365 B1 to RAMANARAYANAN et al. as applied to claims 1 and 2 above, and further in view of US 20230289817 A1 to Ashby (hereinafter ‘ASHBY’) and US 20120110027 A1 to Falcon (hereinafter ‘FALCON’).
Claim 3 (Original)
The combination of PARK, LEE, and RAMANARAYANAN discloses the tangible, non-transitory, computer-readable medium as set forth in claim 2.
The combination of PARK, LEE, and RAMANARAYANAN does not specifically disclose, but ASHBY discloses, wherein: the common video series characteristic comprises: a content name, an owner of the content, or a type of the content, or any combination thereof (see ¶[0207]; identify objects by name, brand serial number, model, version, category, or type).
The combination of PARK and LEE does not specifically disclose, but FALCON discloses, the common site section characteristic comprises: a business unit associated with a playback of content, a platform corresponding to the playback, or a device type of a playback device used for the playback, or any combination thereof (see ¶[0090]; determine affinity between sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). ASHBY discloses providing decentralized support services in a virtual environment that includes identifying objects based on characteristics. It would have been obvious to include the characteristics as taught by ASHBY in the system executing the method of PARK with the motivation to classify data and/or objects.
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). FALCON discloses an audience measurement system that includes clustering media sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria. It would have been obvious to include the clustering criteria as taught by FALCON in the system executing the method of PARK with the motivation to classify data.
Claim 12 (Original)
The combination of PARK and RAMANARAYANAN discloses the computer-implemented method as set forth in claim 11.
The combination of PARK and RAMANARAYANAN does not specifically disclose, but LEE discloses, comprising: identifying the discrete units as rows of the data structure having a common video series characteristic of a content and a common site section characteristic of playback of the content (see ¶[0022]; data matrices that may be co-clustered by the combiner CI-GAN model 200A may include, for example, user behavioral data as the row data and user content viewing data as the column data, video data as the row data and video-caption data as the column data, biological genetic data as the row data and biological condition data as the column data, movie data as the row data and music data as the column data, image data as the row data and audible data as the column data, or other similar data existing in two disparate data dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). LEE discloses data co-clustering to create clusters of data matrices with similar characteristics to create data groups (see ¶[0019]). It would have been obvious to include the video data clusters as taught by LEE in the system executing the method of PARK with the motivation to classify data for analysis.
The combination of PARK, LEE, and RAMANARAYANAN does not specifically disclose, but ASHBY discloses, wherein: the common video series characteristic comprises: a content name, an owner of the content, or a type of the content, or any combination thereof (see ¶[0207]; identify objects by name, brand serial number, model, version, category, or type).
The combination of PARK, LEE, and RAMANARAYANAN does not specifically disclose, but FALCON discloses; and the common site section characteristic comprises: a business unit associated with the playback, a platform corresponding to the playback, or a device type of a playback device used for playback of the content, or any combination thereof (see ¶[0090]; determine affinity between sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). ASHBY discloses providing decentralized support services in a virtual environment that includes identifying objects based on characteristics. It would have been obvious to include the characteristics as taught by ASHBY in the system executing the method of PARK with the motivation to classify data and/or objects.
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). FALCON discloses an audience measurement system that includes clustering media sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria. It would have been obvious to include the clustering criteria as taught by FALCON in the system executing the method of PARK with the motivation to classify data.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al., US 10176365 B1 to RAMANARAYANAN et al., and CN 113657489 A to Yang et al. as applied to claims 1, 2 and 4 above, and further in view of US 20250045480 A1 to Jayadas et al. (hereinafter ‘JAYADAS’).
Claim 5 (Original)
The combination of PARK, LEE, RAMANARAYANAN, and YANG discloses the tangible, non-transitory, computer-readable medium as set forth in claim 4.
