DETAILED ACTION
This action is in response to the claims filed 03/28/2024 for Application number 18/620,668. Claims 1-20 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/28/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered 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.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
generating, [using a first machine learning model, based on the first data structure and content viewing information corresponding to a first term for providing content recommendations], a second data structure that summarizes user content preference information over the first term for providing content recommendations can be considered to be an evaluation in the human mind
generating, [using a second machine learning model and based on the second data structure], one or more content recommendations can be considered to be an evaluation in the human mind
and updating, based on the second data structure, the first data structure can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “by a computing device”, “using a first machine learning model, based on the first data structure and content viewing information corresponding to a first term for providing content recommendations”, and “using a second machine learning model and based on the second data structure”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). 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 claim further recites: receiving, by a computing device, a first data structure that summarizes user content preference information associated with historical content viewing information of a user;
outputting the one or more content recommendations;
These limitations are mere data gathering and outputting steps and thus are insignificant extra-solution activities. 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 claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do 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 elements of utilizing a computing device, using a first machine learning model, and using a second machine learning model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the limitations of receiving, by a computing device, a first data structure that summarizes user content preference information associated with historical content viewing information of a user and outputting the one or more content recommendations are well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”.
These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: extracting user content preference information from the historical content viewing information, wherein the extracted user content preference information corresponds to one or more content items recommended to a user within the first term; and generating, based on determining one or more correlations between the one or more content items and one or more portions of the first data structure, a summary of user content preference information over the first term. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: determining, based on comparing the second data structure to updated content viewing information that corresponds to a second term for providing content recommendations, one or more correlations between the updated content viewing information and the second data structure;
determining, based on the one or more correlations, a set of content attributes; and
selecting, from one or more content repositories, one or more content items corresponding to the set of content attributes. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: modifying, based on comparing the second data structure to the first data structure, one or more portions of the first data structure to match one or more corresponding portions of the second data structure. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
compressing the first data structure to maintain a memory usage associated with the first data structure. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: modifying, based on contextual information, the one or more content recommendations, wherein the contextual information comprises one or more of: a time of day associated with the one or more content recommendations, an event associated with the one or more content recommendations, a user profile associated with the one or more content recommendations, a geographic location associated with the one or more content recommendations, or a geographic location associated with a computing device receiving the one or more content recommendations. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein outputting the one or more content recommendations comprises: inserting, into a content lineup, the one or more content recommendations. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application. The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: sending, to an intermediary computing device, the one or more content recommendations, wherein sending the one or more content recommendations causes the intermediary computing device to provide a content lineup to a second computing device. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application. The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: receiving, based on outputting the one or more content recommendations, one or more credits for outputting the one or more content recommendations. This limitation is an insignificant extra-solution activity and thus the judicial exception is not integrated into a practical application. The claim as a whole is directed to an abstract idea.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the one or more content recommendations comprise recommendations for one or more of: movie content, episodic content, advertising content, video game content, or audio content. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 10, it recites features similar to claim 1 and is rejected for at least the same reasons therein.
Regarding claim 11, the rejection of claim 10 is further incorporated, and further, the claim recites: determining, based on outputting the one or more content recommendations, updated content viewing information; and updating, based on the updated content viewing information and the updated first data structure, the first machine learning model. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 12, the rejection of claim 10 is further incorporated, and further, the claim recites: determining, based on outputting the one or more content recommendations, updated content viewing information corresponding to the one or more content recommendations; and updating, based on the updated content viewing information, the second machine learning model. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding Claim 13, it recites features similar to claim 4 and is rejected for at least the same reasons therein.
Regarding Claim 14, it recites features similar to claim 8 and is rejected for at least the same reasons therein.
Regarding Claim 15, it recites features similar to claim 1 and is rejected for at least the same reasons therein.
Regarding claim 16, the rejection of claim 15 is further incorporated, and further, the claim recites: wherein the user content preference report comprises: the one or more content recommendations, one or more historical content recommendations, and content viewing information corresponding to the one or more historical content recommendations. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 17, the rejection of claim 15 is further incorporated, and further, the claim recites: wherein outputting the user content preference report comprises: determining, based on the one or more content recommendations, one or more content providers associated with the user content preference report; This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim additionally recites: and sending, to the one or more content providers associated with the user content preference report, the user content preference report.
