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 .
This action is in response to the application filed on 12/23/2022. Claims 1-7, 9-12, and 14-22 are pending and have been examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: a receptive field generator in Claim 1, a first neural processing cell in Claim 2, a second neural processing cell in Claim 2, a universal contextual field block in Claim 20, a receptive field generator in Claim 21, a receptive field generator in Claim 22, a transfer function in Claim 22, an activation circuit in Claim 22.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recites sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-7, 9-12, and 14-22 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.
Regarding Claims 1, 2, 20-22:
Claim limitations “a receptive field generator configured to generate a receptive field”, “a first neural processing cell configured to receive inputs”, “a second neural processing cell configured to receive inputs”, “a universal contextual field block configured to”, “a receptive field generator configured to generate a receptive field”, “a receptive field generator configured to generate a receptive field” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is not sufficient structure for the receptive field generator, the transfer function, the activation circuit, the first neural processing cell, and the second neural processing cell. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Regarding claims 2-7, 9-12, and 14-20:
Claims 2-7, 9-12, and 14-20 are rejected as being dependent on a rejected base claim without curing any of the deficiencies.
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 1-7, 9-12, and 14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because it recites a computer program, which is software per se, not a process, machine, article of manufacture, nor composition of matter.
Claims 1-7, 9-12, and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1:
The claim recites a computer program, which is not one of the four statutory categories of patentable subject matter as referenced above.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation execution of a computational neural layer comprising interconnected neural processing cells amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea of generate a receptive field based on inputs to which synaptic weights are applied amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea of a transfer function configured to generate a field variable amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea of generate an output for controlling an activation level of the neural processing cell, based at least in part on the field variable amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using a receptive field generator is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using an activation circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of the transfer function is dependent on:
the receptive field;
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cells of the computational neural layer; and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cells is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MEPEP 2106.05(h).
Step 2B:
The additional element of using a receptive field generator is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using an activation circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of the transfer function is dependent on:
the receptive field;
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cells of the computational neural layer; and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cells is generally linked to the abstract idea, therefore does not amount to significantly more MEPEP 2106.05(h).
Therefore, the claim is ineligible.
Regarding Claim 2:
Claim 2 incorporates the rejection of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites a further additional element a first neural processing cell configured to receive inputs corresponding to a first information modality, and a second neural processing cell configured to receive inputs corresponding to a second information modality which is an insignificant extra solution activity MPEP 2106.05(g). The additional element is further a well understood routine and conventional activity. See MPEP 2106.05(d)(II)(i), (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). The claim is ineligible.
Regarding Claim 3:
Claim 3 which incorporates the rejection of Claim 1, recites a further abstract idea the transfer function is configured to sum a first parameter based on the receptive field, a second parameter based on the local contextual field, and a third parameter based on the universal contextual field which is a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 4:
Claim 4 incorporates the rejection of Claim 3. This claim further recites a description of the transfer function in the transfer function is configured to sum step and is ineligible for the same reasons as set forth in Claim 3. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 5:
Claim 5 incorporates the rejection of Claim 3. This claim further recites a description of the transfer function in the transfer function is configured to sum step and is ineligible for the same reasons as set forth in Claim 3. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 6:
Claim 6 incorporates the rejection of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites a further additional element the transfer function is further dependent on a previous output value of the neural processing cell executing said transfer function which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 7:
Claim 7 incorporates the rejection of Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites a further additional element the transfer function is configured to apply an activation function to the receptive, local, and universal contextual fields and optionally one or more further contextual fields which is generally linked to the abstract idea MPEP 2106.05(h). The claim is ineligible.
Regarding Claim 9:
Claim 9 which incorporates the rejection of Claim 1, recites a further abstract idea the transfer function is configured to shift the field variable in a direction that depends on coherence of the contextual fields and the receptive field with each other, to enable the activation circuit to pass the field variable if the contextual fields and the receptive field are coherent with each other, and suppress or discard the field variable if the contextual fields and the receptive field are not coherent with each other which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 10:
Claim 10 which incorporates the rejection of Claim 1, recites a further abstract idea the universal contextual field comprises a function of individually weighted previous output values of the neural processing cells which is a mathematical concept. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 11:
Claim 11 incorporates the rejection of Claim 10. This claim further recites a description of the function in the universal contextual field step and is ineligible for the same reasons as set forth in Claim 10. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 12:
Claim 12 incorporates the rejection of Claim 10. This claim further recites a description of the function in the universal contextual field step and is ineligible for the same reasons as set forth in Claim 10. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 14:
Claim 14 incorporates the rejection of Claim 12. This claim further recites a description of the activation function in the universal contextual field step and is ineligible for the same reasons as set forth in Claim 12. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 15:
Claim 15 incorporates the rejection of Claim 1. This claim further recites a description of the generating in the receptive field generator configured to generate step and is ineligible for the same reasons as set forth in Claim 1. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 16:
Claim 16 which incorporates the rejection of Claim 1, recites a further abstract apply an activation function to the inputs, the receptive field generator of each neural processing cell having a differently configured activation function which amounts to a mental process as it can be performed in a human mind.
