DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim analysis - 35 USC § 112
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. § 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that § 112(f) (pre-AIA § 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. § 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that § 112(f) (pre-AIA § 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke § 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke § 112(f) except as otherwise indicated in an Office action.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 1-7 has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: “a sensor operable to determine …; a correlator operable to correlate ….; and a demand prediction engine operable to generate …” i.e., Fig. 1,2, and 7 and Para 19 and 21 clearly define network core 112 where the cellular uplink 108 collects data that is assessed and/or filtered by a sensor 113 and compiled into data consumption information 120. Sensor 113 is operable to determine, using cellular uplink 108, data consumption information 120 for plurality of UEs 102 receiving broadcast 104. Data consumption information 120 and broadcast schedule 132 are provided to a correlator 116, which generates demand statistics 122. (This is shown in further detail in FIG. 2). Demand statistics 122 are provided to a demand prediction engine 117 that generates demand predictions 124, based on at least demand statistics 122. Demand predictions 124 indicate a future demand for content data sets in a future time period. Fig. 7 and Para 38 further define computing device having processor 702 and memory 704 allowing information, such as computer executable instructions and/or other data, to be stored and retrieved.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without “significantly more”. Claim(s) 1-20 is/are directed to Abstract Idea such as an idea standing alone such as an instantiated concept, pan or scheme, as well as a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper for example using measurement received from a mobile device, transmitting from the source relay node to a donor access node.
The apparatus and the method claim 1, 8 and 15 recites limitation, “determining, using a cellular uplink, data consumption information for a plurality of user equipment (UEs) receiving a broadcast; correlating the data consumption information with content identification in a first broadcast schedule into demand statistics; and generating demand predictions based on at least the demand statistics, wherein the demand predictions indicate a future demand for content data sets in a future time period”. Since the claim is directed to a process and a machine, which is one of the statutory categories of the invention (Step 1: YES).
The claim is then analyzed to determine whether it is directed to any judicial exception. The claim recites determining, using a cellular uplink, data consumption information for a plurality of user equipment (UEs) receiving a broadcast; correlating the data consumption information with content identification in a first broadcast schedule into demand statistics; and generating demand predictions based on at least the demand statistics. The determining step, correlating the data consumption information and then generating demand prediction step recited in the claim is no more than an abstract idea i.e., mental process of collecting or receiving broadcast, analyzing the information and then generating the demand prediction i.e., outputting certain data etc. i.e., a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) (Step 2A: Prong One Abstract Idea=Yes).
The claim is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. In the current claims, there is no additional elements that would integrate the abstract idea into a practical application (Step 2A: Prong Two Abstract Idea=Yes).
Next the claim as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. The claim requires the additional limitation of a computer with the central processing unit, memory, a printer, an input and output terminal and a program. These generic computer components are claimed to perform the basic functions of storing, retrieving and processing data through the program that enables. In the current scenario, there are no additional elements that would amount to significantly more than the abstract idea. Therefore, the claim does not amount to significantly more than the abstract idea itself (Step 2B: No). Accordingly, the claim is not patent eligible.