The combination of PARK, LEE, RAMANARAYANAN, and YANG does not explicitly disclose, but JAYADAS discloses, comprising computer-readable instructions that, when executed by the one or more processors of the one or more computers, cause the one or more computers to identify the subset of matching strong discrete units, by: for a match with matching site section characteristics: determine if video series similarity criteria between the video series characteristics of the weak unit and a unit corresponding to the match with matching site section characteristics are met (see ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold); and
identify the match with matching site section characteristics as a matching strong discrete unit when the video series similarity criteria are met; and for a match with matching video series characteristics (see ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module):
determine if site section similarity criteria between the site section characteristics of the weak unit and a unit corresponding to the match with matching video series characteristics are met (see again ¶[0094]; a minimum threshold degree of similarity is imposed); and
identify the match with matching video series characteristics as a matching strong discrete unit when the site section similarity criteria are met (see again ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). JAYADAS discloses that KNN clustering may impose a minimum threshold similarity to include the data as a match. It would have been obvious to impose the threshold as taught by JAYADAS in the system executing the method of PARK with the motivation to balance data using KNN.
Claim 6 (Original)
The combination of PARK, LEE, RAMANARAYANAN, YANG, and JAYADAS discloses the tangible, non-transitory, computer-readable medium as set forth in claim 5.
PARK does not specifically disclose, but JAYADAS discloses, wherein the video series similarity criteria comprises: a requirement that a content owner match, a requirement that a type of the content match, a requirement that content names have a threshold level of similarity, or any combination thereof (see again ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). JAYADAS discloses that KNN clustering may impose a minimum threshold similarity to include the data as a match. It would have been obvious to impose the threshold as taught by JAYADAS in the system executing the method of PARK with the motivation to balance data using KNN.
Claim(s) 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al., US 10176365 B1 to RAMANARAYANAN et al., and CN 113657489 A to Yang et al. as applied to claims 1, 2 and 4 above, and further in view of US 20150356508 A1 to Gokhale et al. (hereinafter ‘GOKHALE’).
Claim 8 (Original)
The combination of PARK, LEE, RAMANARAYANAN, and YANG discloses the tangible, non-transitory, computer-readable medium as set forth in claim 4.
The combination of PARK, LEE, RAMANARAYANAN, and YANG does not explicitly disclose, but GOKHALE discloses, comprising computer-readable instructions that, when executed by the one or more processors of the one or more computers, cause the one or more computers to determine the preferred match, by: prioritizing matches by site section over matches by video series (see ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). GOKHALE discloses collaboration using state sharing that uses a nearest neighbor proximity algorithm that weights some dimensions more heavily than others. It would have been obvious to include the state priorities as taught by GOKHALE in the system executing the method of PARK with the motivation to use KNN to balance data.
Claim 9 (Original)
The combination of PARK, LEE, RAMANARAYANAN, and YANG discloses the tangible, non-transitory, computer-readable medium as set forth in claim 4.
The combination of PARK, LEE, RAMANARAYANAN, and YANG does not explicitly disclose, but GOKHALE discloses, comprising computer-readable instructions that, when executed by the one or more processors of the one or more computers, cause the one or more computers to determine the preferred match, by: for a match with matching site section characteristics: prioritize similarities of a particular subset of the video series characteristics to determine the preferred match (see ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions); and
for a match with matching video series characteristics: prioritize similarities of a particular subset of the site section characteristics to determine the preferred match (see again ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). GOKHALE discloses collaboration using state sharing that uses a nearest neighbor proximity algorithm that weights some dimensions more heavily than others. It would have been obvious to include the state priorities as taught by GOKHALE in the system executing the method of PARK with the motivation to use KNN to balance data.
Claim 10 (Original)
The combination of PARK, LEE, RAMANARAYANAN, YANG, and GOKHALE discloses the tangible, non-transitory, computer-readable medium as set forth in claim 9.
