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
The instant application having Application No. 19/353,277 filed on 10/08/2025 in which claims 1-20 are pending in the application, all of which are ready for examination by the examiner.
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. The claims 1, 7, 14 recites generating, by at least one computer processor, a ranking score for each content item in the plurality of content items, wherein the generating the ranking score comprises, for each content item of the plurality of content items, ranking the plurality of content items for presentation to the user based on the ranking score associated with each content item in the plurality of content items.
The limitations of generating…, ranking…, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “method…,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “method…,” “of “generating…, ranking…,” in the context of these claims encompass the user manually generating ranking score of each content item, ranking content items based on ranking score. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – providing…, determining…, calculating…. The “determining” and “calculating” limitation amounts to mere instructions to apply an exception (see MPEP 2106.05f). The “providing” limitations are insignificant extra-solution activity (mere data gathering, outputting, please see MPEP 2106.05g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. “Determining” and “calculating” amounts to mere instructions to apply an exception (see MPEP 2106.05f). The additional elements of “providing” is a well-understood, routine, and conventional activity (data gathering, outputting, see MPEP 2106.05d). The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 2, 8, and 15 recite wherein the input further includes one or more user features associated with the user. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 3, 9, and 16 recite wherein the plurality of content items comprise a plurality of recorded shows, and wherein the one or more content item features associated with the content item comprise one or more of: a content item identifier (ID) associated with the content item; a content type associated with the content item; an audio language associated with the content item; an origin country associated with the content item; a normalized representation of a number of times that a plurality of users have launched the content item for playback over a first predefined time period; a normalized representation of a number of times that the plurality of users have interacted with the UI control associated with the content item to obtain information about the content item over a second predefined time period; a weighted sum of (i) a click through rate (CTR) for the content item when the content item was presented in relation to a submission of the query and (ii) a CTR for the content item when the content item was presented generally, wherein each CTR represents a number of times the plurality of users have interacted with the UI control associated with the content item to obtain information about the content item divided by a number of times the content item was presented to the plurality of users; or a weighted sum of (i) a long watch rate for the content item when the content item was presented in relation to a submission of the query and (ii) a long watch rate for the content item when the content item was presented generally, wherein each long watch rate represents a number of times the plurality of users watched the content item for longer than a third predefined time period. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 4, 10, and 17 recite wherein the plurality of content items comprise a plurality of televised sporting events, and wherein the one or more content item features associated with the content item comprise one or more of: a sports league title associated with the content item; a live event or upcoming event classification associated with the content item; an identifier (ID) of a home team associated with the content item; an ID of an away team associated with the content item; a normalized representation of a number of times that a plurality of users have launched the content item for playback over a first predefined time period; a normalized representation of a number of times that the plurality of users have interacted with the UI control associated with the content item to obtain information about the content item over a second predefined time period; a normalized representation of a measure of similarity between the query and the sports league title associated with the content item; a normalized representation of a measure of similarity between the query and a name of the home team associated with the content item; a normalized representation of a measure of similarity between the query and a name of the away team associated with the content item; or a normalized representation of an amount of time to a start time associated with the content item. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 5, 11, and 18 recite wherein the first type of interaction between the user and the content item comprises the user launching the content item for playback and the second type of interaction between the user and the content item comprises the user interacting with a UI control associated with the content item to obtain information about the content item. These limitations are recited at a high-level of generality (i.e., launching, interacting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claims 6, 13, and 19 recite wherein the generating the ranking score further comprises mapping, by the deep ML model, each of the one or more query features to a plurality of embedding vectors using a corresponding plurality of hash functions, each of the embedding vectors being of a dimension, and wherein the mapping comprises mapping each one of the one or more query features to two different embedding vectors using two different hash functions. These limitations are recited at a high-level of generality (i.e., mapping) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 12 recites wherein the deep ML model comprises a modified version of a deep learning recommendation model (DLRM). The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
The claim 20 recites wherein the mapping uses a MurmurHash non-cryptographic hash function with two different seed values. The limitations only recite additional elements recited at a high level of generality. Accordingly, this additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The additional elements, individually and in combination, also do not amount to significantly more than the abstract idea.
