DETAILED ACTION
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 .
Claims 1-6, 8 and 9 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6 and 8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 1, it is unclear what the limitation “an output time interval for firing and a transmitting spike are divided” means and/or how the limitation is structured. This limitation is indefinite. The phrase “a transmitting spike” is set off by the conjunction “and” as a noun phrase, such that it is unclear whether “a transmitting spike” is intended as a third element that “are divided”, which is unclear because a spike is not a time interval that is divided, or whether the output time interval is intended to be “for firing and transmitting a spike.” Because the claim is amenable to two or more plausible constructions, one of which is unclear, the metes and bounds of the claim cannot be determined with reasonable certainty. For the purposes of examination, Examiner will interpret the limitation as an output time interval for firing and transmitting a spike, for example, the limitation may be interpreted as “for firing and transmitting a spike.”
With respect to claim 4, it is unclear what “such that the membrane potential of the neuron model” [line 2] refers to. Claim 4 is depended on claim 1. However, claim 1 never recites ‘a membrane potential’. For the purposes of examination, Examiner will interpret the limitation as “such that a membrane potential of the neuron model.”
With respect to claim 8, it is unclear how the limitations “a processor configured to execute the instructions to:” and “changing an index value of signal output …; and transmitting a spike …” are linked to each other. The infinitive construction “configured to execute the instructions to:” calls for a verb in the base/infinitive form (to change and to transmit), but the claim instead uses “changing” and “transmitting”. It is unclear whether the limitations are intended as functions the processor is configured to perform (apparatus-style functional limitations) or as method steps recited within an apparatus claim, so that a person of ordinary skill in the art cannot determine with reasonable certainty the scope of the processor configuration. For the purposes of examination, Examiner will interpret the limitation as “a processor configured to execute the instructions to: change an index value of signal output …; and transmit a spike …”
With respect to claims 2, 3, 5 and 6, they are rejected based on their virtual dependency of claim 1.
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 1-6, 8 and 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to a computing device, claim 8 is drawn to a neuron model device comprising a memory and a processor configured to execute instructions, and claim 9 is drawn to a method. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1, 8 and 9 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 8 and 9 recite a method of identifying an input time interval for receiving a spike and an output time interval in which spike transmission is permitted that are divided in association with forcible firing that under its broadest reasonable interpretation enumerates a mathematical concept. A human can perform the calculation using words or using mathematical symbols to identify an input interval after observing a spike and an output interval. Therefore, the step of identifying an input time interval for receiving a spike and an output time interval is nothing more than a mathematical concept (MPEP 2106.04(a)(2)(I)).
Claims 8 and 9 recite further a method of changing an index value of signal output based on an input status of a signal within the input time interval that under its broadest reasonable interpretation enumerates a mathematical concept. A human can perform the calculation using words or using mathematical symbols to change an index value of an output. Therefore, the step of changing an index value of signal output based on an input is nothing more than a mathematical concept (MPEP 2106.04(a)(2)(I)).
Step 2A – Prong 2
Claims 8 and 9 recite further transmitting a spike within the output time interval by firing based on the index value of the signal output that fails to integrate the abstract idea into a practical application. The step of transmitting a spike is a form of insignificant input and output solution activities, where transmitting a spike within a time interval is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B
The additional element in step 2A-Prong 2 that is a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision have determined that this additional element of transmitting a spike within a time interval to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1, 8 and 9 are not patent eligible.
Dependent claims
Claims 2-6 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental process that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 2-6 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claims 2-6 are drawn to a computing device. Therefore, this particular claim group falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claim 6 recites further the mathematical concept by wherein the neuron model starts transmitting the spike in a form of a rectangular waveform in a case where the membrane potential of the neuron model satisfies a predetermined condition within the output time interval, and stops transmitting the spike at a time when the output time interval ends that is based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 2 recites further the insignificant extra solution activities by wherein a firing time at which the neuron model fires within the output time interval is a function of membrane potential of the neuron model at a last time of the input time interval. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 3 recites further the insignificant extra solution activities by wherein the neuron model restricts firing in a case where the membrane potential of the neuron model at the last time of the input time interval is outside a predetermined range. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 4 recites further the insignificant extra solution activities by wherein the neuron model is formed such that the membrane potential of the neuron model within the output time interval increases with a slope unique to each neuron model. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 5 recites further the insignificant extra solution activities by wherein the neuron model is formed to forcibly fire in a case where the membrane potential of the neuron model satisfies a predetermined condition at a posterior edge of the output time interval, or not to fire in the same case. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
As such, dependent claims 2-6 are not patent eligible.
