DETAILED ACTION
Acknowledgements
This Office Action is in response to Applicant’s correspondence filed on 1/20/26.
The Examiner notes that citations to United States Patent Application Publication paragraphs are formatted as [####], #### representing the paragraph number.
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 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.
Status of Claims
Claims 1-3, 5-13, 16, 25-40 are currently pending.
Claims 1-3, 5-13, 16, 25-40 are rejected as set forth below.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/20/26 has been entered.
Response to Arguments
Claim Rejections - 35 U.S.C. § 101
Applicant’s arguments with respect to claim(s) 1 have been fully considered and are persuasive. The rejection (and corresponding rejections to its dependent claims, if applicable) is withdrawn.
Applicant’s arguments with respect to claim(s) 13, 25 have been fully considered but are not persuasive. The rejection (and corresponding rejections to its dependent claims, if applicable) is maintained.
The Examiner suggests amending claims 13 and 25 similar to how claim 1 was amended in order to address the 35 USC 101 rejection.
Claim Rejections - 35 U.S.C. § 103
Applicant’s arguments with respect to claim(s) 1-2, 5-14, 19-21, 25-35 have been fully considered but are not persuasive. The rejection (and corresponding rejections to its dependent claims, if applicable) is maintained.
Applicant contends Epstein fails to teach or suggest tracking attributes of processing multiple requests based on the production of playback information. The Examiner respectfully disagrees. Epstein teaches tracking logins to the account and content consumption from within the account, i.e. attributes of processing multiple requests based on the production of playback information ([0036]). Tracking logins to an account is equivalent to tracking attributes of processing multiple requests.
Specification
The title of the invention contains a misspelling: ‘SUBCSRIPTION’. A new title is required that fixes the misspelling.
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.
Claims 13, 16, 25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
As per claims 13, 25, the claimed invention is directed to an abstract idea without significantly more because:
Claims 13, 25 recites receiving multiple requests specifying video assets for retrieval, each of the multiple requests identified as being associated with a first subscriber account of multiple subscriber accounts; tracking attributes of processing the multiple requests to produce playback information supporting playback of the video assets; and deriving a usage metric for the first subscriber account based on the tracked attributes of processing the multiple requests.
Under Step 1 of the Section 101 analysis, the claim(s) is/are directed to a method, system, and computer-readable storage hardware, which are statutory categories of invention.
Under Step 2A Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claimed invention as drafted includes language (see underlined language above) that recites an abstract idea of receiving requests, tracking attributes of processing the requests, and deriving a usage metric based on the tracked attributes (concepts performed in the human mind, e.g. including an observation, evaluation, judgment) but for the recitation of additional claim elements. That is, nothing in the claim precludes the language from being practically performed in the mind. For example, an administrator is capable of receiving a request for video content, observe who is currently viewing the video content, and derive a basic usage metric based on their observations.
A similar analysis can be applied to dependent claim 16, which further recite the abstract idea of receiving requests, tracking attributes of processing the requests, and deriving a usage metric based on the tracked attributes.
Under Step 2A Prong Two of the 2019 Revised Patent Subject Matter Eligibility Guidance, the additional claim element(s), considered individually, do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and in a manner that integrates the exception into a practical application of the exception. The additional claim elements(s) generally link the use of the judicial exception to a particular technological environment or field of use of protected video streaming. For example, the concepts of generating and distributing playback information over wireless communication links for decryption of video assets generally link the abstract idea of receiving requests, tracking attributes of processing the requests, and deriving a usage metric based on the tracked attributes to the particular technological environment of protected video streaming.
Under Step 2A Prong Two, the additional claim element(s), considered in combination, do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and in a manner that integrates the exception into a practical application of the exception. The combination of elements is no more than the sum of their parts. Unlike the eligible claims in Diehr and Bascom, in which the elements limiting the exception taken together improve a technical field, the instant claim lacks an improvement to the functioning of a computer or to any other technology or technical field.
Under Step 2B, the additional claim element(s), considered individually and in combination, do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself for similar reasons outlined under Step 2A Prong Two.
Claim Rejections - 35 USC § 102
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.
Claims 13, 25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by United States Patent Application Publication No. 20170195337 to Epstein.
