DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 5/27/2026
Claim 1 are amended.
Claims 4 and 8 are cancelled.
Claims 1 – 3, 5 – 7, 9, and 10 are presented for examination.
Final Action
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Response to Arguments
Claim Rejections – 35 USC § 103
The Applicant argues that “Hakiri as best understood treats all agents the same, and recognizes that messages for any agent can be sent less frequently when an error between the extrapolated state of the agent and the real state remains below a threshold, and should be increased when an error exceeds the threshold. This in no way suggests agents should be divided into types based on one type of agent having a first maximum movement speed and a first required movement accuracy, and a second type of agent having a second maximum movement speed and second required movement accuracy.” The Applicant further states that “the Examiner seems to conflate actual speed at a given time within the simulation with maximum movement speed for a given type of simulated agent. Actual speed is simply not the same as maximum speed for a given agent type. As discussed above, maximum movement speed as used herein, is a property of a particular agent. Repeating the illustration, a sports car “agent” and a snail “agent” could have the same actual speed, but vastly different maximum movement speeds.”
In response, the Applicant’s assertion that, in the claims, the maximum movement speed is an attribute of the agent itself (i.e., the maximum seed of a snail vs. the maximum speed of a sports car) and not the same as the simulation speed is not persuasive because the Applicant is misinterpreting the Examiner’s previous arguments and the previous claim mapping.
The Examiner did not equate the “maximum movement speed” to the simulation speed of the individual agents but rather to a maximum movement speed at which the error threshold is exceeded for particular update transmission frequencies. The agent’s simulation speed is simply an indicator of whether or not the agent is above or below such maximum movement speed thresholds. When an agent is below such a threshold it is categorized into a first type of agent which has a lower transmission frequency/granularity. When an agent is above such a threshold it is categorized into a second type of agent requiring more frequent update transmissions (i.e., higher granularity).
Accordingly, simulations with more than one agent have a plurality of types of agents and the plurality of types of agents have different time granularity corresponding to a maximum movement speed and movement accuracy required of each type of agent.
the Applicant’s arguments rely on the claimed “types of agents” corresponding to “a maximum movement speed” being categorically static. The plain meaning of the claim does not support this. The claim merely states that there are:
“a plurality of types of agents including types having different time granularity (i.e., update transmission frequency) corresponding to a maximum movement speed and movement accuracy (i.e., error threshold) of each type of agent”
The claimed “maximum movement speed” is not limited in any way that would require the “maximum movement speed” be some intrinsic static maximum attribute speed of the snail or sports car itself.
To further help illustrate the Examiner’s claim interpretation and mapping see the illustration below.
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The snail is traveling at some speed below a maximum movement speed and its transmission frequency/granularity is below the error threshold of that maximum movement speed. The car is traveling at some speed above the first maximum movement speed which means it requires frequency 2 but is also traveling below the maximum movement speed for frequency 2 at which its error threshold is exceeded. Therefore, there are two types of agents. Snail agent at frequency/granularity 1 and sports car agents at frequency/granularity 2. Each of these agents is evaluated according to a maximum movement speed that is not the simulation speed of the agent but rather a threshold speed which determines the agent’s category.
Please review the Examiner’s response to arguments in the Office action dated 4/22/2026 which articulates how the cited prior art references are combined to achieve this mapping.
Under such an interpretation, the combination of prior art provides for a dynamic categorization of agents into a plurality of types of agents during the simulation. The claim does not exclude dynamic categorization of agents.
End Response to Arguments
Admitted Prior Art
The Applicant admits that multi-agent simulation is known in paragraph 3 – 4 and cites: JP WO2015/132893 and JP2014-174705A and WO2014/196073 as evidence that multi-agent simulation is known in the art.