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 1-6, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Nishi et al (US 12657441 B2) hereafter Nishi, and further in view of Hunzinger et al (US 20130204819 A1) hereafter Hunzinger.
With respect to claim 1, Nishi teaches a computing device comprising: a neuron model in which an input time interval for receiving a spike and an output time interval for firing and a transmitting spike are divided, wherein the neuron model fires within the output time interval (a spiking neural network includes a synaptic element, wherein the synaptic element has a variable weight and outputs, in response to input of a first spike signal, a synaptic signal. During first phase (or cycle), spikes are received and accumulated only. The neuron circuit outputs a second spike signal where a predetermined firing condition is satisfied. During second phase (or cycle), neurons that satisfy a firing condition performs the firing process. The state of neuron is represented by membrane potential. Spike voltages are input to the neuron model, and when the membrane potential reaches a threshold, the neuron fires. A spike voltage is input to the neuron model via a synapse having a weight at time t_pre, and the neuron model fires at time t_post. In the input time, a spike voltage is input to the neuron model, and in the output time, the neuron only fires or transmit [col. 2, lines 40-60; col. 4, line 55 - col. 5, line 15]),
However, Nishi does not particularly disclose wherein the neuron model fires within the output time, with firing within the input time interval being restricted.
In the same field of endeavor, Hunzinger teaches wherein the neuron model fires within the output time, with firing within the input time interval being restricted (an exponentially growing membrane potential and firing of a neuron with respect to time are disclosed. When the membrane potential of the neuron is above zero by a certain amount, the potential grows exponentially. The neuron will typically fire unless there are inhibitory inputs sufficient to bring the potential back to zero or below. On the other hand, excitatory inputs will merely cause the neuron to fire sooner. During such a time, firing may not happen as the neuron may have the inhibitory inputs. Positive and negative input values 802 and positive and negative scaling values 804 may lead to excitatory (firing) and inhibitory (not firing) inputs 806 and 808 on a neuron 810 [par. 0115 and FIGS. 5 & 8]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of learning input delays to achieve a target output delay as suggested by Hunzinger into the concept of outputting a synaptic signal in response to a first spike signal and outputting a second spike signal in a case where the synaptic signal is inputted as suggested by Nishi because both of these systems addressing the process of applying membrane potential in neuron model of a spiking neural network to either perform the firing a spike or restrict the firing. Doing so would be desirable because the concept of Nishi would be more efficient by converting timing information (input time relative to input inference time) in a spiking neural network to input multiple neurons, wherein the timeframe is an excitatory (firing) and/or inhibitory (no firing) potential (Hunzinger, [par. 0023, 0024, 0115]).
With respect to claim 2, the combination of Nishi and Hunzinger teaches wherein a firing time at which the neuron model fires within the output time interval is a function of membrane potential of the neuron model at a last time of the input time interval (Hunzinger, the method includes determining an input time spike time relative to an input reference time based on a logarithm of an input value. The logarithm has a base equal to an exponential value of the coefficient of change of the membrane potential as a function of the membrane potential. A neuron output to a post-synaptic neuron represents a negative value by the absolute value and inhibition for a positive coefficient and excitation for a negative coefficient [par. 0023, 0169-0171 and FIG. 10]).