PARK does not specifically disclose, but GOKHALE discloses, wherein: the prioritized similarities of the particular subset of the video series characteristics comprise: prioritizing a magnitude of similarities of content names (see ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions); and
the prioritized similarities of the particular subset of the site section characteristics comprise: prioritizing a type of user accessing the content over a commonality of an account type used to access the content over a matching device type used to access the content over a matching operating system used to access the content (see again ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). GOKHALE discloses collaboration using state sharing that uses a nearest neighbor proximity algorithm that weights some dimensions more heavily than others. It would have been obvious to include the state priorities as taught by GOKHALE in the system executing the method of PARK with the motivation to use KNN to balance data.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al., US 10176365 B1 to RAMANARAYANAN et al., and CN 113657489 A to Yang et al. as applied to claims 1, 2 and 4 above, and further in view of US 20250045480 A1 to Jayadas et al. as applied to claims 1, 2, 4, and 5 above, and further in view of US 20120110027 A1 to FALCON.
Claim 7 (Original)
The combination of PARK, LEE, RAMANARAYANAN, YANG, and JAYADAS discloses the tangible, non-transitory, computer-readable medium as set forth in claim 6.
The combination of PARK, LEE, RAMANARAYANAN, YANG, and JAYADAS does not specifically disclose, but FALCON discloses, wherein the site section similarity criteria comprises: a requirement that a business unit match, a requirement that a platform that the content is delivered to matches, or both (see ¶[0090]; determine affinity between sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). FALCON discloses an audience measurement system that includes clustering media sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria. It would have been obvious to include the clustering criteria as taught by FALCON in the system executing the method of PARK with the motivation to classify data.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al., US 10176365 B1 to RAMANARAYANAN et al., US 20230289817 A1 to ASHBY, and US 20120110027 A1 to FALCON as applied to claims 11 and 12 above, and further in view of CN 113657489 A to YANG et al..
Claim 13 (Original)
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, and FALCON discloses the computer-implemented method as set forth in claim 12.
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, and FALCON does not specifically disclose, but YANG discloses, comprising identifying the nearest neighbor discrete unit for the weak unit, by: identifying strong discrete units from the identified discrete units having the threshold amount of historical data for forecasting (see abstract; combine all weak classifiers to obtain a strong classifier. Examiner Note: the combination of weak classifiers constitutes a strong classifier. The reference teaches that classifiers are weak/strong according to the iterations);
identifying from the strong discrete units, a subset of matching strong discrete units that match either a video series characteristic or a site section characteristic of the weak unit (see again abstract; update training of samples based on comprehensive similarity determined from similarity theory and Euclidean distance);
determining a preferred match from the subset of matching strong discrete units (see again abstract; update the weight of the training samples according to the classification result of the weak classifier. Examiner Note: the weightings set preferences); and
selecting the preferred match as the nearest neighbor discrete unit for the weak unit (see again abstract; combine the weak classifiers to obtain a strong classifier. Update weights using a k-nearest neighbors algorithm).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0155]). YANG discloses balancing data for classification by using KNN classification and Euclidean distance to combine weak classifiers into strong classifiers. It would have been obvious for one of ordinary skill in the art at the time of invention to create strong classifiers from weak classifiers as taught by YANG in the system executing the method of PARK with the motivation to balance data from classification.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al. and US 10176365 B1 to RAMANARAYANAN et al. as applied to claim 19, and further in view of CN 113657489 A to YANG et al., US 20250045480 A1 to JAYADAS et al., and US 20150356508 A1 to GOKHALE et al.
Claim 20 (Original)
The combination of PARK, LEE, and RAMANARAYANAN discloses the system as set forth in claim 19.
PARK further discloses comprising: a forecast-dependent service configured to receive the forecasting and modify operation based upon the forecasting (see abstract; trainer configured to train the machine learning model using the quality data for learning and a first label relevant to the quality data for learning);
The combination of PARK and LEE does not specifically disclose, but YANG discloses, wherein the nearest neighbor identification service is configured to: identify the nearest neighbor discrete unit, by: identifying a set of strong discrete units having the threshold amount of historical data for forecasting (see abstract; combine all weak classifiers to obtain a strong classifier. Examiner Note: the combination of weak classifiers constitutes a strong classifier. The reference teaches that classifiers are weak/strong according to the iterations);
that matches the one or more particular video series characteristics or the one or more particular site section characteristics (see again abstract; update training of samples based on comprehensive similarity determined from similarity theory and Euclidean distance).
