CTNF 19/245,152 CTNF 83531 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-20 are pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 without significantly more. Each of the independent claims 1, 12 and 20 recites a mental process in the limitations of “…receiving a request…obtaining data…select a next cluster…and selecting one or more content items form the next cluster These limitations could be done mentally based on gathered information. Mental process is directed to one of the abstract ideas groups as set forth by Prong One in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance. Th claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements (e.g. content item recommendation, content items, a set of one or more content items that have been interacted with by the particular user, data specifying a respective cluster of content items to which the content item belongs) are directed to types of information materials, which do not impose a meaningful limit on the judicial exception, such that the claims are more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims. Hence, the claims do not include additional elements or the combination of the elements are sufficient to amount to significantly more than the judicial exception and fail to integrate the judicial exception into practical application according to Prong Two in Step 2A of the 2019 Patent Subject Matter Eligibility Guidance because the claimed elements or their combination do not impose any meaningful limits on practicing the abstract idea. Further, in view of Step 2B of the 2019 Patent Subject Matter Eligibility Guidance, it is determined that the computing elements (such as computer, storage devices, one or more non-transitory computer storage media storing instruction) in the claims amount to no more than usage of a generic computing system having a generic computing components, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known. Dependent claims 2 and 13 each recite the mental process include addition limitations of “…maintaining data …identifying a mapping…selecting the respective next cluster ”, which could be done mentally based on the gathered information. The additional elements (e.g. mappings) in the limitation are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Dependent claims 3 and 14 each recite the mental process include addition limitations of “…generating each of the plurality of mappings…comprising…processing an input sequence…”, which could be done mentally based on the gathered information. The additional elements (e.g. input sequence, prompt, output sequence) in the limitations are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Also, the computing elements (e.g. language model neural network) in the claims amount to no more than usage of generic computing components in a generic computing field, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known. Dependent claims 4 and 15 each recite the mental process include addition limitations of “…selecting the next cluster…comprising…processing an input sequence…”, which could be done mentally based on the gathered information. The additional elements (e.g. input sequence, prompt, output sequence) in the limitations are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Also, the computing elements (e.g. language model neural network) in the claims amount to no more than usage of generic computing components in a generic computing field, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known. Dependent claims 5 and 16 each recite the mental process include addition limitations of “…obtaining data…selecting a fixed number…”, which could be done mentally based on the gathered information. The additional elements (e.g. data specifying an interaction history for the particular user, interaction history) in the limitations are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Dependent claims 6 and 17 each recite the mental process include addition limitations of “…providing an input…obtaining data as output…selecting one or more recommended content items…”, which could be done mentally based on the gathered information. The additional elements (e.g. input characterizing the particular user, data specifying a set of recommended content items, output, content items) in the limitations are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Dependent claims 7 and 18 each recite the mental process include addition limitations of “…data specifying …selecting one or the highest score recommended content items…”, which could be done mentally based on the gathered information. The additional elements (e.g. respective score, highest scoring recommended content items) in the limitations are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Dependent claims 8 and 19 each recite the description of the language model neural network, a computing element that amounts to no more than usage of generic computing components in a generic computing field as any neural network is known the with the cited functionalities including pre-train and fine tune, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known. The claims further recite additional elements (e.g. cluster recommendation, input, target cluster) in the limitations are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception. Dependent claim 9 recites the mental process include addition limitations of “…generating the cluster recommendation training examples…obtaining interaction histories…identifying interaction histories…generating a respective cluster recommendation training example…”, which could be done mentally based on the gathered information. The additional elements (e.g. cluster recommendation training examples, interaction histories, content items) in the limitations are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Dependent claim 10 recites the mental process include addition limitations of “…obtaining data.