Further, dependent claims do not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 5-10, 12-17, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. Patent. No. US 8001217 B1 in view of Lee et al. Pub. No. US 20100146559 A1
Regarding Claim 1, Pan teaches a system comprising (fig. 1):
a sensor operable to determine (Fig. 2 step 204 and Col 5 L 55-60, the number of consumer hits may be used to measure the popularity), using a cellular uplink (Col 6 L 50-52, the method 200 may use Forward Link Push and Reverse Link Signal to indicate the receiving status of an intended client device where forward link push can be refer to as cellular uplink), data consumption information for a plurality of user equipment (UEs) receiving a broadcast (Fig. 1 Unit 106A-106F and Col 5 L 15-25, The channels 110A-110F may also be used to broadcast or multi-cast content to the more than one of the mobile clients 106A-106F i.e., data consumption information for a plurality of user equipment (UEs) receiving a broadcast);
a correlator operable to correlate (Col 5 L 55-60, the method 200 may use correlative studies to anticipate demand of related content) the data consumption information in a first broadcast schedule into demand statistics (Col 6 L 5-20, the method 200, through its predictive and dynamic anticipation of user demand, automatically recognizes popular content. For example, a large number of users may be located in a baseball stadium during a game. If the power in the stadium were to go out, a number of these users may submit requests to view the local news so as to ascertain the cause of the power failure. After receiving a portion of these requests, the method 200 may recognize increased demand in that location for the local news and, thus, may anticipate further requests. In one embodiment, historical demand records are used to anticipate future demand); and
a demand prediction engine (Fig. 3 Unit 304, demand prediction component) operable to generate demand predictions based on at least the demand statistics (Col 7 L 19-25, The demand prediction component 304 is configured to anticipate user demands to view the content stored in the data store 304. For example, the demand prediction component 304 may track received requests and use this tracking information to anticipate future requests i.e., generate demand predictions based on at least the demand statistics), wherein the demand predictions indicate a future demand for content data sets in a future time period (Col 7 L 26-30, the demand prediction component 304 can predict the time and geographic area where a specific piece of content will be requested/popular i.e., the demand predictions indicate a future demand for content data sets in a future time period).
Pan does not specifically teach the data consumption information with content identification in a first broadcast schedule.
However, in the same field of endeavor, Lee teaches the media device 204 can obtain a listing of content available for broadcast from the media distributor 202. The listing can include summaries of content, portions of content, the content itself and/or any suitable information that enables a user to identify and evaluate content i.e., the data consumption information with content identification in a first broadcast schedule (Para 54).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Pan with the method of Lee so as to provide wish list of content based on user schedule (see Lee Para 54).
Regarding Claim 2, Pan teaches further comprising: an anonymizer operable to anonymize data consumption information (Col 8 L 15-20, the transmitted content may be encrypted i.e., an anonymizer operable to anonymize data consumption information).
Regarding Claim 3, Pan teaches wherein correlating the data consumption information with the content identification in the first broadcast schedule comprises: determining amounts of content data sets consumed by the plurality of UEs, wherein the demand statistics indicate the amounts of content data sets consumed by the plurality of UEs (Col 5 L 58-65).
Regarding Claim 5, Pan teaches wherein the sensor is located within a radio access network (RAN) of a wireless network (Col 4 L 46-60 where RAN of the wireless network include sensor or transceiver/receiver refer to as sensor).
Regarding Claim 6, Pan does not specifically teach further comprising: a content schedule optimizer operable to generate a second broadcast schedule based on at least the demand predictions, stability preferences, and content availability information; and a broadcaster operable to broadcast, to the plurality of UEs, content data sets identified in the second broadcast schedule.
However, in the same field of endeavor, Lee teaches from Fig. 7 generating a schedule of broadcast programs according to obtained feedback. In one example, the method 700 can be employed by a media or content distributor and/or a content provider to deliver broadcasted content automatically determined by user (e.g., viewer) feedback. At reference numeral 702, feedback can be obtained from a user. It is to be appreciated that feedback can be further obtained from a plurality of users. For example, feedback can be collected from any user perceiving or desiring to perceive content that can be broadcasted on a distribution network. The feedback can include ratings, votes, comments, reviews, critiques and the like. Further, the feedback can be provided relative to individual content items (e.g., content programs, broadcast programs, etc.) and/or related to a group of content items (Para 67).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Pan with the method of Lee so as to provide wish list of content based on user schedule (see Lee Para 54).
Regarding Claim 7, Pan does not teach wherein the demand prediction engine and the content schedule optimizer each comprises a machine learning (ML) model.
However, in the same field of endeavor, Lee teaches a classifier is a function that maps an input attribute vector, x=(x1, x2, . . . xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed. A support vector machine (SVM) is an example of a classifier that can be employed (Para 64-65).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Pan with the method of Lee so as to provide wish list of content based on user schedule (see Lee Para 54).
Regarding Claim 8, it has been rejected for the same reasons as claim 1.
Regarding Claim 9, it has been rejected for the same reasons as claim 2.
Regarding Claim 10, it has been rejected for the same reasons as claim 3.
Regarding Claim 12, it has been rejected for the same reasons as claim 5.