As per claims 13, 25, Epstein teaches:
A method comprising: receiving multiple requests specifying respective video assets for retrieval, each of the multiple requests identified as being associated with a first subscriber account of multiple subscriber accounts; (Fig 1, [0018], “FIG. 1 is a block diagram of a network 100 including a media sharing detection system 120 in accordance with some implementations. To that end, the network 100 includes a number of user devices 101a-101c that can be used by respective users to access a service provider server 110. The service provider server 110 provides a subscription-based media service. Thus, in response to receiving credentials from a user device 101a-101c, the service provider server 110 provides media content to the user device 101a-101c. The media content can include audio, video, or other media content.”; [0019], “To that end, the network 100 includes a media sharing detection system 120 to detect sharing of credentials for an account with the service provider.”)
tracking attributes of processing the multiple requests to produce multiple instances of playback information supporting playback of the respective video assets; and deriving a usage metric for the first subscriber account based on the tracked attributes of processing the multiple requests. (Fig 4, [0035]-[0039], “Briefly, the method 400 includes receiving data regarding usage of an account, generating a plurality of sharing scores indicative of different types of sharing of the account, and, in response to one of the sharing scores exceeding a respective threshold, presenting a respective challenge. The method 400 begins, at block 410, with the media sharing system receiving usage data regarding usage of a subscription-based media service account. At block 420, the media sharing system generates a plurality of sharing scores based on the usage data. Each of the plurality of sharing scores indicates a confidence that the usage of the subscription-based media service account is subject to a respective type of sharing. In some implementations, generating the sharing scores includes determining a number of different users of the account. By analyzing the usage data, the media sharing system can generate various behavioral profiles per account. Different users of an account are identified and logged, where a given user is classified by some combination of behavioral features. Data science routines can also be used to distinguish between the account owner (e.g, the person who registered for the account) and other projected sharers. Examples of behavioral features that identify a unique user within each subscription account can include, for example, IP address, device properties (ID, type, OS, screen size, etc.), location, viewing times, favorite shows, favorite sports team, genres of content, typical viewing duration, subtitle usage, trick mode usage, and variability of viewing patterns (as indicated by the usage data).”)
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-12, 16, 26-35 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over United States Patent Application Publication No. 20170195337 to Epstein in view of United States Patent Application Publication No. 20020112171 to Ginter.
As per claim 1, Epstein teaches:
A method comprising: receiving multiple requests specifying respective video assets for retrieval, each of the multiple requests identified as being associated with a first subscriber account of multiple subscriber accounts; (Fig 1, [0018], “FIG. 1 is a block diagram of a network 100 including a media sharing detection system 120 in accordance with some implementations. To that end, the network 100 includes a number of user devices 101a-101c that can be used by respective users to access a service provider server 110. The service provider server 110 provides a subscription-based media service. Thus, in response to receiving credentials from a user device 101a-101c, the service provider server 110 provides media content to the user device 101a-101c. The media content can include audio, video, or other media content.”; [0019], “To that end, the network 100 includes a media sharing detection system 120 to detect sharing of credentials for an account with the service provider.”)
tracking attributes of processing the multiple requests to produce multiple instances of playback information supporting playback of the respective video assets; and deriving a usage metric for the first subscriber account based on the tracked attributes of processing the multiple requests. (Fig 4, [0035]-[0039], “Briefly, the method 400 includes receiving data regarding usage of an account, generating a plurality of sharing scores indicative of different types of sharing of the account, and, in response to one of the sharing scores exceeding a respective threshold, presenting a respective challenge. The method 400 begins, at block 410, with the media sharing system receiving usage data regarding usage of a subscription-based media service account. At block 420, the media sharing system generates a plurality of sharing scores based on the usage data. Each of the plurality of sharing scores indicates a confidence that the usage of the subscription-based media service account is subject to a respective type of sharing. In some implementations, generating the sharing scores includes determining a number of different users of the account. By analyzing the usage data, the media sharing system can generate various behavioral profiles per account. Different users of an account are identified and logged, where a given user is classified by some combination of behavioral features. Data science routines can also be used to distinguish between the account owner (e.g, the person who registered for the account) and other projected sharers. Examples of behavioral features that identify a unique user within each subscription account can include, for example, IP address, device properties (ID, type, OS, screen size, etc.), location, viewing times, favorite shows, favorite sports team, genres of content, typical viewing duration, subtitle usage, trick mode usage, and variability of viewing patterns (as indicated by the usage data).”)
