DETAILED ACTION
Claims 1-9 and 21-23 are presented for examination.
Claims 10-20 are withdrawn.
Claims 21-23 are newly presented.
This office action is in response to election submitted on 02-SEP-2025.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/06/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
The last limitation of claim 1 recites the word “ythe”. Examiner interprets the word as “the”.
Appropriate correction is required.
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.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
the generator module being a trainable AI module that has been trained with a computer-supported discriminator module that functions as a generative adversarial network with the generator module so that the generator module generates land-use plan tensor data for good land use plans by repeated training cycles of an adversarial training process in each of which cycles: the generator module generates land-use data from said input data wherein said land-use data defines a land use plan for the associated geographical area; and
The training is developing a land use plan for a region based on the land use data. This amounts to observing the data (land use) and creating judgement (land use plan).
the discriminator module receives the land-use data from the generator module and derives therefrom quality assessment data corresponding to an assessment of quality of the land use plan of the land use data; and
Performing an assessment of the land use plan is an opinion based on the previous judgement.
the assessment data is returned to the generator module; wherein the training cycles are repeated for each input data until the generator module learns to derive said land-use data that defines land-use plans for which the discriminator module derives quality assessment data that reaches a predetermined threshold value; and
The evaluation and judgements are continued until a threshold value is reached.
wherein, responsive to being input context data for a new geographical region, said generator module generates a land-use tensor defining a land use plan for the new geographical region; and
Generating a plan of a new geographical region base is an opinion based on the previous land use data.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post solution activity to be insignificant extra-solution activity.
The following step is merely gathering the data on elements to be used in the calculation:
the generator module receives input data having context data for an associated geographical area;
Post solution activity:
the computer outputs land-use plan output data defining the land-use plan for the new geographical region.
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
a computer having an input receiving data and an output transmitting data to a display viewable by a user;
the computer having data storage with data therein that provides the computer with a generator module;
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post solution activity (Insignificant Extra-Solution Activity) and a general purpose computer do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(g) “Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir.2011)” is merely data gathering.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
2. The computerized system according to claim 1, wherein the land-use plan tensor is organized as a tensor with elements organized in three dimensions, wherein two of said dimensions are geographical dimensions of the geographical area, and the third dimension is different channels each containing data defining a respective type of land use over the geographical area.
Implementing a third dimension is only adding to the evaluation. Step 2A Prong 1.
3. The computerized system according to claim 1, wherein the types of land use include transportation, roads, residences, and stores.
Defining the data types is only furthering the data gathering. MPEP 2106.05(g).
4. The computerized system according to claim 1, wherein the land-use tensor for the new geographical region is transmitted to the discriminator module, and is output only if the assessment data for said land use plan has a value that reaches or exceeds a set value for minimum quality of the land-use plans of the generator module, and where the generator module is caused to generate another land use tensor responsive the assessment data indicating that the assessment data did not reach the set value for minimum quality for the land-use plan.
Setting thresholds for when to end the evaluation is only adding to the mental process. Step 2A Prong 1.
5. The computerized system according to claim 1, wherein input data and the input context data are each vectors containing context data regarding context areas around the associated geographical area or region.
Using vectors for the data is an additional element in the evaluation that remains abstract. Step 2A Prong 1.
6. The computerized system according to claim 5, wherein the context data includes data derived from at least one of housing prices, point of interest data of the context areas, and private or public transportation.
Adding additional data is only additional mere data gathering. MPEP 2106.05(g).
7. The computerized system according to claim 5, wherein said vectors include data from context areas, and said data is organized in a graph database wherein each of the context areas is a respective node.
The graph database is used in the evaluation and performing a judgement. Step 2A Prong 1.
8. The computerized system according to claim 6, wherein the graph database context data is embedded in the vector as a latent vector.
Further defining the vector a latent vector does not remove the vector from being abstract. Step 2A Prong 1.
9. The computerized system according to claim 1, wherein the assessment data contains a Q-value numerically indicative of quality of the respective land-use tensor received by the discriminator module and context data for adjacent areas of the associated land-use tensor.
The Q-value is a calculated value in the evaluation and is abstract. Step 2A Prong 1.
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.
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.
Claim(s) 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over
Zhang et al., “Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks” [2020] (hereinafter ‘Zhang’) [included in IDS] in view of
Albert, U.S. Patent Application Publication 2020/0082041 A1 (hereinafter ‘Albert’).
