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) 1-20 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 1-10 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is drawn to a computer program per se. A computer program per se is abstract instructions and not a machine or apparatus; nor is it a process as the instructions are not “acts” being performed. As such, the claimed invention is not directed to one of the four statutory categories of invention, but instead directed to nonstatutory functional descriptive material. It is noted that computer programs embodied on a computer readable medium or other structure, which would permit the functionality of the program to be realized, would be directed to a product and be within a statutory category of invention, so long as the computer readable medium is not disclosed as non-statutory subject matter per se (e.g., signals or carrier waves).
Claims 1-20 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 system and method.
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:
a purchase system integration component to facilitate transactions with multiple content providers through the entertainment platform's purchase system;
a personalized recommendation engine configured to generate in real time personalized content recommendations based on the user's viewing and purchasing history, cultural background, and emotional reactions;
a cultural background and emotional reactions analysis component configured to consider the user's cultural background and emotional responses when providing content recommendations;
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 or 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:
one or more computers each including respective data processing apparatus coupled to respective computer tangible media having recorded thereon instructions executable by the respective data processing apparatus to cause the one or more computers to implement things;
one or more artificial intelligence and/or machine learning engines configured to generate accurate and personalized recommendations in real time, each engine including an associated neural network executed in a continuous automated feedback loop;
wherein the recommendation engine uses the AI/ML engine;
a feed aggregation module configured to enable user access to feeds from multiple content providers within an entertainment platform;
a content provider integration component configured to integrate feeds from multiple content providers into the system;
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 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, claims 2-3, 5-7, are directed to the abstract idea itself. As for claims 4 and 9-10, 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:
a feed aggregation module configured to enable user access to feeds from multiple content providers within an entertainment platform;
a content provider integration component configured to integrate feeds from multiple content providers into the system;
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) 1-4, 7-14, 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clarke (US 2017/0339462) in view of Koo (US 2004/0193488) in further view of JIKOMES (US 2020/0202409).
Referring to Claim 1, Clarke teaches a system for performing personalization to enhance a user's experience, the system comprising one or more computers each including respective data processing apparatus coupled to respective computer tangible media having recorded thereon instructions executable by the respective data processing apparatus to cause the one or more computers to implement:
a feed aggregation module configured to enable user access to feeds from multiple content providers within an entertainment platform (see Clarke ¶¶0056,15, the content service communication interface receives content from different sources);
a content provider integration component configured to integrate feeds from multiple content providers into the system (see Clarke ¶¶0056-57 , the content service communication interface receives content from different sources and the metadata service is configured to map the content);
a purchase system integration component configured to facilitate transactions with multiple content providers through the entertainment platform's purchase system (see Clarke ¶0097, the system can display interactive content so the user can make online purchases);
a personalized recommendation engine configured to generate personalized content recommendations based on the user's viewing history (see Clarke ¶¶0060-62);
Clarke does not teach wherein the recommendations are further based on the user’s purchasing history, cultural background, and emotional reactions. Nor does Clarke teaches a cultural background and emotional reactions analysis component configured to consider the user's cultural background and emotional responses when providing content recommendations. However, Koo teaches recommendations based on the user’s purchasing history (see Koo ¶¶0078-79, the recommendations can be based on the user’s request history, and purchases are requests, and also subscription data) and based on cultural background and emotional reaction (see Koo ¶¶0055,110-111, the recommendations can further be based on demographic and geographic characteristics of the user (which constitutes culture) and the user’s feedback and how they respond (positively or negatively) which is a type of emotion) and teaches a cultural background and emotional reactions analysis component configured to consider the user's cultural background and emotional responses when providing content recommendations (see Koo ¶¶0136,99,70). It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine these references because the results would be predictable. Specifically, the prior art of Clarke would continue to provide a purchasing system and provide recommendations to a user, except that now those recommendations would be further based on the user’s purchasing history, cultural background, emotional reactions, and would further include a cultural background and emotional reactions analysis component according to the teachings of Koo. This is a predictable result of the combination.
The combination does not teach one or more artificial intelligence and/or machine learning engines configured to generate accurate and personalized recommendations in real time, each engine including an associated neural network executed in a continuous automated feedback loop nor that the personalized recommendation engine uses the AI/ML engine for the generation in real time. However, JIKOMES teaches this (see JIKOMES ¶¶0055-56,62, an AI decision engine that uses a neural network, and performs continuous updates based on user feedback, experience, and changed user preferences, and produces revised recommendations in real time). 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 of Clarke and Koo would continue to teach personalized recommendations, except that now they would be generated using an artificial intelligence and/or machine learning engine and generated in real time according to the teachings of JIKOMES. This is a predictable result of the combination.