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 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 of this title, 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 1-5, 7-11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ruiz et al. (U.S. PGPub 2015/0006280; hereinafter “Ruiz”) in view of Paluri et al. (U.S. PGPub 2017/0132510; hereinafter “Paluri”) and further in view of Specter et al. (U.S. PGPub 2023/0262103; hereinafter “Specter”).
As per claims 1, 7, and 14, Ruiz discloses a computer-implemented method for ranking a plurality of content items for presentation to a user in response to a submission of a query, comprising: generating, by at least one computer processor, a ranking score for each content item in the plurality of content items, wherein the generating the ranking score comprises, for each content item of the plurality of content items: (See paras. 8, 51, wherein determining ranking score for content item are disclosed; as taught by Ruiz.)
determining, by a ML model and based at least on the one or more query features and the one or more content item features, a first probability of a first type of user-item interaction between the user and the content item when the content item is displayed on a user interface (UI) of a media device; (See paras. 81, 129-133, wherein determining probability user will select to interact with content item are disclosed, also See paras. 137-138, wherein determining first probability and second probability of user interaction between user and content item using machine learning technique are disclosed; as taught by Ruiz.)
determining, by a ML model and based at least on the one or more query features and the one or more content item features, a second probability of a second type of user-item interaction between the user and the content item when the content item is displayed on the UI of the media device; (See paras. 81, 129-133, wherein determining probability user will select to interact with content item are disclosed, also See paras. 137-138, wherein determining first probability and second probability of user interaction between user and content item using machine learning technique are disclosed; as taught by Ruiz.)
and calculating the ranking score for the content item based at least on the first probability and the second probability; (See Fig. 2, paras. 71-72, 105, wherein ranking of content items process are disclosed, also See paras. 102-103, 107-109, 142, wherein ordering content items by ranking-score are disclosed; as taught by Ruiz.)
and ranking the plurality of content items for presentation to the user based on the ranking score associated with each content item in the plurality of content items. (See Fig. 2, paras. 71-72, 75, wherein ranking of content items process are disclosed, also See paras. 107-109, 142, wherein ordering content items by ranking-score are disclosed; as taught by Ruiz.)
However, Ruiz fails to disclose providing input to a deep machine learning (ML) model, the input including at least one or more query features associated with the query and one or more content item features associated with the content item.
On the other hand, Paluri teaches providing input to a deep machine learning (ML) model, the input including at least one or more query features associated with the query and one or more content item features associated with the content item. (See Fig. 3, paras. 36-39, 44, wherein training deep-learning model and input content items of deep-learning model in which “identify one or more content items that are similar to a given content item (e.g., a search query) using a deep-learning model. Content items may include, as an example and not by way of limitation, text content (e.g., one or more n-grams), visual content (e.g., one or more images), audio content (e.g., one or more audio recordings), video content (e.g., one or more video clips), any other suitable type of content” [0036] are disclosed, also See Fig. 5, paras. 53, 55-58, 60, wherein utilizing deep-learning model process are disclosed; as taught by Paluri.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Paluri teachings in the Ruiz system. Skilled artisan would have been motivated to incorporate identifying content items using a deep-learning model taught by Paluri in the Ruiz system for effective quality scoring system. In addition, both of the references (Ruiz and Paluri) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as database management system. This close relation between both of the references highly suggests an expectation of success.
However, the combination of Ruiz and Paluri fails to disclose a multi-layer perceptron (MLP) of the deep ML model.
On the other hand, Specter teaches a multi-layer perceptron (MLP) of the deep ML model. (See Fig. 4, paras. 66, wherein machine learning model, linear regression models, such as, multi-task elastic net model, perceptron model are disclosed; as taught by Specter.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Specter teachings in the combination of Ruiz and Paluri system. Skilled artisan would have been motivated to incorporate system for associating dual-path resource locators with streaming content taught by Specter in the combination of Ruiz and Paluri system for effective quality scoring system. In addition, both of the references (Ruiz, Paluri, and Specter) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as database management system. This close relation between both of the references highly suggests an expectation of success.
As per claims 2, 8, 15, the combination of Ruiz, Paluri, and Specter discloses wherein the input further includes one or more user features associated with the user. (See paras. 9, 40, 133, wherein correlating content item with parameters of user’s profile are disclosed; as taught by Ruiz.)