…identifying a respective cluster…”, which could be done mentally based on the gathered information. The additional elements (e.g. data specifying the set of one or more content items that have been interacted with by the particular user, content items) in the limitations are directed to types of information materials. The information materials which do not impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort design to monopolize exception, because the claimed steps could be performed in a same manner to achieve the same outcome with other types of information other than the ones being used in the claims as stated above. Dependent claim 11 further recites additional elements (e.g. videos) in the limitations are directed to types of information materials that are being manipulated, which do not impose a meaningful limit on the judicial exception. Also, the computing elements (e.g. video sharing platform) in the claims amount to no more than usage of generic computing components in a generic computing field, which fails to provide an inventive concept or significantly more than abstract idea because the elements do not necessary improve the functional of a computing system or an improvement to a technical field since network computing is well known. Thus, for at least the reasoning above, the pending claims are not patent eligible. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 3-4 and 14-15 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. With respect to claims 3-4 and 14-15, limitation of “…processing, using a language model neural network, an input sequence that (i) identifies the respective set of clusters in the mapping and (ii) a prompt to generate an…” is not clearly understood rendering the claims being indefinite. It is in unclear what is meant by the input sequence that (i) identifies clusters and (ii) a prompt to generate an output sequence as claimed. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-12-aia AIA (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. 07-15-03-aia AIA Claims 1-2 0 are rej ected under 35 U.S.C. 102 (a)(2) as being ant icipated by K in i at al (Pub No. US 12,342,043, hereinafter Kini). Wit h respect to claim 1, Kini discloses a method performed by one or more computers (abstract) , the method comprising: receiving a request for a content item recommendation for a particular user (Col. 13, lines 39-50, Fig 6-7: receive a request for content item recommendation when user submits a request to a service provide network for content item recommendation, as further disclosed in Col. 18, lines 5-10) ; obtaining data specifying, for each of a set of one or more content items that have been interacted with by the particular user, a respective cluster of content items to which the content item belongs (Col. 3, lines 39-60, Fig 4 & 7: obtain data—e.g. user context data-- specifying a respective cluster represented by category of content in view of user historical behaviors that indicate user interactions with respective content items to which the content items belong, e.g. a category of movies watched, as further disclosed in Col. 18, lines 21-45) ; selecting, using the respective clusters for the content items in the set, a next cluster of content items from a plurality of clusters to recommend to the particular user (Col. 3, lines 60-67, Col. 4, lines 18-30, Fig 4 & 6-7: select next cluster represented by the next category to recommend to user, as further disclosed in Col. 5, lines 30-35, Col. 9, lines 27-35, Col. 18, lines 56-60) ; and selecting, as content items to recommend to the particular user, one or more content items from the next cluster (Col. 5, lines 31-34, Fig 4 & 6-7: select one or more content items as content items to recommend resulting in displaying selected content items on user’s graphical user interface GU, as further disclosed in Col. 19, lines 1-10) . With respect to claim 12, Kini discloses a system comprising one or more computers and one or more storage devices storing instruction that when executed by the one or more computers cause the one or more computers to perform operations (abstract, Fig 2 & 8) comprising: receiving a request for a content item recommendation for a particular user (Col. 13, lines 39-50, Fig 6-7: receive a request for content item recommendation when user submits a request to a service provide network for content item recommendation, as further disclosed in Col. 18, lines 5-10) ; obtaining data specifying, for each of a set of one or more content items that have been interacted with by the particular user, a respective cluster of content items to which the content item belongs (Col. 3, lines 39-60, Fig 4 & 7: obtain data—e.g. user context data-- specifying a respective cluster represented by category of content in view of user historical behaviors that indicate user interactions with respective content items to which the content items belong, e.g. a category of movies watched, as further disclosed in Col. 18, lines 21-45) ; selecting, using the respective clusters for the content items in the set, a next cluster of content items from a plurality of clusters to recommend to the particular user (Col. 3, lines 60-67, Col. 4, lines 18-30, Fig 4 & 6-7: select next cluster represented by the next category to recommend to user, as further disclosed in Col. 5, lines 30-35, Col. 9, lines 27-35, Col. 18, lines 56-60) ; and selecting, as content items to recommend to the particular user, one or more content items from the next cluster (Col. 5, lines 31-34, Fig 4 & 6-7: select one or more content items as content items to recommend resulting in displaying selected content items on user’s graphical user interface GU, as further disclosed in Col. 19, lines 1-10) . With respect to claims 2 and 13, Kini further discloses maintaining data comprising a plurality of mappings, wherein each mapping maps a respective set of clusters to a respective next cluster, and wherein