The combination of PARK, LEE, and RAMANARAYANAN does not specifically disclose, but JAYADAS discloses, when at least one of the set of strong discrete units matches the one or more particular site section characteristics, retain the strong discrete units matching the one or more particular site section characteristics and filter out all of the strong discrete units that match the one or more particular video series characteristics (see ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold);
identify from remaining strong discrete units of the set of strong discrete units, suitable strong discrete units for the nearest neighbor discrete unit, by: for a strong discrete unit matching the one or more particular site section characteristics: determine if video series similarity criteria between the one or more particular video series characteristics of the weak unit and the strong discrete unit are met (see again ¶[0094]; a minimum threshold degree of similarity is imposed); and
identify the strong discrete unit matching the one or more particular site section characteristics as suitable when the video series similarity criteria are met; and for a strong discrete unit matching the one or more particular video series characteristics: determine if site section similarity criteria between the one or more particular site section characteristics of the weak unit and the strong discrete unit are met (see again ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module); and
identify the strong discrete unit matching the particular video series characteristics as suitable when the site section similarity criteria are met (see again ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module); and
The combination of PARK, LEE, and RAMANARAYANAN does not specifically disclose, but GOKHALE discloses, from the suitable strong discrete units, identify a preferred suitable strong discrete unit as the nearest neighbor discrete unit (see again ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0155]). YANG discloses balancing data for classification by using KNN classification and Euclidean distance to combine weak classifiers into strong classifiers. It would have been obvious for one of ordinary skill in the art at the time of invention to create strong classifiers from weak classifiers as taught by YANG in the system executing the method of PARK with the motivation to balance data from classification.
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). JAYADAS discloses that KNN clustering may impose a minimum threshold similarity to include the data as a match. It would have been obvious to impose the threshold as taught by JAYADAS in the system executing the method of PARK with the motivation to balance data using KNN.
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). GOKHALE discloses collaboration using state sharing that uses a nearest neighbor proximity algorithm that weights some dimensions more heavily than others. It would have been obvious to include the state priorities as taught by GOKHALE in the system executing the method of PARK with the motivation to use KNN to balance data.
Claim(s) 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al., US 10176365 B1 to RAMANARAYANAN et al., US 20230289817 A1 to ASHBY, US 20120110027 A1 to FALCON as applied to claims 11 and 12 above, and CN 113657489 A to YANG et al. as applied to claims 11-13 above, and further in view of US 20250045480 A1 to JAYADAS et al.
Claim 14 (Original)
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, FALCON, and YANG discloses the computer-implemented method as set forth in claim 13,
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, FALCON, and YANG does not specifically disclose, but JAYADAS discloses, comprising identifying the subset of matching strong discrete units, by: for a match with matching site section characteristics: determine if video series similarity criteria between the video series characteristics of the weak unit and a unit corresponding to the match with matching site section characteristics are met (see ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold); and
identify the match with matching site section characteristics as a matching strong discrete unit when the video series similarity criteria are met; and for a match with matching video series characteristics (see ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module):
determine if site section similarity criteria between the site section characteristics of the weak unit and a unit corresponding to the match with matching video series characteristics are met (see again ¶[0094]; a minimum threshold degree of similarity is imposed); and
identify the match with matching video series characteristics as a matching strong discrete unit when the site section similarity criteria are met (see again ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). JAYADAS discloses that KNN clustering may impose a minimum threshold similarity to include the data as a match. It would have been obvious to impose the threshold as taught by JAYADAS in the system executing the method of PARK with the motivation to balance data using KNN.
Claim 15 (Original)
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, FALCON, YANG, and JAYADAS discloses the computer-implemented method as set forth in claim 14.