wherein a magnitude of the usage metric for the first subscriber account indicates a degree of video asset demand associated with the first subscriber account; ([0036])
comparing the magnitude of the usage metric to a threshold level; and producing a notification indicating misuse of the digital rights management licenses in response to detecting that the magnitude of the usage metric is greater than the threshold level; ([0024], “A third type of sharing is referred to as stolen account sharing. Stolen account sharing is a type of parasitical sharing that is unknown to the account owner and is enabled by illegally extracted credentials.”; [0048], “In some implementations, once a particular sharing type is detected by comparing the sharing score to a service provider configurable threshold, the media sharing system challenges either the account owner or sharer with a question that is relevant to that sharing type.”)
Epstein does not explicitly teach, but Ginter teaches:
wherein the multiple instances of playback information includes digital rights management licenses; ([0436], [1066], “’Traveling’ objects are a class of VDE objects 300 that can specifically support "out of channel" distribution. Therefore, they include key block(s) 810 and are transportable from one electronic appliance 600 to another. Traveling objects may come with a quite limited usage related budget so that a user may use, in whole or part, content (such as a computer program, game, or database) and evaluate whether to acquire a license or further license or purchase object content.”)
One of ordinary skill in the art would have recognized that applying the known technique of Ginter to the known invention of Epstein would have yielded predictable results and resulted in an improved invention. It would have been recognized that the application of the technique would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such digital rights management features into a similar invention. Further, it would have been recognized by those of ordinary skill in the art that modifying the playback information to include digital rights management licenses results in an improved invention because applying said technique ensures that only authorized entities are able to play the video assets, thus improving the overall security of the invention.
As per claim 2, Epstein teaches:
in response to receiving the multiple requests: i) generating the multiple instances of playback information, and ii) distributing the multiple instances of playback information to communication devices generating the multiple requests; ([0018])
As per claim 3, Epstein does not explicitly teach, but Ginter teaches:
prior to distributing the multiple instances of playback information to the communication devices: generating decryption information to decrypt the video assets; and producing the multiple instances of playback information to specify the generated decryption information to decrypt the video assets specified by the multiple requests; (Fig 2, [1938], “As described in connection with FIG. 2, there are four (4) "participant" instances of VDE 100 in one example of a VDE chain of handling and control used, for example, for content distribution. The first of these participant instances, the content creator 102, is manipulated by the publisher, author, rights owner or distributor of a literary property to prepare the information for distribution to the consumer. The second participant instance, VDE rights distributor 106, may distribute rights and may also administer and analyze customers' use of VDE authored information. The third participant instance, content user 112, is operated by users (included end-users and distributors) when they use information.”; [0436], “Container 302 may contain information content 304 in electronic (such as "digital") form. Information content 304 could be the text of a novel, a picture, sound such as a musical performance or a reading, a movie or other video, computer software, or just about any other kind of electronic information you can think of.”; [1056], “PERCs 808 govern the use of an object 300, specifying methods or combinations of methods that must be used to access or otherwise use the object or its contents. The permission records 808 for an object may include key block(s) 810, which may store decryption keys for accessing the content of the encrypted content stored within the object 300.”)
One of ordinary skill in the art would have recognized that applying the known technique of Ginter to the known invention of Epstein would have yielded predictable results and resulted in an improved invention. It would have been recognized that the application of the technique would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such cryptography features into a similar invention. Further, it would have been recognized by those of ordinary skill in the art that modifying the invention to, prior to distributing the playback information to the communication devices, generate decryption information to decrypt the video assets and produce the playback information to specify the generated decryption information to decrypt the video assets specified by the multiple results in an improved invention because applying said technique ensures that only authorized entities are able to play the video assets, thus improving the overall security of the invention.