A review of the JP WO2015/132893 prior art, for example, shows Figure 13 shown below, which clearly illustrates: a system for simulating a target world using a plurality of agents interacting with each other, comprising: a plurality of agent simulators provided for each of the plurality of agents and configured to simulate a state of each of the plurality of agents (CALC_NODE_1, 2, 3: calculation node) while causing the plurality of agents to interact with each other by exchange of messages (COM_DEV: communication device); and a center controller configured to communicate with the plurality of agent simulators to relay transmission and receipt of the messages between the plurality of agent simulators (COM_SW: communication switch)
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JP_WO2015132893_A1; however, does not explicitly teach “estimate a current state of the interaction agent from a stored past state of the interaction agent” nor to “estimate” current state of the interaction agent.
Accordingly, it appears that the difference between the elements of the independent claims which are admitted by the Applicant as known in the art and that which the Applicant claims as new is estimate[ing] a current state of the interaction agent from a stored past state of the interaction agent” and to “estimate” current state of the interaction agent.
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.
Claims 1, 2, 5, 6, 9, 10 are rejected under 35 U.S.C. 103 as being unpatentable over JP_WO2015132893_A1 in view of Hakiri_2010 (Survey study of the QoS Management in Distributed Interactive Simulation Through Dead Reckoning Algorithms August 2010) in view of Lee_2000 (Adaptive Dead Reckoning Algorithm for Distributed Interactive Simulation, 1999).
Claim 1. JP_WO2015132893_A1 makes obvious “a system for simulating a target world using a plurality of agents interacting with each other, comprising: a plurality of agent simulators provided for each of the plurality of agents and configured to simulate a state of each of the plurality of agents (CALC_NODE_1, 2, 3: calculation node) while causing the plurality of agents to interact with each other by exchange of messages (COM_DEV: communication device); and a center controller configured to communicate with the plurality of agent simulators to relay transmission and receipt of the messages between the plurality of agent simulators (COM_SW: communication switch) wherein each of the plurality of agent simulators is configured to: generate a state of an interaction agent that interacts with a target agent to be simulated based on a message transmitted from the center controller (Page 1 Abstract: “… agent-based simulation is executed under many scenarios… and is a time series indicating a history of messages transmitted and received by each of the plurality of agents under each scenario..” EXAMINER NOTE: a scenario is an interaction between two agents; Page 1 Background-art: “… in modeling a phenomenon, the entire world is sometimes regarded as a collection of many microscopic components that interact and interact with each other. In this case, in the simulation of the phenomenon, the behavior of each microscopic component is modeled and simulated by a computer… a technique called agent-based simulation (ABS) is known…” EXAMINER NOTE: the above teaches a interaction phenomenon/scenario between two components of the world where the components are modeled as agents; Page 2: “… messages are transmitted from one agent to another agent. In the agent-based simulation, the above-described microscopic components are modeled as agents, and the interrelationships between microscopic components are modeled as messages, thereby simulating the target world…” EXAMINER NOTE: a target agent and interaction agent is interpreted as any two agents that are interacting by sending and receiving messages. From the perspective of the target agent, an interaction agent is the agent from which messages are received and the agent to which messages are sent); store a generated state of the interaction agent (abstract: “… a storage device storing simulation result data…”; page 3: “… the agent AGENT shown in FIG. 2a includes a storage area AGENT_STORE that stores an agent behavior program PROG and an internal state STATE… a message reception unit RCV, and a message transmission unit… message MES transmitted and received between agents…”); simulate a current state of the target agent from an current state of the interaction agent;
create a message based on simulated current state of the target agent (page 3: “… when an agent AGENT receives a message through the message receiving unit RCV, the agent program interpretation/execution unit EXEC performs update processing of the internal state STATE of the agent according to the agent behavior program PROG… if necessary in this process, the agent AGENT refers to the message source agent identifier… and the internal state STATE at the time of message reception. further, when the internal sate reaches a condition determined by the agent behavior program as a result of the update process of the internal state, each agent performs a message transmission process to other agents… in agent based simulation, the microscopic components that constitute the target phenomenon are modeled as agents, and the interaction between microscopic components is modeled as a message… in general agent-based simulation, each agent receives a message from another agent and updates its internal state…”); and transmit a create message