DETAILED ACTION
This office action is responsive to the above identified application filed 2/27/2024. The application contains claims 1-8, 10-15, 17-25, all examined and rejected.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The Information Disclosure Statement with references submitted 2/27/2024, 5/2/2025 have been considered and entered into the file.
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.
Claim limitations in claims 10-12 have been interpreted under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, because it uses a non-structural term “module” coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the non-structural term is not preceded by a structural modifier.
Claim 10 recites the limitation " controlling circuitry” coupled with functional language without reciting sufficient structure to achieve the function.
Since these claim limitations invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, claims are interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph limitation: Paragraph [0071] states, “The controlling circuitry may be one or more processors” Based on the guidelines announced from Federal Register Vol. 76, No. 27, this has been interpreted as encompassing a hardware or hardware in combination with software implementation of the module, but not a pure software implementation.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action. Claimed modules also trigger interpretation of the claim language under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph since they are considered a place holder for a corresponding structure in the specification.
If applicant does not wish to have the claim limitation treated under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, applicant may amend the claim so that it will clearly not invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, or present a sufficient showing that the claim recites sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance with 35 U.S.C. § 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 1-8, 21, 23-25 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.
Independent claim 1 disclose “optionally from other processing elements”, dependent claim 3, disclose “optionally low pass”, dependent claim 4, disclose “optionally transforming the first additional compartment input signal”, “optionally the second additional compartment input”. The word “optionally” generate ambiguous claims that do not clearly and precisely define the metes and bounds of the claimed invention as it is unclear if the limitations associated with the term optionally is part of the claims or not. For examination purposes the examiner consider the limitations associated with the term optionally as not a required part of the claims. Dependent claims inherit the deficiency of the independent claims.
Claims 10-12 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.
Independent claim 10 disclose “optionally transformation of the first additional input”, “optionally of the second additional input”. The word “optionally” generate ambiguous claims that do not clearly and precisely define the metes and bounds of the claimed invention as it is unclear if the limitations associated with the term optionally is part of the claims or not. For examination purposes the examiner consider the limitations associated with the term optionally as not a required part of the claims. Dependent claims inherit the deficiency of the independent claims.
Claims 6, 15, 17, 20 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 pre-AIA the applicant regards as the invention.
Regarding claims 6, 15, 17, 20, the phrase "such as" renders the claims indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claims 10-12 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 pre-AIA the applicant regards as the invention.
Claim 10 recites the limitation "the first processing unit". There is insufficient antecedent basis for this limitation in the claim. Dependent claims inherit the deficiency of the independent claims.
Claims 1-8, 11-15, 17-25 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 pre-AIA the applicant regards as the invention.
With regard to Claim 1,
Claim elements “an input unit” “a scaling unit”, “a summing unit”, “a first processing unit”, “an amplifier“, “an addition unit “, and “an output unit” are limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Dependent claims inherit the deficiency of the independent claims.
With regard to Claim 2,
Claim element “a second processing unit” is limitation that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 3,
Claim elements “a second processing unit”, “a comparator”, “an amplifier” are limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 4,
Claim elements “a compartment”, “a second compartment processing unit” are limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 5,
Claim elements “processing element” is limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 8,
Claim elements “a post-processing unit” is limitation that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 11,
Claim elements “a transfer function unit”, “a reception unit “, “an amplifier “, “a first processing unit”, “a first checking unit”, “addition unit”, “ output unit”, “second checking unit” is limitation that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 12,
Claim elements “a transfer function unit”, “a reception unit “, “an amplifier “, “a first processing unit”, “a first checking unit”, “addition unit”, “ output unit”, “second checking unit” is limitation that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 13,
Claim elements “an input unit”, “a scaling unit“, “a summing unit“, “a transfer function unit”, “a reception unit”, “an amplifier”, “a first processing unit”, “a first checking unit”, “an addition unit”, “an output unit” are limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Dependent claims inherit the deficiency of the independent claims.
