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 .
DETAILED ACTION
1. This Office Action is in response to the amendment filed on 08/19/2026.
Claims 1, 10, 11, 13, 20, 21, 22, 29 and 31 have been amended.
Claims 4, 9, 16, 19, 24, 28, 30 and 32 have been canceled.
Claims 33-44 have been added.
Claims 1-3, 5-8, 10-15, 17-18, 20-23, 25-27, 29, 31 and 33-44 are pending.
Information Disclosure Statement
2. The information disclosure statement (IDS) filed on 08/28/2026 and 07/13/2026 comply with the provisions of M.P.E.P. 609. The examiner has considered it.
Response to Arguments
3. Applicant's arguments with respect to claims 1-3, 5-8, 10-15, 17-18, 20-23, 25-27, 29, 31 and 33-44 have been considered but are moot in view of the new ground(s) of rejection.
Notice that the newly amended phrase in independent claim 1 and other independent claims is a little bit different from the canceled claims 4 and 9.
Examiner's Note
4. Is neural network interatomic potential the same as neural network potential? (According to Google): “Yes, a neural network interatomic potential is the exact same thing as a neural network potential. Terminology: (1) Neural Network Potential (NNP): The shorter, widely used term in physics, chemistry, and machine learning literature. (2) Neural Network Interatomic Potential (NNIP): A more explicit version of the same term, emphasizing that the neural network models physical interactions between atoms. (3) Other Names: Both are also referred to as a type of machine-learned interatomic potential (MLIP). Core Function: (1) Purpose: They use artificial neural networks to map atomic coordinates and configurations to a system's potential energy surface. (2) Benefit: They achieve near quantum-mechanical accuracy at a fraction of the computational cost, making large-scale molecular dynamics simulations possible. “
An information processing system (According to Google): "An information processing
system is a mechanism biological (like the human brain) or artificial (like a computer) that
receives, stores, manipulates, and transmits data by transforming raw input into meaningful
output. It operates through a cycle of input, processing, storage, and output, enabling decision-
making, learning, and interaction with the environment."
Inputting atomic information into a neural network (According to Google): "Inputting atomic
information into a neural network involves transforming 3D atomic structures (coordinates and
element types) into numerical, physically meaningful representations that a machine-learning
model can process to predict materials properties. This technique is used to create Machine-
learned interatomic potentials (MLP), which accurately simulate material behaviors-such as energy and force much faster than traditional, expensive quantum mechanical (DFT)
calculations."
Atomic information (According to Google): "In scientific machine learning, specifically Neural
Network Potentials (NNPs) for chemistry and materials science, "atomic information" refers to
the localized, environment-dependent data used to describe an individual atom. Rather than
treating a molecule as a single, massive input, the network decomposes the total system energy
into the sum of individual atomic contributions, where each atom's energy depends only on its
local neighborhood."
Neural network potentials (According to Google): "Neural network potentials (NNPs), or
machine-learned interatomic potentials (MLIPs), are Al-driven models that approximate
quantum mechanical accuracy in molecular simulations at a fraction of the computational cost.
They map atomic structures to potential energy, enabling large-scale, long-timescale simulations
for materials science and chemistry."
A synapse load representing circuit (According to Google): "A synapse load representing circuit,
frequently used in neuromorphic engineering, is an analog, mixed-signal, or digital hardware circuit that emulates the functionality of a biological synapse, specifically acting as the electrical
load and weight between a pre-synaptic neuron and a post-synaptic neuron. "
"These circuits are designed to mimic how synapses pass information, adjust the strength
(weight) of connections (synaptic plasticity), and impose electrical loads that affect the overall
behavior of the neural network."
Is a synapse an information processing device? (According to Google): "Yes, a synapse is
considered a fundamental information processing device, not just a simple bridge for transmitting signals between neurons."
"It functions as a key computational unit where input signals are modified, stored, and
integrated to form complex neural network outputs.'
Serializing to a byte array (According to Google): "Serializing to a byte array is the process of
converting a complex data object-such as a user profile, a game state, or a list-into a
sequence of bytes (1s and Os). This allows the data to be easily saved to a file, stored in a database, or sent over a network. Once serialized, the data can be reconstructed back into its
original object form through a process called deserialization."
A byte array (According to Google): "A byte array is a collection of bytes stored in a sequential
block of memory. Each element in the array is an 8-bit integer, typically ranging from 0 to 255.
Think of it as a raw, low-level container for binary data."