The claim does not include any additional elements that amount to significantly more than the judicial exception. This limitation is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 18, it recites features similar to claim 4 and is rejected for at least the same reasons therein.
Regarding Claim 19, it recites features similar to claim 6 and is rejected for at least the same reasons therein.
Regarding Claim 20, it recites features similar to claim 8 and is rejected for at least the same reasons therein.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-12, 14-17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kadam et al. ("US 20210271967 A1", hereinafter "Kadam").
Regarding claim 1, Kadam teaches A method comprising:
receiving, by a computing device, a first data structure that summarizes user content preference information associated with historical content viewing information of a user (“The system may receive information corresponding to content consumption. The information corresponding to content consumption may also be referred to, herein, as content consumption data. In some embodiments, the information may include information about user activity corresponding to content consumption (i.e. user activity data).” [¶0008]);
generating, using a first machine learning model, based on the first data structure and content viewing information corresponding to a first term for providing content recommendations, a second data structure that summarizes user content preference information over the first term for providing content recommendations (“For example, the personalized model may be used to output recommendations similar to Game of Thrones such as Lord of the Rings. The content recommendations may include previously consumed and unconsumed content. (“second data structure”) For example, the recommendations may be provided at a time when a user may enjoy consuming the content. For example, a user may have watched Game of Thrones over a year ago. The system, using the personalized model, may determine the user might enjoy rewatching Game of Thrones after a year based on the activity data. (corresponds to “a first term”) The personalized model may be used to generate Game of Thrones for recommendation based on that determination.” [¶0010]);
generating, using a second machine learning model and based on the second data structure, one or more content recommendations (“A second updated model may be generated based on the additional information and on the first updated model. In some embodiments, the second updated model is the personalized model which is updated using the additional information corresponding to content consumption.” [¶0012]);
outputting the one or more content recommendations (“For example, the system may use the second personalized model to generate recommendations similar to any one or more content item consumed in the content consumption data. The additional content recommendations may be a second set of content recommendations. The system may cause to be provided the second content recommendations.” [¶0013]); and
updating, based on the second data structure, the first data structure (“FIG. 2 shows an illustrative block diagram of a system 200 using a model to generate content recommendations and continually update the model based on new content consumption data to generate improved content recommendations, in accordance with some embodiments of the disclosure.” [¶0046]).
Regarding claim 2, Kadam teaches The method of claim 1, wherein generating the second data structure comprises: extracting user content preference information from the historical content viewing information, wherein the extracted user content preference information corresponds to one or more content items recommended to a user within the first term (“At 1402, a preferred portion of a content item may be determined based on information about consumption of portions of content items (i.e. content portion consumption data 1404). For example, a user prefers watching a battle scene from Lord of the Rings based on content portion consumption data 1404. In some embodiments, a system may determine a preferred portion of a content item based on user activity (corresponds to “extracting”) corresponding to the content item.” [¶0116]); and
generating, based on determining one or more correlations between the one or more content items and one or more portions of the first data structure, a summary of user content preference information over the first term (“The information about content consumption may include information about consumption of portions of content items. A system may generate content recommendations using the trained model. In some embodiments, the system may generate content portion recommendations based on content recommendations and on information about consumption of portions of content items. The system may provide the content portion recommendations. For example, Lord of the Rings may be recommended using any of the models described in the present disclosure based on content consumption data. A system may generate and provide scene recommendations from Lord of the Rings and/or a similar series like Game of Thrones based on the content consumption data.” [¶0020]).
