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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Application 18915966 and US Patent 12149492
Claim 1 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 of US Patent 12149492.
Limitations of the Claim 1 in the instant application 18915966 “train, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects most likely to reciprocate”, and the limitations in claim 1 in the US Patent 12149492 “train, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects most likely to reciprocate” have similar subject matters.
Limitations of the claim 1 in the instant application 18915966 “ iteratively predicting, based on a profile for each unknown object, which of the unknown objects are most likely to reciprocate, the predicting being based on at least one output category;”, and the limitations in claim 1 in the US Patent 12149492 “iteratively predicting which of the unknown objects, based on the profile for each unknown object, which of the unknown objects are most likely to reciprocate, the predicting being based on at least one output category;” have similar subject matters.
Limitations of the claim 1 in the instant application 18915966 “testing and comparing each of the unknown objects predicted during each iteration against a target variable”, and the limitations in claim 1 in the US Patent 12149492 “testing and comparing the unknown objects predicted during each iteration against a target variable” have similar subject matters.
Limitations of the claim 1 in the instant application 18915966 “indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects are necessary to improve predictability of the target variable”, and the limitations in claim 1 in the US Patent 12149492 “indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects are necessary to improve predictability of the target variable” have similar subject matters.
Limitations of the claim 1 in the instant application 18915966 “deploy the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object”, and the limitations in claim 1 in the US Patent 12149492 “deploy the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object” have similar subject matters.
Limitations of the claim 1 in the instant application 18915966 “trigger a communication, based on the profiles of each unknown object, to each unknown object of the reduced set of unknown objects”, and the limitations in claim 1 in the US Patent 12149492 “trigger a communication, based on the profiles of each unknown object, to each unknown object of the reduced set of unknown objects” have similar subject matters.
Claim 2 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of US Patent 12149492.
Limitations of the claim 2 in the instant application 18915966 “ wherein the memory device further stores code that, when executed, causes the at least one processor to record, in an interaction map, which of the unknown objects have been sent a communication”, and the limitations in claim 2 in the US Patent 12149492 “wherein the memory device further stores code that, when executed, causes the at least one processor to record, in the interaction map, which of the unknown objects have been sent a communication” have similar subject matters.
Claim 3 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 3 of US Patent 12149492.
Limitations of the claim 3 in the instant application 18915966 “ wherein the memory device further stores code that, when executed, causes the at least one processor to receive a reciprocating communication from one of the unknown objects; and record, in the interaction map, that the unknown object has reciprocated the communication”, and the limitations in claim 3 in the US Patent 12149492 “wherein the memory device further stores code that, when executed, causes the at least one processor to receive a reciprocating communication from one of the unknown objects; and record, in the interaction map, that the unknown object has reciprocated the communication” have similar subject matters.
Claim 4 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of US Patent 12149492.
Limitations of the claim 4 in the instant application 18915966 “wherein the machine learning model comprises a neural network to predict information about unknown objects based on relationships with known objects and generate a reduced set of unknown objects most likely to reciprocate”, and the limitations in claim 4 in the US Patent 12149492 “wherein the machine learning model comprises a neural network to predict information about unknown objects based on relationships with known objects and generate a reduced set of unknown objects most likely to reciprocate” have similar subject matters.
Claim 5 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 5 of US Patent 12149492.
Limitations of the claim 5 in the instant application 18915966 “wherein the neural network is one of a recurrent neural network (RNN), a convolution neural network (CNN), or a feed-forward network”, and the limitations in claim 5 in the US Patent 12149492 “wherein the neural network is one of a recurrent neural network (RNN), a convolution neural network (CNN), or a feed-forward network” have similar subject matters.
Claim 6 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 6 of US Patent 12149492.
Limitations of the claim 6 in the instant application 18915966 “wherein the machine learning model comprises a Bayesian machine learning algorithm to predict information about the unknown object based on relationships with known objects and generate a reduced set of unknown objects most likely to reciprocate”, and the limitations in claim 6 in the US Patent 12149492 “wherein the machine learning model comprises a Bayesian machine learning algorithm to predict information about the unknown object based on relationships with known objects and generate a reduced set of unknown objects most likely to reciprocate” have similar subject matters.
Claim 7 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 7 of US Patent 12149492.
Limitations of the claim 7 in the instant application 18915966 “wherein the data within the interaction map is used to continuously train the machine learning model”, and the limitations in claim 7 in the US Patent 12149492 “wherein the data within the interaction map is used to continuously train the machine learning model” have similar subject matters.
Claim 8 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claims 8 of US Patent 12149492.
Limitations of the Claim 8 in the instant application 18915966 “ train, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects most likely to reciprocate”, and the limitations in claim 8 in the US Patent 12149492 “train, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects most likely to reciprocate” have similar subject matters.