to the center controller” (FIG. 13 COM_SW); wherein the plurality of agents include a plurality of types of agents including types having different time granularity (Page 7: “… in the agent-based simulation, as described above, agents configure a network and send and receive messages to and from each other. Usually, the network between agents is not uniform. Agents that frequently send and receive messages to and from many agents (hub agents), and agents that send and receive messages to only a small number of agents at low frequencies is also present. Therefore, when the behavior of the target agent in all scenarios, that is, the transition of the internal state and the history of all messages sent and received by the agent are indexed, the variation of individual agent behavior between scenarios is indexed. Variations in average agent behavior between scenarios are different between an agent that sends a receive messages and an agent that sends and receives messages only with a small number of agents…” Page 10: “… attention is paid to the number of messages received by the target agent, the message transmission source agent, and the message transmission time as variation in agent behavior… the message transmission time…” page 12: “… the inter-scenario variation of agent behavior… considering that the update processing of the internal state of the agent is performed at the timing when the agent receives the message, the variation in the agent behavior scenario calculated in this way has a strong correlation with the frequency at which the agent receives the message… calculating the amount of agent behavior variation between scenarios caused by the network structure between agents, it is necessary to consider the frequency of messages received by each agent…” EXAMINER NOTE: The reason is that the above citations teach a plurality of agents where some agents send messages frequently while other agents send messages less frequently. The fact that the agents are sending messages at a different frequency indicates that the agents are at least of two types. Those that send frequent messages and those that send less frequent messages. The above citations clearly recite “message transmission time” and also uses the word “frequency” of received messages. The word frequency directly implies “time granularity” to those of ordinary skill in the art because the word frequency is defined as the number of times an event occurs within a specific, set period of time. A common unit of frequency is Hertz (Hz) and is defined as events per second, linking frequency directly to time measurement. ) the plurality of types of agents comprising a first type of agent having a first [behavior scenario], and a second type of agent having a second [behavior scenario]; wherein the time granularity of the first type of agent is longer than a time granularity of the second type of agent” (Page 7: “… in the agent-based simulation, as described above, agents configure a network and send and receive messages to and from each other. Usually, the network between agents is not uniform. Agents that frequently send and receive messages to and from many agents (hub agents), and agents that send and receive messages to only a small number of agents at low frequencies is also present. Therefore, when the behavior of the target agent in all scenarios, that is, the transition of the internal state and the history of all messages sent and received by the agent are indexed, the variation of individual agent behavior between scenarios is indexed. Variations in average agent behavior between scenarios are different between an agent that sends a receive messages and an agent that sends and receives messages only with a small number of agents…”; Page 10: “… attention is paid to the number of messages received by the target agent, the message transmission source agent, and the message transmission time as variation in agent behavior… the message transmission time…”; page 12: “… the inter-scenario variation of agent behavior… considering that the update processing of the internal state of the agent is performed at the timing when the agent receives the message, the variation in the agent behavior scenario calculated in this way has a strong correlation with the frequency at which the agent receives the message… calculating the amount of agent behavior variation between scenarios caused by the network structure between agents, it is necessary to consider the frequency of messages received by each agent…”
EXAMINER NOTE: The above citations teach that some agents send/receive messages frequently while some agents send/receive messages less frequently. The above citations also teach that “variation of agent behavior” is strongly correlated with the frequency of messages sent/received by the agents. The above citations clearly recite “message transmission time” and also uses the word “frequency” of received messages. The word frequency directly implies “time granularity” to those of ordinary skill in the art because the word frequency is defined as the number of times an event occurs within a specific, set period of time. A common unit of frequency is Hertz (Hz) and is defined as events per second, linking frequency directly to time measurement. Accordingly, the above citations make obvious two behavior scenarios. A first behavior scenario that causes a variation where agents sending messages less frequently and a second behavior scenario that causes a variation where agents sending messages more frequently.)