As per claims 3, 9, and 16, the combination of Ruiz, Paluri, and Specter discloses wherein the plurality of content items comprise a plurality of recorded shows, and wherein the one or more content item features associated with the content item comprise one or more of: a content item identifier (ID) associated with the content item;
a content type associated with the content item; (See para. 66, wherein content items, such as news items, business-related items, sports-related items are disclosed; as taught by Ruiz.)
an audio language associated with the content item; (See paras. 18, 37, 55, wherein audio/visual content services are disclosed; as taught by Ruiz.)
an origin country associated with the content item;
a normalized representation of a number of times that a plurality of users have launched the content item for playback over a first predefined time period;
a normalized representation of a number of times that the plurality of users have interacted with the UI control associated with the content item to obtain information about the content item over a second predefined time period;
a weighted sum of (i) a click through rate (CTR) for the content item when the content item was presented in relation to a submission of the query and (ii) a CTR for the content item when the content item was presented generally, wherein each CTR represents a number of times the plurality of users have interacted with the UI control associated with the content item to obtain information about the content item divided by a number of times the content item was presented to the plurality of users; or
a weighted sum of (i) a long watch rate for the content item when the content item was presented in relation to a submission of the query and (ii) a long watch rate for the content item when the content item was presented generally, wherein each long watch rate represents a number of times the plurality of users watched the content item for longer than a third predefined time period.
As per claims 4, 10, and 17, the combination of Ruiz and Specter fails to disclose wherein the plurality of content items comprise a plurality of televised sporting events, and wherein the one or more content item features associated with the content item comprise one or more of: a sports league title associated with the content item; a live event or upcoming event classification associated with the content item; an identifier (ID) of a home team associated with the content item; an ID of an away team associated with the content item; a normalized representation of a number of times that a plurality of users have launched the content item for playback over a first predefined time period; a normalized representation of a number of times that the plurality of users have interacted with the UI control associated with the content item to obtain information about the content item over a second predefined time period; a normalized representation of a measure of similarity between the query and the sports league title associated with the content item; a normalized representation of a measure of similarity between the query and a name of the home team associated with the content item; a normalized representation of a measure of similarity between the query and a name of the away team associated with the content item; or a normalized representation of an amount of time to a start time associated with the content item.
On the other hand, Paluri wherein the plurality of content items comprise a plurality of televised sporting events, and wherein the one or more content item features associated with the content item comprise one or more of: a sports league title associated with the content item; (See paras. 30-31, wherein sports team, name, title, location of content items are disclosed; as taught by Paluri.)
a live event or upcoming event classification associated with the content item; (See paras. 22, 25, wherein events or calendar entries are disclosed; as taught by Paluri.)
an identifier (ID) of a home team associated with the content item;
an ID of an away team associated with the content item;
a normalized representation of a number of times that a plurality of users have launched the content item for playback over a first predefined time period; a normalized representation of a number of times that the plurality of users have interacted with the UI control associated with the content item to obtain information about the content item over a second predefined time period;
a normalized representation of a measure of similarity between the query and the sports league title associated with the content item;
a normalized representation of a measure of similarity between the query and a name of the home team associated with the content item;
a normalized representation of a measure of similarity between the query and a name of the away team associated with the content item; or
a normalized representation of an amount of time to a start time associated with the content item.
See claims 1, 7, and 14 for motivation above.
As per claims 5, 11, and 18, the combination of Ruiz and Paluri fails to disclose wherein the first type of interaction between the user and the content item comprises the user launching the content item for playback and the second type of interaction between the user and the content item comprises the user interacting with a UI control associated with the content item to obtain information about the content item.
On the other hand, Specter teaches wherein the first type of interaction between the user and the content item comprises the user launching the content item for playback and the second type of interaction between the user and the content item comprises the user interacting with a UI control associated with the content item to obtain information about the content item. (See Figs. 1, 4, 8, paras. 35, 59, 84, wherein playback features and user interaction with graphical user interface are disclosed; as taught by Specter.)
See claims 1, 7, and 14 for motivation above.
Claims 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ruiz et al. (U.S. PGPub 2015/0006280; hereinafter “Ruiz”) in view of Paluri et al. (U.S. PGPub 2017/0132510; hereinafter “Paluri”) and further in view of Specter et al. (U.S. PGPub 2023/0262103; hereinafter “Specter”) and further in view of Bai et al. (U.S. PGPub 2010/0185659; hereinafter “Bai”).
