DETAILED ACTION
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 .
Claims 1-20 are present in this application. Claims 1-20 are pending in this office action.
Drawings
The drawings received on 28 May 2024 are accepted by the Examiner.
This Office Action is Non-Final.
Specification Objection
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 28, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Claims rejection 35 U.S.C. 103
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 1-3, 5-7, 8-10, 12-14, 15-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Le et al. (US 20240061504 A1) in view of Kuperman et al. (US 20160182672 A1) further in view of Patel et al. (US 20230060575 A1).
Regarding claims 1, 8 and 15 Le discloses a system for storage cache personalization and object generation using advanced computational models for data analysis and automated processing, the system comprising: a processing device; a non-transitory storage device containing instructions that, when executed by the processing device, causes the processing device to perform the steps of:
receive a user interaction from a user account, wherein the user interaction comprises transaction details associated with a transaction, and wherein the transaction is initiated by a user via a user device (see Le paragraph [0049], In relation to Block S120, types of interactions with content can include: an introductory interaction (e.g., a users initial interaction with content that has not previously been presented to the user), a repeat interaction (e.g., an interaction with content that the user has previously engaged with), a reference interaction (e.g., a user interaction with reference content), and/or any suitable type of interaction. An interaction can be associated with timeframes corresponding to the entirety or a portion of the user's interaction with the content or portions of the content. For example, an interaction can be associated with the duration that the user is on a particular webpage. However, interactions can be associated with any suitable timeframe);
transmit the user interaction to a service layer, wherein the service layer comprises a personalization knowledge model and a bronze storage, wherein the bronze storage comprises unmodified data associated with the user interaction (see Le paragraph [0073], datasets can be collected corresponding to the first and the second user interactions within a same user session with the content-providing source, where the first interaction is with unmodified content, and the second interaction is with tailored content. As such, collecting a dataset S150 and/or other portions of the method 100 can be performed in real-time, non-real time, or in any suitable fashion);
determine a candidate element, wherein the candidate element is based on a preference of the user (see Le paragraph [0079], content tailoring in Block S150 is preferably based on analysis of cognitive state data associated with a user's interaction with unmodified and/or reference content. The analysis associated with providing tailored content can include comparing cognitive state data with user baselines (e.g., baseline cognitive state metrics, baseline bioelectrical signal data, etc.), comparing cognitive state metrics captured at different timeframes, processing with supplemental data and/or content stream data, employing machine learning models (e.g., based on values of the cognitive state data, selecting certain forms of advertising known to have induced certain cognitive states of the user, etc.), applying thresholds (e.g., if a cognitive state metric exceeds a threshold value for user excitement with the reference content, then deliver similar content to the reference content, etc.), and/or any suitable analysis approach);
Kuperman expressly discloses transmit the candidate element to a cache compute engine, wherein the cache compute engine builds a personalized object using the candidate element… transmit the personalized object to a memory (see Kuperman paragraph [0022], the proxy server may store the user identifier in association the tag identifier locally and/or separate from the personalized cache such that the proxy server maintains a record of the cached dynamic content. The user identifier references the cached dynamic content with the user account and/or user device it is specific to).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Kuperman into the method of Le have the cache compute engine builds a personalized object using the candidate element. Here, combining Kuperman with Le, which are both related to data processing, improves Le, by providing systems and methods that supports caching dynamic content specific to a user at the edge of a network to decrease loading times for web pages (see Kuperman paragraph [0003]).
Patel expressly discloses a memory fabric layer, wherein the memory fabric layer is near a user interface of the user device (see Patel paragraph [0027], the multi-tenant cloud environment includes host 210. On host 210, there is host running multiple applications 211, including A, B, C, D, E, and F. Host 210 performs I/O operations to storage system 220, through common queue layer 212 and NVMeF layer 213. After a successful NVMe fabric connection between NVMeF layer).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Patel into the method of Le have receiving a plurality of sub-results, each of the plurality of sub-results provided by one of the plurality of PNM apparatuses. Here, combining Patel with Le, which are both related to data processing, improves Le, by providing systems and methods that supports multiple cores process I/O requests simultaneously, system performance increases due to optimal utilization of CPU resource (see Patel paragraph [0003]).
Regarding claims 2, 9 and 16 Le discloses wherein the transaction details comprise interaction events and session objects associated with the user interaction (see Le paragraph [0073], datasets can be collected corresponding to the first and the second user interactions within a same user session with the content-providing source, where the first interaction is with unmodified content, and the second interaction is with tailored content. As such, collecting a dataset S150 and/or other portions of the method 100 can be performed in real-time, non-real time, or in any suitable fashion).
Regarding claims 3, 10 and 17 Kuperman discloses wherein transmitting the candidate element to a cache compute engine further comprises proactively pulling the candidate element into a silver storage, wherein the silver storage comprises the personalization knowledge module enriching the candidate element (see Kuperman paragraph [0020], Examples of dynamic content include images, text, audio, video, etc. in a web page that are user specific—such as those included in a shopping cart, targeted recommendation, targeted advertisement, and account information specific (or personalized) for the user on the web page. Dynamic content within the web page is wrapped with a dynamic content tag registered with the proxy server).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Kuperman into the method of Le have the cache compute engine builds a personalized object using the candidate element. Here, combining Kuperman with Le, which are both related to data processing, improves Le, by providing systems and methods that supports caching dynamic content specific to a user at the edge of a network to decrease loading times for web pages (see Kuperman paragraph [0003]).