Referring to Claim 2, the combination teaches the system of claim 1, further comprising a user profile management module configured to capture and store user preferences, viewing history, and emotional responses (see Clarke ¶0059).
Referring to Claim 3, the combination teaches the system of claim 1, further comprising a data collection and analysis module configured to collect and analyze user behavior, preferences, viewing history, and emotional responses (see Koo ¶¶0096-99).
Referring to Claim 4, the combination teaches the system of claim 1, wherein the one or more AI/ML engines are further configured to generate accurate and personalized recommendations based on the collected data (see JIKOMES ¶¶0055-56,62).
Referring to Claim 7, the combination teaches the system of claim 1, wherein each continuous automated feedback loop is configured to incorporate user feedback and evolving user preferences into the system (see JIKOMES ¶¶0055-56,62).
Referring to Claim 8, the combination teaches the system of claim 1, further comprising a scalable architecture and cloud deployment to ensure system performance and accommodate increasing user demands (see Clarke ¶¶00125-129).
Referring to Claim 9, the combination teaches the system of claim 1, further comprising an interface for users to access feeds from multiple content providers, transact with them through the purchase system, and choose from a variety of content options (see Clarke ¶0095 and Fig. 6-7).
Referring to Claim 10, the combination teaches the system of claim 1, further comprising synchronization mechanisms to ensure content delivery and synchronization across multiple screens or feeds (see Clarke ¶¶0043,46-52).
Referring to Claims 11-14 and 16-19, these claims are similar to claims 1-4 and 7-10 and therefore rejected using the same reasons and rationale.
Claim(s) 5-6, 15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clarke (US 2017/0339462) in view of Koo (US 2004/0193488) in further view of JIKOMES (US 2020/0202409) in further view of Chakrabarti (US 2009/0006373).
Referring to Claim 5, the combination teaches the system of claim 1 but does not a recommendation candidate selection module configured to select a subset of candidate recommendations based on normalized scores and weighted factors. However, Chakrabarti teaches this (see Chakrabarti ¶0040, the candidate selector selects a subset of the candidate recommendations from the normalization engine; ¶¶0036,46, a normalization engine which scores the recommenders to produce normalized recommendations; ¶0038, applying weights to the output from each recommender). It would have been obvious to one of ordinary skill in the art before the effective filing date to combine these references because the results would be predictable. Specifically, the combination of Clarke and Koo and JIKOMES would continue to teach personalized recommendation system except that now that recommendations system would include selecting a subset of candidate recommendations based on normalized scores and weighted factors. This is a predictable result of the combination.
Referring to Claim 6, the combination teaches the system of claim 1 but does not teach a personalization and tailoring module configured to customize the recommendations based on the user's profile, preferences, and real-time interactions. However, Chakrabarti teaches this (see Chakrabarti ¶¶0021,103, the recommendations are based on the user’s profile; ¶0038, and also the user’s preferences; ¶¶0099,92, and also real-time information from the user). It would have been obvious to one of ordinary skill in the art before the effective filing date to combine these references because the results would be predictable. Specifically, the combination of Clarke and Koo and JIKOMES would continue to teach personalized recommendation system except that now that recommendations system would include customizing the recommendations based on the user's profile, preferences, and real-time interactions according to the teachings of Chakrabarti. This is a predictable result of the combination.
Referring to Claims 15 and 20, these claims are similar to claim 5 and therefore rejected under the same reasons and rationale.
Remarks
Additional prior art relevant to the claimed application but not relied upon includes:
Piepenbrink (US 2008/0235101) teaches presenting advertising content related to media content.
Patel (US 2018/0124473) teaches presenting related content overlaid on media content a user is watching.
Reference U (see PTO-892) teaches product recommendations based on a customer's emotions.
Applicant’s arguments regarding the prior art are moot because of the new grounds of rejection in response to the amendment.
Applicant respectfully argues on page 8 of the remarks that using AI and machine learning to create product recommendations improves the types of associations between data elements leading to an improvement in the accuracy of the recommendations. Because of this improvement, the applicant argues that the claims should be found eligible. The examiner respectfully disagrees. The applicant is arguing an improvement to a business process, and not to an underlying technology or technical field. The applicant has not improved the field of machine learning but is simply using machine learning to improve the business process of product recommendations. For these reasons, the applicant’s arguments are not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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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
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