With regard to Claim 14,
Claim elements “a second checking unit” is limitation that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 15,
Claim elements “classifier ” are limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 21,
Claim elements “a first processing unit ”, “a first accumulator” “a first checking unit”, “a second checking unit” are limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 23,
Claim elements “a first processing unit ”, “a first accumulator” “a first checking unit”, “a second checking unit” are limitations that invoke 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 the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
With regard to Claim 24,
Claim elements “a post-processing unit” is limitation that invoke 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 the claimed function. Therefore, the claim is indefinite and is 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.
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 13-15, 17-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004). Independent claim 13 recites a “system,” which is not comprehensively defined by the specification. The broadest reasonable interpretation of a claim drawn to a system covers software per se in view of the ordinary and customary meaning of system, particularly when the specification is silent. Software per se is not a “process,” a “machine,” a “manufacture,” or a “composition of matter” as defined in 35 U.S.C. § 101. Examiner suggests adding a recitation of a “processor.”. Dependent claims inherit the deficiency in the independent claims.
Claims 1-8, 10-15, 17-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 10 and 13 are each directed to a statutory category, it recites a series of steps pertaining to analyze received data to identify features that are used to predict machine failure, which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-8, 10-15, 17-25 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process and machine as in independent Claim 1 and 10, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to Mental Process and Mathematical concepts (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“scaling, by a scaling unit of the processing element, each of the plurality of input signals with a respective weight to obtain weighted input signals; calculating, by a summing unit of the processing element, a sum of the weighted input signals to obtain a sum signal; processing the sum signal, by a first processing unit of the processing element, to obtain a first additional input signal; amplifying the sum signal, by an amplifier of the processing element, to obtain an amplified sum signal; adding, by an addition unit of the processing element, the first additional input signal to the amplified sum signal to obtain an activity potential signal; utilizing, by an output unit of the processing element, the activity potential signal as a third additional input signal to the first processing unit of the processing element to provide a positive feedback loop to make a state machine within the processing element non-linear; and utilizing, by the output unit of the processing element, the activity potential signal as an output signal for the processing element” (Mental process, observation, evaluation and judgment) (Mathematical concept).
Claim 10
“scaling of each of the plurality of input signals with a respective weight to obtain weighted input signals; calculation of a sum of the weighted input signals to obtain a sum signal; processing of the sum signal to obtain a first additional input signal; amplification of the sum signal to obtain an amplified sum signal; optionally transformation of the first additional input signal to obtain a second additional input signal; addition of the first additional input signal, and optionally of the second additional input signal, to the amplified sum signal, to obtain an activity potential signal; and utilization of the activity potential signal as a third additional input signal to the first processing unit of the processing element to provide a positive feedback loop which makes a state machine within the processing element non-linear; utilization of the activity potential signal and as an output signal for the processing element to dynamically adapt the range of the output signal processing element to separate or identify entities or measurable characteristics thereof”, “processing of the sum signal to obtain a first additional input signal by causing: checking, by a first checking unit, of whether the sum signal is positive or negative; if the sum signal is negative, feeding, by the first checking unit, of the sum signal to a first accumulator which functions as an independent state memory, thereby charging the first accumulator; if the sum signal is positive or zero, feeding, by the first checking unit, of the sum signal to a discharge unit connected to the first accumulator to discharge the first accumulator through the discharge unit; and utilization of an output of the discharge unit as the first additional input signal”, and/or “cause utilization of the activity potential signal as a third additional input signal to the first processing unit of the processing element by causing: checking, by a second checking unit, of whether the activity potential signal is positive or negative; if the activity potential signal is negative, feeding, by the second checking unit, of the activity potential signal to the first accumulator, thereby charging the first accumulator; and if the activity potential signal is positive or zero, feeding, by the second checking unit, of the activity potential signal to the discharge unit to discharge the first accumulator“ (Mental process, observation, evaluation and judgment) (Mathematical concept).