Regarding claims 5, 12 and 19 Kuperman expressly discloses wherein the personalization knowledge module further comprises generating personalization context of the candidate elements, wherein the personalization context comprises: transactional updates, wherein the transactional updates comprise events associated with the user interaction; common updates, wherein the common updates comprise general configurations associated with the system; and product rules (The proxy server may subsequently receive the placeholder request for the updated version of the dynamic content. The placeholder request includes the tag identifier and user identifier such that the proxy server may retrieve the referenced updated version of the cached dynamic content once it has been received from the origin server and stored in the personalized cache. In turn, the proxy server generates a placeholder response that includes the updated version of the cached dynamic content. Once the user device receives the updated version of the cached dynamic content for the placeholder, the updated version of the dynamic content is inserted for display on a screen of the user device).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Kuperman into the method of Le have the cache compute engine builds a personalized object using the candidate element. Here, combining Kuperman with Le, which are both related to data processing, improves Le, by providing systems and methods that supports caching dynamic content specific to a user at the edge of a network to decrease loading times for web pages (see Kuperman paragraph [0003]).
Patel expressly discloses wherein the product rules comprise configurations of products associated with the bronze storage (see Patel paragraph [0027], the multi-tenant cloud environment includes host 210. On host 210, there is host running multiple applications 211, including A, B, C, D, E, and F. Host 210 performs I/O operations to storage system 220, through common queue layer 212 and NVMeF layer 213. After a successful NVMe fabric connection between NVMeF layer; see Patel paragraph [0019], applications C and D as silver customer(s) who have paid moderate amount for storage space and performance, and applications E and F as bronze customer(s) who have paid low for storage space and performance. The storage space mentioned here is the amount of storage space from backend physical storage (drives) 123. Although gold, silver, and bronze customers have paid different amount but all of them suffer the performance degrade. The gold customers who have paid the highest amount also suffers the performance issue due to cache miss as the current cache association is consumed by silver or bronze applications I/O queues).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Patel into the method of Le have receiving a plurality of sub-results, each of the plurality of sub-results provided by one of the plurality of PNM apparatuses. Here, combining Patel with Le, which are both related to data processing, improves Le, by providing systems and methods that supports multiple cores process I/O requests simultaneously, system performance increases due to optimal utilization of CPU resource (see Patel paragraph [0003]).
Regarding claims 6, 13 and 20 Kuperman discloses wherein the cache compute engine further comprises building the personalized object based on the personalization context provided by the personalization knowledge module (see Kuperman paragraph [0021], In order to service subsequent requests with cached content, having retrieved the requested online content from the origin server the proxy server parses out tagged dynamic content within the online content and stores the dynamic content within a personalized cache while non-tagged static content within the online content may be stored within a static cache).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Kuperman into the method of Le have the cache compute engine builds a personalized object using the candidate element. Here, combining Kuperman with Le, which are both related to data processing, improves Le, by providing systems and methods that supports caching dynamic content specific to a user at the edge of a network to decrease loading times for web pages (see Kuperman paragraph [0003]).
Regarding claims 7 and 14 Patel expressly discloses, wherein the memory fabric layer comprises a first layer of interaction for the user device (see Patel paragraph [0027], the multi-tenant cloud environment includes host 210. On host 210, there is host running multiple applications 211, including A, B, C, D, E, and F. Host 210 performs I/O operations to storage system 220, through common queue layer 212 and NVMeF layer 213. After a successful NVMe fabric connection between NVMeF layer).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Patel into the method of Le have receiving a plurality of sub-results, each of the plurality of sub-results provided by one of the plurality of PNM apparatuses. Here, combining Patel with Le, which are both related to data processing, improves Le, by providing systems and methods that supports multiple cores process I/O requests simultaneously, system performance increases due to optimal utilization of CPU resource (see Patel paragraph [0003]).
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Le et al. (US 20240061504 A1) in view of Kuperman et al. (US 20160182672 A1) further in view of Patel et al. (US 20230060575 A1) further in view of Sadr et al. (US 20240330381 A1).
Regarding claims 4, 11 and 18 Le discloses see Kuperman paragraph [0062], applying a machine learning model can to various types of input data in order to output an appropriate cognitive state metric indicating the cognitive state of a user at a given timeframe.
Sadr expressly discloses wherein the personalization knowledge module further comprises a large language model (LLM), and wherein the LLM proactively determines the candidate element based on historical user interactions (see Sadr paragraph [0080], the model-generated user-specific terms 212 can be generated by machine-learned models (e.g., LLM) based user personalization data, search engine data, and merchant data. The user personalization data can include explicit personalization that is user generated and/or user controlled. Additionally, the user personalization data can include interest graph data. The search engine data can include fashion knowledge data, recent trends data, and implicit personalization data. The implicit personalization can be based on a plurality of characteristics derived from the user (e.g., time, third-party data, location, intent). The merchant data can include merchant assets data. In some instances, the user personalization data, search engine data, and merchant data can be inputted into a machine-learned model (e.g., large language model (LLM)) to generate user-specific terms for a user).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Sadr into the method of Le have a large language model (LLM), and wherein the LLM proactively determines the candidate element based on historical user interactions. Here, combining Sadr with Le, which are both related to data processing, improves Le, by providing systems and methods that supports using a machine-learned model to generate user-specific terms and inputting the user-specific terms to a text-to-image machine-learned model to generate image content items (see Sadr paragraph [0003]).
Remarks
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chaman et al. (US 20060047643 A1) discloses the Personalization Object Index 90, that creates a personalization object for each entity. The Personalization Object is comprised of the Root Set and Extended Set of the User and refreshes it on a periodic basis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DINKU W GEBRESENBET whose telephone number is (571)270-1636. The examiner can normally be reached between 8:00AM-5:00PM.
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/DINKU W GEBRESENBET/Primary Examiner, Art Unit 2164
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