As per claims 5, Epstein teaches:
tracking attributes of processing the multiple requests associated with the first subscriber account without regard to identities of the respective video assets being requested for playback; ([0036], “The method 400 begins, at block 410, with the media sharing system receiving usage data regarding usage of a subscription-based media service account. The usage data can include data regarding, for example, creation of the account, updating of account information, logins to the account, and content consumption from within the account. With respect to account access, creation or modification, the usage data can indicate, among other things, an IP address of a user device used to access, create, or modify the account, device properties of a user device used to access, create, or modify the account (e.g, device ID, type, OS, screen size, etc.), a location of a user device used to access, create, or modify the account, or a time a user device accessed, created, or modified the account. With respect to content consumption, the usage data can indicate, among other things, content selected for consumption, a time or duration content was consumed, the presence or absence of subtitles during content consumption, or usage of trick modes (e.g., fast-forward and rewind) during content consumption.”)
As per claims 6, Epstein teaches:
tracking the attributes of processing the multiple requests associated with the first subscriber account without regard to identities of users of the communication devices generating the multiple requests; ([0036])
As per claim 7, Epstein teaches:
wherein the tracked attributes include a first attribute and a second attribute; wherein the first attribute is based on a magnitude of the multiple requests received within a predetermined time duration; and wherein the second attribute is based on unique identities of communication devices generating the multiple requests; ([0036])
As per claim 8, Epstein teaches:
wherein deriving the usage metric includes: producing a first numerical value from the first tracked attribute; and producing a second numerical value from the second tracked attribute; ([0036]-[0037])
As per claim 9, Epstein teaches:
wherein deriving the usage metric includes: applying a first weight value to the first numerical value to produce a first weighted value; applying a second weight value to the second numerical value to produce a second weighted value; and generating the usage metric for the first subscriber account based on summing the first weighted value and the second weighted value; ([0043], “In some implementations, the sharing scores are generated based on prediction algorithms using tools such as Bayes networks decision trees, random forests, logistic regression, or support vector machines, taking into account the extracted features based on the usage data. Thus, in various implementations, the sharing scores can indicate a likelihood or probability that an account is subject to particular type of sharing”; The Examiner notes that machine learning regression models inherently apply weighted values, i.e. coefficients, to variables in order to predict a value.)
As per claims 10, Epstein teaches:
deriving the usage metric for the first subscriber account based on authentication tracking logs associated with authenticating communication devices generating the multiple requests. ([0036])
As per claims 11, Epstein teaches:
deriving the usage metric for the first subscriber account based on management logs associated with producing the multiple instances of playback information in response to the multiple requests. ([0036])
As per claims 12, Epstein teaches:
wirelessly communicating the playback information over wireless communication links to multiple communication devices generating the multiple requests. (Fig 1, [0067], “Some or all of the methods and tasks described herein may be performed and fully automated by a computer system. The computer system may, in some cases, include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, etc.) that communicate and interoperate over a network to perform the described functions.”)
As per claim 16, Epstein teaches:
wherein a magnitude of the usage metric for the first subscriber account indicates a degree of video asset demand associated with the first subscriber account; ([0036])
comparing the magnitude of the usage metric to a threshold level; and producing a notification indicating misuse of the digital rights management licenses in response to detecting that the magnitude of the usage metric is greater than the threshold level; ([0024], “A third type of sharing is referred to as stolen account sharing. Stolen account sharing is a type of parasitical sharing that is unknown to the account owner and is enabled by illegally extracted credentials.”; [0048], “In some implementations, once a particular sharing type is detected by comparing the sharing score to a service provider configurable threshold, the media sharing system challenges either the account owner or sharer with a question that is relevant to that sharing type.”)
Epstein does not explicitly teach, but Ginter teaches:
wherein the multiple instances of playback information includes digital rights management licenses; ([0436], [1066], “’Traveling’ objects are a class of VDE objects 300 that can specifically support "out of channel" distribution. Therefore, they include key block(s) 810 and are transportable from one electronic appliance 600 to another. Traveling objects may come with a quite limited usage related budget so that a user may use, in whole or part, content (such as a computer program, game, or database) and evaluate whether to acquire a license or further license or purchase object content.”)
One of ordinary skill in the art would have recognized that applying the known technique of Ginter to the known invention of Epstein would have yielded predictable results and resulted in an improved invention. It would have been recognized that the application of the technique would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such digital rights management features into a similar invention. Further, it would have been recognized by those of ordinary skill in the art that modifying the playback information to include digital rights management licenses results in an improved invention because applying said technique ensures that only authorized entities are able to play the video assets, thus improving the overall security of the invention.
