Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 09/25/2025.
Claims 1-8 are currently pending and have been examined.
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 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because Independent claim 8 is directed to “AN information processing program”, which is a software. Applicant’s specification teaches “ the information processing apparatus (rating point prediction apparatus) 30 is implemented by causing predetermined software (program) to be loaded on hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs an arithmetic operation, and controls communication by the communication apparatus 1004, reading and/or writing of data in the memory 1002 and the storage 1003 (e.g. a system), and the like [62]. The examiner suggests amending the claims to recite “a system”. Such an amendment would overcome the rejection above.
Claims 1-8 are directed to a system and a method which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes). Assuming that claim 8 is amended as suggested above, is directed to a system.
However, claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the following abstract idea:
an acquisition unit configured to acquire past programs that are candidates for a related program to be used for rating point prediction of a target program;
an extraction unit configured to extract the related program from among the past programs based on a broadcast station, a broadcast date and time, a program name, and a program content of the past program;
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of a computer with one or more hardware processors ( e.g. unit).
The additional technical elements above are recited at a high-level of generality (i.e., as a generic processor and generic computer components performing a generic computers function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and generic computer components. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on one or more computers, or merely uses computers as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer with one or more hardware processors (e.g. Unit) to perform the claimed functions amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and one or more generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on one or more computers, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires one or more general-purpose computer and generic computer components (as evidenced from paragraphs 22, 24 of the applicant’s specification) ; therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e., “PEG” Step 2B=No). For the same reason these elements are not sufficient to provide an inventive concept. For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible. Same Judicial analysis is applied here to independent claims 7-8.
The dependent claims 2-6 appear to merely further limit the abstract idea by further limiting the prediction unit which is considered part of the abstract idea (Claim 2); further limiting the extraction unit which is considered part of the abstract idea (Claims 3-6); and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No). Thus, based on the detailed analysis above, claims 1-8 are not patent eligible.
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 1-5, 7-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by EPSTEIN, US Pub No: 2019/0082206 A1.
As per claims 1, 7-8, EPSTEIN teaches:
an acquisition unit configured to acquire past programs that are candidates for a related program to be used for rating point prediction of a target program (see at least paragraph 29 (While individual performances may vary widely, collectively acquisitions tend to perform worse (in terms of rating points) than the acquiring network's average during the specific day part in which the acquisition airs. As illustrated in graph 200 of FIG. 2, historical acquisitions have produced ratings up to 4 percent below the acquiring networks' mean performance, on average, each month to date in 2016. This implies that there are relatively more unsuccessful acquisitions than successful acquisitions. The exemplary systems and methods described herein will leverage advanced statistical and machine-learning techniques to offer data-driven acquisition recommendations and thus reverse these statistics);
an extraction unit configured to extract the related program from among the past programs based on a broadcast station, a broadcast date and time, a program name, and a program content of the past program (see at least paragraph 5 (systems and methods for predicting audience measurements of a television program. The method may include receiving historical data from the external resources, retrieving prediction data by applying a plurality of acquisition performance predictors on the historical data, creating a prediction model based on the plurality of acquisition performance predictors, inputting a target program for acquisition into the prediction model, and generating a recommendation as to whether the target program should be acquired based on the prediction model and the plurality of acquisition performance predictors on the historical data); paragraph 30 (According to the exemplary systems and methods described herein, the predictive acquisition modeling may use data including a telecast dataset (e.g., live viewing and on-demand viewing) as well as a viewer dataset (e.g., more than 100 demographic and socioeconomic factors) provided by audience research and measurement outlets (e.g., Nielsen). A primary predictor of acquisition performance may be centered around one driving factor, namely, the program's audience. A network may prefer to acquire programs that target their own core audience. This may be due to a belief that if their viewers are similar to the program's original audience, their viewers will enjoy the content, and thus the acquisition will perform well. Accordingly, the exemplary systems and methods may be utilized to quantify how similar the acquiring network's core audience is to the program's historical fan bas); paragraph 49 (An exemplary acquisition system and/or method may also consider program performance during previous transmissions of the series. Accordingly, a feature to include as a predictor may be a program's Nielsen ratings on the source network within the acquiring network's target demographic during each of the source day parts in which it aired. In the same vein, a further feature may be calculated to indicate how much the program over-indexes or under-indexes compared to the network average by taking a ratio of program ratings compared to the source network's average ratings during the specified day part. Since acquired programming implies the rights to air repeats, special attention may be paid to how ratings for repeat episodes compare to ratings for original episodes. Thus, a feature called “repeat degradation” may be constructed that is a function of the ratio of the repeat ratings and the original ratings);
As per claim 2, EPSTEIN teaches:
a prediction unit configured to predict a rating point of the target program by using the related program (see at least paragraph 5 (systems and methods for predicting audience measurements of a television program. The method may include receiving historical data from the external resources, retrieving prediction data by applying a plurality of acquisition performance predictors on the historical data, creating a prediction model based on the plurality of acquisition performance predictors, inputting a target program for acquisition into the prediction model, and generating a recommendation as to whether the target program should be acquired based on the prediction model and the plurality of acquisition performance predictors on the historical data);
As per claim 3, EPSTEIN teaches:
