DETAILED ACTION
This non-final office action is responsive to application 18/503,618 as submitted 07 Nov. 2023.
Claim status is currently pending and under examination for claims 1-9 of which independent claims are 1 and 8-9.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The application has an effective filing date of 12/23/22.
Information Disclosure Statement
As required by MPEP 609(c), the applicant’s submissions of the Information Disclosure Statements dated 11/07/23 – 01/27/25 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by MPEP 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Specification
The disclosure is objected to because of the following informalities:
The title of the invention “Information Processing Apparatus, Information Processing Method, and Storage Medium” is objected to as non-descript. A new title is required that is clearly indicative of the invention to which the claims are directed, see MPEP 606.01.
Specification at [0003] recites http://arxiv.org/abs/1609.02907 as containing an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Specification at [0041] recites “2 hours+4 hours=0.5” the addition symbol should be division.
Appropriate correction is required.
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.
Claims 1-7 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Particularly, claim 1 recites "the learning unit" in limitations perform and output. There is insufficient antecedent basis for “the learning unit” as recited in the claim which does not first introduce a learning unit. As such, claim 1 lacks proper antecedent basis and claims 2-7 depend therefrom further failing to cure the deficiency. Accordingly claims 1-7 are rejected as indefinite under 35 U.S.C. 112(b).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-6 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over:
Yang et al., US PG Pub No 2023/0252644A1 hereinafter Yang, in view of
Wu et al., “Unsupervised Domain Adaptive Graph Convolutional Networks” hereinafter Wu, in view of
Li et al., “Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction” hereinafter Li.
With respect to claim 1, Yang teaches:
An information processing apparatus configured to classify a plurality of delivery destinations into a plurality of groups {Yang [031] “electronic device including a processor and memory” see Fig 1, for classification [003-04, 078] e.g. segmentation type of classification, and/or clustering [047,098]. The destinations are parcels of land [002, 027] e.g. lat/long., address, geographic location of interest [040], so as for [027] “parcel segmentation” see Figs 2, 7-8}, comprising:
a memory storing a program {Yang Fig 1:103, [0037] “memory 103 and/or storage 104 may be configured to store programs(s)”}; and
at least one processor that, by executing the program stored in the memory {Yang Fig 1:102 [0037] “processor 102 … memory 103 and/or storage 104 may be configured to store programs(s) that may be executed by processor 102”}, is configured to:
perform unsupervised learning to train a graph convolutional neural network {Yang discloses [0027,29] “unsupervised GCN-based framework incorporates the graph-learning” learning i.e. training by loss functions e.g. [0080,82], GCN is graph convolution network Figs 4, 2}, which is determined using an adjacency matrix indicating a connection relationship of the plurality of delivery destinations {Yang [0071-72] “adjacency matrix… spatial distance between the two nodes” Eq.2 and shown at Fig 4 input}, and […] the learning unit performing unsupervised learning using a first loss function defined such that the smaller a value for distance between delivery destinations belonging to a same group {Yang [060] “a small Euclidean distance value” for merging superpixel patches [0059] e.g. less than ‘ < ‘ λ for merging or aggregating thus into aggregate or merged group, e.g. [067] “aggregated group of superpixels” and/or [049] “neighbors can be dynamically learned during the training” trained with loss functions [080-82] e.g. Eqs. 4-5 where loss includes reduced-sum} and
However, Yang does not expressly disclose the following limitations which are met by Wu:
receives as input a feature matrix indicating a feature of the plurality of delivery destinations {Wu [P.4 Sect. 4.1] “input feature matrix X” indicating feature Eq.1, Tbl.1, Fig 2 Xt is target/destination nodes in a GCN graph convolution network for unsupervised learning}, […] and the smaller a difference in feature between delivery destinations belonging to a same group, the less a loss {Wu [P.6 Sect. 4.3] Eq. 14 overall loss combines 3 loss functions Eqs.15-17 each prefaced by negative sign ‘ – ‘ is difference, the smaller difference and less a loss is interpreted to comprise “minimize the cross-entropy loss” see Figs 2,1, Alg.1}; and
Wu is directed to unsupervised graph convolution networks thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include input feature matrix and minimize loss per Wu in combination for a motivation [P.6 Sect.4.3.2] “feature extraction process… jointly optimized” to enable [P.2 Rt.Col] “combining source information, domain information and target information into a unified deep model” and [P.10 Conc.] “to learn the better representation for nodes in both source and target graphs… reduce the domain discrepancy and enable efficient domain adaptation.”