As per claims 6, 13, and 19, the combination of Ruiz, Paluri, and Specter fails to disclose wherein the generating the ranking score further comprises mapping, by the deep ML model, each of the one or more query features to a plurality of embedding vectors using a corresponding plurality of hash functions, each of the embedding vectors being of a dimension, and wherein the mapping comprises mapping each one of the one or more query features to two different embedding vectors using two different hash functions.
On the other hand, Bai teaches wherein the generating the ranking score further comprises mapping, by the deep ML model, each of the one or more query features to a plurality of embedding vectors using a corresponding plurality of hash functions, each of the embedding vectors being of a dimension, and wherein the mapping comprises mapping each one of the one or more query features to two different embedding vectors using two different hash functions. (See Fig. 6, paras. 7, 18, 34, wherein search engine rankings process are disclosed, also See paras. 36-38, 44-46, wherein embedding data process and hashing functions are disclosed; as taught by Bai.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Bai teachings in the combination of Ruiz, Paluri, and Specter system. Skilled artisan would have been motivated to incorporate supervised semantic indexing and its extensions taught by Bai in the combination of Ruiz, Paluri, and Specter system for effective quality scoring system. In addition, both of the references (Ruiz, Paluri, Specter, and Bai) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as database management system. This close relation between both of the references highly suggests an expectation of success.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ruiz et al. (U.S. PGPub 2015/0006280; hereinafter “Ruiz”) in view of Paluri et al. (U.S. PGPub 2017/0132510; hereinafter “Paluri”) and further in view of Specter et al. (U.S. PGPub 2023/0262103; hereinafter “Specter”) and further in view of Paul et al. (U.S. PGPub 2025/0156356; hereinafter “Paul”).
As per claim 12, the combination of Ruiz, Paluri, and Specter fails to disclose wherein the deep ML model comprises a modified version of a deep learning recommendation model (DLRM).
On the other hand, Paul teaches wherein the deep ML model comprises a modified version of a deep learning recommendation model (DLRM). (See para. 19, wherein deep learning recommendation model (DLRM) workloads are disclosed; as taught by Paul.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Paul teachings in the combination of Ruiz, Paluri, and Specter system. Skilled artisan would have been motivated to incorporate techniques to utilize near memory compute circuitry for memory-bound workloads taught by Paul in the combination of Ruiz, Paluri, and Specter system for effective quality scoring system. In addition, both of the references (Ruiz, Paluri, Specter, and Paul) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as database management system. This close relation between both of the references highly suggests an expectation of success.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Ruiz et al. (U.S. PGPub 2015/0006280; hereinafter “Ruiz”) in view of Paluri et al. (U.S. PGPub 2017/0132510; hereinafter “Paluri”) and further in view of Specter et al. (U.S. PGPub 2023/0262103; hereinafter “Specter”) and further in view of Voros et al. (U.S. PGPub 2022/0353284; hereinafter “Voros”).
As per claim 20, the combination of Ruiz, Paluri, and Specter fails to disclose wherein the mapping uses a MurmurHash non-cryptographic hash function with two different seed values.
On the other hand, Voros teaches wherein the mapping uses a MurmurHash non-cryptographic hash function with two different seed values. (See Fig. 12C, paras. 76, 106, 122, wherein MurmurHash, non-cryptographic hash function are disclosed; as taught by Voros.)
Therefore, it would have been obvious to a person of ordinary skill in the computer art before the effective filing date of the claimed invention to incorporate the Voros teachings in the combination of Ruiz, Paluri, and Specter system. Skilled artisan would have been motivated to incorporate method for using machine learning to classify malicious infrastructure taught by Voros in the combination of Ruiz, Paluri, and Specter system for effective quality scoring system. In addition, both of the references (Ruiz, Paluri, Specter, and Voros) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as database management system. This close relation between both of the references highly suggests an expectation of success.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Duan et al. (U.S. PGPub 2021/0312237) discloses click intention machine learned models.
2) Ramanath et al. (U.S. PGPub 2020/0004835) discloses generating candidates for search using scoring/retrieval architecture.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIN LIN M HTAY whose telephone number is (571)272-7293. The examiner can normally be reached on M-F, 7am-3pm, PST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached on (571)272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L. L. H./
Examiner, Art Unit 2153
/KAVITA STANLEY/Supervisory Patent Examiner, Art Unit 2153
