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 . Claims 1-9 are pending.
Claim Objections
Claim 9 is objected to because of the following informalities.
In claim 9, applicant recites “the computer program allows one or more processors,” which should read: “the computer program allows the one or more processors,” for consistency with the previously introduced “one or more processors.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 9, the preamble attempts to claim an article of manufacture (a computer-readable medium storing instructions) but mid-sentence it shifts into a method claim format ("...the method comprises:"). This renders the claim scope indefinite because one cannot tell if the applicant is claiming the article or the process, thereby rendering the metes and bounds indeterminate.
Appropriate correction is required.
Claim Rejections - 35 USC § 101 (Signal Per Se)
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 9 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 the preamble doesn’t exclude transitory signals (page 22 of applicant’s specification as filed states that “The computer readable storage media include volatile and non-volatile media, transitory and non-transitory media…”).
Applicant can overcome this rejection by amending the claims to recite “A computer program stored in a non-transitory computer-readable storage medium…” Such an amendment does not constitute new matter.
Appropriate correction is required.
Claim Rejections - 35 USC § 101 (Abstract Idea)
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a method (i.e., a process), claim 8 to a device (i.e., a machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
(Examiner notes that claim 9, while failing to pass Step 1, is included in the Abstract Idea rejection for purposes of compact prosecution. Assuming applicant amends properly, then claim 9 is to “A computer program stored in a non-transitory computer-readable storage medium,” i.e., an article.)
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 8, and 9 is (claim 1 being representative):
training a model through training data to predict the user information from the playlog data, wherein the training data is generated based on first playlog data of account identification information in which the user information is identified; and
predicting the user information related to advertisement identification information from second playlog data of the advertisement identification information by the model.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, rudimentary “training” of a model (e.g., via simple linear regression with pen and paper) and using the model to predict user information related to advertisement identification information. This is further supported by page 1 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe a marketing activity that pertains to predicting user information related to advertisement identification information, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by page 1 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions that pertain to predicting user information related to advertisement identification information, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by page 1 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe rudimentary “training” of a model (e.g., via simple linear regression with pen and paper) and using the model to predict user information related to advertisement identification information, which constitutes a process that, under its broadest reasonable interpretation, covers mathematical concepts. This is further supported by page 1 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1 recites the following additional elements: by a computing device; deep learning.
Claim 8 recites the following additional elements: a computing device; a processor including at least one core; a memory including program codes executable in the processor; deep learning.
Claim 9 recites the following additional elements: a computer program stored in a computer-readable storage medium; executed by one or more processors; one or more processors; deep learning.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in pages 7-8 of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-7 further narrow the abstract idea above with additional abstract steps and/or information. This narrowing of the abstract idea does not integrate it into practical application or add significantly more, and the narrowed abstract idea(s) would fall into the groupings highlighted above.
Accordingly, claims 1-9 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 and 7-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Taifi (US 20220230205).
Claims 1, 8, and 9
Taifi discloses:
[Claim 1: A method for predicting user information using playlog data performed by a computing device {method described in Abstract, [0001]. predicting user information using playlog data performed by a computing device described in [0071], where model output corresponds to a prediction: The content embeddings model 242 returns a ranked list of digital content identification information, or DIGITAL_CONTENT_ID values, that correspond to digital content items which match most closely television content items corresponding to the set of TV_CONTENT_ID values provided as an input to the content embeddings model 242 at the end of step 270. The highest-ranked digital content items are most similar to the television content items targeted by the television advertising campaign. computing device further described in [0091].}, the method comprising:]
[Claim 8: A computing device for performing a method for predicting user information using playlog data {See previous citations to Abstract, [0001], [0071], [0091].}, comprising: a processor including at least one core; and a memory including program codes executable in the processor {processor including at least one core, memory described in [0115] and [0138].}, wherein the processor is configured to]
[Claim 9: A computer program stored in a computer-readable storage medium, wherein when the computer program is executed by one or more processors, the computer program allows one or more processors to perform operations for performing a method for predicting user information using playlog data {See previous citations to Abstract, [0001], [0071], [0091], [0115], [0138]. computer program stored in a computer-readable storage medium further described in [0095].}, the method comprises:]
training [train] a deep learning model through training data to predict the user information from the playlog data {training a deep learning model through training data to predict the user information from the playlog data, where playlog data represented by digital viewership information: [0062] FIG. 2C depicts an illustrative embodiment of a method 252 in accordance with various aspects described herein. In the method 252, digital viewership information is joined with the household television viewership information associated with advertising of a television advertiser to build content rankings of the digital content identification information for digital content items that are closest to television content items identified by the television content identification information. In particular, the method 252 illustrates a model training algorithm for a machine learning model. deep learning model also described in [0091]: For example, computing environment 400 can facilitate in whole or in part collecting television viewership information for an advertiser's television campaign and recommending digital content items such as websites for a corresponding digital campaign, including developing a deep learning model that encodes the relationship between television content and digital content. The model is trained on historical television viewership data and online browsing data.}, wherein the training data is generated based on first playlog data of account identification information in which the user information is identified {See previous citation to [0062].}; and
predicting [predict] the user information related to advertisement identification information from second playlog data of the advertisement identification information by the deep learning model {See previous citation to [0091].}.