Backward processing in a neural network (According to Google): "Backward processing in a
neural network, commonly known as backpropagation (backward propagation of errors), is the
fundamental algorithm used to train neural networks by reducing errors. While forward
propagation passes data from the input layer to the output layer to make predictions, backward
propagation works in reverse from the output back to the input to compute gradients and
update weights based on the errors"
Is the quote "wherein weights for the first plurality of neural networks can be shared with the
second plurality of neural networks" implying the backward processing of the neural network?
(According to Google): "Yes, the concept of sharing weights between a first and second plurality
of neural networks strongly implies that the same weight parameters are updated during the
backward processing (backpropagation) phase, based on the combined gradients from both
networks"
Yang et al, US 12,511,349, [Yang: Column 8, lines 62-67 through column 9, lines 1-17 ("when
doing computer vision tasks, different images may be processed using different neural networks. In at least one embodiment, at a time of inferencing, each layer of a potential neural network has
many different operations (e.g., convolutions) that is performed. In at least one embodiment, for
each layer, all operations are performed and results are combined (e.g., averaged) and fed into
a next layer. In at least one embodiment, results are fed into a generator network that
reconstructs an image. In at least one embodiment, a reconstruction loss function compares how
a reconstructed image compares to an original image and values of reconstruction loss function
(e.g., information from inferencing) indicate which operation to select at each layer in network
504 to construct a neural network for that image. ", i.e., 'calculate a processing result
inputting the atomic information into a neural network')] [Yang: Column 85, lines 13-38 ("In at
least one embodiment, SoC interface 3037 can also enable communication with fixed function
devices within an SoC, such as camera imaging pipelines, and enables use of and/or implements
global memory atomics that may be shared between graphics core 3000 and CPUs within an
SoC. ")].
Arima et al, US, 5,148,514, [Arima: Column 3, lines 54-62 ("The minimum energy state
regularly has the highest probability at an arbitrary temperature. In general, it takes a long time
to reach the thermal equilibrium state, and hence it is considered preferable to start the
annealing from a high temperature and gradually reduce the annealing temperature. This state
transition is similar to such a state that each crystal atom is transferred to a position for taking
the minimum energy state at a given temperature in a crystal lattice", i.e., 'atomic information')]
[Arima : Column 53, lines 4-27 ("Thus, the educator signal control circuits 506 to 510 hold the
educator data of three patterns. In order to change the educator data which are supplied to the
neural network, the signal potentials of the control signals CTI and CT2 supplied to the selection circuit 616 may be simply changed SO as to change the educator data selected and
outputted by the selection circuit 616. FIG. 50 shows such an example that first educator data is
selected when the control signals CTI and CT2 of the selection circuits 616 are at high and low
levels while second educator data is selected when the control signals CT1 and CT2 are at low
and high levels respectively. Due to such structure, there is no need to again shift new educator
data in from the exterior, whereby the time required for setting the educator data can be
reduced", i.e., 'calculate a processing result inputting the atomic information into a neural
network ')] [Arima: Column 69, lines 50-65 ("the educator information is supplied from the
exterior not to be shifted in and out through shift registers, whereby it is not necessary to
successively shift educator information in the neuron unit NU every learning cycle, and hence
learning can be executed at a higher speed")].
Sedlmayr, US 20070095823, [Sedlmayr: Paragraph 135 ("Since the elemental "object" of the
universe is to have the lowest average energy of individual systems while maintaining the overall
total energy, then each individual energy system tries to have the lowest average stable energy.
For this law to be observed, each of the particles must interact with the other particles making
up the system of the atom to be as stable as possible. If some of the particles are "unstable", not
in the lowest stable energy state possible, then they move towards stability by interaction among
intra-molecular forces and intermolecular forces. ", i.e., 'atomic information )] [Sedlmayr:
Paragraph 137 ("All of the particles in an atomic system interact with the individual forces of all
of the other particles to arrive at an energy level. If the spins, rotations, repulsions, speeds,
directions, etc. of the individual particles are so balanced that it (the atom) is in the lowest
energy level, then this atom is stable and is an inactive element, i.e., it does not readily interact with other elemental atoms, although the individual particles will change their energy under the
influence of the proper force or energy field, this causing the entire system to be unbalanced and
interact with other atoms. However, the energy required to have a particle change its energy
level is greater than an atom that does not have all of its particles balanced in the lowest energy
levels", i.e., 'atomic information ')].
Claim Rejections - 35 USC § 103
5. 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 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.
6. 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.