JP_WO2015132893_A1; however, does not explicitly teach “estimate a current state of the interaction agent from a stored past state of the interaction agent” nor to “estimate” current state of the interaction agent nor “corresponding to a maximum movement speed and movement accuracy required of each type of agent” nor “a first maximum movement speed and a first required movement accuracy” nor “a second maximum movement speed faster than the first maximum movement speed and a second required movement accuracy greater than the first required movement accuracy and each of the plurality of agent simulators is configured to transmit a message to the center controller at a transmission time interval corresponding to time granularity of the target agent”
Hakiri_2010 makes obvious “estimate a current state of the interaction agent from a stored past state of the interaction agent” and to “estimate” current state of the interaction agent (page 1 introduction: “… Dead Reckoning (DR) algorithms were proposed as message filtering techniques. It is a process consisting in the estimation one’s current position of an entity based upon a previously determined position and advance this position based upon known or estimated speed over elapsed time…”; page 1 2. Dead Reckoning Algorithm: “… Dead Reckoning technique was proposed. This technique dates back to the navigational techniques used to estimates ship’s current position based on start position, traveling velocity and elapsed time… by sending update messages less frequently and estimating the state information between updates. Each remote site maintains in addition to the real representation of the entity, a high fidelity model to estimate the remote entity state locally. The anticipated entity states are computed from the last states based on the extrapolation of the position, velocity, rotation and acceleration of the entity…”).
Hakiri_2010 further makes obvious “corresponding to a maximum movement speed and movement accuracy required of each type of agent (Page 1: “… Distributed Interactive Simulations (DIS) applications… in order to reduce the bottleneck and help to better manage the available network resources, Dead Reckoning (DR) algorithms were proposed as message filtering techniques. It is a process consisting in the estimation one’s current position of an entity based upon a previously determined position and advance this position based upon known or estimated speed over elapsed time… it seeks to… improve the spatial and the temporal coherence of remote entity. Consequently, a diminution of network traffic data can be achieved through the decrease of frequently data transmission… in order to reduce the number of updating message sent, Dead Reckoning technique was proposed… to estimate… current position based on start position, traveling velocity and elapsed time. DR is used to reduce the bandwidth consumption by sending update message less frequently… based on the extrapolation of the position, velocity, rotation and acceleration of the entity. When the gap between extrapolated states and the real state exceeds a defined threshold… the simulator transmits messages more frequently…” EXAMINER NOTE: The above citation clearly teaches to have agents that change the frequency of messages based on change in position based upon speed, velocity, rotation, and acceleration. Additionally, the above citation teaches to “improve the spatial and the temporal coherence.” To one of ordinary skill in the art the concept of improving coherence of a simulation means to improve the consistency, synchronization and logical unity of the simulated world across the computing nodes/agents by ensuring a shared accurate state of the simulation. Further, the above citation teaches that when there is a “gap between the extrapolated state and the real state that exceeds a threshold” (i.e., an error in estimated movement) to increase the message frequency. By increasing the frequency of the messages, the error is reduced and thereby increasing the accuracy.) “a first maximum movement speed and a first required movement accuracy” (Page 1: “… Distributed Interactive Simulations (DIS) applications… in order to reduce the bottleneck and help to better manage the available network resources, Dead Reckoning (DR) algorithms were proposed as message filtering techniques. It is a process consisting in the estimation one’s current position of an entity based upon a previously determined position and advance this position based upon known or estimated speed over elapsed time… it seeks to… improve the spatial and the temporal coherence of remote entity. Consequently, a diminution of network traffic data can be achieved through the decrease of frequently data transmission… in order to reduce the number of updating message sent, Dead Reckoning technique was proposed… to estimate… current position based on start position, traveling velocity and elapsed time. DR is used to reduce the bandwidth consumption by sending update message less frequently… based on the extrapolation of the position, velocity, rotation and acceleration of the entity. When the gap between extrapolated states and the real state exceeds a defined threshold… the simulator transmits messages more frequently…”; Page 2: “… Dead Reckoning Algorithm… in order to reduce the number of updating message set, Dead Reckoning technique was proposed… current position based on start position, travelling velocity and elapsed time. DR is used to reduce the bandwidth consumption by sending update messages less frequently and estimating the state information between updated… the anticipated entity states are computed from the last states based on the extrapolation of the position, velocity, rotation and acceleration of the entity. When the gap between the extrapolated states and the real states exceeds a defined threshold (THpos for the position and Thor for the orientation), the simulator transmits messages more frequently to anticipate the entities states of motion…”
EXAMINER NOTE: The above citation clearly teaches to have agents that change the frequency of messages based on change in position based upon speed, velocity, rotation, and acceleration. Further, the above citation teaches that when there is a “gap between the extrapolated state and the real state that exceeds a threshold” (i.e., an error in estimated movement) to increase the message frequency. By increasing the frequency of the messages, the error is reduced and thereby increasing the accuracy. Velocity is a vector, the magnitude of which is speed. As illustrated in Figure 3 of Hakiri_2010, positional error is a result of the magnitude of the velocity vector (i.e., speed) and therefore, there is a maximum speed, beyond which, a positional error threshold is exceeded.