Claim 13,
an input unit, configured to receive a plurality of input signals from a plurality of sensors and/or from other processing elements; a scaling unit, configured to scale each of the plurality of input signals with a respective weight to obtain weighted input signals; a summing unit, configured to calculate a sum of the weighted input signals to obtain a sum signal; and a transfer function unit for adjusting the dynamics of a signal, the transfer function unit comprising: an amplifier configured to amplify the input signal to obtain an amplified input signal; a first processing unit comprising a first checking unit, wherein the first checking unit is configured to check whether the input signal is positive or negative, wherein the first checking unit is configured to feed the input signal to a first accumulator if the input signal is negative and wherein the first checking unit is configured to feed the input signal to a discharge unit connected to the first accumulator if the input signal is positive or zero, and wherein the first processing unit is configured to process the input signal to obtain a first additional input signal by utilizing an output of the discharge unit as the first additional input signal; an addition unit configured to add the first additional input signal to the amplified input signal to obtain an activity potential signal; and an output unit configured to provide the activity potential signal as a third additional input signal to the first processing unit and as an output signal, the dynamics of the output signal being different from the dynamics of the input signal(Mental process, observation, evaluation and judgment) (Mathematical concept).
The claim recites additional elements as
Claim 1
“a computer-implemented method for separation or identification of entities using a network of processing elements, each processing element of the network being independent of global control signals” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
receiving, by an input unit of a processing element, a plurality of input signals from a plurality of sensors and optionally from other processing elements of the network of processing elements, wherein the plurality of input signals changes dynamically over time (insignificant extra-solution activity, MPEP 2106.05(g));
“wherein the range of the output signal of the processing element is dynamically adapted to separate or identify entities or measurable characteristics thereof “ (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)).
Claim 10
“An apparatus for separation or identification of entities using a network of processing elements, each processing element of the network being independent of global control signals, the apparatus comprising controlling circuitry configured to cause, at a processing element of the network of processing elements”, “controlling circuitry” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
reception of a plurality of input signals from a plurality of sensors and/or from other processing elements of the network of processing elements, wherein the plurality of input signals changes dynamically over time; (insignificant extra-solution activity, MPEP 2106.05(g)).
Claim 13
“A system for separating or identifying entities using a network of processing elements, each processing element of the network being independent of global control signals”, “a plurality of processing elements, each processing element having an independent memory“ (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
an input unit, configured to receive a plurality of input signals from a plurality of sensors and/or from other processing elements (insignificant extra-solution activity, MPEP 2106.05(g));
“wherein the sum signal is utilized as the input signal for the transfer function unit and wherein the output signals of the transfer function units of the plurality of processing elements are utilized to separate or identify entities “ (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1, 10, and 13 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
Claim 1
“a computer-implemented method for separation or identification of entities using a network of processing elements, each processing element of the network being independent of global control signals” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
receiving, by an input unit of a processing element, a plurality of input signals from a plurality of sensors and optionally from other processing elements of the network of processing elements, wherein the plurality of input signals changes dynamically over time (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i))
“wherein the range of the output signal of the processing element is dynamically adapted to separate or identify entities or measurable characteristics thereof “ (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)).
Claim 10
“An apparatus for separation or identification of entities using a network of processing elements, each processing element of the network being independent of global control signals, the apparatus comprising controlling circuitry configured to cause, at a processing element of the network of processing elements”, “controlling circuitry” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
reception of a plurality of input signals from a plurality of sensors and/or from other processing elements of the network of processing elements, wherein the plurality of input signals changes dynamically over time (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
Claim 13
“A system for separating or identifying entities using a network of processing elements, each processing element of the network being independent of global control signals”, “a plurality of processing elements, each processing element having an independent memory“ (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
an input unit, configured to receive a plurality of input signals from a plurality of sensors and/or from other processing elements (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
“wherein the sum signal is utilized as the input signal for the transfer function unit and wherein the output signals of the transfer function units of the plurality of processing elements are utilized to separate or identify entities “ (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)).