Regarding Claim 1: A computerized system for generating a land-use plan, said system comprising:
Zhang teaches
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the generator module being a trainable AI module that has been trained with a computer-supported discriminator module that functions as a generative adversarial network with the generator module so that the generator module generates land-use plan tensor data for good land use plans by repeated training cycles of an adversarial training process in each of which cycles: (Pg. 843 left col 2nd paragraph and Figure 2 Zhang “…To estimate the urban traffic, many estimation methods have been proposed from different perspectives. The classic traffic estimation methods have been extensively studied in the literature [3, 11, 32, 33]. These works train machine learning models using historical traffic data trying to capture the correlations among the past traffic, environmental features and the future traffic. However, when predicting the traffic impacts of drastic increased (or decreased) travel demands, these models would fail because they cannot capture the future traffic changes caused by the travel demand changes due to the lack of training samples…”)
Zhang teaches the generator module receives input data having context data for an associated geographical area; (Pg. 843 right col last paragraph Zhang “…The travel demand of an area captures the total number of departures in a period of time. Thus, we denote the travel demand of a grid cell s in time slot t…”)
Zhang teaches the generator module generates land-use data from said input data wherein said land-use data defines a land use plan for the associated geographical area; and (Pg. 844 right col 3rd paragraph Zhang “…In urban areas, the strength of traffic spatial auto-correlations is often heterogeneous, which mostly relies on the locations and the complex underlying road structures. Based on the First Law of Geography [24], nearby locations and closely connected roads often have stronger traffic spatial auto-correlations. Hence, we apply dynamic convolutional layers in both G and D, which can better capture the diverse footprints of spatial auto-correlations of traffic status…”)
Zhang teaches the discriminator module receives the land-use data from the generator module and derives therefrom quality assessment data corresponding to an assessment of quality of the land use plan of the land use data; and (Pg. 844 left col 3rd paragraph Zhang “…After applying the dynamic convolutional layer to capture the spatial auto-correlations of urban traffic, we are seeking a way to capture the temporal dependencies. Self-attention mechanism [26], which is mostly used in Seq2Seq models, achieves excellent performance when dealing with language modeling and machine translation problems. Self-attention mechanism handles sequential data including text, audios and videos, and learn the temporal dependencies from it. Compared with LSTM and GRU, self-attention mechanism is computed in parallel, and thus requires less time to train and results in higher training quality…”)
Zhang teaches the assessment data is returned to the generator module; (Pg. 846 left col last paragraph Zhang “…In each training iteration, we update the parameters θD of D with Eq. 5 and Eq. 6, where ηD is the learning rate…”)
Zhang teaches wherein the training cycles are repeated for each input data until the generator module learns to derive said land-use data that defines land-use plans for which the discriminator module derives quality assessment data that reaches a predetermined threshold value; and (Pg. 846 right col 4th paragraph “…The hourly average traffic speed is extracted from GPS records collected from taxis in Shenzhen, China from Jul 1st to Dec 31st, 2016. In this estimation task, we first partition Shenzhen City into 40 × 50 grid cells. The traffic status in each grid cell is measured by average traffic speed, and there are 4416 time slots (i.e., one hour) over 6 months. Then for each time slot (i.e., one hour), we obtain traffic distributions and travel demands of training regions, and use the daily traffic distribution sequences and travel demand sequences of training regions to train the model. The details of training region selection are in 4.4 and Appendix A…”)
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Zhang teaches wherein, responsive to being input context data for a new geographical region, said generator module generates a land-use tensor defining a land use plan for the new geographical region; and (Pg. 845 right col 3rd paragraph Zhang “…The generator G aims to generate daily sequential traffic distributions with respect to the daily travel demand sequence DR in a specific region. The input of the generator G includes three parts, i) a noise tensor Z = {z1, . . . , zNt } ∈ RNt ×Ns×Ns , randomly sampled from Gaussian distribution, ii) a condition tensor CR = {CR 1 , . . . ,CRNt } ∈ RNt ×Ns×4, where CR t is a matrix of size Ns × 4 defining the region location of R, travel demand and current time slot, i.e., CR t = Repeat(Concat(i, j, dR t , t )), where (i, j, dR t , t ) are concatenated to one vector and repeat for Ns times to form the matrix CR t , and iii) a traffic correlation matrix tensor AR. In generator, CR is concatenated into Z and it builds the mapping from distribution pZ(Z) to a traffic distribution G(CR,AR,Z)…”)