Regarding claim 3, Kadam teaches The method of claim 1, wherein generating the one or more content recommendations comprises:
determining, based on comparing the second data structure to updated content viewing information that corresponds to a second term for providing content recommendations, one or more correlations between the updated content viewing information and the second data structure (“In some embodiments, a system may determine a portion of a content item is preferred based on information about content consumption. For example, a system may determine a scene of Lord of the Rings is preferred if a user repeatedly watches the scene. A portion may be preferred based on a consumption preference associated with a profile. A consumption preference may include content genres and/or content lengths. A system may generate content portion recommendations based on the preferred portion.” [¶0021]);
determining, based on the one or more correlations, a set of content attributes (“In some embodiments, portions (e.g. scenes) of interest may be recommended which may be filtered content from previously consumed content. That filtered content may be sorted and/or ranked based on a variety of criteria (e.g. by preferred content themes and/or genres based on content consumption data 108) and may be recommended.” [¶0044]); and
selecting, from one or more content repositories, one or more content items corresponding to the set of content attributes. (“In some embodiments, portions (e.g. scenes) of interest may be recommended which may be filtered content from previously consumed content. That filtered content may be sorted and/or ranked based on a variety of criteria (e.g. by preferred content themes and/or genres based on content consumption data 108) and may be recommended.” [¶0044; filtered corresponds to “selecting”])
Regarding claim 5, Kadam teaches The method of claim 1, further comprising: modifying, based on contextual information, the one or more content recommendations, wherein the contextual information comprises one or more of: a time of day associated with the one or more content recommendations, an event associated with the one or more content recommendations, a user profile associated with the one or more content recommendations, a geographic location associated with the one or more content recommendations, or a geographic location associated with a computing device receiving the one or more content recommendations. (“In some embodiments, trained model 1202 may have been updated based on information about content consumption associated with a profile.” [¶0112; note the claims recite “one or more of” therefore under BRI, the examiner is only required to map to one of recited elements])
Regarding claim 6, Kadam teaches The method of claim 1, wherein outputting the one or more content recommendations comprises: inserting, into a content lineup, the one or more content recommendations. (“For example, the ordering of the recommendations may be from most preferred genres to least preferred genres. For example, a system may recommend content such as Lord of the Rings (fantasy), the Tudors (drama), and Spartacus (action). Content consumption data may have indicated a user prefers action over fantasy and fantasy over drama. In this non-limiting example, the system may generate a preferred genre ranking of 1) action, 2) fantasy, and 3) drama. The system may cause to provide the content recommendations in the order of 1) Spartacus, 2) Lord of the Rings, and 3) Tudors.” [¶0104])
Regarding claim 7, Kadam teaches The method of claim 1, wherein outputting the one or more content recommendations comprises:
sending, to an intermediary computing device, the one or more content recommendations, wherein sending the one or more content recommendations causes the intermediary computing device to provide a content lineup to a second computing device (“In some embodiments, guidance data from media guidance data source 718 may be provided to users' equipment using a client-server approach. For example, a user equipment device may pull media guidance data from a server, or a server may push media guidance data to a user equipment device. In some embodiments, a guidance application client residing on the user's equipment may initiate sessions with source 718 to obtain guidance data when needed (e.g. when the guidance data is out of date or when the user equipment device receives a request from the user to receive data).” [¶0078]).