In the instant case, Claim 1, 10, 13 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Independent claims 10 and 11 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “transforming the first additional input signal, by a second processing unit of the processing element, to obtain a second additional input signal”; and wherein adding, by an addition unit of the processing element, the first additional input signal to the amplified sum signal to obtain an activity potential signal further comprises adding, by the processing element, the second additional input signal to the amplified sum signal to obtain the activity potential signal (mental process, Mathematical concept). )). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “transforming the first additional input signal, by a second processing unit of the processing element, to obtain a second additional input signal, and wherein the adding further comprises adding, by the processing element, the second additional input signal to the amplified sum signal to obtain the activity potential signal, wherein transforming the first additional input signal, by a second processing unit of the processing element, to obtain a second additional input signal comprises: providing the first additional input signal to a second accumulator; low pass filtering an output of the second accumulator with a low pass filter to create a low-pass filtered version of the output of the second accumulator; comparing, with a comparator, the output of the second accumulator with the low-pass filtered version to create a negative difference signal; amplifying the negative difference signal with an amplifier, and optionally low pass or high pass filter the amplified negative difference signal, to obtain a second additional input signal” (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “receiving, at a compartment of the processing element, a plurality of compartment input signals from a plurality of sensors and/or from other processing elements” (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “scaling, by the compartment, each of the plurality of compartment input signals with a respective weight to obtain weighted compartment input signals; calculating, by the compartment, a sum of the weighted compartment input signals to obtain a compartment sum signal; processing the compartment sum signal, by a first compartment processing unit, to obtain a first additional compartment input signal; optionally transforming the first additional compartment input signal, by a second compartment processing unit, to obtain a second compartment additional input signal; amplifying the compartment sum signal, by an amplifier of the compartment, to obtain an amplified compartment sum signal; adding, by the compartment, the first and optionally the second additional compartment input signals to the amplified compartment sum signal to obtain a compartment activity potential signal; and utilizing the compartment activity potential signal as a third additional compartment input signal to the first compartment processing unit and as a compartment output signal to adjust the sum signal based on a transfer function“ (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “ adjusting, by the processing element), the activity potential signal based on a threshold function” (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose “wherein each respective weight is updated based on a combination, such as a correlation, of the activity potential signal and an input activity or a state of each respective weight” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 disclose “utilizing the activity potential signal to identify an entity: comparing over a time period the activity potential signal to known activity potential signals associated with known entities; and identifying the entity as the known entity which is associated with the known activity potential signal which is most similar to the activity potential signal” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 8 disclose “wherein the variation of the activity potential signal over time is measured by a post-processing unit, wherein the post-processing unit is configured to compare the measured variation to known measurable characteristics of entities comprised in a list associated with the post-processing unit” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 11 disclose “wherein at least one of the processing elements in the network of processing elements comprises a transfer function unit for adjusting the dynamics of a signal, where the transfer function unit comprises” (mental process, Mathematical concept), “a reception unit configured to receive an input signal” (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “an amplifier configured to amplify the input signal to obtain an amplified input signal; a first processing unit comprising a first checking unit, wherein the first checking unit is configured to check whether the input signal is positive or negative, wherein the first checking unit is configured to feed the input signal to a first accumulator if the input signal is negative and wherein the first checking unit is configured to feed the input signal to a discharge unit connected to the first accumulator if the input signal is positive or zero, and wherein the first processing unit is configured to process the input signal to obtain a first additional input signal by utilizing an output of the discharge unit as the first additional input signal;