Claim 2
Taifi further discloses: wherein the user information includes gender information {[0032] Content providers such as advertisers collect information about one or more audiences for television programming. The information may include demographic information such as age and gender of the viewing audience, psychographic information and geographic information.}.
Claim 3
Taifi further discloses: wherein the user information includes age information {See previous citation to [0032].}.
Claim 4
Taifi further discloses: wherein the user information includes the gender information and the age information {See previous citation to [0032].}.
Claim 7
Taifi further discloses: wherein the training data is generated by labeling the user information with respect to the first playlog data {[0064] As indicated in FIG. 2B, the digital content identification information and digital identification information labelled and stored as pairs.}.
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.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Taifi in view of Chittilappilly (US 20150186925).
Claim 5
Taifi further discloses: deep learning; gender {See pervious citations to [0032], [0091].}
Taifi, while disclosing the features above, doesn’t explicitly disclose, however, Chittilappilly, in a similar field of endeavor directed to advertising portfolio management, teaches: wherein evaluation metrics of the model for prediction of the information is defined by accuracy {[0115] As described above, validations are performed on the learning model 116.sub.3 using historical data itself (e.g., where both the stimulus and response are measured data) to ensure goodness of fit and prediction accuracy. In addition to model validation using the training dataset, additional validation steps are performed to check prediction accuracy and to ensure the model is not just doing a data fitting.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Taifi to include the features of Chittilappilly. Given that Taifi is directed to cross-media recommendations using machine learned models, one of ordinary skill in the art would have been motivated to look to Chittilappilly, in order to facilitate accurately predicting the overall effectiveness of a particular change in advertising spending {[0009] of Chittilappilly}.
Claim 6
Taifi further discloses: deep learning; age {See pervious citations to [0032], [0091].}
Taifi, while disclosing the features above, doesn’t explicitly disclose, however, Chittilappilly, in a similar field of endeavor directed to advertising portfolio management, teaches: wherein evaluation metrics of the model for prediction of the information is defined by a mean absolute percentage error (MAPE) {[0116] Model validation can occur at any moment in time. For example, the model developer module 504 can update the learning model 116.sub.3 upon receipt of new input data. In such as case, a training model can be trained using training data up to the latest available date, which training model in turn can be used to predict the values in the historical data (e.g., data captured in the past). The error in the training model can be calculated. Statistical metrics can be employed to calculate errors in the training model.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Taifi to include the features of Chittilappilly. Given that Taifi is directed to cross-media recommendations using machine learned models, one of ordinary skill in the art would have been motivated to look to Chittilappilly, in order to facilitate accurately predicting the overall effectiveness of a particular change in advertising spending {[0009] of Chittilappilly}.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Personalized Channel Recommendation Deep Learning From a Switch Sequence” (NPL attached), which teaches: We finally developed a separate learning method to fairly recommend for popular (hot) or unpopular (cold) channels, respectively, based on channel popularity in the training set with an extra price of a possible hit lag after recommendation, in order to alleviate the Matthew effect arising from the conventional recommendation based on historical information.
US 20150189351, which teaches: Television is the largest advertising category in the United States with over 65 billion spent by advertisers per year. A variety of different targeting algorithms are compared, ranging from the traditional age-gender targeting methods employed based on Nielsen ratings, to new approaches that attempt to target high probability buyers using Set Top Box data. The performance of these different algorithms on a real television campaign is shown, and the advantages and limitations of each method are discussed. In contrast to other theoretical work, all methods presented herein are compatible with targeting the existing 115 million Television households in the United States and are implementable on current television delivery systems.
US 20170161773, which teaches: Methods and systems have been provided for matching advertising requests (e.g., for advertisements in an advertising campaign) to advertising events (e.g., during display of a media asset or within a media guidance application) based on predicted viewership information determined using machine learning techniques. Performance of an artificial neural network (ANN) based approach and a support vector machine (SVM) based approach have been described, along with a hybrid approach that combines information from the ANN and SVM approaches. These techniques described with the ANN and SVM are equally applicable to other machine learning techniques.
US 20210299577, which teaches: Methods, systems, and computer readable media for predictive preloading of game data are described. In some implementations, a method can include using a machine learning model of player data to predict which games (or game-related data assets) to begin preloading prior to a user making a game selection. Once the user makes a selection, any preloading not related to the selection can be terminated. Thus, during the time period when the user is viewing available game selections, a given number of selections can begin to be preloaded based on predictions from the machine learning model. The preloading can help reduce latency from game selection by the user to game start time.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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