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
Claim(s) 11-30 have been examined.
Claim(s) 1-10 have been canceled.
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 11-30 are rejected under 35 U.S.C. 101 because the claims recite a judicial exception which is not integrated into a practical application and the claims lack an inventive concept.
Step 1 is the first inquiry into eligibility analysis and asks whether the claims are directed to a statutory category. In this instance, the answer must be in the affirmative because they recite a method and system.
Step 2A prong 1 is the next step in the eligibility analyses and asks whether the claimed invention recites a judicial exception. In this instance, the claims recite the following limitations which comprise the abstract idea:
establishing a user profile derived from input information provided by the user, wherein the user profile is continuously updated based on additional or modified input information provided by the user, and historical usage data elements automatically recognized and applied by the system to the user profile;
attributing one or more tags or meta-tags to data among at least one of the input information and the data elements;
displaying information curated in real time about a product or service that is available for purchase through the same user interface on which the media content is streamed, wherein the curated information is curated based on the user profile, and the data attributed with the one or more tags or meta-tags;
This is an abstract idea because it is a certain method of organizing human activity because it involves commercial or legal interactions such as marketing and sales activities and/or behaviors.
Step 2A prong 2 is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims 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.
In this instance, the claims recite the additional elements such as:
streaming media content on the user interface, wherein the media content includes at least one trigger therein;
when the trigger occurs in the media content, displaying data on a user interface wherein the streaming media content continues to be displayed in the user interface at the same time the curated information is being displayed;
wherein the user interface enables the user to perform an interactive function related to the product or service while the streaming media content continues to be displayed;
using a machine learning model, wherein the machine learning model include an associated neural network executed in a continuous, automated feedback loop;
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use 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.
In addition, the recitations of these additional limitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use 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.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use 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. For example, claim 12, 14, and 18-20 are directed to the abstract idea itself. As for claims 13, 15-17, these claims do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
streaming media content on the user interface, wherein the media content includes at least one trigger therein;
when the trigger occurs in the media content, displaying data on a user interface wherein the streaming media content continues to be displayed in the user interface at the same time the curated information is being displayed;
wherein the user interface enables the user to perform an interactive function related to the product or service while the streaming media content continues to be displayed;
using a machine learning model, wherein the machine learning model include an associated neural network executed in a continuous, automated feedback loop;
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use 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.
In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
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.
Claim(s) 11 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clarke (US 2017/0339462) in view of McGovern (WO 2013126648 A1) in further view of Bright (US 2011/0099122).
Referring to Claim 11, Clarke teaches a computerized method for providing entertainment and e-commerce to a user through a user interface of a computing device, the method comprising the steps of:
streaming media content on the user interface (see Clarke ¶0056), wherein the media content includes at least one trigger therein (see Clarke ¶0025);
when the trigger occurs in the media content, displaying on the user interface information curated in real time about a product or service that is available for purchase through the same user interface on which the media content is streamed (see Clarke ¶¶0025,95 and Fig. 6D, the user is watching a movie on their device and the movie has associated metadata which has “sync time triggers” and content items for a particular triggers, such that when the time in the movie is reached that activates the trigger, the device displays a pop-up purchase window over the movie of the content item, such as a soundtrack to the movie along with a buy now button);
wherein the streaming media content continues to be displayed in the user interface at the same time the curated information is being displayed (see Clarke Fig. 6C-D);
wherein the user interface enables the user to perform an interactive function related to the product or service while the streaming media content continues to be displayed (see Clarke Fig. 6C-D).
Clarke does not explicitly teach establishing a user profile derived from input information provided by the user, wherein the user profile is continuously updated based on additional or modified input information provided by the user, and historical usage data elements automatically recognized and applied to the user profile, nor attributing one or more tags or meta-tags to data among at least one of the input information and the data elements, nor wherein the curated information is curated based on the user profile, and the data attributed with the one or more tags or meta-tags.
However, McGovern teaches establishing a user profile derived from input information provided by the user, wherein the user profile is continuously updated based on additional or modified input information provided by the user, and historical usage data elements automatically recognized and applied to the user profile (see McGovern ¶¶0114,57, the consumer provides their age, height, weight, location, etc., and the recommendation engine stores this information; ¶0055, the consumer profile can periodically change, such as a consumer purchases something, so the recommendation engine periodically scans it for changes; ¶¶0066-69, the recommendation system also stores behavior properties for the consumer based on their preferences and behaviors, and periodically amends these as things change with the consumer) and attributing one or more tags or meta-tags to data among at least one of the input information and the data elements (see McGovern ¶¶0051-53, the recommendation engine stores meta data from the user’s purchase history that describes salable items, including SKUs, categories assigned to salable items, colors, sizes, brand information, and other descriptive data; this meta data constitutes tags or meta-tags) and wherein curated information is curated based on the user profile, and the data attributed with the one or more tags or meta-tags (see McGovern ¶¶0087-89, the recommendation engine also uses preferences and behaviors of the consumer to recommend salable items). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, the prior art of Clarke would continue to provide entertainment and-ecommerce through a user interface, except that now the e-commerce would be targeted for the user based on a continuously updated user profile derived from input information provided by the user according to the teachings of McGovern. This is a predictable result of the combination.