an addition unit configured to add the first additional input signal to the amplified input signal to obtain an activity potential signal; and an output unit configured to provide the activity potential signal as a third additional input signal to the first processing unit and as an output signal, the dynamics of the output signal being different from the dynamics of the input signal“ (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 12 disclose “ at least one of the processing elements in the network of processing elements comprises a transfer function unit for adjusting the dynamics of a signal, where the transfer function unit” (mental process, Mathematical concept), a reception unit configured to receive an input signal” (insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “an amplifier configured to amplify the input signal to obtain an amplified input signal; a first processing unit comprising a first checking unit, wherein the first checking unit is configured to check whether the input signal is positive or negative, wherein the first checking unit is configured to feed the input signal to a first accumulator if the input signal is negative and wherein the first checking unit is configured to feed the input signal to a discharge unit connected to the first accumulator if the input signal is positive or zero, and wherein the first processing unit is configured to process the input signal to obtain a first additional input signal by utilizing an output of the discharge unit as the first additional input signal; an addition unit configured to add the first additional input signal to the amplified input signal to obtain an activity potential signal; and an output unit configured to provide the activity potential signal as a third additional input signal to the first processing unit and as an output signal, the dynamics of the output signal being different from the dynamics of the input signal; and wherein the first processing unit further comprises a second checking unit, wherein the second checking unit is configured to check whether the activity potential signal is positive or negative; wherein the second checking unit is configured to feed the activity potential signal to the first accumulator if the activity potential signal is negative, and wherein the second checking unit is configured to feed the activity potential signal to the discharge unit if the activity potential signal is positive or zero” (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 14 disclose “ a second checking unit wherein the second checking unit is configured to check whether the activity potential signal is positive or negative; wherein the second checking unit is configured to feed the activity potential signal to the first accumulator if the activity potential signal is negative, and wherein the second checking unit is configured to feed the activity potential signal to the discharge unit if the activity potential signal is positive or zero” (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 15 disclose “a classifier comprising a list of known entities, such as objects, wherein each known entity is mapped to a respective distribution of activity potential signals of each processing element and wherein the classifier is configured to receive the activity potential signal of each processing element wherein the classifier is configured to compare the activity potential signal of each processing element to the distributions of activity potential signals of the known entities over a time period, and configured to identify the entity as one of the entities of the list based on the comparison” (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 17 disclose wherein the plurality of input signals changes dynamically over time and follows a sensor input trajectory, and wherein the plurality of input signals comprises pixel values, such as intensity, of images captured by a camera and wherein the activity potential signal of each processing element is further utilized to control a position of the camera by rotational and/or translational movement of the camera, thereby controlling the sensor input trajectory and wherein the entity identified is an object or a feature of an object present in one or more images of the captured images” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 18 disclose “wherein the plurality of input signals changes dynamically over time and follows a sensor input trajectory, and wherein the plurality of sensors are touch sensors and the input from each of the plurality of sensors comprises a touch event signal with a force dependent value and wherein the activity potential signal of each processing element is utilized to identify the sensor input trajectory as a new contact event, the end of a contact event, a gesture or as an applied pressure” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 19 disclose “wherein the plurality of input signals changes dynamically over time and follows a sensor input trajectory, and wherein each sensor of the plurality of sensors is associated with a different frequency band of an audio signal, wherein each sensor reports an energy present in the associated frequency band, and wherein the combined input from the plurality of sensors follows a sensor input trajectory, and wherein the activity potential signal of each processing element is utilized to identify a speaker and/or a spoken letter, a syllable, a phoneme, a word or a phrase present in the audio signal” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 20 disclose “wherein the plurality of input signals changes dynamically over time and follows a sensor input trajectory, and wherein the plurality of sensors comprise a plurality of sensors related to a speaker, such as microphones, and wherein the output signal for the processing element is utilized to separate or identify one or more speakers” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 21 disclose “checking, by a first checking unit, whether the sum signal is positive or negative; if the sum signal is negative, feeding, by the first checking unit, the sum signal to a first accumulator which functions as an independent state memory, thereby charging the first accumulator; if the sum signal is positive or zero, feeding, by the first checking unit, the sum signal to a discharge unit connected to the first accumulator to discharge the first accumulator through the discharge unit; and wherein utilizing the activity potential signal as a third additional input signal to the first processing unit of the processing element comprises: checking, by a second checking unit, whether the activity potential signal is positive or negative; if