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 .
Election/Restrictions
Applicant's election with traverse of claims 1-8 and 16-17 in the reply filed on 07/28/2026 is acknowledged. The traversal is on the ground(s) that the restriction is not practical per MPEP 1850 (II). This is not found persuasive because claims 9-15 and 18 are not merely an implementation of the method of claims 1-8 and 16-17 on a computer, i.e. a recommender system with a processor and memory which stores instructions that when executed by the processor performs the method. Claims 9-15 and 18 contain distinct structures not present in claims 1-8 and 16-17. Furthermore, as shown herein, at least claim 1 lacks novelty and therefore claims 1-18 lack unity of invention.
The requirement is still deemed proper and is therefore made FINAL.
Claims 9-15 and 18 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 07/28/2026.
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 1-8 and 16-17 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.
Regarding claim 1, “x” is not defined and therefore “(t+x)” is indefinite as it is unclear what is being added to “t”.
Regarding claim 1, “the initial knowledge graph” lacks antecedent basis.
Regarding claim 1, “classifying the knowledge graphs” renders the claim indefinite because it is unclear if the knowledge graphs claimed are the expected temporal knowledge graphs previously claimed and if so, it is unclear if “predicting expected temporal knowledge graphs” implies the production of expected temporal knowledge graphs.
Regarding claim 1, “and for the simulated situations” renders the claim indefinite because the claim appears to recite that knowledge graphs are produced for (1) respective points in time and (2) for the simulated situations whereas previously the claim recites that situations resulting from the execution of a particular action or a combination of actions at the certain points in time are simulated.
Regarding claim 1, “the classification result” lacks antecedent basis.
Regarding claim 2, “the initial knowledge graph” lacks antecedent basis.
Regarding claim 3, “the temporal knowledge graphs” lacks antecedent basis.
Regarding claim 3, “pairs of present and future temporal knowledge graphs” renders the claim indefinite because it is unclear if these knowledge graphs are those produced in claim 1.
Regarding claim 5, “the difference between a respective present knowledge graph and a corresponding future knowledge graph” lacks antecedent basis.
Regarding claim 6, “the respective action” renders the claim indefinite because it is unclear if it refers to the action of “each action” and lacks antecedent basis.
Regarding claim 6, “the respective present knowledge graph” lacks antecedent basis.
Regarding claim 6, “the corresponding future knowledge graph” lacks antecedent basis.
Regarding claim 6, “the further developments” lacks antecedent basis.
Regarding claim 7, “the received knowledge graphs” lacks antecedent basis.
Regarding claim 7, “their corresponding weight matrices” lacks antecedent basis.
Regarding claim 7, “the differences between the weight matrices and the confidence scores” lacks antecedent basis.
Regarding claim 7, “a knowledge graph” renders the claim indefinite because it is unclear which knowledge graph is being referred to.
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) 1-5 and 8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhou (Zhou, Sijin, et al. "Interactive recommender system via knowledge graph-enhanced reinforcement learning." Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020.)
Regarding claim 1, a method for providing recommendations to users based on a state of interest, the method comprising:
organizing domain of interest information in an initial temporal knowledge graph (KGt), wherein t is a timestamp that refers to a present point in time (see Equation (9), “…we perform a sampling strategy based on the k-hop neighborhood in KG. In each timestep t, the user’s historical interacted items serve as the seed set
ℇ
t
0
=
{
i
1
,
i
2
,
…
i
n
}
. The k-hop neighborhood set starting from the seed entities is denoted as…[Equation 9]”
ℇ
t
k
can be an initial temporal knowledgegraph KGt because it is a knowledge graph for a timestamp “t” and “t” can be considered a present point in time) ;
predicting, for at least one future point in time (t + x), future entities, future links between entities and/or future attributes of entities for the initial knowledge graph (KGt) and producing at least one new knowledge graph (KGt+x) based on the predictions (§4.2-4.3, “Then all candidate items get their embedding through the graph convolutional layers.” – Producing candidate selections, see Figure 2(b), results in the production of a set of candidate sets, which corresponds to predicting, for at least one future point in time (t+x), future entities, future links between entities, and/or future attributes of entities for the initial knowledge graph and producing at least one new knowledge graph based on the predictions because each candidate set, as shown in Figure 2(b), is itself a knowledge graph which corresponds to a next action which may be taken);
simulating situations resulting from the execution of a particular action or a combination of actions at certain points in time and predicting expected temporal knowledge graphs for the simulated situations (§4, “In the interactive recommendation process, at each timestep t , the IRS sequentially recommends items it to users…” – The iterative nature of the recommendations means expected temporal knowledge graphs are generated for multiple simulated situations at multiple points in time);
classifying the knowledge graphs produced for the respective points in time and for the simulated situations based on the state of interest (“Then the IRS calculates the highest-scored item in the candidate set through Q-network and recommends it to the user.” – this constitutes classifying a knowledge graph as the best pick in each iteration); and
providing, based on the classification result, a ranked list of recommended actions (“Then the IRS calculates the highest-scored item in the candidate set through Q-network and recommends it to the user.” – the action provided can be considered a ranked list where the action recommended is the top ranked action).
Regarding claim 2, Zhou teaches all of the limitations of claim 1, wherein the predicting future entities, future links between entities and/or future attributes of entities for a the initial
performed by a neural network, wherein weights of the neural network are trained with stochastic gradient descent-(SGD} using past prediction results as training data (see Algorithm 1, line 21, which updates weights with SGD using past prediction results as training data and Figure 2 shows that a Graph Convolutional Network, i.e. a neural network, is utilized).