Regarding Claim 13, it has been rejected for the same reasons as claim 6.
Regarding Claim 14, it has been rejected for the same reasons as claim 7.
Regarding Claim 15, it has been rejected for the same reasons as claim 1 and further Pan teaches One or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations (Col 3 L 35-42).
Regarding Claim 16, it has been rejected for the same reasons as claim 2.
Regarding Claim 17, it has been rejected for the same reasons as claim 3.
Regarding Claim 19, it has been rejected for the same reasons as claim 5.
Regarding Claim 20, it has been rejected for the same reasons as claim 6.
Claim(s) 4, 11, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pan et al. Patent. No. US 8001217 B1 in view of Lee et al. Pub. No. US 20100146559 A1 and further in view of Prasad et al. Pub. No. US 20200404460 A1
Regarding Claim 4, Pan and Lee does not specifically teach further comprising: a self-improver operable to further train the demand prediction engine using a demand predictions history and an anonymized demand statistics history.
However, in the same field of endeavor, Prasad teaches from Fig. 4 that the optimization engine 407 comprises an algorithm 401 for predicting 410 requested content and content schedule and at least one data consumption area. The algorithm 401 may be, for example, a machine learning algorithm executed in the optimization engine 407. A machine learning algorithm enables predictive and proactive operation based on pre-determined inputs. In the example of FIG. 4, the algorithm 401 receives as input 408A information on previous cell associations 403 of the user device 406, a 408B broadcast content and event schedule 404 and 408C at least one user group with which the user device 406 is associated. The one or more previous cell associations 403 comprise a cell association history. A cell association may comprise information on a network element that a user device has previously connected. For example, information on a cell association may comprise a cell ID. Information on a cell association may also comprise a point in time when the user device was connected to the network element. One or more previous cell associations may be used for more accurately predicting locations where data might be consumed. Broadcast content and event schedule 404 comprises information on content to be broadcast and a point in time or a time interval when it is scheduled to be broadcasted (Para 96).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Pan with the method of Lee and further in view of Prasad so as to provide data transmission at the first point in time according to the determined data transmission mode (See Prasad Abstract).
Regarding Claim 11, it has been rejected for the same reasons as claim 4.
Regarding Claim 18, it has been rejected for the same reasons as claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sivakumar et al. Patent No. US 12063400 B2 - Systems and methods for time-shifted prefetching of predicted content for wireless users
Chen et al. Pub. No. US 20240193629 A1 - METHOD AND SYSTEM FOR PREDICTING OPERATIONAL INDICATORS WHERE A DEMAND MODEL IS TRAINED AND UPDATED
Pejhan Pub. No. US 20240031629 A1 - METHOD FOR DYNAMIC CONFIGURATION OF MULTIMEDIA CONTENT ENCODER AND APPARATUS FOR IMPLEMENTING THE SAME
Mappus et al. Pub. No. US 20220329640 A1 - Method for providing content streaming such as video streaming, involves collecting a set of time series features which relate to requests for a content item at a content distribution node in a communication network
Kaufman et al. Pub. No. US 20200037035 A1 - DEMAND BASED SELECTION FOR CELLULAR BROADCAST STREAMING MEDIA
Kim et al. Pub. No. US 20150032506 A1 - DEMAND FORECAST SEGMENTATION APPARATUS AND METHOD, APPARATUS AND METHOD FOR ADJUSTING DEMAND FORECAST AND RECORDING MEDIUM RECORDING PROGRAM THEREOF
Kee et al. Pub. No. US 20140214535 A1 - Method for selecting advertisement for HTML based webpage/website in online environment, involves determining bid for impression in requested content based on estimate of conditional probability of conversion, and transmitting bid
Quality-Assured Cloud Bandwidth Auto-Scaling for Video-on-Demand Applications – 2012
On Proactive Caching with Demand and Channel Uncertainties – 2015
Proactive Resource Allocation with Predictable Channel Statistics - 2018
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NIZAR N. SIVJI
Primary Examiner
Art Unit 2647
/NIZAR N SIVJI/ Primary Examiner, Art Unit 2647
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