7. Claims 1-3, 7-8, 12-16, 18, 20-23 and 27, 29, 31, 33-34, 38 and 41-42 are rejected under 35 U.S.C. 103 as being unpatentable over Arima et al (US, 5,148,514), in view of Kornbluth et al (US 20220100931).
Claim 1:
Arima suggests an information processing system comprising: a first information
processing device [Arima: Column 16, lines 63-67 through column 17, lines 1-47 ("A synapse
load representing circuit according to still another aspect of the present invention includes
synapse load value storage means which stores a specific synapse load value in the form of
charges, means for changing the synapse load value stored in the synapse load value storage
means in response to a pulsing signal from the aforementioned learning control circuit, and
means for transferring a received axon signal onto a corresponding dendrite signal line in the
form of a current signal with a weight represented by the stored synapse load value", i.e., any of
the "synapse load representing circuit" is considered as 'a first information processing device'
or 'a second information processing device ')]. Arima suggests a second information processing
device configured to transmit atomic information to the first information processing device
[Arima: Column 3, lines 54-62 ("The minimum energy state regularly has the highest probability
at an arbitrary temperature. In general, it takes a long time to reach the thermal equilibrium
state, and hence it is considered preferable to start the annealing from a high temperature and
gradually reduce the annealing temperature. This state transition is similar to such a state that
each crystal atom is transferred to a position for taking the minimum energy state at a given
temperature in a crystal lattice", i.e., 'atomic information')]. Arima suggests wherein the first
information processing device is configured to: receive the atomic information from the second
information processing device [Arima: Column 3, lines 54-62 ("The minimum energy state
regularly has the highest probability at an arbitrary temperature. In general, it takes a long time
to reach the thermal equilibrium state, and hence it is considered preferable to start the
annealing from a high temperature and gradually reduce the annealing temperature. This state
transition is similar to such a state that each crystal atom is transferred to a position for taking the minimum energy state at a given temperature in a crystal lattice", i.e., 'atomic
information')]. Arima suggests calculating a processing result corresponding to the atomic
information by inputting the atomic information into a neural network [Arima : Column 53, lines
4-27 ("Thus, the educator signal control circuits 506 to 510 hold the educator data of three
patterns. In order to change the educator data which are supplied to the neural network, the
signal potentials of the control signals CT1 and CT2 supplied to the selection circuit 616 may be
simply changed so as to change the educator data selected and outputted by the selection circuit
616. FIG. 50 shows such an example that first educator data is selected when the control signals
CT1 and CT2 of the selection circuits 616 are at high and low levels while second educator data
is selected when the control signals CT1 and CT2 are at low and high levels respectively. Due to
such structure, there is no need to again shift new educator data in from the exterior, whereby
the time required for setting the educator data can be reduced", i.e., 'calculate a processing
result inputting the atomic information into a neural network')]. Arima suggests transmitting
the processing result to the second information processing device [Arima: Column 69, lines 50-
65 ("the educator information is supplied from the exterior not to be shifted in and out through
shift registers, whereby it is not necessary to successively shift educator information in the
neuron unit NU every learning cycle, and hence learning can be executed at a higher speed")].
Kornbluth suggests wherein the neural network is an NNP (Neural Network Potential) model, wherein the first information processing device is configured to perform backward processing of the neural network in order to calculate force acting on each atom, and the processing result includes at least one of information related to the force or information obtained by using the force [Kornbluth: Title ("Neural-Network Interatomic Potential”)] [Kornbluth: Abstract (“For each processor of the plurality of processors, force vectors are predicted for the real elements within the multi-element system by making a backward pass through a graph neural network (GNN) having multiple layers and parallelized across multiple processors, the predicting including adjusting neighbor distance separately for each of the multiple layers of the GNN. A physical phenomenon is described based on the force vectors”)] [Kornbluh: Paragraph
2 ("Methodologies exist to calculate the underlying atomic forces used in the simulation of the motion of atoms")] [Kornbluh: Paragraphs 4-5 ("predict force vectors for the subset of real elements included within the partition by making a backward pass through a GNN having multiple layers and parallelized across the plurality of processing nodes")] [Kornbluh: Paragraph 19 (“what is desirable is a computational methodology for calculating atomic forces that delivers an adequate level of accuracy while consuming a reasonable amount of computing resources”)].
Both references (Arima and Kornbluh) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the
teachings of Arima and Kornbluh before him/her, to modify the system of Arima with the
teaching of Kornbluh in order to convey features of atomic information [Kornbluh: Title and Paragraphs 2, 4, 5 and 19].