Limitations of the claim 8 in the instant application 18915966 “ iteratively predicting, based on a profile for each unknown object, which of the unknown objects are most likely to reciprocate, the predicting being based on at least one output category”, and the limitations in claim 8 in the US Patent 12149492 “iteratively predicting which of the unknown objects, based on the profile for each unknown object, which of the unknown objects are most likely to reciprocate, the predicting being based on at least one output category;” have similar subject matters.
Limitations of the claim 8 in the instant application 18915966 “test and compare the unknown objects predicted during each iteration against a target variable”, and the limitations in claim 8 in the US Patent 12149492 “testing and comparing the unknown objects predicted during each iteration against a target variable” have similar subject matters.
Limitations of the claim 8 in the instant application 18915966 “indicate, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects are necessary to improve predictability of the target variable”, and the limitations in claim 8 in the US Patent 12149492 “indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects are necessary to improve predictability of the target variable” have similar subject matters.
Limitations of the claim 8 in the instant application 18915966 “deploy the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object”, and the limitations in claim 8 in the US Patent 12149492 “deploy the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object” have similar subject matters.
Limitations of the claim 8 in the instant application 18915966 “trigger a communication, based on the profiles of each unknown object, to each unknown object of the reduced set of unknown objects”, and the limitations in claim 8 in the US Patent 12149492 “trigger a communication, based on the profiles of each unknown object, to each unknown object of the reduced set of unknown objects” have similar subject matters.
Claim 9 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 10 of US Patent 12149492.
Limitations of the claim 9 in the instant application 18915966 “wherein the data in the interaction map is used to continuously train the machine learning model”, and the limitations in claim 10 in the US Patent 12149492 “wherein the data in the interaction map is used to continuously train the machine learning model” have similar subject matters.
Claim 10 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 9 of US Patent 12149492.
Limitations of the claim 10 in the instant application 18915966 “wherein the interaction map is displayed, to a user of the system; wherein the interaction map displays to the user connections between objects, which of the unknown objects have been sent a communication, and which of the unknown objects have reciprocated the communication”, and the limitations in claim 9 in the US Patent 12149492 “wherein the interaction map is displayed, to a user of the system; wherein the interaction map displays to the user connections between objects, which of the unknown objects have been sent a communication, and which of the unknown objects have reciprocated the communication” have similar subject matters.
Claim 11 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of US Patent 12149492.
Limitations of the claim 11 in the instant application 18915966 “wherein the machine learning model comprises a neural network”, and the limitations in claim 11 in the US Patent 12149492 “wherein the machine learning model comprises a neural network” have similar subject matters.
Claim 12 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of US Patent 12149492.
Limitations of the claim 12 in the instant application 18915966 “wherein the neural network is one of a recurrent neural network (RNN), a convolution neural network (CNN), or a feed-forward neural network”, and the limitations in claim 12 in the US Patent 12149492 “wherein the neural network is one of a recurrent neural network (RNN), a convolution neural network (CNN), or a feed-forward neural network” have similar subject matters.
Claim 13 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 13 of US Patent 12149492.
Limitations of the claim 13 in the instant application 18915966 “wherein the machine learning model comprises a Bayesian machine learning algorithm”, and the limitations in claim 13 in the US Patent 12149492 “wherein the machine learning model comprises a Bayesian machine learning algorithm” have similar subject matters.
Claim 14 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claims 14 of US Patent 12149492.
Limitations of the Claim 14 in the instant application 18915966 “training, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects most likely to reciprocate”, and the limitations in claim 14 in the US Patent 12149492 “training, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects most likely to reciprocate” have similar subject matters.
Limitations of the claim 14 in the instant application 18915966 “ iteratively predicting, based on a profile for each unknown object, which of the unknown objects are most likely to reciprocate, the predicting being based on at least one output category”, and the limitations in claim 14 in the US Patent 12149492 “iteratively predicting which of the unknown objects, based on the profile for each unknown object, which of the unknown objects are most likely to reciprocate, the predicting being based on at least one output category;” have similar subject matters.
Limitations of the claim 14 in the instant application 18915966 “testing and comparing the unknown objects predicted during each iteration against a target variable”, and the limitations in claim 14 in the US Patent 12149492 “testing and comparing the unknown objects predicted during each iteration against a target variable” have similar subject matters.
Limitations of the claim 14 in the instant application 18915966 “ indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects are necessary to improve predictability of the target variable”, and the limitations in claim 14 in the US Patent 12149492 “indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects are necessary to improve predictability of the target variable” have similar subject matters.
Limitations of the claim 14 in the instant application 18915966 “deploying the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object”, and the limitations in claim 14 in the US Patent 12149492 “deploying the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object” have similar subject matters.
Limitations of the claim 14 in the instant application 18915966 “triggering a communication, based on the profile of each unknown object, to each unknown object of the reduced set of unknown objects”, and the limitations in claim 14 in the US Patent 12149492 “triggering a communication, based on the profile of each unknown object, to each unknown object of the reduced set of unknown objects” have similar subject matters.
Claim 15 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 15 of US Patent 12149492.