However, Yang and Wu does not appear to disclose the following limitation which is met by Li:
output information about a group to which the plurality of delivery destinations belongs, the information being obtained by inputting the feature matrix into the graph convolutional neural network trained by the learning unit {Li [P.5] Fig 1 shows Output Destination1…DestinationT subject to spatial attention, fusion and graph convolution described [P.6 Sect. 3.4] groups are a neighbor aggregate for spatial neighborhood in destination grids, and further discloses feature matrix for the GCN Fig 1. Additional detail provided [Sect.3]}.
Li is directed to destination prediction with graph convolution networks thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to output information pertaining to destinations per Li in combination to arrive at the invention as claimed for a motivation [P.2 Rt.Col] “significantly help online car-hailing scheduling and urban traffic management… Accurate and efficient destination prediction helps to organize traffic flow, improve vehicle utilization, reduce waiting time, and ease traffic congestion.”
With respect to claim 2, the combination of Yang, Wu and Li teaches the information processing apparatus according to claim 1, wherein the at least one processor is further configured to
perform the unsupervised learning using a second loss function, in addition to the first loss function, the second loss function being defined such that the smaller a sum of values calculated for each of the plurality of groups, the values being based on a difference between a total probability that each delivery destination belongs to a given group and an average number of delivery destinations per group, the less a loss {Yang [027] “unsupervised GCN” second loss is Eq.5 [082] combined at [080], in [082] Eq.5 term ∑reduce-sum is summation ∑-sigma and subscript “reduce-sum” is smaller sum of values, the group is from k=1 to g as per ∑gk=1 for a “probability that a node belongs to a partition k” [083], a difference is denoted with ‘ – ‘ subtraction operand, and an average is disclosed [097,103], see also [024] “graph partitioning aims to divide a vertex set under constraints, such that the edge cut across the partitions is minimized”}.
With respect to claim 5, the combination of Yang, Wu and Li teaches the information processing apparatus according to claim 1, wherein
the feature matrix includes information on a desired time slot for delivery as a feature of the plurality of delivery destinations {Li discloses [P.6 Sect. 3.4] “feature matrix… time slot t” e.g. [P.4 ¶1] “time slot destination matrix” see Figs 1,3 temporal attention with LSTM encoder-decoder for dest.’s}.
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include time slots for feature matrix as a feature of destinations per Li in combination to arrive at the invention as claimed for a motivation being that [P.9 ¶1] “Most of the destinations of online car-hailing during the morning rush hour… temporal attention mechanism learns the long time dependent characteristics of historical data and assigns higher weights to more relevant destinations over a particular time period.”
With respect to claim 6, the combination of Yang, Wu and Li teaches the information processing apparatus according to claim 5, wherein
the feature matrix includes, as a feature of the plurality of delivery destinations, information on a ratio of hours during which a delivery vehicle is in operation overlapping a desired delivery time slot {Li [P.9 Sect. 3.6.2] Eq. 24 is ratio for temporal attention, “car-hailing during the morning rush hour” conveys the ratio in terms of hours during which cars/vehicles are in operation, overlapping includes e.g. [P.4 ¶4] “same time”}. Motivation for combination is applied similarly as in claim 5.
With respect to claim 8, the rejection of claim 1 is incorporated. The difference in scope being an information processing method executed by information processing apparatus configured to perform steps of limitations similar to claim 1. Yang discloses [027] “method” Figs 5-6 and [033] “executed by processor” Fig 1. The remainder of this claim is rejected for the rationale as applied to claim 1.
With respect to claim 9, the rejection of claim 1 is incorporated. The difference in scope being a computer-readable non-transitory storage medium storing a program that makes a computer execute steps of limitations similar to claim 1. Yang discloses [006] “non-transitory computer-readable storage medium is configured to store instructions which, in response to an execution by a processor” again at [036-37] “software units implemented by processor” Fig 1. The remainder of this claim is rejected for the rationale as applied to claim 1.
Claims 3-4 are rejected under 35 U.S.C. 103 as unpatentable over Yang, Wu and Li in view of
Jiang et al., “MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling” hereinafter Jiang (arXiv: 2212.05989v1, Univ. Tokyo).
With respect to claim 3, the combination of Yang, Wu and Li teaches the information processing apparatus according to claim 1, wherein the at least one processor is further configured to
perform the unsupervised learning using a third loss function, in addition to the first loss function {Wu Fig 1 unsupervised GCN, [P.6 Sect.4.3] Eq.14 loss function adds three losses Eqs.15-17},
However, Wu does not appear to disclose the following limitation which is met by Jiang:
the third loss function being defined such that the closer a maximum probability of each delivery destination belonging to one of the plurality of groups is to a maximum value of values that can be taken as probabilities, the less a loss {Jiang [P.7-8 Sect. 4.3] Eq.9 “max” loss function combined at Eq.10 is totaled loss for minimization Alg.1 Line16, see also Figs 1-2}.