Regarding Claim 17:
Claim 17 which incorporates the rejection of Claim 1, recites a further abstract idea generate the output in dependence on the field variable and in dependence on a previous output value of the activation circuit which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 18:
Claim 18 which incorporates the rejection of Claim 1, recites a further abstract idea apply an activation function setting an activation threshold of the neural processing cell which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 19:
Claim 19 which incorporates the rejection of Claim 1, recites a further abstract idea each neural processing cell comprises one or more trainable weights to be applied to each of one or more of: the inputs, when generating the receptive field, such that the synaptic weights are trainable weights; the plurality of receptive fields, when generating the local contextual field; or the previous output values of the neural processing cells, when generating the universal contextual field which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. The claim is ineligible.
Regarding Claim 20:
Claim 20 which incorporates the rejection of Claim 1, recites a further abstract idea hidden layers each configured as a neural processing layer as defined in claim 1 which amounts to a mental process as it can be performed in a human mind. The claim does not recite any additional elements that integrate the abstract idea into practical application or amount to significantly more. Specifically, the claim recites a further additional element a universal contextual field block configured to store and provide one or more of the hidden layers at a next time step a universal contextual field parameter which is an insignificant extra solution activity MPEP 2106.05(g). The additional elements are further well understood routine and conventional activities. See MPEP 2106.05(d)(II)(iv), (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). The claim recites a further additional element a universal contextual field block which is a generic computer component amounting to mere instructions to apply the abstract idea MPEP 2106.05(f).
The claim is ineligible.
Regarding Claim 21:
Step 1:
The claim recites a computational neural layer circuit, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generate a receptive field based on inputs to which synaptic weights are applied amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea of generate a field variable amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea of generate an output for controlling an activation level of the neural processing cell circuit, based at least in part on the field variable amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using neural processing cells is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a receptive field generator is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a transfer circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using an activation circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of the transfer circuit is dependent on:
the receptive field;
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cell circuits of the computational neural layer circuit; and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cell circuits is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MEPEP 2106.05(h).
Step 2B:
The additional element of using neural processing cells is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a receptive field generator is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a transfer circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using an activation circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of the transfer circuit is dependent on:
the receptive field;
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cell circuits of the computational neural layer circuit; and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cell circuits is generally linked to the abstract idea, therefore does not amount to significantly more MEPEP 2106.05(h).
Therefore, the claim is ineligible.
Regarding Claim 22:
Step 1:
The claim recites a method, which is one of the four statutory categories of patentable subject matter.
Step 2A prong 1:
The claim recites an abstract idea. Specifically, the limitation generate a receptive field based on inputs to which synaptic weights are applied amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea of a transfer function configured to generate a field variable amounts to a mental process as it can be performed in a human mind.
The claim recites an additional abstract idea of generate an output for controlling an activation level of the neural processing cell, based at least in part on the field variable amounts to a mental process as it can be performed in a human mind.
Step 2A prong 2:
The additional element of using neural processing cells is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using a receptive field generator is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of using an activation circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not integrate the abstract idea into practical application MPEP 2106.05(f).
The additional element of the transfer function is dependent on:
the receptive field;
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cells of the computational neural layer; and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cells is generally linked to the abstract idea, therefore does not integrate the abstract idea into practical application MEPEP 2106.05(h).
Step 2B:
The additional element of using neural processing cells is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using a receptive field generator is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of using an activation circuit is a generic computer component amounting to mere instructions to apply the abstract idea, therefore does not amount to significantly more MPEP 2106.05(f).
The additional element of the transfer function is dependent on:
the receptive field;
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cells of the computational neural layer; and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cells is generally linked to the abstract idea, therefore does not amount to significantly more MEPEP 2106.05(h).