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In other words, the above citation clearly teaches that objects with the behavior of moving below the speed which results in that object exceeding its positional error threshold transmit messages less frequently and therefore transmit with a longer time granularity while objects with the behavior of moving above the speed which results in that object exceeding its positional error threshold transmits messages more frequently and therefore transmit with a shorter time granularity.
Accordingly, Hakiri_2010 teaches a first behavior scenario and a second behavior scenario that explicitly relate the first and second behavior scenario to maximum movement speed and required movement accuracy
The first behavior scenario is when an object is moving below (i.e., slower than) the speed which results in that object exceeding its positional error threshold (i.e., a first maximum movement speed) and transmit messages less frequently and therefore transmit with a longer time granularity because the position error of the object is below its Epos threshold indicating that the objects required movement accuracy is within tolerance.
The second behavior scenario is when an object is moving above (i.e., faster than ) the speed which results in that object exceeding its positional error threshold (i.e., a second maximum movement speed) and transmits messages more frequently and therefore transmit with a shorter time granularity because the position error of the object is above its Epos threshold indicating that the objects required movement accuracy is outside of tolerance and more frequent messages will improve to coherence of the simulation and bring the object’s position error back within its required movement accuracy.)
Hakiri_2010 further makes obvious “and each of the plurality of agent simulators is configured to transmit a message to the center controller at a transmission time interval corresponding to time granularity of the target agent” (Page 4: “… the spatial coherence requires that at any time of the simulation, the gap between the entity state in the sender site Se and that in the receiver site Sr does not exceed the threshold. For example, in figure 3, the gap of the position shift represented by Ep should fulfill the following condition: Thpos
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|Epos| and the orientation error should satisfy the relation Thor
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|Eor|… every remote site have to know the occurrence of all events occurring on all other sites within a bounded delay…”; Figure 4; page 5: “… in the sender site Se… at Te1: at that time the high fidelity DR model begins sending data packets… In the receiver site Sr: At Tro the extrapolation error Er remains the same as in site Se at the same time Te0; particularly, the error Er reach the maximum allowed value (Thpos at Te1, the data when refresh packets are sent…” EXAMINER NOTE: The above teaches that the transmission occurs according to a time granularity (i.e., frequency) determined by the dead reckoning error and this error is the same in the receiver and sender. Accordingly, “and each of the plurality of agent simulators is configured to transmit a message to the center controller at a transmission time interval corresponding to time granularity of the target agent” is made obvious because amount of time between messages corresponds to the amount of time required to reach the error threshold on sender and/or receiver.)
JP_WO2015132893_A1 and Hakiri_2010 are analogous art because they are from the same field of endeavor called simulations. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine JP_WO2015132893_A1 and Hakiri_2010. The rationale for doing so would have been that JP_WO2015132893_A1 teaches to perform an agent based simulation over a network and also to determine the degree of difference between simulation results in the plurality of simulation results caused by the network structure. Hakiri_2010 teaches to reduce the number of messages sent on the network by using Dead Reckoning algorithms to reduce the burden on the network and also to improve temporal coherence of remote entities/agents on the network. Therefore, it would have been obvious to combine JP_WO2015132893_A1 and Hakiri_2010 for the benefit of reducing network traffic and improving agent coherence in a distributed interactive simulation to obtain the invention as specified in the claims.