The combination does not teach using a machine learning model, wherein the machine learning model include an associated neural network executed in a continuous, automated feedback loop. However, Bright teaches this (see Bright ¶0020,22, fit prediction based on customer purchases, returns, satisfaction data, via an automatic feedback loop that can improve the accuracy of fit predictions over time for better recommendations; the fit prediction uses machine learning and neural networks). It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, the combination would continue to teach displaying information curated in real time about a product except that now the information could be curated using a machine learning model as taught by Bright. This is a predictable result of the combination.
Referring to Claim 21, this claim is similar to claims 11 and therefore rejected under the same reasons and rationale.
Claim(s) 12-20 and 22-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clarke (US 2017/0339462) in view of McGovern (WO 2013126648 A1) in further view of Bright (US 2011/0099122) and in further view of XIE (AU 2016360122 A1).
Referring to Claim 12, the combination teaches the method of claim 11, but does not teach processing the data attributed with the one or more tags or meta-tags through a plurality of engines, each identifying engine being a recommender engine or personalizer engine and a different type of personalized recommendation for items and interactions. However, XIE teaches processing data using different recommendation algorithms (e.g., recommenders / personalizer engines) to determine different recommendations such that “two recommended information sets may not be the same” and then using the different recommendations to determine a final recommendation list (see XIE ¶0021, the server uses different recommendation algorithms to determine the plurality of recommended information sets based on historical behavioral information such that two recommended information sets may not be the same). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine these references because the results would be predictable. Specifically, the combination would continue to teach processing the data attributed with the one or more tags or meta-tags through a recommendation engine to determine personalized recommendations for items and interactions within the platform, except that now it would be done through multiple recommenders / personalizer engines as taught by XIE. This is a predictable result of the combination.
Referring to Claim 13, the combination teaches the method of claim 12, wherein the media content comprises a video (see Clarke ¶0022).
Referring to Claim 14, the combination teaches the method of claim 12, wherein the product or service displayed comprises a product or service displayed in the streaming media content (see Clarke ¶0098).
Referring to Claim 15, the combination teaches the method of claim 12, wherein the curated information is displayed in the user interface by providing a display gradient over a portion of the user interface that highlights the information while still allowing the user to view the media content (see Clarke Fig. 6D, the left edge of the pop-up is darker than the left edge of the image in the pop-up; also see Fig. 6C).
Referring to Claim 16, the combination teaches the method of claim 15, wherein the display gradient incrementally increases or decreases in opacity across the user interface (see Clarke Fig. 6C-D).
Referring to Claim 17, the combination teaches the method of claim 16, wherein the display gradient comprises a top layer overlay in the user interface, the top layer overlay having a color that becomes increasingly darker across the user interface (see Clarke Fig. 6C-D).
Referring to Claim 18, the combination teaches the method of claim 12, wherein the interactive function is selected from the group consisting of: purchase the product or service, add the product or service to a virtual shopping cart, add the product or service to a wish list, and add the product or service to a virtual folder, that can later be viewed by the user for later decision-making (see Clarke Fig. 6D).
Referring to Claim 19, the combination teaches the method of claim 12, wherein the product or service is chosen at least in part based upon the input information (see McGovern ¶¶0087-89).
Referring to Claim 20, the combination teaches the method of claim 12, wherein the user profile includes preferences derived from interactions of the user with the computing device (see McGovern ¶¶0042,102).
Referring to Claims 22-30, these claims are similar to claims 12-20 and therefore rejected under the same reasons and rationale.
Remarks
In regards to the rejection under 35 U.S.C. 101, the applicant argues that using a machine learning model leads to all sorts of improvements and proceeds to recite those improvements from paragraph [0046] of the specification. However, the claims recite “displaying…information…curated…using a machine learning model”, and does not recite actually using the machine learning model, training the machine learning model, etc., (nor any of the other technical details about the machine learning model in paragraph [0046] and its subsequent paragraphs). The claims simply recite displaying information and then details where that information originated from. For these reasons, the applicant’s arguments are not persuasive.
In regards to the applicant’s arguments regarding the prior art, they are persuasive and thus the examiner has found additional prior art to teach the newly amended limitations above.
Conclusion
Additional prior art relevant to the current application but not relied upon includes:
Pedro (US 2013/0085851) teaches targeted advertising of products shown in media content.
Klinger (US 2012/0116897) teaches interactive online advertisements associated with media.
Reference U (see PTO-892) which teaches time-dependent user profiling for TV recommendations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW E ZIMMERMAN whose telephone number is (571)270-5278. The examiner can normally be reached 8-4pm M-T, 8-12pm W.
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, Marissa Thein can be reached at (571)272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW E ZIMMERMAN/Primary Examiner, Art Unit 3688
