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 .
REMARKS
The Replacement drawing, filed January 28, 2026, has been acknowledged.
On pages 2-4, Applicant’s argument by claim amendment to overcome the 35 USC 102 and 103 prior art rejections in view of Buriano et al. based on previous citations and basis. The rejection has been withdrawn. However, Buriano et al. has been re-applied based on new citations and basis.
NON-FINAL
The drawing, filed January 28, 2026, has been accepted.
The amendment to the specification, filed January 28, 2026, has been accepted.
Claims 1 and 15-20 are cancelled.
Claims 2-14, filed January 28, 2026, are examined on the merits.
Claim 2 is objected to because the new limitations from claim 1 are not underlined. Appropriate correction is required.
Claim 21-26 are objected to because the listing of claims does not include the text of all pending claims (including withdrawn claims).
Claims 2-14, filed January 28, 2026, are examined on the merits.
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.
Claim(s) 2-14, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Buriano et al. (Buriano hereafter, US 2009/0234784 A1).
Claim 1, Buriano discloses a method comprising:
providing a data store comprising first data elements therein ([0030], e.g. the first set of content items is stored in a content item repository);
providing a first processing system having access to the data store ([0195], e.g. A content item may be delivered by the service provider directly or indirectly to the user, for example by providing an address (e.g. an Internet address) where the content item is located or can be accessed);
providing a first metadata store comprising first metadata elements therein relating to the first data elements (page 5, [0117], e.g. starting from numerical metadata, symbolic ranges may be generated through the use of discretization techniques that are able to group numerical values; this is a way of giving a more compact and semantic representation of numerical values);
providing second metadata comprising additional metadata for use in generating one or more predictive models, the second metadata including data derived from at least some of the first metadata and the first data elements (page 5, [0116], e.g. metadata can be derived (second) from any other metadata, and [0118], e.g. Metadata can be also derived through the use of ontologies. An ontology is the formalization of a conceptualization, using a machine-readable representation. Ontologies can be used to organize taxonomies and relationships among metadata; this allows a user preference model to be built onto higher-order semantic categories and concepts);
generating a predictive model based on at least the second metadata (page 5, [0167], e.g. Most machine learning methods use the feature vector representation. In case those methods are applied to build a predictive model of user preferences related to the provided content, each feature vector is associated to a content item comprises and [0168], e.g. independent features, each feature corresponding to a derived or a pre-assigned), wherein generating the predictive model is performed absent access to the first data elements ([0236], e.g. selecting an appropriate machine learning algorithm (STEP 302 in FIG. 3) adapted to build a predictive model of the user preferences based on the information contained in the interaction history. The predictive model of Buriano is performed with information contained in the interaction history which has been interpreted as “performed absent access to the first data elements” from the content repository cited in paragraph [0030].
Claim 3, Buriano discloses the additional metadata includes metadata relating to data stored outside the datastore (page 10, [0195], e.g. A content item may be delivered by the service provider directly or indirectly to the user, for example by providing an address (e.g. an Internet address) where the content item is located or can be accessed. Content items are generally provided directly by content providers through a communication network, such as a packet-data network (e.g., Internet) or a mobile telephone network (e.g. UTMTS network)).
Claim 4, Buriano discloses the additional metadata includes metadata relating to data stored on another computer system (page 10, [0195], e.g. A content item may be delivered by the service provider directly or indirectly to the user, for example by providing an address (e.g. an Internet address) where the content item is located or can be accessed. Content items are generally provided directly by content providers through a communication network, such as a packet-data network (e.g., Internet) or a mobile telephone network (e.g. UTMTS network)).
Claim 5, Buriano discloses discloses a method comprising: providing a data store comprising first data elements therein; providing a first processing system having access to the data store([0030], e.g. the first set of content items is stored in a content item repository);
providing a first metadata store comprising first metadata elements therein relating to the first data elements; providing second metadata comprising additional metadata for use in generating one or more predictive models, the second metadata including data derived from at least some of the first metadata and the first data elements (page 5, [0117], e.g. starting from numerical metadata, symbolic ranges may be generated through the use of discretization techniques that are able to group numerical values; this is a way of giving a more compact and semantic representation of numerical values); and generating a predictive model based on at least the second metadata, wherein the additional metadata includes metadata relating to data stored outside the datastore (page 5, [0167], e.g. Most machine learning methods use the feature vector representation. In case those methods are applied to build a predictive model of user preferences related to the provided content, each feature vector is associated to a content item comprises and [0168], e.g. independent features, each feature corresponding to a derived or a pre-assigned),
wherein the additional metadata includes metadata relating to data stored on another computer system (page 10, [0195], e.g. A content item may be delivered by the service provider directly or indirectly to the user, for example by providing an address (e.g. an Internet address) where the content item is located or can be accessed. Content items are generally provided directly by content providers through a communication network, such as a packet-data network (e.g., Internet) or a mobile telephone network (e.g. UTMTS network)), and wherein the another computer system is a computer system to which the first processing system does not have read access (It is noted that Buriano does not describe the first processing system needing read access to the another computer. Therefore, the discloses of Buriano has been interpreted as more likely than not having read access to “the another computer system.”)
