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 .
According to paper filed February 14th 2023, claims 1-20 are pending for examination with a priority date of February 15th 2022 under 35 USC §119(a)-(d) or (f).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. §112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. §112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. §112(b) or 35 U.S.C. §112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. §112, the applicant), regards as the invention.
In claim 1, the “a first group” and “a second group” of the recited limitation “the group of users including a first group of users and a second group of users” are unclear. It is unclear if the “first group” and “second group” of the group mean “a first subgroup” and “a second subgroup”?
For claim examination purpose, the first group and the second group are construed and cited as “a first subgroup” and “a second subgroup” in the present Office action until further clarification provided. Same rationale applies to claim 12.
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.
The claimed invention is directed to non-statutory subject matter. Claims 12-20 do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to an abstract idea. The claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
The claims do not include additional elements, particularly, hardware elements, that are sufficient to amount to significantly more than an abstract idea. Claims 12-20 recite a method of providing and inputting a machine learning algorithm, and performing machine learning training steps. Said steps merely provide instructions to implement an abstract idea and do not integrate them into a practical application.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. §102(b)(2)(C) for any potential 35 U.S.C. §102(a)(2) prior art against the later invention.
Claims 1, 3-4, 9, and 12 are rejected under 35 U.S.C. §103 as being unpatentable over Drapeau et al. (WO 2022/005913), hereinafter Drapeau, and further in view of Lefkofsky et al. (US 2021/0125731), hereinafter Lefkofsky.
Claim 1
“receive a first set of data representing, for each user of a group of users, one or more categories of user attribute data, the group of users including a first group of users and a second group of users, the first and second groups of users having no users in common; receive a second set of data representing, for each of the users of the first group of users, one or more behavioural characteristics” Lefkofsky [0010] teaches dividing a group of patients into a first subgroup and a second subgroup based on predicted target values that are generated for each patient of the group of patients and for each of the plurality of forward features and the plurality of prior features;
“train a weighted processing network to form, for each of the first group of users, relationships between the categories of user attribute data of the first set of data and the behavioural characteristics of the second set of data” Drapeau [0027][0048] teaches using a deep neural network (DNN) technique to train prediction models to detect fraudulent transactions, the prediction models is weighted, and creating a relationship
between the attributes for the user;
“generate, using the relationships formed by the trained weighted processing network, a third set of data representing, for each of the users of the second group of users, one or more behavioural characteristics present in the second set of data” Drapeau [0039] teaches a labeling technique of cluster generation that processes the large set of graphs where subsets/clusters are formed by joining subgraphs sharing an attribute.
Drapeau and Lefkofsky disclose analogous art. However, Drapeau does not spell out the “first group and second group” as recited above. Said feature is taught in Lefkofsky. Hence, it would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Lefkofsky into Drapeau to enhance its grouping and management functions of user data sets.
Claim 3
“wherein the user attribute data includes user identification information” Drapeau [0027] teaches user identifier as one of identity transaction attributes.
Claim 4
“wherein the user identification information is an email address” Drapeau [0026] teaches attribute information including email addresses.
Claim 9
“wherein the weighted processing network is a machine learning algorithm” Lefkofsky [0085] teaches machine learning algorithms.
Claim 12
Claim 1 is rejected for the similar rationale given for claim 1.
Claims 2, 7, 10-11, 13-14, 17, and 19-20 are rejected under 35 U.S.C. §103 as being unpatentable over Drapeau et al. (WO 2022/005913), hereinafter Drapeau, and further in view of Lefkofsky et al. (US 2021/0125731), hereinafter Lefkofsky, and Raviv et al. (US 2020/0211048), hereinafter Raviv.
Claim 2
“wherein the one or more processors are further configured to: receive, for a third group of users who have no users in common with the first and second groups of users, a fourth set of data representing one or more behavioural characteristics” Raviv [0048] teaches a third subset of user data and a fourth subset of activity data;
“input to the trained weighted processing network the fourth set of data; and generate, using the relationships formed by the trained weighted network, for the user from the third group of users, a fifth set of data representing one or more categories of user data and/or one or more behavioural characteristics of the users” Drapeau [0027][0048] teaches using a deep neural network (DNN) technique to train prediction models to detect fraudulent transactions, the prediction models is weighted, and creating a relationship between the attributes for the user.
