DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claims 1, 15, and 20 recite the limitation "the clutter" in the last line of each claim, respectively. There is insufficient antecedent basis for this limitation in the claim. The Examiner has interpreted the limitation to be intended to mean “the cluster”. Appropriate correction is required.
Claims 9, 11 and 18 recite “cognitively analyzing”, respectively. This limitation is vague and unclear as it does not appear to have a fixed definition in the art and the specification does not provide any idea of what it actually means.
Additionally, claim 20 recites “the computer system” at lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-8, 10, 12-14 and 16-17, and 19 are rejected as they incorporate the deficiencies of the claims upon which they depend.
Claim Rejections - 35 USC § 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, 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, 6, 10, 12-13, 15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US20250021941, hereinafter Liu) in view of Agarwal et al. (US20210125238, hereinafter Agarwal).
Regarding claims 1, 15 and 20 (the Examiner notes the exclusion in the Applicant’s original disclosure of the media of claim 20 being a signal, at least at [0025]), Liu discloses a computer-implemented method for temporal targeted digital content transmission (see Liu, at least at Fig. 5, and related text), comprising:
obtaining, by one or more processors, digital content (see Liu, at least at [0025]-[0027], [0039]-[0041], [0068], [0071], and other related text);
determining, by the one or more processors, a target group for the digital content, wherein the target group comprises a cluster (i.e., at least particular users and/or users of tailored content, see Liu, at least at [0036], [0040], [0068], and other related text);
determining, by the one or more processors, historical usage metrics on a digital content platform for one or more users in the cluster (see Liu, at least at [0006]-[0008], [0038], [0069], and other related text); and
utilizing, by the one or more processors, the historical usage metrics for identifying an optimized release time for the digital content to the cluster (see Liu, at least at [0036], ]0068], and other related text).
Liu does not specifically disclose utilizing the historical usage metrics to determine a threshold value for identifying the optimized release time for the digital content; or
determining, by the one or more processors, that current usage metrics exceed the threshold value, the determining that the current usage metrics exceed the threshold value, comprising:
monitoring, by the one or more processors, in real-time, the one or more users in the cluster to obtain usage data;
calculating, by the one or more processors, based on the usage data, the current usage metrics; and
comparing, by the one or more processors, the current usage metrics to the threshold value; and
based on determining that the current usage metrics exceed the threshold value at a given time, transmitting the digital content to the clutter via the digital content platform.
In an analogous art relating to a system for providing content, Agarwal discloses utilizing historical usage metrics to determine a threshold value for identifying an optimized release time for digital content to a cluster (see Agarwal, at least at [0011], [0021]-[0025], [0040], [0046]-[0048], [0051], [0054]-[0055], [0063], [0068], [0079], and other related text); and
determining, by the one or more processors, that current usage metrics exceed the threshold value (see Agarwal, at least at [0011], [0021]-[0025], [0040], [0047]-[0048], [0051], [0054]-[0055], [0063], [0068], [0081], and other related text), the determining that the current usage metrics exceed the threshold value, comprising:
monitoring, by the one or more processors, in real-time, the one or more users in the cluster to obtain usage data (see Agarwal, at least at [0022]-[0025], [0050], [0093], and other related text);
calculating, by the one or more processors, based on the usage data, the current usage metrics (see Agarwal, at least at [0039], [0063]-[0065], and other related text); and
comparing, by the one or more processors, the current usage metrics to the threshold value (see Agarwal, at least at [0011], [0021]-[0025], [0040], [0047]-[0048], [0051], [0054]-[0055], [0063]-[0065], [0068], [0081], and other related text); and
based on determining that the current usage metrics exceed the threshold value at a given time, transmitting the digital content to the clutter via the digital content platform (see Agarwal, at least at [0021], [0085], [0090], [0093], and other related text).
It would have been obvious to a person having ordinary skill in the art before the effective date of the invention to modify the system of the system of Liu to include the limitations as taught by Agarwal for the advantage of optimizing system resources.
Regarding claim 3, Liu in view of Agarwal discloses wherein the threshold value comprises a given usage score (see Agarwal, at least at [0011], [0021]-[0025], [0040], [0047]-[0048], [0051], [0054]-[0055], [0063], [0068], [0081], and other related text), and wherein calculating the current usage metrics comprises determining a usage score for each user in the cluster (see Agarwal, at least at [0023]-[0025], [0049], [0095], and other related text).
Regarding claim 6, Liu in view of Agarwal discloses wherein the obtaining is from a digital content creator (see Liu, at least at [0006], [0026], and other related text).
Regarding claims 10 and 17, wherein utilizing the historical usage metrics to determine the threshold value, comprises:
analyzing, by the one or more processors, the historical usage metrics, to derive attributes of the digital content relevant to responsiveness of the one or more users in the cluster to the digital content (see Liu, at least at [0040], and other related text, and see Agarwal, at least at [0023], and other related text);
applying, by the one or more processors, a machine learning algorithm to predict the threshold value based on the attributes of the digital content see Agarwal, at least at [0055]-[0059], and other related text).
Regarding claim 12, Liu in view of Agarwal discloses wherein an attribute of the attributes of the digital content comprises placement of the digital content in a graphical user interface on the digital content platform (see Agarwal, at least at [0023, [0046], and other related text).