wherein the extraction unit extracts the related program from among the past programs based on whether or not a broadcast station of the target program and the broadcast station of the past program are same ( see at least paragraph 5 (systems and methods for predicting audience measurements of a television program. The method may include receiving historical data from the external resources, retrieving prediction data by applying a plurality of acquisition performance predictors on the historical data, creating a prediction model based on the plurality of acquisition performance predictors, inputting a target program for acquisition into the prediction model, and generating a recommendation as to whether the target program should be acquired based on the prediction model and the plurality of acquisition performance predictors on the historical data); paragraph 7 ( a system for predicting audience measurements of a television program. The system may include a memory storing a plurality of rules, and a processor coupled to the memory and configured to perform actions that include receiving historical data from the external resources, retrieving prediction data by applying a plurality of acquisition performance predictors on the historical data, creating a prediction model based on the plurality of acquisition performance predictors, inputting a target program for acquisition into the prediction model, and generating a recommendation as to whether the target program should be acquired based on the prediction model and the plurality of acquisition performance predictors on the historical data);
As per claim 4, EPSTEIN teaches:
wherein the extraction unit extracts the related program from among the past programs based on whether or not there is a predetermined common matter between broadcast dates of the target program and the past program, and whether or not start times, end times, and broadcast duration of the target program and the past program are same or within a predetermined range (see at least paragraph 24 ( predictors for acquisition performance prediction modeling may be limitless. However, a few examples of the model predictors may include, but are not limited to, the brand and/or network acquiring the program (e.g., the acquiring network, the brand/network that the program is being acquiring from (e.g., the source network), the day part during which the acquiring network will air the program, the day part during which the source network aired the program, etc. As will be described in greater detail below, further predictors may include the current size (or average ratings) of the acquiring network during the acquiring day part, the rank of the source network in terms of recency of air, audience similarity, duplication (e.g., the percent of acquiring network's audience that has already seen the acquisition on the source network), a resting period (e.g., days since the network has aired the acquisition), program ratings on source network in source day part in the acquiring network's target demographic, etc); paragraph 26 ( predictors for acquisition performance prediction modeling may be limitless. However, a few examples of the model predictors may include, but are not limited to, the brand and/or network acquiring the program (e.g., the acquiring network, the brand/network that the program is being acquiring from (e.g., the source network), the day part during which the acquiring network will air the program, the day part during which the source network aired the program, etc. As will be described in greater detail below, further predictors may include the current size (or average ratings) of the acquiring network during the acquiring day part, the rank of the source network in terms of recency of air, audience similarity, duplication (e.g., the percent of acquiring network's audience that has already seen the acquisition on the source network), a resting period (e.g., days since the network has aired the acquisition), program ratings on source network in source day part in the acquiring network's target demographic, etc ;
As per claim 5, EPSTEIN teaches:
wherein the extraction unit extracts the related program from among the past programs based on whether or not an editing amount for making the program names of the target program and the past program into a same character string exceeds a predetermined editing amount (see at least paragraph 49 (a further feature may be calculated to indicate how much the program over-indexes or under-indexes compared to the network average by taking a ratio of program ratings compared to the source network's average ratings during the specified day part. Since acquired programming implies the rights to air repeats, special attention may be paid to how ratings for repeat episodes compare to ratings for original episodes. Thus, a feature called “repeat degradation” may be constructed that is a function of the ratio of the repeat ratings and the original ratings);
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.
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 6 is rejected under 35 U.S.C. §103 as being unpatentable over by EPSTEIN, US Pub No: 2019/0082206 A1 in view of D ‘Auria, US Pub No: 2022/0351236 A1.
Claim 6:
EPSTEIN discloses the limitations as shown above.
EPSTEIN does not specifically disclose, but D ‘Auria however discloses:
wherein the extraction unit using bidirectional encoder representations from transformers (BERT), and extracts based on cosine similarity between the vectorized target program and the past program (see at least paragraph 368 (Pattern Recognition 114 computes similarities between one or more sample video attributes and gathered metadata for videos that appear in public and/or private domains. The computation of similarity may include, for example, computing cosine similarity, Jaccard, Doc2vec, Word2vec, Universal Sentence Encoder, BERT, and/or computing one or more similarity matrices. The similarity computation is used to identify one or more most similar metadata entries to the one or more sample videos. In some embodiments, the top 50 most similar entries to the sample are computed by Pattern Recognition 114. One or more identifiers of the computed most similar entries are supplied to Data Retrieval 106. Data Retrieval 106 uses the similarity identifiers to gather additional data about the similar entries. The additional data may include, for example, audiovisual data associated with the identifiers and/or audiovisual content associated with the channel to which the identifiers are associated);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to have combined the teaching of TV acquisition performance predictors with the teaching of D ‘Auria to predict winning TV content before production with the motivation of predicting TV concepts and attributes likely to succeed at achieving at least one performance objective [2] as taught by D ‘Auria over that EPSTEIN.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
SCHNOOR et al, US Pub No: 20190066170 A1, teaches A technique for predictive modeling to generate ratings forecasts in a media network is described. An episode-level programming schedule is imported into a viewership forecasting application to generate episode-level ratings predictions. Episode-level ratings predictions for media content in the episode-level programming schedule are generated by implementing multiple different predictive algorithms in parallel for each instance of specific media content in the programming schedule. In addition, for each such predicted viewership value, an accuracy value is generated that indicates the likely accuracy of that predicted viewership value. The episode-level ratings predictions can be uploaded by a business unit of the media network, and merged with a programming schedule currently employed by the business unit.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Affaf Ahmed whose telephone number is 571-270-1835. The examiner can normally be reached on [M- R 8-6 pm ].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AFAF OSMAN BILAL AHMED/Primary Examiner, Art Unit 3622
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