Therefore, the claim is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-7, 9-12, and 14-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Adeel, “Conscious Multisensory Integration: Introducing a Universal Contextual Field in Biological and Deep Artificial Neural Networks”, from applicant IDS, hereinafter “Adeel”.
Regarding Claim 1, Adeel teaches:
A computer program that, when run on a computer, performs execution (Adeel trains and tests a machine learning model in different ways, demonstrating that Adeel performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 10, col. 1, ¶2, “deep LSTM model is trained only with visual cues (RF only) considering multiple prior frames (ranging from 1 visual frame to 18 prior visual frames). The simulation results are shown in Figure 9”, p. 12, Figure 13 shows performance of model testing”) of a computational neural layer comprising interconnected neural processing cells (p. 6, Figure 5B) each comprising:
a receptive field generator configured to generate a receptive field based on inputs to which synaptic weights are applied (p. 1, Abstract, “integrated with the receptive field (RF) to develop a new class of contextually-adaptive neuron (CAN)”, p.7, Equations 8-10);
a transfer function configured to generate a field variable (Synaptic weights are a field variable, p. 4, col. 2, ¶1, “The firing from neuron y to succeeding neuron w in the network is according to the Poisson process, represented by the synaptic weights w+yw = ry[P+yx + P+yz + P+yu] and w−yw = ry[P−yx + P−yz + P−yu]”, p. 5, Equation 2); and
an activation circuit configured to generate an output for controlling an activation level of the neural processing cell, based at least in part on the field variable (p .5, “The probability that CAN (Y) is excited”, Equation 7 shows controlling activation level of neural processing cell based on field variable),
wherein the transfer function is dependent on (Transfer function is dependent on the three field variables, p. 4, col. 2, ¶1, “P + yx, P + yz, P + yu and P − yx, P − yz, P − yu represent the probabilities of excitatory and inhibitory RF, LCF, and UCF signals”):
the receptive field (p. 4, col. 2, ¶1, “excitatory and inhibitory RF”);
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cells of the computational neural layer (p. 4, col. 2, ¶1, “excitatory and inhibitory… LCF”, p. 6, Figure 5B shows local contextual field cells in layer 2 are dependent on a plurality of receptive field cells in layer 1); and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cells (p. 4, col. 2, ¶1, “excitatory and inhibitory… UCF”, p. 1, Abstract, “UCF defines the outside environment and anticipated behavior (based on past learning and reasoning)”, p. 6, Figure 5B shows UCF cells based on output of LCF cells).
Regarding Claim 2, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the neural processing cells comprise a first neural processing cell configured to receive inputs corresponding to a first information modality, and a second neural processing cell configured to receive inputs corresponding to a second information modality (p .3, col. 2, ¶2, “two distinctive multimodal multistreams”, p. 9, Figure 8 shows different modalities inputting into separate neural cells), such that the universal contextual field is indicative of a cross-modal memory state (p. 1, Abstract, “UCF defines the outside environment and anticipated behavior (based on past learning and reasoning)”, p. 9, Figure 8 UCF neurons).
Regarding Claim 3, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the transfer function is configured to sum a first parameter based on the receptive field, a second parameter based on the local contextual field, and a third parameter based on the universal contextual field (p. 5, Equation 2, w+yw = ry[P+yx + P+yz + P+yu] and w−yw = ry[P−yx + P−yz + P−yu], p. 4, col. 2, ¶1, “probabilities of excitatory and inhibitory RF, LCF, and UCF signals,”).
Regarding Claim 5, Adeel teaches the computer program of Claim 3 as referenced above. Adeel further teaches:
wherein the relative contribution of each of the first, second and third parameters to the transfer function is tunable via coefficients (p. 5, equation 2, ry is coefficient that tunes contribution of parameters).
Regarding Claim 6, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the transfer function is further dependent on a previous output value of the neural processing cell executing said transfer function (p. 5, Equation 2 transfer function is dependent on previously calculated output firing rate of Equation 3).
Regarding Claim 7, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the transfer function is configured to apply an activation function to the receptive, local, and universal contextual fields (Probability that CAN (Y) is excited is activation function, p. 5, Equation 7 applies to contextual fields) and optionally one or more further contextual fields
Regarding Claim 9, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the transfer function is configured to shift the field variable in a direction that depends on coherence of the contextual fields and the receptive field with each other, to enable the activation circuit to pass the field variable if the contextual fields and the receptive field are coherent with each other (p. 5, equation 2, variable is passed based on coherence of contextual and receptive field w+yw = ry[P+yx + P+yz + P+yu]), and suppress or discard the field variable if the contextual fields and the receptive field are not coherent with each other (p. 5, equation 2, variable is suppressed based on coherence of contextual and receptive field , w−yw = ry[P−yx + P−yz + P−yu]).