Drapeau, Lefkofsky, Raviv disclose analogous art. However, Drapeau does not spell out the “third group and fourth group” as recited above. Said feature is taught in Raviv. Hence, it would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Raviv into Drapeau to enhance its application of the trained weighted network to generate user data sets.
Claim 7
“wherein the one or more processors are further configured to: receive a single category of the one or more categories from the first set of data for a user of the first group of users; and generate, using the trained weighted processing network, first and/or second sets of data associated to the unique user” Raviv [0048] teaches a predictive model that can be a machine-learned model indicates a first set of coupon features is associated with a first set of user features and a second set of coupon features is associated with a second set of user features.
Claim 10
“wherein, in training the weighted processing network to form relationships between the first set of data and the second set of data, the one or more processors are configured to: compare the first sets of data and the second sets of data for each of the first group of users to other users from the first group of users” Raviv [0048][0049] teaches a predictive model indicates a first set of coupon features is associated with a first set of user features and a second set of coupon features is associated with a second set of user features, and one or more actions performed by a second user or a second set of users determined to be similar to the user;
“identify combinations of the one or more user attributes from the first sets of data that are present in combination with one or more behavioural characteristics, for a plurality of users from the first group of users” Raviv [0049] teaches prediction of one or more actions, i.e., clicks, views, purchases and so on, performed by a second user or a second set of users determined to be similar to the user.
Claim 11
“wherein, when generating the third set of data for the second group of users, the one or more processors are further configured to: generate one or more probabilities that each of the second group of users has one or more behavioural characteristics that form the third set of data based on one or more user attributes that form the first set of data for the second group of users, wherein the probability is based on the relationships formed between the first set of data and the second set of data of the first group of users” Raviv [0049] teaches a predictive model that indicates a first set of coupon features is associated with a first set of user features and one or more actions performed by a second user or a second set of users determined to be similar to the user.
Claims 13-14 & 17 & 19-20
Claims 13-14, 17, and 19-20 are rejected for the similar rationale given for claims 2-3, 7, and 10-11 respectively.
Claims 5, 8, 15, and 18 are rejected under 35 U.S.C. §103 as being unpatentable over Drapeau et al. (WO 2022/005913), hereinafter Drapeau, and further in view of Lefkofsky et al. (US 2021/
0125731), hereinafter Lefkofsky, and Lu et al. (US 2024/0028752), hereinafter Lu.
Claim 5
“wherein the one or more behavioural characteristics include user listening data having information about user listening habits based on the audio content consumption of the first group of users” Lu [0037] teaches user habits including listening habits.
Drapeau, Lefkofsky, Lu disclose analogous art. However, Drapeau does not spell out the “listening habits” as recited above. Said feature is taught in Lu. Hence, it would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Lu into Drapeau to expand and enhance its categorizing functions of user data sets.
Claim 8
“wherein the second set of data includes information relating to the user’s preferences and/or interests” Lu [0090] teaches the Personal Data Store tracks browser history and stores or removes data in accordance with user preferences.
Claims 15 & 18
Claims 15 and 18 are rejected for the similar rationale given for claims 5 and 8 respectively.
Claims 6 and 16 are rejected under 35 U.S.C. §103 as being unpatentable over Drapeau et al. (WO
2022/005913), hereinafter Drapeau, in view of Lefkofsky et al. (US 2021/0125731), hereinafter Lefkofsky, and further in view of Raviv et al. (US 2020/0211048), hereinafter Raviv, and Lu et al. (US 2024/0028752), hereinafter Lu.
Claim 6
“receive user identification information and/or user listening habit data specific to a unique user; and generate, using the trained weighted processing network, a fifth set of data for the unique user” Drapeau [0027] teaches user identifier as one of identity transaction attributes; and Lu [0037] teaches user habits including listening habits.
Drapeau, Lefkofsky, Raviv, and Lu disclose analogous art. However, Drapeau does not spell out the “user identification” and “listening habit” as recited above. Said features are taught in Raviv and Lu respectively. Hence, it would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said features of Raviv and Lu into Drapeau to enhance the user data sets categorizing and management functions.
Claim 16
Claim 16 is rejected for the similar rationale given for claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUAY HO whose telephone number is (571)272-6088. The examiner can normally be reached Monday to Friday 9am - 5pm.
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/Ruay Ho/Primary Patent Examiner, Art Unit 2142