PARK does not specifically disclose, but JAYADAS discloses, wherein the video series similarity criteria comprises: a requirement that a content owner of the content described in the video series characteristics match, a requirement that a type of the content described in the video series characteristics match, a requirement that content names described in the video series characteristics have a threshold level of similarity, or any combination thereof (see again ¶[0094]; a minimum threshold degree of similarity is imposed such that the sorted similarities do not include any match having a degree of similarity that did not meet the threshold. The sorted similarities data may be provided as an input to the recommendation module).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). JAYADAS discloses that KNN clustering may impose a minimum threshold similarity to include the data as a match. It would have been obvious to impose the threshold as taught by JAYADAS in the system executing the method of PARK with the motivation to balance data using KNN.
Claim 16 (Original)
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, FALCON, YANG, and JAYADAS discloses the computer-implemented method as set forth in claim 15.
PARK does not specifically disclose, but FALCON discloses, wherein the site section similarity criteria comprises: a requirement that a business unit of the site section characteristics match, a requirement that a platform that the content is delivered to matches, or both (see ¶[0090]; determine affinity between sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]). FALCON discloses an audience measurement system that includes clustering media sessions based on distribution platform, type of media device used for consumption, time-shift range, content genre, or any other suitable clustering criteria. It would have been obvious to include the clustering criteria as taught by FALCON in the system executing the method of PARK with the motivation to classify data.
Claim(s) 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230041209 A1 to PARK in view of US 20210097372 A1 to LEE et al., US 10176365 B1 to RAMANARAYANAN et al., US 20230289817 A1 to ASHBY, US 20120110027 A1 to FALCON, CN 113657489 A to YANG et al., and US 20250045480 A1 to JAYADAS et al. as applied to claims 11-14 above, and further in view of US 20150356508 A1 to GOKHALE et al.
Claim 17 (Original)
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, FALCON, YANG, and JAYADAS discloses the computer-implemented method as set forth in claim 14.
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, FALCON, YANG, and JAYADAS does not specifically disclose, but GOKHALE discloses, comprising determining the preferred match, by: prioritizing matches by site section over matches by video series (see ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions); and
for a match with matching site section characteristics: prioritize similarities of a particular subset of the video series characteristics to determine the preferred match (see ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions); and
for a match with matching video series characteristics: prioritize similarities of a particular subset of the site section characteristics to determine the preferred match (see again ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). GOKHALE discloses collaboration using state sharing that uses a nearest neighbor proximity algorithm that weights some dimensions more heavily than others. It would have been obvious to include the state priorities as taught by GOKHALE in the system executing the method of PARK with the motivation to use KNN to balance data.
Claim 18 (Original)
The combination of PARK, LEE, RAMANARAYANAN, ASHBY, FALCON, YANG, JAYADAS, and GOKHALE discloses the computer-implemented method as set forth in claim 17.
PARK does not specifically disclose, but GOKHALE discloses, wherein: the prioritized similarities of the particular subset of the video series characteristics comprise: prioritizing a magnitude of similarities of content names (see ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions); and
the prioritized similarities of the particular subset of the site section characteristics comprise: prioritizing a type of user accessing the content over a commonality of an account type used to access the content over a matching device type used to access the content over a matching operating system used to access the content (see again ¶[0033]; some dimensions may be weighted more heavily, for example may count as two buckets when determining proximity, in order to prioritize states matched in those dimensions).
PARK discloses an data analysis system that classifies data according to criterion (see ¶[0112]) and uses KNN to balance data (see ¶[0153]-[0154]). GOKHALE discloses collaboration using state sharing that uses a nearest neighbor proximity algorithm that weights some dimensions more heavily than others. It would have been obvious to include the state priorities as taught by GOKHALE in the system executing the method of PARK with the motivation to use KNN to balance data.
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 RICHARD N SCHEUNEMANN whose telephone number is (571)270-7947. The examiner can normally be reached M-F 9am-5pm EST.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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