Hakiri_2010 does not explicitly illustrates that the positional error threshold (Epos) is different for the first object in the first behavior scenario and the second object in the second behavior scenario. Therefore, while Hakiri_2010 clearly teaches “a first maximum movement speed and a first required movement accuracy” of the “first type of agent” and clearly teaches a “second type of agent” with a movement speed above the first maximum movement speed, it is not explicitly taught that a “second type of agent” has “a second maximum movement speed faster than the first maximum movement speed and a second required movement accuracy greater than the first required movement accuracy”
Lee_2000, however, makes obvious “a second maximum movement speed faster than the first maximum movement speed and a second required movement accuracy greater than the first required movement accuracy” (page 23 section 3 par 1 states that “generally, DR algorithms are implemented with a fixed threshold. Although it is easy for the simulator to operate the DR model, a fixed threshold may not adequately handle the dynamic relationship between entities”. Indeed, Figure 1 illustrates that agents may have different thresholds for positional accuracy. This is made more clear on page 24 which states: “… in all these cases, extrapolation of A needs to be accurate, though the degree of accuracy required is different… for each local simulation entity e that belongs to a simulator I, these four levels of threshold will be used in the extrapolation…”. This is illustrated in Figure 2 and Figure 3 where the movement of agents A, B, C, D, E are determined to have different accuracy threshold levels. Further, page 22 section 2, as illustrated in Table 1, the position is based upon velocity. As stated previously, velocity is a vector, the magnitude of which is speed. Therefore, similarly to Hakiri_2010, there is a maximum movement speed above which a positional error occurs (Epos) for a given movement accuracy. Section 3.2.1 and Figure 6 illustrates a corner case example where a motionless entity (ME) has zero update (i.e., message) frequency as there is no positional error while a test entity (TE) is moving with a velocity and has greater than zero update (i.e., message) frequency due to positional error exceeding the desired accuracy of the test entity. Therefore, this illustrates a corner case with a first type of simulation entity with a first maximum movement speed (i.e., zero) and a second type of simulation entity with a second maximum movement speed (i.e., greater than zero) and a second required movement accuracy greater than the first required movement accuracy.)
Hakiri_2010 and Lee_2000 are analogous art because they are from the same field of endeavor called simulations. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Hakiri_2010 and Lee_2000. The rationale for doing so would have been that Hakiri_2010 teaches to use a dead reckoning algorithm is an agent-based simulation and Lee_2000 teaches that “generally, DR algorithms are implemented with a fixed threshold. Although it is easy for the simulator to operate the DR model, a fixed threshold may not adequately handle the dynamic relationship between entities” Therefore, it would have been obvious to combine Hakiri_2010 and Lee_2000 for the benefit of handling the dynamic relationship between entities that require different accuracies as they move in the simulation environment to obtain the invention as specified in the claims.
Claim 5. The limitations of claim 5 are substantially the same as those of claim 1 and are therefore rejected due to the same reasons as outlined above for claim 1. Additionally, JP_WO2015132893_A1 makes obvious the further limitations of “a method” (technical-field: “the present invention relates to a simulation analysis method and an information processing system and more particularly to a method and apparatus…”).
Claim 2, 6. Hakiri_2010 makes obvious “wherein each of the plurality of agent simulators is configured to, when a number of stored past states of the interaction agent is two or more, estimate the current state of the interaction agent by linear extrapolation based on two or more latest past states of the interaction agent” (Page 1 - 3 section 2 Dead Reckoning Algorithm: “… the anticipated entity state are computed from the last states based on the extrapolation of the position, velocity, rotation and acceleration of the entity. When the gap between the extrapolated states and the real states exceeds a defined threshold (THpos for the postion and Thor for the orientation)… DIS standard [1] specifies a duration… 5 seconds… using first order or second order (equation(3)) extrapolation equations…” EXAMINER NOTE:
above citation recites “the last states” is plural which indicates 2 or more. Also, the dead reckoning algorithm is used at each time step to calculate the state as long as the gap/error does not exceed a threshold and is only reset every 5 seconds. Accordingly, this would have been obvious to those of ordinary skill in the art to estimate the current state when a number of stored past states of the interaction is two or more because the dead reckoning extrapolation is performed at every time up to 5 seconds.