Zhang teaches the computer outputs land-use plan output data defining the land-use plan for ythe new geographical region. (Pg. 848 right col 3rd paragraph Zhang “…To clearly validate the estimated traffic by Curb-GAN against the ground-truth, we visualize the traffic distributions over the road networks. As shown in Figure 9, we pick two time slots of a day, i.e., rush hour (7:00-8:00) and non-rush hour (15:00-16:00), and visualize the traffic status on the road map of a specific region in Shenzhen. The ground-truth visualizations are compared with the estimation visualizations of Curb-GAN and the other four competitive baseline models. Due to page limit, we only use traffic speed to measure the traffic status but we got similar results using taxi inflow…”)
Zhang does not appear to explicitly disclose
a computer having an input receiving data and an output transmitting data to a display viewable by a user;
the computer having data storage with data therein that provides the computer with a generator module;
However, Albert teaches a computer having an input receiving data and an output transmitting data to a display viewable by a user; ([0066] Albert “…At least some embodiments include implementation of a flexible Application Programming Interfaces (APIs) and User Interfaces (Uis) layer designed for exposing platform capability for use-cases of interest…”)
Albert teaches the computer having data storage with data therein that provides the computer with a generator module; ([0006] Albert “…Another embodiment includes a system for generating simulations of physical variables of a physical system. The system includes a plurality of sensors, sensing physical data, one or more computing devices connected through a network to the plurality of sensors, and memory including instructions. When the instructions are executed, the one or more computing devices enable the system to obtain observational data, wherein the observational data includes at least one source of physical data sensed by the plurality of sensors, obtain numeric simulation data, and fuse the observation data and the numeric simulation data…”)
Zhang and Albert are analogous art because they are from the same field of endeavor, simulation in spatio-temporal models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the wherein, responsive to being input context data for a new geographical region, said generator module generates a land-use tensor defining a land use plan for the new geographical region as disclosed by Zhang by a computer having an input receiving data and an output transmitting data to a display viewable by a user and the computer having data storage with data therein that provides the computer with a generator module as disclosed by Albert.
One of ordinary skill in the art would have been motivated to make this modification in order to improve the computational efficiency as discussed in [0004] of Albert “…Thus, it is desirable to have methods, apparatuses, and systems for estimating physical parameters of a physical system based on models that emulate the complex spatialtemporal dynamics of numerical physics models, yet employ highly efficient computational architectures to perform these estimations, deployable on a variety of computing devices…”
Regarding Claim 2: Zhang and Albert teach The computerized system according to claim 1,
Albert teaches wherein the land-use plan tensor is organized as a tensor with elements organized in three dimensions, wherein two of said dimensions are geographical dimensions of the geographical area, and the third dimension is different channels each containing data defining a respective type of land use over the geographical area. ([0051] Albert “…At least some embodiments include generating one or more of the emulator models or modules/operators, and connecting them (in that, the output of one or more models become the input of other models) in ways specified by the application of interest, generating new, derived emulator models. For at least some embodiments, connecting emulators includes 1) formatting the output or one or more emulators in the form of tensors; 2) concatenating these output tensors in specific ways required by the input requirements of the other emulators that will ingest this output; 3) set up another set of emulators to ingest this concatenated output tensor data as inputs. For example, as indicated in FIG. 3, a wildfire emulator model may be comprised of a wind emulator module, of a temperature emulator module, and of a hydroclimate emulator module as sub-components. The output of such emulator modules will be in the form of tensors (gridded data), either 1 D, 2D, or in more dimensions, ingestible as input by other emulator modules…”)
Regarding Claim 3: Zhang and Albert teach The computerized system according to claim 1,
Zhang teaches wherein the types of land use include transportation, roads, residences, and stores. (Pg. 842 right col 1st paragraph Zhang “…For example, as shown in Figure 1, new sports village was planed to be built in Vaughan, Canada by the local government in 2019, which would increase the local travel demands to a great extent. Considering the potential traffic pressure the construction would bring, the plan was finally rejected [2]. Thus, urban traffic estimation is a critical step when evaluating an urban development plan before its deployment…”)
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Regarding Claim 4: Zhang and Albert teach The computerized system according to claim 1,