Claim 2:
The combined teachings of Arima and Kornbluh suggest a plurality of the second
information processing devices [Arima: Column 16, lines 63-67 through column 17, lines 1-47
("A synapse load representing circuit according to still another aspect of the present invention
includes synapse load value storage means which stores a specific synapse load value in the
form of charges, means for changing the synapse load value stored in the synapse load value
storage means in response to a pulsing signal from the aforementioned learning control circuit,
and means for transferring a received axon signal onto a corresponding dendrite signal line in
the form of a current signal with a weight represented by the stored synapse load value", i.e.,
any of the "synapse load representing circuit" is considered as 'a first information processing
device' or 'a second information processing device')].
Claim 3:
The combined teachings of Arima and Kornbluh suggests wherein the first information
processing device is able to execute the calculating the processing result using the neural
network at a higher speed than the second information processing device [Arima: Column 69,
lines 50-65 ("the educator information is supplied from the exterior not to be shifted in and out
through shift registers, whereby it is not necessary to successively shift educator information in
the neuron unit NU every learning cycle, and hence learning can be executed at a higher speed")].
Claim 7:
Arima and Kornbluh suggest wherein the atomic information includes information
related to a type of an atom and a position of the atom [Kornbluh: Paragraph 21 ("These simulations include the following setup: (i) obtain the atomic positions; (ii) calculate forces based on the atomic positions; (iii) update velocities based on the forces as calculated; (iv) update the atomic positions based on velocities; and (v) repeat as desired")] [Feinberg:
Paragraph 27 (“atomic positions are mapped onto a graph with nodes (v) and edges (e). Then, key features, such as atom chemical type and distances")].
Both references (Arima and Kornbluh) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the
teachings of Arima and Kornbluh before him/her, to modify the system of Arima with the
teaching of Kornbluh in order to convey features of atomic information [Kornbluh: Paragraphs
21 and 27].
Claim 8:
The combined teachings of Arima and Kornbluh suggest wherein the processing result
includes at least one of energy, information calculated based on the energy, information
calculated using the neural network, or information related to a result of an analysis using an
output of the neural network [Arima: Column 5, lines 23-34 ("The equation (5) expresses an
energy gap between a state Si of "0" and a state Si of "1" of the unit i with respect to the energy E of the overall neural network. The equation (6) expresses such a probability that a new state Si
of the unit i takes the value "1" when such an energy gap takes place ")].
Claim 12:
The combined teachings of Arima and Kornbluh suggest wherein the second information processing device transmits, with the atomic information, at least one of information designating the neural network, metadata about the second information processing device, or metadata about a request for the first information [Kornbluh: Paragraph 21 (“For many properties, the length scale and time scale of interest is accessible by MD simulations, which is a simulation that predicts the motion of individual atoms. These simulations include the following setup: (i) obtain the atomic positions; (ii) calculate forces based on the atomic positions; (iii) update velocities based on the forces as calculated; (iv) update the atomic positions based on velocities; and (v) repeat as desired”)] [Kornbluh: Paragraphs 18-19 (“Methodologies exist to calculate the underlying atomic forces used in the simulation of the motion of atoms”)].
Both references (Arima and Kornbluh) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the
teachings of Arima and Kornbluh before him/her, to modify the system of Arima with the teaching of Kornbluh in order to convey features of atomic information [Kornbluh: Paragraphs
18, 19 and 21].
Claim 13:
Claim 11 is essentially the same as claim 1 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 14:
Claim 14 is essentially the same as claim 7 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 15:
Claim 15 is essentially the same as claim 3 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 18:
Claim 18 is essentially the same as claim 8 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 20:
Claim 18 is essentially the same as claim 10 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 21:
Claim 21 is essentially the same as claim 11 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 22:
Claim 22 is essentially the same as claim 1 and rejected under the same reasons as
applied above.
Claim 23:
Claim 23 is essentially the same as claim 3 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 24:
Claim 24 is essentially the same as claim 4 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 27:
Claim 27 is essentially the same as claim 7 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 28:
Claim 28 is essentially the same as claim 9 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 30:
Claim 30 is essentially the same as claim 9 except that it sets forth the claimed invention
as a device rather than a system and rejected under the same reasons as applied above.
Claim 31:
Claim 31 is essentially the same as claim 1 except that it sets forth the claimed invention
as a device rather than a system and rejected under the same reasons as applied above.