With respect to claim 3, the combination of Nishi and Hunzinger teaches wherein the neuron model restricts firing in a case where the membrane potential of the neuron model at the last time of the input time interval is outside a predetermined range (Hunzinger, when the membrane potential of the neuron is above zero by a certain amount, the potential grows exponentially. The neuron will typically fire unless there are inhibitory inputs sufficient to bring the potential back to zero or below. On the other hand, excitatory inputs will merely cause the neuron to fire sooner. During such a time, firing may not happen as the neuron may have the inhibitory inputs. Positive and negative input values 802 and positive and negative scaling values 804 may lead to excitatory (firing) and inhibitory (not firing) inputs 806 and 808 on a neuron 810 [par. 0115 and FIGS. 5 & 8]).
With respect to claim 4, the combination of Nishi and Hunzinger teaches wherein the neuron model is formed such that the membrane potential of the neuron model within the output time interval increases with a slope unique to each neuron model (Nishi, a slope increased indicates the rate of change of the membrane potential with respect to time. The integration circuit integrates the synaptic current to convert the same to a voltage called a membrane potential. The temporal change in the membrane potential is determined by a neuron model set in advance in the integration circuit. For example, a leaky integrate and fire (LIF) model may be used as the neuron model [col. 8, lines 50-60]).
With respect to claim 5, the combination of Nishi and Hunzinger teaches wherein the neuron model is formed to forcibly fire in a case where the membrane potential of the neuron model satisfies a predetermined condition at a posterior edge of the output time interval, or not to fire in the same case (Nishi, when a predetermined firing condition is satisfied, the neuron circuit fires and outputs a spike signal. For example, the neuron circuit fires when the value exceeds a predetermined threshold and emits a spike signal toward a downstream neuron circuit. The threshold comparator circuit compares the membrane potential outputted from the integration circuit with the predetermined threshold. The spike generation circuit generates and outputs a spike voltage when the membrane potential exceeds the threshold [col. 7, lines 10-40; col. 8, line 60 – col. 9, line 30]).
With respect to claim 6, the combination of Nishi and Hunzinger teaches wherein the neuron model starts transmitting the spike in a form of a rectangular waveform in a case where the membrane potential of the neuron model satisfies a predetermined condition within the output time interval, and stops transmitting the spike at a time when the output time interval ends (Nishi, the neuron circuit outputs a second spike signal (second phase/cycle) in a case where the synaptic signal is inputted and a predetermined firing condition for the synaptic signal is satisfied [col. 2, lines 40-55; col. 7, lines 10-40; col. 10, line 55 - col. 11, line 30]).
With respect to claim 8, Nishi teaches a neuron model device, wherein an input time interval for receiving a spike and an output time interval in which spike transmission is permitted are divided in association with forcible firing (a spiking neural network includes a synaptic element, wherein the synaptic element has a variable weight and outputs, in response to input of a first spike signal, a synaptic signal. During first phase (or cycle), spikes are received and accumulated only. The neuron circuit outputs a second spike signal where a predetermined firing condition is satisfied. During second phase (or cycle), neurons that satisfy a firing condition performs the firing process. The state of neuron is represented by membrane potential. Spike voltages are input to the neuron model, and when the membrane potential reaches a threshold, the neuron fires. A spike voltage is input to the neuron model via a synapse having a weight at time t_pre, and the neuron model fires at time t_post. In the input time, a spike voltage is input to the neuron model, and in the output time, the neuron only fires or transmit [col. 2, lines 40-60; col. 4, line 55 - col. 5, line 15]).
However, Nishi does not disclose the neuron model device comprising: a memory configured to store instructions; and a processor configured to execute the instructions to: changing an index value of signal output based on an input status of a signal within the input time interval; and transmitting a spike within the output time interval by firing based on the index value of the signal output.