the activity potential signal is negative, feeding, by the second checking unit, the activity potential signal to the first accumulator, thereby charging the first accumulator; and if the activity potential signal is positive or zero, feeding, by the second checking unit, the activity potential signal to the discharge unit to discharge the first accumulator” (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 22 disclose “wherein the method is implemented at least partially in hardware” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 23 disclose “checking, by a first checking unit, whether the sum signal is positive or negative; if the sum signal is negative, feeding, by the first checking unit, the sum signal to a first accumulator which functions as an independent state memory, thereby charging the first accumulator; if the sum signal is positive or zero, feeding, by the first checking unit, the sum signal to a discharge unit connected to the first accumulator to discharge the first accumulator through the discharge unit; utilizing an output of the discharge unit as the first additional input signal; and utilizing the activity potential signal as a third additional input signal to the first processing unit of the processing element, wherein the positive feedback loop is formed by: checking, by a second checking unit, whether the activity potential signal is positive or negative; if the activity potential signal is negative, feeding, by the second checking unit, the activity potential signal to the first accumulator, thereby charging the first accumulator; and if the activity potential signal is positive or zero, feeding, by the second checking unit, the activity potential signal to the discharge unit to discharge the first accumulator” (mental process, Mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 24 disclose “wherein the variation of the activity potential signal over time is measured by a post-processing unit, wherein the post-processing unit is configured to compare the measured variation to known measurable characteristics of entities comprised in a list associated with the post-processing unit, and wherein the post-processing unit is configured to identify an entity based on the comparison” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 25 disclose “wherein each processing element of the network has a global network clock independent memory” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 2-18,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-8, 10-15, 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ren Hualong [US 2023/0087722 A1] in view of Bazhenov et al. [US 2022/0374679 A1, hereinafter Bazhenov]
With regard to Claim 1,
Ren teach a computer-implemented method for separation or identification of entities using a network of processing elements, each processing element of the network being independent of global control signals (¶327, “Each neuron of the target area forms a unidirectional excitatory or inhibitory connection with one or more neurons of the readout layer 920A, 920B, 920C, 920D, 920E and 920F. … For example, each label could be “Apple,” “car,”, “grassland”, etc “, ¶103, ¶174, “each neuron and each connection (including neuron-neuron connection and synapse-synapse connection) can be represented by vector or matrix”, ¶176, “ the brain-like neural network can also be implemented in the form of firmware …”, ¶565, “present invention has been verified by software simulation, and its source code has been registered and obtained the software copyright”, ¶5, “The present invention is used to improve the effectiveness and accuracy of the intelligent agent's ability of object recognition, spatial navigation, reasoning and autonomous decision-making”, ¶107, “For one or more of the neurons, membrane potential is calculated to determine whether to activate the neurons, and if the neurons are determined to be activated, each downstream neuron is made to accumulate the membrane potential so as to determine whether to activate the neurons, such that the activation of the neurons will propagate in the brain-like neural network”, ¶122, “If the conditionally spontaneous firing neurons are not activated by external input in a first pre-set time interval, the conditionally spontaneous firing neurons are self-activated according to probability P”, ¶123, “The unconditionally spontaneous firing neurons automatically gradually accumulate the membrane potential without external input”), the method comprising:
receiving, by an input unit of a processing element, a plurality of input signals from a plurality of sensors and optionally from other processing elements of the network of processing elements (¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”, ¶105, “Samples (images, video streams) can be acquired in real time … a camera mounted on a movable platform”, ¶182, “the perceptual module 1 can also accept audio input or other modal information input. For example, the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons “, ¶183, ¶106, ¶178, ¶89, ¶99), wherein the plurality of input signals changes dynamically over time (¶105, “Samples (images, video streams) can be acquired in real time … a camera mounted on a movable platform”, ¶356, “ the input sample is a video stream, and the sampling frequency is 30 frames/second, … Input samples (the 1st to 60th frames in the video stream) in the time period of 0 to 2 seconds … Input sample (the 61st to 120th frames in the video stream) in the time period of 2 to 4 seconds”, ¶350, “ information input to the memory module 8 in a continuous period of time can be encoded as a time series memory”);
scaling, by a scaling unit of the processing element, each of the plurality of input signals with a respective weight to obtain weighted input signals (¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”, ¶103, “encodes, stores, and transmits information through the (synaptic) connections (with weights) between the neurons”, ¶174, ¶295, “ this condition can be satisfied by making each of the weights of these connections 0.4 …”, ¶316, “Weights of unidirectional excitatory connections between the concrete information source neurons/abstract information source neurons and the matched differential information decoupling neurons is constant (such as 0.1), or is dynamically adjusted through the synaptic plasticity process”);