As per claim 26, Epstein teaches:
wherein each of the multiple requests includes a corresponding token provided by a respective communication device, the corresponding token configured to associate the respective communication device with the first subscriber account; and wherein the respective token operates to authenticate the corresponding communication device for retrieval of a corresponding instance of playback information of the multiple instances of playback information; ([0018], “Thus, in response to receiving credentials from a user device 101a-101c, the service provider server 110 provides media content to the user device 101a-101c. The media content can include audio, video, or other media content. The provided credentials are the means by which users of the user devices 101a-101c can prove their identities to the service provider web server 110 during a login or content request. The credentials can include, for example, a username and password. In various implementations, the credentials can include secure cookies, OAuth (open standard for authorization) tokens, or SAML (Security Assertion Markup Language) assertions received as a result of a previous authentication (e.g., using a username and password).”)
As per claim 27, Epstein teaches:
wherein each of the multiple instances of playback information include respective digital rights management licenses enabling playback of the respective video assets from a content delivery network; and wherein a magnitude of the derived usage metric above a threshold level indicates misuse of the respective digital rights management licenses; ([0026], “To that end, the media sharing detection system 120 includes data analytic modules (as described further below with respect to FIG. 3) and receives logs of account creation, logins, and content consumption, e.g., from the service provider server 110, license generators, CDNs (content delivery networks), subscriber management systems, etc.”; [0045]-[0047], “At block 425, the media sharing system compares each of the plurality of sharing scores to a respective threshold. At block 430, the media sharing system presents a challenge associated with the respective type of sharing. For example, if the sharing score for casual sharing exceeds the casual sharing threshold, a casual sharing challenge is presented. As another example, if the sharing score for stolen account sharing exceeds the stolen account sharing threshold, a stolen account sharing challenge is presented.”)
As per claim 28, Epstein teaches:
producing a notification indicating the misuse of the respective digital rights management licenses in response to detecting that the magnitude of the usage metric is greater than a threshold level; and in response to receiving the notification, disabling distribution of content to communication devices associated with the first subscriber account; ([0052], “In some implementations, the method 400 includes further action, in response to determining that that the challenge has been failed, e.g., that an answer returned in response to a question does not match an expected response. In various implementations, the media sharing policy described above can include proactive events to be taken if the challenge associated with the respective type of sharing is failed. As described further below, the method 400 includes, in various implementations, warning an owner of the account, suspending the account, or blacklisting the account in response to determining that the challenge has been failed.”)
As per claim 29, Epstein teaches:
wherein the multiple requests are received from multiple communication devices using the first subscriber account as a basis in which to retrieve the respective video assets; ([0018])
As per claim 30, Epstein teaches:
wherein the tracked attributes include a first attribute and a second attribute, the first attribute based on the magnitude of the multiple requests, the second attribute based on concurrency of the multiple communication devices requesting retrieval of the respective video assets within a duration of time; ([0042]-[0044], “When an account is identified being shared by multiple users, the media sharing system can use multi-variate data science algorithms to generate the plurality of sharing scores for a plurality of different sharing types according to features that differentiate the various sharing types. For example, the number of sharers can be used to determine the sharing scores for different sharing types. In particular, the number of sharers can be greater for business sharing than for casual sharing. As another example, the number of concurrency violations (e.g., the number of times multiple users are logged in simultaneously) can be used to determine the sharing scores for different sharing types. In particular, the number of concurrency violations can be greater for business sharing than for casual sharing. As another example, the larger the number of accounts a single user is identified on can be used to determine that each of those accounts is more likely subject to various sharing types. In particular, users of stolen accounts can appear on multiple accounts. As another example, the number of users over time can be used to determine the sharing scores for different types of sharing. In particular, the number of users can be relatively fixed for casual sharing, but variable for business sharing or stolen account sharing. Further, the number of users can grow over time relatively constantly for business sharing. FIG. 6 shows an example graph of the number of users over time for three different sharing types. As another example, the viewing patterns can be used to determine the sharing scores for different types of sharing. In particular, whereas viewing patterns are relatively fixed for casual sharing and business sharing, they may be relatively variable for stolen accounts. In general, sensing changes in activity of the account over time (e.g., the viewing patterns, the amount of content consumed, the number of log-ins, or other characteristics) can be indicative of sharing. For example, increased activity over time (particularly, abrupt increases in activity) for an account can indicate the addition of a new user to that account.”)