Limitations of the claim 15 in the instant application 18915966 “further comprising recording, in an interaction map, which of the unknown objects have been sent a communication”, and the limitations in claim 15 in the US Patent 12149492 “ comprising recording, in the interaction map, which of the unknown objects have been sent a communication” have similar subject matters.
Claim 16 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 16 of US Patent 12149492.
Limitations of the claim 16 in the instant application 18915966 “receiving a reciprocating communication from one of the unknown objects; and recording, in the interaction map, that the unknown object has reciprocated the communication”, and the limitations in claim 16 in the US Patent 12149492 “receiving a reciprocating communication from one of the unknown objects; and recording, in the interaction map, that the unknown object has reciprocated the communication” have similar subject matters.
Claim 17 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 17 of US Patent 12149492.
Limitations of the claim 17 in the instant application 18915966 “continuously training the machine learning model using the data in the interaction map”, and the limitations in claim 17 in the US Patent 12149492 “continuously training the machine learning model using the data in the interaction map” have similar subject matters.
Claim 18 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 18 of US Patent 12149492.
Limitations of the claim 18 in the instant application 18915966 “displaying the interaction map to a user of the system”, and the limitations in claim 18 in the US Patent 12149492 “displaying the interaction map to a user of the system” have similar subject matters.
Claim 19 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 of US Patent 12149492.
Limitations of the claim 19 in the instant application 18915966 “the machine learning model comprises a neural network”, and the limitations in claim 19 in the US Patent 12149492 “wherein the machine learning model comprises a neural network” have similar subject matters.
Claim 20 is non-provisionally (anticipated) rejected on the ground of nonstatutory double patenting as being unpatentable over claim 20 of US Patent 12149492.
Limitations of the claim 20 in the instant application 18915966 “wherein the machine learning model comprises a Bayesian machine learning algorithm”, and the limitations in claim 20 in the US Patent 12149492 “wherein the machine learning model comprises a Bayesian machine learning algorithm” have similar subject matters.
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 failing to set forth 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.
Regarding claims 1, 8, 14: the instant claims recite “to identify a reduced set of unknown objects most likely to reciprocate”. The term “most likely” in instant claims is a relative term which renders the claim indefinite. The term “most likely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner interprets this limitation as “to identify a reduced set of unknown objects
Furthermore, the instant claims recite “deploy the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object”. The term “most likely” in instant claims is a relative term which renders the claim indefinite. The term “most likely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner interprets this limitation as “deploy the trained machine learning model to generate a reduced set of unknown objects objects
Regarding claims 2-7, 9-13, 15-20: The dependent claims 2-7, 9-13, 15-20 are rejected as they depends on claims 1, 8, and 14.
Regarding claim 4: the instant claim recites the terms “wherein the machine learning model comprises a neural network to predict information about unknown objects based on relationships with known objects and generate a reduced set of unknown objects most likely to reciprocate”. The term “most likely” in instant claims is a relative term which renders the claim indefinite. The term “most likely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner interprets this limitation as “wherein the machine learning model comprises a neural network to predict information about unknown objects based on relationships with known objects and generate a reduced set of unknown objects
Regarding claim 6: the instant claim recites the terms “wherein the machine learning model comprises a Bayesian machine learning algorithm to predict information about the unknown object based on relationships with known objects and generate a reduced set of unknown objects most likely to reciprocate”. The term “most likely” in instant claims is a relative term which renders the claim indefinite. The term “most likely” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner interprets this limitation as “wherein the machine learning model comprises a Bayesian machine learning algorithm to predict information about the unknown object based on relationships with known objects and generate a reduced set of unknown objects
Allowable Subject Matter
Claims 1, 8 and 14 would be allowable if overcoming the double patenting rejection and 112(b) rejection set forth in this office action.
The reason for the allowable subject matter in claims 1, 8 and 14.
The closed prior arts (“LOWE”, US 20230334339 A1), (“Reardon”, US 9871757 B1), (“Guo”, A Deep Graph Neural Network Based Mechanism For Social Recommendations) fairly fail to teach or suggest train, via machine learning and using a set of training data, a machine learning model configured to identify a reduced set of unknown objects most likely to reciprocate, the training including: ii. iteratively predicting, based on a profile for each unknown object, which of the unknown objects are most likely to reciprocate, the predicting being based on at least one output category; iii. testing and comparing each of the unknown objects predicted during each iteration against a target variable; and iv. indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain profile data for each of the unknown objects are necessary to improve predictability of the target variable; v. deploy the trained machine learning model to generate a reduced set of unknown objects most likely to reciprocate, and based thereon determining a reduced set of unknown objects most likely to reciprocate and, pull properties of each unknown object in the reduced set from a database storing the profiles of each unknown object; and vi. trigger a communication, based on the profiles of each unknown object, to each unknown object of the reduced set of unknown objects.
Conclusion
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/FADI HAJ SAID/Primary Examiner, Art Unit 2444