DETAILED ACTION
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Lu et al. (US 2022/0277204 A1).
For claim 1, Lu et al. teaches:
A method for recommending data, comprising: obtaining first feature data of an object [feature data extracted, 0049: Lu], the first feature data comprises a plurality of feature dimensions of the object [first feature extraction layer with vector including proximity information, 0049-0051; proximity information having many dimensional information including sequencing and ID, 0037: Lu]; obtaining a permission type for using the first feature data, the permission type specifying a portion of the first feature data allowed to be used in data recommendation [when receiving pre-determined training instruction and user requested, giving recommendation, 0006; for first feature 0049: Lu], the permission type indicates at least a portion of the plurality of feature dimensions [proximity data vector that is shared has only id and sequencing for a recommended data for users, 0037 and 0051: Lu]; updating the first feature data based on the permission type to generate second feature data [first feature data used to generate second feature data, 0051-0052: Lu]; and determining, from a data set comprising a plurality of data items, a group of data items matching the second feature data based on the second feature data [result from estimated, preset and recommended result from a set, 0039: Lu].
For claim 2, Lu et al. teaches:
The method of claim 1, wherein generating the second feature data comprises: generating the second feature data based on the at least a portion of the plurality of feature dimensions [second feature from feature vector, 0051-0052: Lu].
For claim 3, Lu et al. teaches:
The method of claim 1, wherein the at least a portion of the plurality of feature dimensions comprises at least one of: first type data associated with the object, the first type data comprising first type data within the data set and first type data beyond the data set; and second type data associated with the object [having two different feature data, 0055: Lu].
For claim 4, Lu et al. teaches:
The method of claim 2, further comprising: determining region information corresponding to the object; and determining the at least a portion of the plurality of feature dimensions based on the region information [calculating feature data based on vectoring, 0051: Lu].
For claim 5, Lu et al. teaches:
The method of claim 1, wherein determining the group of data items comprises: determining, based on the permission type, a request type of a query request for querying the data set; and obtaining, from the plurality of data items, the group of data items matching the request type [data related to behavior of user used to determine recommendation, 0028: Lu].
For claim 6, Lu et al. teaches:
The method of claim 5, wherein a data item of the plurality of data items has a data type indicating an association relationship between the data item and the query type, and obtaining the group of data items comprises: in response to determining that the association relationship indicates the data type of the data item matching the query type, adding the data item to the group of data items [adding that are associated to the recommended pre-determined data model, 0006: Lu].
For claim 7, Lu et al. teaches:
The method of claim 6, wherein the plurality of data items is provided by at least one data provider and the data type of the data items is set based on configuration data from the at least one data provider of the plurality of data items [device configured to provide data items for user behavior, 0007: Lu].
For claim 8, Lu et al. teaches:
The method of claim 5, further comprising: generating the query request for querying the data set based on the second feature data; determining, using a recommendation model, a data item matching the query request; and updating the group of data items based on the data item [training unit to add to library based on recommendation modeling, 0007: Lu].
For claim 9, Lu et al. teaches:
The method of claim 8, wherein the recommendation model is determined based on reference feature data of a reference object, the reference feature data comprising the plurality of feature dimensions [reference recommendation results based on vectors, 0054-0058: Lu].
For claim 10, Lu et al. teaches:
The method of claim 2, wherein a first number of dimensions of the first feature data is the same as a second number of dimensions of the second feature data, and feature dimensions other than the at least a portion of the plurality of feature dimensions in the second feature data are set to null [feature vector used to gather first feature data to second feature data using proximity information as necessary, 0051-0056: Lu].
Claim 11 is a system of the method taught by claim 1. Lu et al. teaches the limitations of claim 1 for the reasons stated above.
Claim 12 is a system of the method taught by claim 2. Lu et al. teaches the limitations of claim 2 for the reasons stated above.
Claim 13 is a system of the method taught by claim 3. Lu et al. teaches the limitations of claim 3 for the reasons stated above.
Claim 14 is a system of the method taught by claim 4. Lu et al. teaches the limitations of claim 4 for the reasons stated above.
Claim 15 is a system of the method taught by claim 5. Lu et al. teaches the limitations of claim 5 for the reasons stated above.
Claim 16 is a system of the method taught by claim 6. Lu et al. teaches the limitations of claim 6 for the reasons stated above.
Claim 17 is a system of the method taught by claim 7. Lu et al. teaches the limitations of claim 7 for the reasons stated above.
Claim 18 is a system of the method taught by claim 8. Lu et al. teaches the limitations of claim 8 for the reasons stated above.
Claim 19 is a system of the method taught by claim 9. Lu et al. teaches the limitations of claim 9 for the reasons stated above.
Claim 20 is a computer readable medium of the method taught by claim 1. Lu et al. teaches the limitations of claim 1 for the reasons stated above.
Response to Arguments
Applicant's arguments filed January 22, 2026 have been fully considered and the arguments do not overcome the 35 U.S.C. 102 rejection.
Applicant argues that Lu et al. (US 2022/0277204 A1) does not teach “obtaining
a permission type for using the first feature data”, “updating the first feature data based on the permission type to generate second feature data”, “determining, from a data set comprising a plurality of data items” and “the first feature data comprising a plurality of feature dimensions of the object, ... the permission type indicating at least a portion of the plurality of feature dimensions”. The amended language is addressed in detail in the rejection above with updated referencing.
Lu et al. teaches first feature extracting, with various vectors, with recommended
models of data related to the first feature available to be obtained [0037 and 0049-0052: Lu]. The reference then teaches the updating of the first feature data through a calculation layer that extracts and is configured to calculate feature data corresponding to the feature vector with the recommended model data [0052: Lu]. Lu et al. continues to teach using the reference recommendation result and first feature set of data to generate a matching second feature set [0039-0040: Lu]. Lu et al. further teaches calculating based on the proximity information and the first and second feature set, a recommendation result from the second recommendation model that is more accurate as results from a set [0033 and 0039-0040: Lu].
Thus, Lu et al. teaches “obtaining a permission type for using the first feature
data”, “updating the first feature data based on the permission type to generate second feature data”, “determining, from a data set comprising a plurality of data items” and “the first feature data comprising a plurality of feature dimensions of the object, ... the permission type indicating at least a portion of the plurality of feature dimensions”.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJITH M JACOB whose telephone number is (571)270-1763. The examiner can normally be reached on Monday-Friday: Flexible Hours.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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4/1/2026
/AJITH JACOB/Primary Examiner, Art Unit 2161
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