As per claim 31, Epstein teaches:
wherein deriving the usage metric includes: producing a first numerical value from the first tracked attribute, the first numerical value indicating the magnitude of the multiple requests; and producing a second numerical value from the second tracked attribute, the second attribute indicating how many of the multiple communication devices concurrently request retrieval of respective video assets within the duration time; ([0042]-[0044])
As per claim 32, Epstein teaches:
wherein deriving the usage metric includes: applying a first weight value to the first numerical value to produce a first weighted value; applying a second weight value to the second numerical value to produce a second weighted value; and generating the usage metric for the first subscriber account based on a summation of the first weighted value and the second weighted value; ([0042]-[0044]; The Examiner notes prediction algorithms such as decision trees, random forests, logistic regressions, and support vector machines necessarily uses weighted values for parameters in their algorithms.)
As per claim 33, Epstein teaches:
wherein the multiple requests are received from multiple communication devices using the first subscriber account as a basis in which to retrieve the respective video assets; wherein a first attribute of the tracked attributes is based on a magnitude of total requests from the multiple communication devices for retrieval of the respective video assets within a duration of time; wherein deriving the usage metric includes: i) producing a first numerical value indicating the magnitude of the total requests, ii) applying a first weight value to the first numerical value to produce a first weighted numerical value; ([0042]-[0044]; The Examiner notes prediction algorithms such as decision trees, random forests, logistic regressions, and support vector machines necessarily uses weighted values for parameters in their algorithms.)
As per claim 34, Epstein teaches:
wherein a second attribute of the tracked attributes is based on a number of the multiple communication devices requesting retrieval of the respective video assets; wherein deriving the usage metric includes: i) producing a second numerical value indicating the number of the multiple communication devices requesting for retrieval of the respective video assets, ii) applying a second weight value to the second numerical value to produce a second weighted numerical value, and iii) summing the first weighted numerical value and the second weighted numerical value; ([0042]-[0044]; The Examiner notes prediction algorithms such as decision trees, random forests, logistic regressions, and support vector machines necessarily uses weighted values for parameters in their algorithms.)
As per claim 35, Epstein teaches:
wherein a second attribute of the tracked attributes is based on concurrency of a portion of the multiple communication devices requesting retrieval of the respective video assets within a duration of time; wherein deriving the usage metric includes: i) producing a second numerical value indicating a magnitude of the portion, and ii) applying a second weight value to the second numerical value to produce a second weighted numerical value, and iii) summing the first weighted numerical value and the second weighted numerical value; ([0042]-[0044]; The Examiner notes prediction algorithms such as decision trees, random forests, logistic regressions, and support vector machines necessarily uses weighted values for parameters in their algorithms.)
Claims 36-40 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over United States Patent Application Publication No. 20170195337 to Epstein in view of United States Patent Application Publication No. 20020112171 to Ginter, and further in view of United States Patent Application Publication No. 20210248623 to Scheidiger.