Claim 33:
The combined teachings of Arima and Kornbluh suggest wherein the information obtained by using the force includes at least one of a result of a dynamic calculation using the force or a result of a calculation of physical property values [Kornbluh: Paragraphs 20-21 (“to calculate interatomic forces of each atom, which are consequently used to modify the velocities of atoms in the simulation.”)].
Both references (Arima and Kornbluh) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the
teachings of Arima and Kornbluh before him/her, to modify the system of Arima with the teaching of Kornbluh in order to calculate of atomic forces [Kornbluh: Paragraphs
20 and 21].
Claim 34:
The combined teachings of Arima and Kornbluh suggest wherein the information obtained by using the force includes information obtained by using the NNP model multiple times [Kornbluh: Tile and Paragraph 1 (“using a neural-network force field (NNFF) across multiple CPUs.”)].
Both references (Arima and Kornbluh) taught features that were directed to analogous art and they were directed to the same field of endeavor, such as data processing. It would have been obvious to one of ordinary skill in the art at the time the invention was made, having the
teachings of Arima and Kornbluh before him/her, to modify the system of Arima with the
Claim 38:
Claim 38 is essentially the same as claim 34 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
Claim 41:
Claim 41 is essentially the same as claim 33 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
Claim 42:
Claim 42 is essentially the same as claim 34 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
8. Claims 5-6, 10-11, 17 and 25-26 are rejected under 35 U.S.C. 103 as being
unpatentable over Arima et al (US, 5,148,514), in view of Kornbluth et al (US 20220100931),
and further in view of Kroepfl et al (US 20210063200).
Claim 5:
The combined teachings of Arima, Kornbluh and Kroepfl suggest wherein the second
information processing device transmits a byte array of the atomic information stored in a
memory of the second information processing device to the first information processing device
without converting a data format [Kroepfl: Abstract and paragraph 55 ("more deep neural
networks (DNNs) ")] [Kroepfl: Paragraphs 80-81 ("For example, the sensor data 102 and/or the
outputs (e.g., in either case without conversion or after conversion where not generated in a 3D
coordinate space relative to the vehicle 1500) may be compressed and/or minimized.. ")I
[Kroepfl: Paragraph 71 ("Additional compression may be realized by chunking data into smaller e.g., 10,000 byte chunks, converting the data to a byte array, and then transmitting or
uploading to the cloud for map creation")].
Three references (Arima, Kornbluh and Kroepfl) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made,
having the teachings of Arima, Kornbluh and Kroepfl before him/her, to modify the system of
Arima and Kornbluh with the teaching of Kroepfl in order to either convert or not convert data
[Kroepfl: Paragraphs 71 and 81-81].
Claim 6:
The combined teachings of Arima, Kornbluh and Kroepfl suggest wherein the second
information processing device transmits a byte array of the atomic information stored in a
memory of the second information processing device to the first information processing device
without serializing the byte array [Kroepfl: Abstract and paragraph 55 ("more deep neural
networks (DNNs) ")] [Kroepfl: Paragraphs 80-81 ("For example, the sensor data 102 and/or the
outputs (e.g., in either case without conversion or after conversion where not generated in a 3D
coordinate space relative to the vehicle 1500) may be compressed and/or minimized. ")]
[Kroepfl: Paragraph 71 ("Additional compression may be realized by chunking data into
smaller-e.g., 10,000 byte-chunks, converting the data to a byte array, and then transmitting or
uploading to the cloud for map creation")] [Kroepfl: Paragraph 285 ("may utilize distributed
file system 1738 for large-scale data processing (e.g., "big data"). ")].
Three references (Arima, Kornbluh and Kroepfl) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made, having the teachings of Arima, Kornbluh and Kroepfl before him/her, to modify the system of
Arima and Kornbluh with the teaching of Kroepfl in order to either convert or not convert large data [Kroepfl: Paragraphs 71, 81-81 and 285].
Claim 10:
The combined teachings of Arima, Kornbluh and Kroepfl suggest wherein the first
information processing device transmits a byte array of the information related to the force stored
in a memory of the first information processing device to the second information processing
device without converting a data format [Kroepfl: Abstract and paragraph 55 ("more deep
neural networks (DNNs) ")] [Kroepfl: Paragraphs 80-81 ("For example, the sensor data 102
and/or the outputs (e.g., in either case without conversion or after conversion where not
generated in a 3D coordinate space relative to the vehicle 1500) may be compressed and/or
minimized.. ")] [Kroepfl: Paragraph 71 ("Additional compression may be realized by chunking
data into smaller-e.g., 10,000 byte-chunks, converting the data to a byte array, and then
transmitting or uploading to the cloud for map creation")].