Regarding claim 13, Liu in view of Agarwal discloses wherein an attribute of the attributes of the digital content comprises an alert type for the digital content (see Agarwal, at least at [0023, [0046], and other related text).
Claims 4-5, 7-9, 11 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (previously cited) in view of Agarwal (previously cited), as applied to claims 1 above, and further in view of Hirsch et al. (US20170171580, hereinafter Hirsch).
Regarding claims 4 and 19, Liu in view of Agarwal discloses transmitting based on a threshold condition, but does not specifically disclose wherein the given usage score is an aggregate score for all the one or more users in the cluster, and wherein the transmitting is based on the aggregate score exceeding the threshold value.
In an analogous art relating to a system for providing content, Hirsch discloses wherein a given usage score is an aggregate score for all of one or more users in a cluster, and wherein a provision is based on the aggregate score exceeding a threshold value (see Hirsch, at least at [0058], [0104], [0244], and other related text).
It would have been obvious to a person having ordinary skill in the art before the effective date of the invention to modify the system of the system of Liu in view of Agarwal to include the limitations as taught by Hirsch for the advantage of further optimizing system resources.
Regarding claim 5, Liu in view of Agarwal, and further in view of Hirsch discloses wherein the given usage score is a usage score for a first user of the one or more users in the cluster (see Hirsch, at least at [0058], and other related text), and wherein the transmitting is based on the usage score for the first user exceeding the threshold value (see Hirsch, at least at [0058], and other related text).
Regarding claim 7, Liu in view of Agarwal and Hirsch discloses wherein determining the target group for the digital content comprises:
obtaining, by the one or more processors, with the digital content, metadata indicating the target group (see Hirsch, at least at [0037], [0050], [0071]-[0072], and other related text).
It would have been obvious to a person having ordinary skill in the art before the effective date of the invention to modify the system of the system of Liu in view of Agarwal to include the limitations as taught by Hirsch for the advantage of further optimizing system resources.
Regarding claim 8, Liu in view of Agarwal and Hirsch discloses wherein determining the target group for the digital content comprises:
analyzing, by the one or more processors, the digital content, to extract attributes relevant to one or more target groups (see Hirsch, at least at [0136], [0162], [0166]-[0167], [0173]-[0175], and other related text); and
applying, by the one or more processors, a machine learning algorithm to classify the digital content, based on the attributes, as being relevant to the target group (see Hirsch, at least at [0136], [0154]-[0155], [0160], and other related text).
Regarding claim 9, Liu in view of Agarwal and Hirsch discloses training, by the one or more processors, the machine learning algorithm, to classify the digital content, based on the attributes, as being relevant to the target group (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text), the training comprising:
obtaining, by the one or more processors, user attribute data and historical interaction data relevant to the user attribute data (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text); and
cognitively analyzing, by the one or more processors, the user attribute data and the historical interaction data to identify the attributes predicting relevance of the digital content to the target group (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text).
Regarding claims 11 and 18, Liu in view of Agarwal and Hirsch discloses training, by the one or more processors, the machine learning algorithm, to predict the threshold value based on the attributes of the digital content (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text), the training comprising:
obtaining, by the one or more processors, live user activity data of the one or more users in the cluster (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text);
cognitively analyzing, by the one or more processors, the live user activity data to identify the attributes of the digital content relevant to the responsiveness of the one or more users in the cluster (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text);
identifying, by the one or more processors, patterns related to the responsiveness of the one or more users in the cluster (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text); and
training, by the one or more processors, the machine learning algorithm, based on the patterns (see Liu, at least at [0080], Fig. 6, and other related text, see Agarwal, at least at [0055]-[0059], and other related text, and see Hirsch, at least at [0067], and other related text).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Liu (previously cited) in view of Agarwal (previously cited), as applied to claims 1 above, and further in view of Yi et al. (US20140280890, hereinafter Yi).
Regarding claim 14, Liu in view of Agarwal discloses applying, by the one or more processors, at least one machine learning algorithm, to determine the threshold value for identifying the optimized release time for the digital content to the cluster (please see rejections above);
but does not specifically disclose based on the transmitting, monitoring, by the one or more processors, responses by the one or more users in the cluster to the digital content to obtain data related to response timing; and
updating, by the one or more processors, the at least one machine learning algorithm based on the data related to the response timing.
In an analogous art relating to a system for providing content, Yi discloses based on transmitting, monitoring, by the one or more processors, responses by the one or more users in the cluster to the digital content to obtain data related to response timing (see Yi, at least at [0093], [0097], [0150], and other related text); and
updating, by the one or more processors, the at least one machine learning algorithm based on the data related to the response timing (see Yi, at least at [0093], [0097], [0150], and other related text).
It would have been obvious to a person having ordinary skill in the art before the effective date of the invention to modify the system of the system of Liu in view of Agarwal to include the limitations as taught by Yi for the advantage of further optimizing system resources.
Allowable Subject Matter
Claims 2 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, AND if overcoming all other rejections of record.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHENEA DAVIS whose telephone number is (571)272-9524 and whose email address is CHENEA.SMITH@USPTO.GOV. The examiner can normally be reached M-F: 8:00 am - 4:00 pm.
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/CHENEA DAVIS/Primary Examiner, Art Unit 2421