In the same field of endeavor, Hunzinger teaches the neuron model device comprising: a memory configured to store instructions; and a processor configured to execute the instructions (a computer product for learning using a spiking neural network that includes a computer-readable medium having instructions executable to provide at each of one or more learning neuron models a set of logical inputs [par. 0012, 0020]) to:
changing an index value of signal output based on an input status of a signal within the input time interval; and transmitting a spike within the output time interval by firing based on the index value of the signal output (an exponentially growing membrane potential and firing of a neuron with respect to time are disclosed. When the membrane potential of the neuron is above zero by a certain amount, the potential grows exponentially. The neuron will typically fire unless there are inhibitory inputs sufficient to bring the potential back to zero or below. On the other hand, excitatory inputs will merely cause the neuron to fire sooner. During such a time, firing may not happen as the neuron may have the inhibitory inputs. Positive and negative input values 802 and positive and negative scaling values 804 may lead to excitatory (firing) and inhibitory (not firing) inputs 806 and 808 on a neuron 810. The difference between a self-referential relative time and a non- self-referential relative time may be considered by spike trains, wherein the spiking output of a neuron having an index of a spike and multiple spikes of a neuron [par. 0104, 0105, 0115 and FIGS. 5 & 8]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of learning input delays to achieve a target output delay as suggested by Hunzinger into the concept of outputting a synaptic signal in response to a first spike signal and outputting a second spike signal in a case where the synaptic signal is inputted as suggested by Nishi because both of these systems addressing the process of applying membrane potential in neuron model of a spiking neural network to either perform the firing a spike or restrict the firing. Doing so would be desirable because the concept of Nishi would be more efficient by converting timing information (input time relative to input inference time) in a spiking neural network to input multiple neurons, wherein the timeframe is an excitatory (firing) and/or inhibitory (no firing) potential (Hunzinger, [par. 0023, 0024, 0115]).
With respect to claim 9, it is a computing method claim that is corresponding to the neuron model device of claim 8. Therefore, it is rejected for the same reason as claimed in claim 8 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chang et al (US 11449735 B2) disclosed a system for computing conditional probabilities of random variables for Bayesian inference. The system implements a spiking neural network of neurons to compute the conditional probability of two random variables X and Y. The spiking neural network includes an increment path for a synaptic weight that is proportional to a product of the synaptic weight and a probability of X, a decrement path for the synaptic weight that is proportional to a probability of X, Y, and delay and spike timing dependent plasticity (STDP) parameters such that the synaptic weight increases and decreases with the same magnitude for a single firing event.
Yudanov (US 20220156549 A1) disclosed a method directed to search and match operations of a spiking neural network (SNN) that performs in-memory operations. To model a computer-implemented SNN after a biological neural network, the architecture in the present disclosure involves different memory sections for storing inbound spike messages, synaptic connection data, and synaptic connection parameters. The section of memory containing synaptic connection data to identify matching inbound spike messages. Various embodiments are directed to an efficient search and match operation performed in memory to determine targeted synaptic connections.
Linares-Barranco et al (US 11301753 B2) disclosed a neuron circuit performing synapse learning on weight values includes a first sub-circuit, a second sub-circuit, and a third sub-circuit. The first sub-circuit is configured to receive an input signal from a pre-synaptic neuron circuit and determine whether the received input signal is an active signal having an active synapse value. The second sub-circuit is configured to compare a first cumulative reception counter of active input signals with a learning threshold value based on results of the determination. The third sub-circuit is configured to perform a potentiating learning process based on a first probability value to set a synaptic weight value of at least one previously received input signal to an active value, upon the first cumulative reception counter reaching the learning threshold value, and perform a depressing learning process based on a second probability value to set each of the synaptic weight values to an inactive value.
Lam et al (US 20220101107 A1) disclosed artificial neuromorphic circuit includes synapse circuit and post-neuron circuit. Synapse circuit includes phase change element, first switch, and second switch. First switch is coupled to phase change element, and is configured to receive first pulse signal. Second switch is coupled to phase change element. Input terminal of post-neuron circuit is coupled to switch circuit, and input terminal is coupled to phase change element. Input terminal charges capacitor through switch circuit in response to first pulse signal. Post-neuron circuit is configured to generate firing signal based on voltage level at input terminal and threshold voltage, and is further configured to generate first control signal and second control signal based on firing signal. Post-neuron circuit turns off switch circuit according to first control signal. Second control signal is configured to cooperate with second pulse signal to control second switch so as to control a state of phase change element.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quoc Phung whose telephone number is (703) 756 1330. The examiner can normally be reached on Monday through Friday from 9am to 5pm PT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) athttp://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
Prosecution Insights