selecting the next cluster of content items to recommend to the particular user comprises (Col. 3, lines 40-67, Fig 1& 4: maintaining data by comprise mapping of clusters represented by categories represented by data that link or map a category to the next category, involving in recommendation selection as further disclosed in Col. 5, lines 20-30, Col. 8, lines 25-30, Col. 9, lines 1-30): identifying, in the maintained data, a mapping that has a respective set of clusters that includes the respective clusters for the content items in the set (Col. 3, lines 60-67, Fig 4: identify a mapping, such as but not limited to a rank or relevancy mapping in the data, as further disclosed in Col. 8, lines 25-30, Col. 9, lines 1-30) ; and selecting, as the next cluster of content items to recommend to the particular user, the respective next cluster in the identified mapping (Col. 3, lines 60-67, Col. 4, lines 16-30, Fig 4 & 6-7: select a cluster represented by a selected category in the mapping via user data) . With respect to claims 3 and 14, Kini further discloses generating each of the plurality of mappings in the maintained data, comprising, for each mapping: processing, using a language model neural network, an input sequence that (i) identifies the respective set of clusters in the mapping and (ii) a prompt to generate an output sequence that identifies the respective next cluster in the mapping, wherein the prompt instructs the language model neural network to predict a cluster of data items that a user that has interacted with the respective set of clusters would interact with next (Col. 3, lines 56-67, Fig 3: processing input sequence that identify mapping and prompt represented by transformer instruction to predict the cluster or category using a transformer neural network representing the language model neural network, as further disclosed in Col. 6,lines 30-48) . With respect to claims 4 and 15, Kini further discloses wherein selecting the next cluster of content items to recommend to the particular user comprises: processing, using a language model neural network, an input sequence that (i) identifies the respective clusters for the content items in the set and (ii) a prompt to generate an output sequence that identifies the next cluster, wherein the prompt instructs the language model neural network to predict a cluster of data items that a user that has interacted with the respective clusters for the content items in the set would interact with next (Col. 3, lines 56-67, Fig 3: processing input sequence that identify mapping and prompt represented by transformer instruction to predict the cluster or category using a transformer neural network representing the language model neural network, as further disclosed in Col. 6,lines 30-48) . With respect to claims 5 and 16, Kini further discloses wherein obtaining data specifying, for each of a set of one or more content items that have been interacted with by the particular user, a respective cluster of content items to which the content item belongs comprises: obtaining data specifying an interaction history for the particular user (Col. 3, lines 54, Fig 2: obtain data represented by user historical behaviors data specifying interaction history) ; and selecting a fixed number of clusters from the interaction history (Col. 6, lines 15-20: select a fixed number of cluster/categories, such as not limited to K categories, as further disclosed in Col. 7, lines 1-6) . With respect to claims 6 and 17, Kini further discloses wherein selecting, as content items to recommend to the particular user, one or more content items from the next cluster comprises: providing an input characterizing the particular user to a content recommendation system (Col. 3, lines 56-67, Fig 3: provide an input with user data via transformer neural network of the recommendation system, as further disclosed in Col. 6, lines 30-48) ; obtaining, as output from the content recommendation system, data specifying a set of recommended content items (Col. 3, lines 56-67, Fig 3: obtain an output via transformer neural network of the recommendation system, as further disclosed in Col. 6, lines 30-48) ; and selecting, as content items to recommend to the particular user, one or more of the recommended content items that are in the next cluster (Col. 3, lines 56-67, Fig 3 & 6-7: select one or more recommended content items in the next cluster such as the predicted category, as further disclosed in Col. 6, lines 30-48, Col. 19, line s1-15) . With respect to claims 7 and 18, Kini further discloses wherein the data specifying a set of recommended content items comprises a respective score for each of the recommended content items and wherein selecting, as content items to recommend to the particular user, one or more of the recommended content items that are in the next cluster comprises: selecting, from the recommended content items that are in the next cluster, one or more highest scoring recommended content items (Col. 3, lines 60-67, Fig 4: the data comprise score, and select items with highest scoring as set forth by the highest scored category with respect to relevancy, as further disclosed in Col. 9, lines 13-30) . With respect to claims 8 and 19, Kini further discloses wherein the language model neural network is a pre-trained language model neural network that has been fine-tuned on cluster recommendation training examples, each cluster recommendation training example being associated with a respective user and identifying (i) an input set of one or more clusters associated with data items that have been interacted with by the respective user and (ii) a target cluster associated with a data item that was interacted with by the respective user after interacting with the data items associated with the input set of one or more clusters (the limitations are directed to non-functional descriptive material that do not impact the functionally of