Three references (Arima, Kornbluh and Kroepfl) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made,
having the teachings of Arima, Kornbluh and Kroepfl before him/her, to modify the system of
Arima and Kornbluh with the teaching of Kroepfl in order to either convert or not convert data
[Kroepfl: Paragraphs 71 and 81-81].
Claim 11:
The combined teachings of Arima, Kornbluh and Kroepfl suggest wherein the first
information processing device transmits a byte array of the information related to the force to the second information processing device without serializing the byte array [Kroepfl: Abstract and
paragraph 55 ("more deep neural networks (DNNs) ")] [Kroepfl: Paragraphs 80-81 ("For
example, the sensor data 102 and/or the outputs (e.g., in either case without conversion or after
conversion where not generated in a 3D coordinate space relative to the vehicle 1500) may be
compressed and/or minimized.. ")] [Kroepfl: Paragraph 71 ("Additional compression may be
realized by chunking data into smaller-e.g., 10,000 byte-chunks, converting the data to a byte
array, and then transmitting or uploading to the cloud for map creation")] [Kroepfl: Paragraph
285 ("may utilize distributed file system 1738 for large-scale data processing (e.g., "big
data"). ")].
Three references (Arima, Kornbluh and Kroepfl) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made,
having the teachings of Arima, Kornbluh and Kroepfl before him/her, to modify the system of
Arima and Kornbluh with the teaching of Kroepfl in order to either convert or not convert large data [Kroepfl: Paragraphs 71, 81-81 and 285].
Claim 17:
Claim 17 is essentially the same as claim 6 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 25:
Claim 25 is essentially the same as claim 5 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
Claim 26:
Claim 26 is essentially the same as claim 6 except that it sets forth the claimed invention
as a method rather than a system and rejected under the same reasons as applied above.
9. Claims 35, 37, 39 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Arima et al (US, 5,148,514), in view of Kornbluth et al (US 20220100931), and further in view of Yamada et al (US 20220005866).
Claim 35:
The combined teachings of Arima, Kornbluh and Yamada suggest wherein the
processing result includes an output from an intermediate layer of the neural network [Yamada: Paragraph 113 (“For example, such connecting corresponds to the process of returning a result of the third intermediate layer L.sub.m3 to the first intermediate layer L.sub.m1 in the neural network NN illustrated in FIG. 7. In other words, by performing such connecting, the integrated device 200 can realize a recurrent neural network. The recurrent neural network can handle data of a nonlinear time series”)].
Three references (Arima, Kornbluh and Yamada) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made,
having the teachings of Arima, Kornbluh and Yamada before him/her, to modify the system of
Arima and Kornbluh with the teaching of Yamada in order to return results to intermediate layers of a neural network [Yamada: Paragraph 113].
Claim 37:
Claim 37 is essentially the same as claim 35 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
Claim 39:
Claim 37 is essentially the same as claim 35 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
Claim 43:
Claim 42 is essentially the same as claim 35 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
10. Claims 36, 40 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Arima et al (US, 5,148,514), in view of Kornbluth et al (US 20220100931), and further in view of Rawal et al (US 20190303028).
Claim 36:
The combined teachings of Arima, Kornbluh and Rawal suggest wherein the first information processing device is configured to function as a Software as a Service (SaaS) [Rawal: Paragraph 121 (“The program instructions can also be contained in, and provided as, an electronic signal, for example in the form of software as a service (SaaS) delivered from a server (e.g., a distributed system and/or a cloud computing system).”)].
Three references (Arima, Kornbluh and Rawal) taught features that were directed to
analogous art and they were directed to the same field of endeavor, such as data processing. It
would have been obvious to one of ordinary skill in the art at the time the invention was made,
having the teachings of Arima, Kornbluh and Rawal before him/her, to modify the system of
Arima and Kornbluh with the teaching of Rawal in order to implement a Software as a Service, SaaS [Rawal: Paragraph 121].
Claim 40:
Claim 40 is essentially the same as claim 36 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
Claim 44:
Claim 44 is essentially the same as claim 36 except that it sets forth the claimed invention as a device rather than a system and rejected under the same reasons as applied above.
Conclusion
11. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404]. The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached on [571-272-4080]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Hung Le
09/24/2026
/HUNG D LE/Primary Examiner, Art Unit 2161
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