Regarding Claim 10, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the universal contextual field comprises a function of individually weighted previous output values of the neural processing cells (p. 6, Figure 5A, weight of universal contextual field is based on weighted output of neural cell y).
Regarding Claim 11, Adeel teaches the computer program of Claim 10 as referenced above. Adeel further teaches:
wherein the universal contextual field is based on a sum of the individually weighted previous output values of the neural processing cells (p. 6, wu is based on weighted output of y).
Regarding Claim 12, Adeel teaches the computer program of Claim 10 as referenced above. Adeel further teaches:
wherein the function of the universal contextual field comprises an activation function (Function for calculating Wu contributes to activation of neuron and is therefore an activation function p. 6, Figure 5A, weight function for UCF).
Regarding Claim 14, Adeel teaches the computer program of Claim 12 as referenced above. Adeel further teaches:
wherein the activation function is configured to be applied to the sum of the previous output values of the neural processing cells (p. 6, Figure 5A, Wu = wyu+ + wyu-).
Regarding Claim 15, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the receptive field generator is configured to generate the receptive field in dependence on the inputs and in dependence on a previous receptive field state of the receptive field generator (p. 7, col. 1, ¶1, “The RF input (qx) is given as: ”, Equations 8-10, “qv is the potential of the preceding neuron v and qu and qz are potentials of the incoming UCF and LCF neurons”).
Regarding Claim 16, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the receptive field generator is configured to apply an activation function to the inputs (p. 7, Equations 9 and 10 are activation functions applied to input), the receptive field generator of each neural processing cell having a differently configured activation function (p. 6, Figure 5B shows multiple CAN cells that will each have activation function).
Regarding Claim 17, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the activation circuit is configured to generate the output in dependence on the field variable and in dependence on a previous output value of the activation circuit (p. 5, Equation 7 shows dependence on field variables and dependence on previously calculated weights).
Regarding Claim 18, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the activation circuit is configured to apply an activation function setting an activation threshold of the neural processing cell (p. 5, Equation 7 sets threshold for neural processing cell to be excited, “The probability that CAN (Y) is excited”).
Regarding Claim 19, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein each neural processing cell comprises one or more trainable weights to be applied to each of one or more of:
the inputs, when generating the receptive field, such that the synaptic weights are trainable weights (p. 5, col. 1, ¶1, “For training and weights update, state-of-the-art gradient descent algorithm is used. The RF input (qx) is given as:”, Equations 9 and 10 show applying trainable weights to inputs when generating receptive field);
the plurality of receptive fields, when generating the local contextual field; or
the previous output values of the neural processing cells, when generating the universal contextual field.
Regarding Claim 20, Adeel teaches the computer program of Claim 1 as referenced above. Adeel further teaches:
wherein the computer program, when run on a computer, performs execution of a computational neural network comprising:
hidden layers each configured as a neural processing layer as defined in claim 1 (p. 6, Figure 5b showing neural processing layers); and
a universal contextual field block configured to store and provide to one or more of the hidden layers at a next time step a universal contextual field parameter based on the previous output values of the neural processing cells of a first one or more of the hidden layers (p. 3, col. 2, ¶2, “UCF defines the outside environment and anticipated behavior (based on past learning and reasoning)”, p. 9, Figure 8 shows UCF neurons storing and providing parameters based on past-learning and reasoning, p. 5, col. 1, ¶2, “the potential of CAN then in n number of neurons… can be modeled as a continuous-time Markov process”).