Regarding claim 8, Kadam teaches The method of claim 1, further comprising: receiving, based on outputting the one or more content recommendations, one or more credits for outputting the one or more content recommendations. (“For example, the subscription data may identify to which sources or services a given user subscribes and/or to which sources or services the given user has previously subscribed but later terminated access (e.g., whether the user subscribes to premium channels, whether the user has added a premium level of services, whether the user has increased Internet speed). In some embodiments, the viewer data and/or the subscription data may identify patterns of a given user for a period of more than one year.” [¶0079; subscription corresponds to credit])
Regarding claim 9, Kadam teaches The method of claim 1, wherein the one or more content recommendations comprise recommendations for one or more of: movie content, episodic content, advertising content, video game content, or audio content. (“For example, Lord of the Rings may be recommended using any of the models described in the present disclosure based on content consumption data. A system may generate and provide scene recommendations from Lord of the Rings and/or a similar series like Game of Thrones based on the content consumption data.” [¶0020])
Regarding claim 10, it is substantially similar to claim 1 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 11, Kadam teaches The method of claim 10, further comprising: determining, based on outputting the one or more content recommendations, updated content viewing information (“In some embodiments, the media guidance data may include viewer data. For example, the viewer data may include current and/or historical user activity information (e.g., what content the user typically watches, what times of day the user watches content, whether the user interacts with a social network, at what times the user interacts with a social network to post information, what types of content the user typically watches (e.g., pay TV or free TV), mood, brain activity information, etc.)… ” [¶0079]); and
updating, based on the updated content viewing information and the updated first data structure, the first machine learning model. (“A personalized model may be generated by updating the trained model with content consumption data from when a user watched Game of Thrones. In some embodiments, the personalized model may be a first updated model.” [¶0009])
Regarding claim 12, Kadam teaches The method of claim 10, further comprising: determining, based on outputting the one or more content recommendations, updated content viewing information corresponding to the one or more content recommendations (“In some embodiments, the media guidance data may include viewer data. For example, the viewer data may include current and/or historical user activity information (e.g., what content the user typically watches, what times of day the user watches content, whether the user interacts with a social network, at what times the user interacts with a social network to post information, what types of content the user typically watches (e.g., pay TV or free TV), mood, brain activity information, etc.)… ” [¶0079]); and
updating, based on the updated content viewing information, the second machine learning model. (“Personalized model 302 and activity data 308 may be used to generate an updated personalized model 308. Updated personalized model 310 may be a second updated model.” [¶0050])
Regarding claim 14, it is substantially similar to claim 8 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 15, it is substantially similar to claims 1 and 10 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 16, Kadam teaches The method of claim 15, wherein the user content preference report comprises: the one or more content recommendations (“The system may generate content recommendations using the personalized model. For example, the personalized model may be used to output recommendations similar to Game of Thrones such as Lord of the Rings. The content recommendations may include previously consumed and unconsumed content.” [¶0010]), one or more historical content recommendations (“The content recommendations may include previously consumed and unconsumed content.” [¶0010])), and content viewing information corresponding to the one or more historical content recommendations (“For example, the activity data may include skipped scenes or repeated watching of a scene in Game of Thrones. In some embodiments, the information corresponding to content consumption may be associated with a profile. For example, a user may have watched and enjoyed an action scene in a series such as Game of Thrones. The corresponding activity indicating enjoyment of watching the action scene may be received by the system and saved in a profile.” [¶0008]).
Regarding claim 17, Kadam teaches The method of claim 15, wherein outputting the user content preference report comprises:
determining, based on the one or more content recommendations, one or more content providers associated with the user content preference report (“Content and/or media guidance data delivered to user equipment devices 702, 704, and 706 may be over-the-top (OTT) content.” [¶0081]); and
sending, to the one or more content providers associated with the user content preference report, the user content preference report. (“OTT content delivery allows Internet-enabled user devices, including any user equipment device described above, to receive content that is transferred over the Internet, including any content described above, in addition to content received over cable or satellite connections.” [¶0081])
Regarding claim 19, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 20, it is substantially similar to claim 8 respectively, and is rejected in the same manner, the same art, and reasoning applying.
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.
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.
Claims 4, 13, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kadam in view of Chen et al. ("Learning Elastic Embeddings for Customizing On-Device Recommenders", hereinafter "Chen").
Regarding claim 4, Kadam teaches The method of claim 1, wherein updating the first data structure comprises: modifying, based on comparing the second data structure to the first data structure, one or more portions of the first data structure to match one or more corresponding portions of the second data structure (“In this manner, a recommendations engine may generate content recommendations by determining whether a content item matches the criteria based on the weights.” [¶0111]); and
However fails to explicitly teach compressing the first data structure to maintain a memory usage associated with the first data structure.
Chen teaches compressing the first data structure to maintain a memory usage associated with the first data structure. (“A common practice in building compact on-device recommender systems is to compress their embeddings which are normally the cause of excessive parameterization.” [Abstract])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Kadam’s teachings in order to compress embeddings to maintain memory usage as taught by Chen. One would have been motivated ot make this modification for allowing efficient specialization of elastic embeddings under any memory constraint for on-device recommendation. [Abstract, Chen]
Regarding claim 13, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding claim 18, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
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