Regarding claim 3, Zhou teaches all of the limitations of claim 1, further comprising:
characterizing present and expected situations by analyzing the temporal knowledge graphs for the different points in time be means of a graph analyzer (Figure 2, “Q-Network”- this constitutes a “graph analyzer” because it is used to analyze the temporal knowledge graphs).
Regarding claim 4, Zhou teaches all of the limitations of claim 3, wherein the graph analyzer comprises
a neural network (Figure 2, “Q-Network”, see Equation (11) “Here the approximation of value function and advantage function are accomplished by multi-layer perceptrons.”) that uses a set of classified pairs of present and future temporal knowledge graphs as training data that classifies a temporal knowledge graph based on a state of interest that is defined by a set of classification labels by determining a confidence score for each state of interest (see Equations 12 and 13, where equation 12 describes the present temporal knowledge graph as training data and Equation 13 describes the future temporal knowledge graph being used, and the overall output of the Q-Network is a classification of an action to be taken, i.e. an action is classified as “the best” and therefore labeled as “the best” and it is determined via a highest-scored item in the candidate list, see §4, “Then the IRS calculates the highest-scored item in the candidate set through Q-network and recommends it to the user.”)
Regarding claim 5, Zhou teaches all of the limitations of claim 3, further comprising:
calculating, by the graph analyzer, a weight matrix that reflects the difference between a respective present knowledge graph and a corresponding future knowledge graph (see Equation (12), θQ constitutes a weight matrix and according to Equation (12) it reflects a difference between yt, the future knowledge graph representation in the Q-Network, and Q(), the current knowledge graph representation in the Q-Network).
Regarding claim 8, Zhou teaches all of the limitations of claim 1, wherein the domain of interest information is collected and/or acquired by observing online communications of a person in an online community (§5.1.1, “Movielens-20M”).
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.
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.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou (Zhou, Sijin, et al. "Interactive recommender system via knowledge graph-enhanced reinforcement learning." Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020.) in view of McRaith (US20170329917A1).
Regarding claim 16, Zhou teaches all of the limitations of claim 8, but does not teach wherein the domain of interest information is collected from a monitoring system that monitors vital parameters of a patient, wherein the vital parameters of the patient comprise blood pressure levels and the method further comprises providing treatment recommendations to optimize a condition of the patient.
McRaith teaches wherein the domain of interest information is collected from a monitoring system that monitors vital parameters of a patient, wherein the vital parameters of the patient comprise blood pressure levels and the method further comprises providing treatment recommendations to optimize a condition of the patient (¶44, “FIG. 4 is a flow diagram of an exemplary method 400 for providing a treatment recommendation [providing treatment recommendations to optimize a condition of the patient]. In some examples, the depicted method 400 may be used for automatic clinical-decision making. As shown in FIG. 4, at step 401, electronic device 19 and/or server 29 may obtain initial data from a user 8 before generating a treatment plan. The user 8 may enter the data into the electronic device 19, which may be sent to server 29. In some examples, at step 401 in FIG. 2, server 29 may receive data that is relevant to a healthcare provider, e.g., a doctor may enter related patient healthcare information into server 29. This data may be electronically transmitted by the provider and/or the user 8 and received by the server 29 at step 401. The data may be electronically transmitted and received by the server 29 at step 401 in any suitable manner. For example, the provider may access mHealth application 1 or secure server and send or drop electronic data files via a network so that the files may be accessed by mHealth application 1. In some examples, the provider may allow mHealth application 1 limited access, in compliance with any healthcare privacy regulations and other applicable regulations, to any electronic medical records, user prescription records, referral records, etc. In some examples, the service may electronically retrieve healthcare data from such electronic records (e.g., automatically). In other examples, user data may be electronically transmitted by a user 8 and may be electronically received by the service in any suitable manner. The user data may be manually input by the user 8 via mHealth application 1 and/or may be automatically retrieved by the service from an electronic device of the user (e.g., device 19) that may measure user health values, such as heart rate, blood glucose, blood oxygen, blood pressure [the domain of interest information is collected from a monitoring system that monitors vital parameters of a patient, wherein the vital parameters of the patient comprise blood pressure levels], activity, stress, mood, and/or sleep either periodically or continuously. In some examples, the user 8 may be required to complete a questionnaire and/or survey at step 401. The questionnaire may be presented to the user 8 during the setup of mHealth application 1.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize Zhou in treatment recommendations for patients, i.e. wherein the domain of interest information is collected from a monitoring system that monitors vital parameters of a patient, wherein the vital parameters of the patient comprise blood pressure levels and the method further comprises providing treatment recommendations to optimize a condition of the patient, in order to provide accurate treatment plans.
Allowable Subject Matter
Claims 6-7 and 17 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.
Regarding claim 6, the prior art does not anticipate or render obvious the limitations of claim 6. In particular, impermissible hindsight would be required to modify the closest prior art, Zhou (cited herein), to arrive at the invention of claim 6 because it would not be prima facie obvious to incorporate the elements of claim 6 and to make the non-trivial modifications to Zhou to ensure operability.
Claims 7 and 17 depend on claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhao (Zhao, Pengpeng, et al. "Where to go next: A spatio-temporal gated network for next poi recommendation." IEEE Transactions on Knowledge and Data Engineering 34.5 (2020): 2512-2524.) describes knowledge graphs for providing recommendations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCHYLER S SANKS whose telephone number is (571)272-6125. The examiner can normally be reached 06:30 - 15:30 Central Time, M-F.
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/SCHYLER S SANKS/ Primary Examiner, Art Unit 2129
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