Regarding Claim 21, Adeel teaches:
A computational neural layer circuit comprising interconnected neural processing cell circuits (Adeel trains and tests a machine learning model in different ways, demonstrating that Adeel performs their method on a computer, in which processor, memory, and storage devices are inherent, p. 10, col. 1, ¶2, “deep LSTM model is trained only with visual cues (RF only) considering multiple prior frames (ranging from 1 visual frame to 18 prior visual frames). The simulation results are shown in Figure 9”, p. 12, Figure 13 shows performance of model testing”) each comprising:
a receptive field generator configured to generate a receptive field based on inputs to which synaptic weights are applied (p. 1, Abstract, “integrated with the receptive field (RF) to develop a new class of contextually-adaptive neuron (CAN)”, p.7, Equations 8-10);
a transfer circuit configured to generate a field variable (Synaptic weights are a field variable, p. 4, col. 2, ¶1, “The firing from neuron y to succeeding neuron w in the network is according to the Poisson process, represented by the synaptic weights w+yw = ry[P+yx + P+yz + P+yu] and w−yw = ry[P−yx + P−yz + P−yu]”, p. 5, Equation 2); and
an activation circuit configured to generate an output for controlling an activation level of the neural processing cell circuit, based at least in part on the field variable (p .5, “The probability that CAN (Y) is excited”, Equation 7 shows controlling activation level of neural processing cell based on field variable),
wherein the transfer circuit is dependent on (Transfer function is dependent on the three field variables, p. 4, col. 2, ¶1, “P + yx, P + yz, P + yu and P − yx, P − yz, P − yu represent the probabilities of excitatory and inhibitory RF, LCF, and UCF signals”):
the receptive field (p. 4, col. 2, ¶1, “excitatory and inhibitory RF”);
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cell circuits of the computational neural layer circuit (p. 4, col. 2, ¶1, “excitatory and inhibitory… LCF”, p. 6, Figure 5B shows local contextual field cells in layer 2 are dependent on a plurality of receptive field cells in layer 1); and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cell circuits (p. 4, col. 2, ¶1, “excitatory and inhibitory… UCF”, p. 1, Abstract, “UCF defines the outside environment and anticipated behavior (based on past learning and reasoning)”, p. 6, Figure 5B shows UCF cells based on output of LCF cells).
Regarding Claim 22, Adeel teaches:
A method of executing a computational neural layer comprising interconnected neural processing cells, the method comprising, for each neural processing cell:
causing execution of a receptive field generator configured to generate a receptive field based on inputs to which synaptic weights are applied (p. 1, Abstract, “integrated with the receptive field (RF) to develop a new class of contextually-adaptive neuron (CAN)”, p.7, Equations 8-10);
causing execution of a transfer function configured to generate a field variable (Synaptic weights are a field variable, p. 4, col. 2, ¶1, “The firing from neuron y to succeeding neuron w in the network is according to the Poisson process, represented by the synaptic weights w+yw = ry[P+yx + P+yz + P+yu] and w−yw = ry[P−yx + P−yz + P−yu]”, p. 5, Equation 2); and
causing execution of an activation circuit configured to generate an output for controlling an activation level of the neural processing cell, based at least in part on the field variable (p .5, “The probability that CAN (Y) is excited”, Equation 7 shows controlling activation level of neural processing cell based on field variable),
wherein the transfer function is dependent on (Transfer function is dependent on the three field variables, p. 4, col. 2, ¶1, “P + yx, P + yz, P + yu and P − yx, P − yz, P − yu represent the probabilities of excitatory and inhibitory RF, LCF, and UCF signals”):
the receptive field (p. 4, col. 2, ¶1, “excitatory and inhibitory RF”);
a local contextual field dependent on a plurality of receptive fields of the other ones of the neural processing cells of the computational neural layer (p. 4, col. 2, ¶1, “excitatory and inhibitory… LCF”, p. 6, Figure 5B shows local contextual field cells in layer 2 are dependent on a plurality of receptive field cells in layer 1); and
a universal contextual field indicative of a cross-cell memory state, based at least in part on previous output values of the neural processing cells (p. 4, col. 2, ¶1, “excitatory and inhibitory… UCF”, p. 1, Abstract, “UCF defines the outside environment and anticipated behavior (based on past learning and reasoning)”, p. 6, Figure 5B shows UCF cells based on output of LCF cells).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Adeel in view of Lee et al. “Training Deep Spiking Neural Networks Using Backpropagation”, hereinafter “Lee”.
Regarding Claim 4, Adeel teaches the computer program of Claim 3 as referenced above. Adeel does not teach, but Lee teaches:
wherein the transfer function is configured to compute the square of the sum (Lee, p. 6, col. 2, ¶3, Equations 14-15, “The decay rate is exponentially proportional to the squared sum of weights”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lee regularizing weights involving computing a square of a sum with the weights from the transfer function taught by Adeel. The modification would have been motivated to improve stability and generation of the spiking neural network (Lee, p. 6, col. 2, ¶3, “Weight decay regularization is used to improve the stability of SNNs as well as their generalization capability”).
Conclusion
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/JESSE C COULSON/
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122