the claimed steps; Col. 2, lines 48-55, Col. 3, lines 55-67, Col. 4, lines 1-2, Fig 3: the transformer neural network is a pretrained mode with fine tuning via learned process in view of user historical behaviors as input and a target cluster represented by a predicted category) . With respect to claim 9, Kini further discloses generating the cluster recommendation training examples, comprising: obtaining a plurality of interaction histories, each interaction history corresponding to a respective user (Col. 3, lines 40-45, Fig 2-3: obtain user historical behaviors for interaction histories) ; for each of the plurality of clusters: identifying one or more interaction histories that each include an interaction with a content item from the cluster preceded by respective interactions with one or more content items from clusters that are different from the cluster (Col. 3, lines 40-55, Col. 4, lines 15-30, Fig 2-3: identify interaction histories in view of user historical behaviors) ; and generating a respective cluster recommendation training example from each identified interaction history. (Col. 3, lines 40-55, Col. 4, lines 15-30, Fig 2-3: generate training example in view of training data as described in Col. 2, lines 48-55). With respect to claim 10, Kini further discloses wherein obtaining data specifying, for each of a set of one or more content items that have been interacted with by the particular user, a respective cluster of content items to which the content item belongs comprises: obtaining data specifying the set of one or more content items that have been interacted with by the particular user (Col. 3, lines 40-55, Col. 4, lines 15-30, Fig 2-3: identify interaction histories in view of user historical behaviors) ; and identifying, for each of the content items in the set and from the plurality of clusters of content items, a respective cluster of content items to which the content item belongs (Col. 3, lines 35-55: identify a respective cluster with categorization and patten identification) . With respect to claim 11, Kini further discloses wherein the one or more content items to recommend to the particular user are videos maintained by a video sharing platform (the limitation is directed to non-functional descriptive material for describe the content item and not functionally involved; Col. 2, lines 30-32, Fig 6:the content items are videos such as movies mainitnig by a movie sharing platform) . With respect to claim 20, Kini discloses one or more non-transitory computer storage media storing instruction that when executed by the one or more computers cause the one or more computers to perform operations (abstract, Fig 8) comprising: receiving a request for a content item recommendation for a particular user (Col. 13, lines 39-50, Fig 6-7: receive a request for content item recommendation when user submits a request to a service provide network for content item recommendation, as further disclosed in Col. 18, lines 5-10) ; obtaining data specifying, for each of a set of one or more content items that have been interacted with by the particular user, a respective cluster of content items to which the content item belongs (Col. 3, lines 39-60, Fig 4 & 7: obtain data—e.g. user context data-- specifying a respective cluster represented by category of content in view of user historical behaviors that indicate user interactions with respective content items to which the content items belong, e.g. a category of movies watched, as further disclosed in Col. 18, lines 21-45) ; selecting, using the respective clusters for the content items in the set, a next cluster of content items from a plurality of clusters to recommend to the particular user (Col. 3, lines 60-67, Col. 4, lines 18-30, Fig 4 & 6-7: select next cluster represented by the next category to recommend to user, as further disclosed in Col. 5, lines 30-35, Col. 9, lines 27-35, Col. 18, lines 56-60) ; and selecting, as content items to recommend to the particular user, one or more content items from the next cluster (Col. 5, lines 31-34, Fig 4 & 6-7: select one or more content items as content items to recommend resulting in displaying selected content items on user’s graphical user interface GU, as further disclosed in Col. 19, lines 1-10) . Examiner Note Examiner has cited particular columns/paragraph and line numbers in the references 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 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 entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Owyang whose telephone number is (571)270-1254. The examiner can normally be reached Monday-Friday, 8am-6pm EST. 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, Charles Rones can be reached at (571)272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MICHELLE N OWYANG/Primary Examiner, Art Unit 2168 Application/Control Number: 19/245,152 Page 2 Art Unit: 2168 Application/Control Number: 19/245,152 Page 3 Art Unit: 2168 Application/Control Number: 19/245,152 Page 4 Art Unit: 2168 Application/Control Number: 19/245,152 Page 5 Art Unit: 2168 Application/Control Number: 19/245,152 Page 6 Art Unit: 2168 Application/Control Number: 19/245,152 Page 7 Art Unit: 2168 Application/Control Number: 19/245,152 Page 8 Art Unit: 2168 Application/Control Number: 19/245,152 Page 9 Art Unit: 2168 Application/Control Number: 19/245,152 Page 10 Art Unit: 2168 Application/Control Number: 19/245,152 Page 11 Art Unit: 2168 Application/Control Number: 19/245,152 Page 12 Art Unit: 2168 Application/Control Number: 19/245,152 Page 13 Art Unit: 2168 Application/Control Number: 19/245,152 Page 14 Art Unit: 2168 Application/Control Number: 19/245,152 Page 15 Art Unit: 2168 Application/Control Number: 19/245,152 Page 16 Art Unit: 2168 Application/Control Number: 19/245,152 Page 17 Art Unit: 2168 Application/Control Number: 19/245,152 Page 18 Art Unit: 2168
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