Jiang is directed to graph convolutional network training for connected cars on road networks thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to define a loss term by maximization per Jiang in combination to arrive at the invention as claimed as applying known techniques to known methods ready for improvement to yield predictable results and/or a stated motivation [P.3 ¶2] “we are motivated to propose a novel spatio-temporal meta-graph learning framework… empowers our modularized Meta-Graph Learner to essentially distinguish spatio-temporal patterns on different roads over time.”
With respect to claim 4, the combination of Yang, Wu and Li teaches the information processing apparatus according to claim 1, wherein the at least one processor is further configured to
perform the unsupervised learning using, in addition to the first loss function {Yang [027] discloses “unsupervised GCN” uses loss functions [080,82]},
a second loss function that is defined such that the smaller a sum of values calculated for each of the plurality of groups, the values being based on a difference between a total probability that each delivery destination belongs to a given group and an average number of delivery destinations per group, the less a loss {Yang [082] Eq.5 is second loss function, combined at [080], in [082] Eq.5 term ∑reduce-sum is summation ∑-sigma and subscript “reduce-sum” is smaller sum of values, the group is from k=1 to g as per ∑gk=1 for a “probability that a node belongs to a partition k” [083], a difference is denoted with ‘ – ‘ subtraction operand, and an average is disclosed [097,103], see also [024] “graph partitioning aims to divide a vertex set under constraints, such that the edge cut across the partitions is minimized”}, and
However, Yang does not disclose the following limitation which is met by Jiang:
a third loss function that is defined such that the closer a maximum probability of each delivery destination belonging to one of the plurality of groups is to a maximum value of values that can be taken as probabilities, the less a loss {Jiang [P.7-8 Sect. 4.3] Eq.9 “max” loss function combined at Eq.10 is totaled loss for minimization Alg.1 Line16, see also Figs 1-2 }.
Jiang is directed to graph convolutional network training for connected cars on road networks thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to define a loss term by maximization per Jiang in combination to arrive at the invention as claimed as applying known techniques to known methods ready for improvement to yield predictable results and/or a stated motivation [P.3 ¶2] “we are motivated to propose a novel spatio-temporal meta-graph learning framework… empowers our modularized Meta-Graph Learner to essentially distinguish spatio-temporal patterns on different roads over time.”
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yang, Wu and Li in view of:
Ye et al., “A Heterogeneous Graph Convolution based Method for Short-term OD Flow Completion and Prediction in a Metro System” hereinafter Ye (arXiv: 2108.03900v8).
With respect to claim 7, the combination of Yang, Wu and Li teaches the information processing apparatus according to claim 1. Ye teaches wherein
the feature matrix includes, as a feature of the plurality of delivery destinations, information about a direction from a delivery depot to a delivery destination or from a delivery destination to the delivery depot, and information about a distance between each of the plurality of delivery destinations and the delivery depot {Ye [P.3 Sect.III] Eq.1 “OD matrix” is origin-destination (from-to) metro stations are depots, the direction information includes “Inflow, Outflow” and further discloses “geographical distance between stations” see Figs 1-4, noting Fig 2 “Graph Convolution” arrow inputs from latest OD Matrix and Geo Distance Map, detail at [P.7 ¶3-4]}.
Ye is directed to graph convolution networks for routing and travel tasks thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include OD matrix with inflow/outflow and distance for graph convolution per Ye in combination to arrive at the invention as claimed for a motivation [P.1 ¶3] “OD prediction can better support metro systems for train scheduling, fine-grained abnormal flow warning, passenger route planning and it is also an essential input for sectional passenger flow prediction task. For example, the OD flow is the basis to get the travel demand of each metro line... help manager to recommend proper routes for passengers to achieve global traffic balance in whole metro systems” see contributions [P.2 Last¶].
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Osanlou et al., “Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search” arXiv: 2108.01036v4 discloses destination encoding as end-node feature
Senuma et al., “GEAR: A Graph Edge Attention Routing Algorithm Solving Combinatorial Optimization Problem with Graph Edge Cost” Waseda Univ., Tokyo GCN, depot Eq.12
Wang et al., JP2024011066A Waseda/Nippon, GCN. Recommend JPO consider
Akashi et al., US PG Pub No 2023/0274216A1 Nippon, Tokyo discloses delivery plan with destination and trained neural network.
Mo et Yamana, “GN-GCN: Combining Geographical Neighbor Concept with Graph Convolution Network for POI Recommendation” Waseda Univ., Tokyo see Figs 1-2
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chase P Hinckley whose telephone number is (571)272-7935. The examiner can normally be reached M-F 9:00 - 5:00.
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/CHASE P. HINCKLEY/Examiner, Art Unit 2124
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