DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
2. 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.
3. Claims 1-20 are 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 particular independent claim 1, lines 1+, recites: “…one or more features and short-term time window”; “…one or more user intent embeddings…”; it is unclear as to the underlined claim limitations.
Independent claims 12 and 20, recites similar claim limitations and hence discussed on the same ground(s) of rejection.
Claim Rejections - 35 USC § 102
4. 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 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.
5. Claim(s) 1-3, 5-10, 12-14 and 16-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by LIN et al (2021/0374577).
As to claims 1-3, LIN discloses systems and methods for cross-domain action prediction, generating action sequence embeddings based on autoencoder trained to utilized the action sequence embeddings and further discloses a computer-implemented method for generating recommendations, the method comprising:
Generating (figs.1-9, Server or Service Switch Classifier “SSC”, [0002-0007] and [0023-0035]), based on one or more features and a short-term time window, an input feature sequence(s); generating, based on the input feature sequence, one or more user intent embeddings using a first machine learning model (Service Switch Classifier Model “SSCM”); and generating, based on the input feature sequence and the one or more user intent embeddings, one or more recommendations using a second machine learning model; and, wherein the one or more features comprises one or more categorical features and one or more numerical features (figs.1-9, Abstract, [0002-0007], [0023-0035] and [0037-0059), SSC collects past user actions (interactions and/or other interactive actions) sequence(s), that occur within a threshold timespan, across various media content type services, accessing shopping websites, various streaming services: news, sports, movies, VOD, PPV, other provider or content, etc., specific topics: variety of domain topics, that maybe interested to the user, purchases, etc., are collected as past user action sequences, defined as action chains; and utilized as heterogeneous inputs or comprehensive understanding of human behavior; past actions sequence(s) are series of actions, in which users switch between domains/services given intent transitions due to the information collected in prior domains/services; SSCM is trained to predict user intents or switches to recommend actions to users that relate to the service(s) or domain(s), uses categorical information and/or vectors and various threshold(s); and generating, based on the one or more numerical features and the one or more numerical features, one or more interaction features; generating, based on the one or more interaction features and the short-term time window, one or more short-term interest features; and generating, based on the one or more short-term interest features and the one or more interaction features, the input feature sequence ([0002-0007], [0023-0035] and [0037-0059), SSC collects past user actions (interactions and/or other interactive actions) sequence(s), that occur within a threshold timespan, across various media content type services, accessing shopping websites, various streaming services: news, sports, movies, VOD, PPV, other provider or content, etc., specific topics.
As to claims 5-8, LIN further discloses concatenating the one or more short-term interest features and the one or more interaction features, wherein generating the one or more user intent embeddings using the first machine learning model comprises: generating, based on the input feature sequence, one or more processed input features using one or more fully connected and normalization layers; generating, based on the one or more processed input features, an intent encoding using an intent encoding transformer; generating, based on the intent encoding, one or more intent predictions using one or more fully connected layers; and generating, based on intent predictions, the one or more user intent embeddings using one or more attention layers; wherein generating the intent encoding comprises using a causal mask ([0047-0055]) and wherein generating the one or more user intent embeddings comprises aggregating the one or more intent predictions ([0002-0007], [0023-0035] and [0037-0059), note remarks in claims 1-3.
As to claims 9-10, LIN further discloses wherein generating the one or more recommendations using the second machine learning model comprises: generating, based on the one or more user intent embeddings, one or more concatenated features using one or more concatenation layers; generating, based on the one or more concatenated features, one or more processed concatenated features using one or more fully connected and normalization layers; generating, based on the one or more processed concatenated features, an item encoding using an item encoding transformer; and generating, based on the item encoding, the one or more recommendations using one or more fully connected layers and wherein the first machine learning model is trained based on an intent loss computed using the one or more user intent embeddings and one or more ground truth intents; and the second machine learning model is trained based on a content item loss computed using the one or more recommendations and one or more ground truth recommendations ([0002-0007], [0023-0035] and [0037-0059), note remarks in claims 1-3.
As to claims 12-14, the claimed “One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors…” is composed of the same structural elements that were discussed with respect to claims 1-3.
Claims 16-18 are met as previously discussed in claims 5-8.
Claims 19 is met as previously discussed in claims 9-10.
As to claim 20, the claimed “A system…” is composed of the same structural elements that were discussed with respect to claims 1-3.
Claim Rejections - 35 USC § 103
6. 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.
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.
7. Claim(s) 4, 11 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over LIN et al (2021/0374577) in view of GHARIBSHAH et al (2024/0161165).
As to claims 4 and 15, LIN discloses all the claim limitation as discussed with respect to claims 3 and 14 respectively using encoder and other time information markers, BUT appears silent as to processing the input feature sequence using timestamp-based positional encoding.
However, in the same field of endeavor, GHARIBSHAH discloses cross-domain recommendation via contrastive learning of user behaviors in attentive sequence models and further discloses where the input feature sequence uses timestamp-based positional encoding (figs.1-8, [0064-0067]).
Hence it would have been obvious before the effective filing date of the claimed invention to incorporate the teaching of GHARIBSHAH into the system of LIN to use the most efficient or recent timestamped dataset for processing of information efficiently.
As to claim 11, LIN discloses all the claim limitation as discussed with respect to claim 10, including using some probability function to determine other predictions ([0042-0044] and [0054-0055]), BUT appears silent as to wherein: the intent loss is a binary cross-entropy loss; and the content item loss is a weighted cross-entropy loss function
However, in the same field of endeavor, GHARIBSHAH further discloses wherein: the intent loss is a binary cross-entropy loss; and the content item loss is a weighted cross-entropy loss function ([0054-0057]).
Hence it would have been obvious before the effective filing date of the claimed invention to incorporate the teaching of GHARIBSHAH into the system of LIN to use other functions to predict results with a desired application.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNAN Q SHANG whose telephone number is (571)272-7355. The examiner can normally be reached Monday-Friday 7-4.
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/ANNAN Q SHANG/Primary Examiner, Art Unit 2424
ANNAN Q. SHANG
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