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
This action is response to the communication filed on July 25, 2025. Claims 1-20 are pending.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites, inferring, by a content recommendation system based on user activities on the content recommendation system, a user request for one or more content items from a real-time stream of content items; obtaining a plurality of topics to which the content items are distributed; obtaining an interest distribution of the user on the plurality of topics; obtaining a classification distribution of each content item to the plurality of topics; estimating a rating of the user to each content item based on the interest distribution and the classification distribution; and providing the one or more of the content items based on the ratings.
The claim recited the limitation of inferring --- as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can infer a request form data by reading which is a mental process. Similarly, the limitation estimating a rating of the user to each content item based on the interest distribution and the classification distribution also a mental process as human can mentally calculates rating using interest distribution and classification distribution as recited. If necessary, user can also use physical aid such as pencil and paper. Hence, the limitation is a mental process. See MPEP 2106.04(a)(2) III, B, If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."). Hence, the limitation is a mental process.
The claim recited four additional elements: All the obtaining … limitation and providing … limitation. The obtaining steps as recited amounts to mere data gathering, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362(utilizing an intermediary computer to forward information)). Similarly, the providing step as recited is nothing but data manipulation which is an insignificant extra-solution activity. Accordingly, even in combination, the additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of obtaining and providing steps amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of content recommendation. The claim recites the limitations of wherein the user activities correspond to the user browsing a category of content items, and the user request corresponds to receiving the one or more content items in the category of content items, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of content recommendation. The claim recites the limitations of wherein the interest distribution of the user includes a plurality of values each representing an interest of the user in a respective one of the plurality of topics, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 4 is dependent on claim 3 and includes all the limitations of claim 3. Therefore, claim 4 recites the same abstract idea of content recommendation. The claim recites the limitations of wherein the interest distribution is determined by a first initiation model based on activities of the user related to the content items, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of content recommendation. The claim recites the limitations of wherein the classification distribution includes a plurality of values each representing a frequency of each content item being classified into a respective one of the plurality of topics, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 6 is dependent on claim 5 and includes all the limitations of claim 5. Therefore, claim 6 recites the same abstract idea of content recommendation. The claim recites the limitations of wherein the classification distribution is determined by a second initiation model based on activities performed with respect to the content items by one or more other users, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of content recommendation. The claim recites the limitations of estimating a set of correlation values based on the interest distribution and the classification distribution, wherein the providing the one or more of the content items is further based on the set of correlation values, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claims 8-20, they have similar limitations as claims 1-7 above. Hence, they are rejected under the same rational as claims 1-7 above.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yee et al. (Pub. No. : US 9129227 B1) in the view of Nickerson et al. (Pub. No. : US 20170124593 A1)
As to claim 1 Yee teaches a method for recommending content items, the method comprising:
inferring, by a content recommendation system based on user activities on the content recommendation system, a user request for one or more content items from a stream of content items (Column 4 lines 34-37, column 3 lines 51-65: detecting that a user has logged into a content hosting service (e.g., by entering a username and password), information relating to the content items that the user has accessed can be stored in a user interest profile, wherein a model of user interests can be generated using a machine learning approach, where a subset of topics, such as the top K topics);
obtaining a plurality of topics to which the content items are distributed (Column 6 lines 39-50: determining the list of entities and topics that a user has shown interest based on viewing history (e.g., the content items accessed by the user, the content items watched for a substantial period of time by the user, the content items that the user has provided a favorable indication, the content items that the user has shared with other users, etc.), the content recommendation system can use various input features and conjunctions to generate an interest model for a user using one or more machine learning techniques);
obtaining an interest distribution of the user on the plurality of topics (column 6 line 65 to column 7 line 3: At 210, the content recommendation system can access a user interest profile of topics associated with the user. As described above, the topics can be based on the content item accessed by the user and, in some implementations, an interest weight can be associated with each of the topics in the user interest profile. For example, an interest weight for the topic "science" can be high compared to other interest weights in response to determining that the user associated with the user interest profile watches a substantial number of video content items that are annotated with the topic "science.");
obtaining a classification distribution of each content item to the plurality of topics (Column 8 lines 45-54: the content recommendation system can cluster each topic with one or more related topics based on the distance measure. For example, the content recommendation system can create topic clusters using any suitable clustering approaches, such as a hierarchical agglomerative clustering approach. The content recommendation system can identify the topic clusters having a distance measure less than a given threshold value (e.g., grouping topics together that are closer in distance) and combine the two or more topics into a topic cluster);
estimating a rating of the user to each content item based on the interest distribution and the classification distribution (Column 8 lines 66 to column 9 line 1, 18-20, column 12 lines 45-55: the content recommendation system can map the user interest profile (that includes topics associated with content items accessed by the user) with the topic clusters and generate a matrix or any other suitable data representation that provides probabilities that a user of a user interest); and
providing the one or more of the content items based on the ratings (Column 13 lines 1-3, 18-19: the content recommendation system can select a subset of the content items from the ranked list and provide the user with the recommended content items).
Yee does not explicitly disclose but Nickerson teaches the one or more content items being a real-time stream (paragraph [0044]: a recommendation engine (50) that automatically analyzes content requirements, across multiple web properties or networks of web properties that are associated with the platform, and analyzes content available through the content hub (20), and in real time or near real time, recommends specific content for distribution). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Yee by adding above limitation as taught by Nickerson to improve the relevance and effective distribution of content (Nickerson, paragraph [0052]).
As to claim 2 Yee together with Nickerson teaches a method according to claim 1. Yee teaches wherein the user activities correspond to the user browsing a category of content items, and the user request corresponds to receiving the one or more content items in the category of content items (Column 3 lines 45-47, Column 4 52-56).
As to claim 3 Yee together with Nickerson teaches a method according to claim 1. Yee teaches wherein the interest distribution of the user includes a plurality of values each representing an interest of the user in a respective one of the plurality of topics (column 6 line 65 to column 7 line 3).
As to claim 4 Yee together with Nickerson teaches a method according to claim 3. Yee teaches wherein the interest distribution is determined by a first initiation model based on activities of the user related to the content items (Column 5 lines 45-39).
As to claim 5 Yee together with Nickerson teaches a method according to claim 1. Yee teaches wherein the classification distribution includes a plurality of values each representing a frequency of each content item being classified into a respective one of the plurality of topics (Column 5 lines 29-44).
As to claim 6 Yee together with Nickerson teaches a method according to claim 6. Yee teaches wherein the classification distribution is determined by a second initiation model based on activities performed with respect to the content items by one or more other users (Column 8 lines 55-64).
As to claim 7 Yee together with Nickerson teaches a method according to claim 1. Yee teaches estimating a set of correlation values based on the interest distribution and the classification distribution, wherein the providing the one or more of the content items is further based on the set of correlation values (Column 8 lines 66 to column 9 line 1, 18-20, column 12 lines 45-55).
As to claims 8-20, they have similar limitations as claims 1-7 above. Hence, they are rejected under the same rational as claims 1-7 above.
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
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/MD I UDDIN/Primary Examiner, Art Unit 2169
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