Claim 6, Buriano discloses the another system provides metadata updates at intervals, the metadata updates for updating the second metadata ([0038] to [0044], e.g. in order to update the profile of a user the following steps are performed…Preferably, records include also user requests, raw context metadata and content metadata, derived and pre-assigned, associated to the content items…Said feature vector comprises a plurality of elements corresponding to the metadata, derived and (if any) pre-assigned, associated to a specific content item and comprises a user feedback. If derived metadata are not present in the record, they can be retrieved from the content item repository that stores all the content items of the first set and in general the content items selected following a query;…[0044] updating the profile of the above user by substituting the old predictive model with the new predictive model).
Claim 7, Buriano discloses the predictive model is based at least in part on data within the metadata updates ([0044], e.g. updating the profile of the above user by substituting the old predictive model with the new predictive model).
Claim 8, Buriano discloses the metadata updates comprise data derived from data on the another system and data derived from metadata on the another system, the metadata updates reflective of trend data for the data stored on the another system ([0038] to [0044], e.g. in order to update the profile of a user the following steps are performed…Preferably, records include also user requests, raw context metadata and content metadata, derived and pre-assigned, associated to the content items…Said feature vector comprises a plurality of elements corresponding to the metadata, derived and (if any) pre-assigned, associated to a specific content item and comprises a user feedback. If derived metadata are not present in the record, they can be retrieved from the content item repository that stores all the content items of the first set and in general the content items selected following a query).
Claim 9, Buriano discloses yet another system provides other metadata updates at intervals, the other metadata updates for updating the second metadata ([0038] to [0044], e.g. in order to update the profile of a user the following steps are performed…Preferably, records include also user requests, raw context metadata and content metadata, derived and pre-assigned, associated to the content items…Said feature vector comprises a plurality of elements corresponding to the metadata, derived and (if any) pre-assigned, associated to a specific content item and comprises a user feedback. If derived metadata are not present in the record, they can be retrieved from the content item repository that stores all the content items of the first set and in general the content items selected following a query).
Claim 10, Buriano discloses the other metadata updates comprise data derived from data on the yet another system and data derived from metadata on the yet another system, the other metadata updates reflective of trend data for the data stored on the yet another system ([0038] to [0044], e.g. in order to update the profile of a user the following steps are performed…Preferably, records include also user requests, raw context metadata and content metadata, derived and pre-assigned, associated to the content items).
Claim 11, Buriano discloses different trend data is used to predict future events ([0174], e.g. data set (to be processed by machine learning methods in order to build the predictive models of the user profiles) preferably comprises content metadata (authored and derived) and preferably context metadata (raw and derived), as independent features. The user feedback is the target feature. The goal of the machine learning methods is to find a model (referred to as user model or predictive model) predicting user preferences, i.e. a machine learning model expressing the relationships between the metadata and the user feedback. The predictive model thus obtained can then be used for estimating the user's evaluation with respect to new content items (providing new metadata) when they become available).
Claim 12, Buriano discloses the second metadata includes context data for supporting predictive models within different contexts ([0076], e.g. interaction context is formed by different aspects. Typically, the most important aspects are: "date and time" (when the interaction takes place), "user location" (where the interaction takes place), "interaction device" (used by the user for the interaction), "content channel" (through which the interaction takes place), "environment state" (during the interaction), "physical world state" (during the interaction), "user state" (during the interaction)).
Claim 13, Buriano discloses different contexts comprise at least one of temporal, geographic and event-based context ([0076], e.g. interaction context is formed by different aspects. Typically, the most important aspects are: "date and time" (when the interaction takes place), "user location" (where the interaction takes place), "interaction device" (used by the user for the interaction), "content channel" (through which the interaction takes place), "environment state" (during the interaction), "physical world state" (during the interaction), "user state" (during the interaction)).
Claim 14, Buriano discloses different contexts comprise at least one of demographic and financial context ([0121], e.g. the classification of date values in "working-day").
PERTINENT PRIOR ART
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Husain et al. (US 20180285748 A1) disclose machine learning model 122 is an analytical predictive model built from sample inputs that produces reliable, repeatable decisions and results and may uncover hidden insights through learning from historical relationships and trends in the stored information describing the delivery of the content items and feature vectors extracted from the content items ([0042]).
Kapoor et al. (US 11521077 B1) discloses Model Manager is a computerized module to capture the predictive model metadata for building machine learning model and deploying the predictive model in production. The metadata includes a technical library on which the predictive model is to be created, training data on which the predictive model is to be trained, a machine learning algorithm that is used for generating the predictive model, hyperparameters and their values for the machine learning algorithm and algorithm scripts for customizing the predictive model (column 2, lines 38-50).
CONCLUSION
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Dune Ly, whose telephone number is (571) 272-0716. The examiner can normally be reached on Monday-Friday from 8 A.M. to 4 PM ET.
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/Cheyne D Ly/
Primary Examiner, Art Unit 2152
5/25/2026
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