Albert teaches wherein the land-use tensor for the new geographical region is transmitted to the discriminator module, and is output only if the assessment data for said land use plan has a value that reaches or exceeds a set value for minimum quality of the land-use plans of the generator module, and where the generator module is caused to generate another land use tensor responsive the assessment data indicating that the assessment data did not reach the set value for minimum quality for the land-use plan. ([0083] Albert “…iterating steps 1-3 until convergence to a desired reduction in size of the trained spatial-temporal emulator model, yielding a compressed trained spatial-temporal model (that is, the steps of generating candidate mutations, evaluating the performance of the mutations, and retaining a subset of the candidate mutations is repeated over and over until a number of parameters or connections of parameters has been reduced which reduces the size of the trained spatial-temporal emulator model to a desired size)…”)
Regarding Claim 5: Zhang and Albert teach The computerized system according to claim 1,
Zhang teaches wherein input data and the input context data are each vectors containing context data regarding context areas around the associated geographical area or region. (Pg. 844 left col 4th paragraph Zhang “…The input and output of a self-attention layer are two sequences of vectors. In the self-attention process, each vector in the input sequence is linearly transformed into three vectors called query, key and value. Each output vector is computed as a weighted sum of all the values, where the weights are the outputs of a softmax layer, and the inputs of the softmax layer are scaled dot products of the corresponding query with all keys. Since a sequence of queries, keys and values can be combined in matrices form Q, K and V and computed in parallel, the self-attention function is calculated in Eq. 2…”)
Regarding Claim 6: Zhang and Albert teach The computerized system according to claim 5,
Zhang teaches wherein the context data includes data derived from at least one of housing prices, point of interest data of the context areas, and private or public transportation. (Pg. 844 right col 1st paragraph Zhang “…Hence, we propose a novel generative model – Curb-GAN which can more accurately capture the spatial auto-correlations and temporal dependencies of traffic, control the generation results with desired travel demands, and generate realistic traffic estimations in consecutive time slots…”)
Regarding Claim 7: Zhang and Albert teach The computerized system according to claim 5,
Zhang teaches wherein said vectors include data from context areas, and said data is organized in a graph database wherein each of the context areas is a respective node. (Pg. 843 right col 7th paragraph Zhang “…target region R is a square geographic region in the city, formed by Ns = ℓ × ℓ grid cells. The whole city can be split into overlapping regions R = {Ri j }, where Ri j = ⟨si j , ℓ⟩ is uniquely defined by an anchor grid cell si j on its top-left corner and a number ℓ of grid cells on the side…”)
Regarding Claim 8: Zhang and Albert teach The computerized system according to claim 6,
Albert teaches wherein the graph database context data is embedded in the vector as a latent vector. ([0077] Albert “…At least some embodiments include mechanisms to allow fine-grained controllability of the conditional generative architectures and explicit structuring of the latent space to relate it to interpretable physical simulation parameters. These mechanisms are implanted by 1) incorporating features and latent noise at different stages in the network architecture, 2) imposing assumptions about the distributions followed by the latent space random vectors, 3) incorporating boundary and initial conditions such as complex geometries/terrain elevation and remote-sensing observations to enable fast, realistic interpolation in simulation parameter configuration space…”)
Regarding Claim 9: Zhang and Albert teach The computerized system according to claim 1,
Zhang teaches wherein the assessment data contains a Q-value numerically indicative of quality of the respective land-use tensor received by the discriminator module and context data for adjacent areas of the associated land-use tensor. (Pg. 849 left col 2nd paragraph Zhang “…Since the number of stacked Building Block 1 (DyConv layers) and Building Block 2 (self-attention layers) inside generator and discriminator could influence the final estimation results, we evaluate the Curb-GAN with 2,3,4 stacked layers of Building Block 1 and 1,2,3 stacked layers of Building Block 2. The evaluations results are shown in Table 3. In both tasks, the more layers of Building Block 2 (self-attention) we use, the lower error we get in both metrics. It is because more layers of self-attention can better learned the temporal dependencies of traffic. When the number of DyConv layers increases from 2 to 4, the errors significantly decrease, which indicates too few of DyConv layers are not enough to capture the spatial auto-correlations, the structure of Curb-GAN should be adjusted to get the best estimation results for different datasets…”)
Allowable Subject Matter
Claim 21 is 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.
Claims 22 and 23 are also objected to and dependent on claim 21. Claims 22 and 23 are objected to for the same reasons as claim 21.
Conclusion
Claims 1-9 are rejected.
Claims 21-23 are object to.
Claims 10-20 are withdrawn.
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/JOHN E JOHANSEN/Examiner, Art Unit 2187