A first order dead reckoning extrapolation is a linear extrapolation as the equation PDR(t) = Pi +V(t-ti) + Ai(t-ti)2 is a linear equation of the form PDR’(t) = V + 2A(t-ti). Additionally, in the context of dead reckoning a “first-order” model uses position and velocity to extrapolate motion linearly while a “second-order” model incorporates acceleration. The above citation indicates to use either the “first-order”: PDR(t) = Pi +V(t-ti) OR the “second-order”: PDR(t) = Pi +V(t-ti) + Ai(t-ti)2
The DIS standard cited above is the IEEE 1278.1A-1998 “standard for Distributed Interactive Simulation – Application protocols” which further outlines the dead reckoning is used with 2 or more past states and also explicitly recites to use first order dead reckoning.
Claim 9, 10. Hakiri_2010 makes obvious “Wherein the time granularity is changeable, and upper and lower limits of the time granularity are set for each type of the interaction agent” (Page 3: “…specifies a duration which is necessary to refresh the entities states… HEART BEAT TIMER to 5 seconds…” EXAMINER NOTE: this is the lower limit on time between messages.
Page 5: “… the interval between [Te1, Tr1] corresponds to the network delay, where packets move from the sender to the receiver. In fact, it seems within this interval the error Er is not predictable and can exceed the Dead Reckoning threshold (Thpos) generating the spatial incoherence…” Figure 5: Emax vs Thpos and the Ermax region. EXAMINER NOTE: the implication of these teachings is that due to network delays if message frequency is controlled only by Thpos (i.e., Dead Reckoning Threshold) then, there is the potential to enter a zone where the error is growing faster than the packets can be received resulting in more packets to be sent which will ultimately overwhelm the network. In other words, if the frequency of data packets exceeds the network's capacity (handling rate), it will cause network congestion. This occurs because the excessive, rapid influx of data fills up device buffers (queues) faster than they can be processed, leading to increased latency, packet loss, and severe performance degradation. By taking into consideration Emax, Hakiri_2010 is effectively teaching to put an upper limit on the time granularity (i.e., frequency of packets being send) of the packets being sent to avoid overwhelming the network and causing congestion in the system.).
Claims 3, 7 are rejected under 35 U.S.C. 103 as being unpatentable over JP_WO2015132893_A1 in view of Hakiri_2010 in view of Lee_2000 in view of IEEE 1278.1A-1998 (IEEE Standard for Distributed Interactive Simulation – Application Protocols, 19 August 1998).
Claim 3, 7. Hakiri_2010 makes obvious “wherein each of the plurality of agent simulators is configured to, when a number of stored past states of the interaction agent is only one, estimate the current state of the interaction agent by regarding an only past state of the interaction agent as the current state of the interaction agent” (page 3: “DIS standard [1] specifies a duration which is necessary to refresh the entities states… the reception of new packets implies the updating of the entity state…”)
IEEE 1278.1A-1998 makes obvious “wherein each of the plurality of agent simulators is configured to, when a number of stored past states of the interaction agent is only one, estimate the current state of the interaction agent by regarding an only past state of the interaction agent as the current state of the interaction agent” (page 13 section 4.5.2.4 Entity State Update PDU – 4.5.2.4.3 Receipt of the Entity State Update PDU: “upon receipt of an Entity State Update PDU… the simulation application shall use the information contained therein to model the position…”)
Hakiri_2010 and IEEE 1278.1A-1998 are analogous art because they are from the same field of endeavor called simulation. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Hakiri_2010 and IEEE 1278.1A-1998. The rationale for doing so would have been that Hakirir_2010 explicitly teaches to use the IEEE 1278.1A-1998 international standard during simulations. Therefore, it would have been obvious to combine Hakiri_2010 and IEEE 1278.1A-1998 for the benefit of complying with international standards and the explicit teaching to obtain the invention as specified in the claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN S COOK whose telephone number is (571)272-4276. The examiner can normally be reached 8:00 AM - 5:00 PM.
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/BRIAN S COOK/Primary Examiner, Art Unit 2187
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