As per claim 36, Epstein teaches:
wherein the multiple requests are received from multiple communication devices using the first subscriber account as a basis in which to retrieve the respective video assets; ([0018]-[0019])
Epstein as modified does not explicitly teach, but Scheidiger teaches:
wherein the tracked attributes include a first attribute and a second attribute. ([0024], “Described herein are methods, devices and systems for determining watch-time variability and using watch-time variability for detection of credential sharing. Watch-time variability is a numeric indicator which measures the likelihood that an account's credentials have been shared outside an immediate household.”; [0059], “In general, a method for determining watch-time variability includes obtaining, from a plurality of streaming devices, account and streaming data for all streams viewed on an account using an account password, generating, by a watch-time variability unit, a viewing probability distribution for the account, generating, by the watch-time variability unit, an account entropy based on the viewing probability distribution, grouping, by the watch-time variability unit, the streams into two or more groups, wherein the grouping uses an account-stream characteristic which has a probabilistic utility to indicate account password sharing, generating, by the watch-time variability unit, a group entropy for each of the two or more groups, determining, by the watch-time variability unit, a watch-time variability based on the account entropy and each group entropy, wherein the watch-time variability measures the increase in disorder when the two or more groups are unrelated with respect to the account-stream characteristic, and providing, by the watch-time variability unit, an indication of account password sharing to limit activity on the account. In implementations, the method includes determining, by the watch-time variability unit, a total amount of content streamed in a defined analysis period, determining, by the watch-time variability unit, an amount of content streamed in a defined time bin during a defined recurring interval for the defined analysis period, and normalizing, by the watch-time variability unit, the amount of content streamed in each defined time bin by the total amount of content streamed to generate the viewing probability distribution. In implementations, the account-stream characteristic uses streaming device identifiers and Internet Protocol (IP) addresses as a probabilistic indicator of single household localization. In implementations, the method includes identifying, by the watch-time variability unit, each streaming device which was used for streaming content using the account password from the account and streaming data, identifying, by the watch-time variability unit, each IP address which was used for streaming content using the account password from the account and streaming data, determining, by the watch-time variability unit, relationships between the identified streaming devices and identified IP addresses, identifying, by the watch-time variability unit, clusters which have disconnected streaming devices and IP addresses, and dividing, by the watch-time variability unit, the streams into the two or more groups based on the streams associated with the streaming devices in each cluster. In implementations, the method includes determining, by the watch-time variability unit, a weight for each group entropy, and subtracting, by the watch-time variability unit, each weighted group entropy from the account entropy to determine the watch-time variability. In implementations, the method includes determining, by the watch-time variability unit, the weight based on a watch-time for the streams in each group divided by the total amount of watch-time for all streams. In implementations, the method includes obtaining, by a fraud detection unit, fraud detection factors related to the account including the watch-time variability, and providing, by the fraud detection unit, an indication of account password sharing to limit activity on the account.”)
One of ordinary skill in the art would have recognized that applying the known technique of Scheidiger to the known invention of Epstein as modified would have yielded predictable results and resulted in an improved invention. It would have been recognized that the application of the technique would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such account sharing detection features into a similar invention. Further, it would have been recognized by those of ordinary skill in the art that modifying the tracked attributes so they include a first attribute and a second attribute results in an improved invention because applying said technique ensures that the tracked attributes more accurately describe the characteristics of the multiple requests, thus improving the overall accuracy of the invention.
As per claim 37, Scheidiger teaches:
wherein tracking the attributes of processing the multiple requests includes: producing a first count value from the first tracked attribute, the first count value indicating a first number, the first number indicating a magnitude of detected occurrences of a first type of event associated with the processing of the multiple requests; and producing a second count value from the second tracked attribute, the second count value indicating a second number, the second number indicating a magnitude of detected occurrences of a second type of event associated with the processing of the multiple requests. ([0059])
As per claim 38, Scheidiger teaches:
wherein deriving the usage metric includes: in response to detecting that the first count value falls within a first range, where the first range is selected from first ranges associated with the first type of event, converting the first count value into the first score value; and in response to detecting that the second count value falls within a second range, where the second range is selected from second ranges associated with the second type of event, converting the second count value into the second score value. ([0059])
As per claim 39, Scheidiger teaches:
wherein deriving the usage metric further includes: generating the usage metric for the first subscriber account based on a summation of at least the first score value and the second score value. ([0059])
As per claim 40, Scheidiger teaches:
wherein deriving the usage metric includes: i) applying a first weight value to the first count value to produce a first score value; and ii) applying a second weight value to the second count value to produce a second score value; wherein deriving the usage metric further includes: generating the usage metric for the first subscriber account based on a summation of at least the first score value and the second score value. ([0059])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
United States Patent No. 10992972 to Zhang discloses a method for detecting impermissible account sharing among user accounts of a streaming media service including the steps of determining a plurality of locations accessed by a given user account of the user accounts; determining a device access count for each of the locations, the device access count indicating how many times the corresponding location was accessed by at least one device associated with the given user account; identifying one of the locations having the highest device access count as a base location; calculating a risk coefficient for each remaining location; generating a sharing score for the given user account by summing the risk coefficients; and determining impermissible account sharing of the given user account has occurred when the sharing score exceeds a threshold.
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/JAY HUANG/Primary Examiner, Art Unit 3619