calculating, by a summing unit of the processing element, a sum of the weighted input signals to obtain a sum signal (¶126, “step m2: summing all inputs weighted and superimposed to Vm”, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶174, “the signal propagation of the brain-like neural network can be expressed as the dot multiplication operation of the firing rate vector of the neuron and the weight vector of the connection (that is, the weighted sum of the input)”);
processing the sum signal, by a first processing unit of the processing element, to obtain a first additional input signal (¶120, “each neuron of the brain-like neural network adopts spiking neuron and leaky integrate-and-fire neurons (LIF neuron model) in addition to those with a given specific working process”, ¶119, “one way to implement spiking neurons is to use leaky integrate-and-fire neurons (LIF neuron model)”, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶125, “step m1: letting membrane potential to be Vm=Vm+Vc“, ¶128, “where Vm is the membrane potential, Vc is the cumulative constant, Vrest is the resting potential, and threshold is the threshold”, ¶107, “each downstream neuron is made to accumulate the membrane potential so as to determine whether to activate the neurons”);
adding, by an addition unit of the processing element, the first additional input signal to the amplified sum signal to obtain an activity potential signal (¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”;
utilizing, by an output unit of the processing element, the activity potential signal as a third additional input signal to the first processing unit of the processing element to provide a positive feedback loop (¶245, “ Each of the time encoding neurons 610 can also have excitatory connections connected back to itself (called self-connection) so that the time encoding neurons 610 can be continuously activated until this time encoding neuron is shut down by inhibitory input of a next time encoding neuron 610”, ¶337, “step c4: allowing each activated target neuron to establish the unidirectional or bidirectional excitatory connections with one or more of the other target neurons, or establish self-circulating excitatory connections with itself, adjusting the weights of the unidirectional or bidirectional excitatory connections or the self-circulating excitatory connections through the synaptic plasticity process”, ¶87, “The excitatory connection is: when the upstream neurons of the excitatory connection are activated, non-negative input is provided to the downstream neurons through the excitatory connection”) to make a state machine within the processing element non-linear (¶245, “each time encoding neuron 610 forms a time-sequential switch loop”, ¶127, “step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3”); and
utilizing, by the output unit of the processing element, the activity potential signal as an output signal for the processing element (¶117, “One or more of the neurons are mapped to corresponding labels as output. For example, 10,000 instance encoding neurons 20 are mapped to 1 label as output”),
the output signal of the processing element to separate or identify entities or measurable characteristics thereof (¶327, “Each neuron of the target area forms a unidirectional excitatory or inhibitory connection with one or more neurons of the readout layer 920A, 920B, 920C, 920D, 920E and 920F. … For example, each label could be “Apple,” “car,”).
Ren does not explicitly teach amplifying the sum signal, by an amplifier of the processing element, to obtain an amplified sum signal;
Bazhenov teach a computer-implemented method for separation or identification of entities using a network of processing elements, each processing element of the network being independent of global control signals (¶¶5-6, “the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶66, “Sleep Promotes Separation of Internal Representations for Different Inputs”, ¶67, “ these correlation graphs suggest that sleep promotes decorrelating the internal representations of the input categories”), the method comprising:
receiving, by an input unit of a processing element, a plurality of input signals, wherein the plurality of input signals changes dynamically over time (Fig. 2, ¶44, “he input layer in the SNN is represented as a Poisson-distributed spike train with mean firing rate given by the average value of that unit in the ANN for all tasks seen so far”);
scaling, by a scaling unit of the processing element, each of the plurality of input signals with a respective weight to obtain weighted input signals (¶44, “The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer.”);
calculating, by a summing unit of the processing element, a sum of the weighted input signals to obtain a sum signal (¶44, “ The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer.”);
processing the sum signal, by a first processing unit of the processing element, to obtain a first additional input signal (¶44, “Otherwise, the potential decays exponentially”, decayed potential is the processed carried forward);
amplifying the sum signal, by an amplifier of the processing element, to obtain an amplified sum signal (¶44, “Each layer has 2 important parameters that dictates its firing rate: a threshold and a synaptic scaling factor. The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, ¶46, Table 1, ¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”);
adding, by an addition unit of the processing element, the first additional input signal to the amplified sum signal to obtain an activity potential signal (¶44, “ The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, a is applied to Wx first, and the product is added to the stored potential);
the range of the output signal of the processing element is dynamically adapted (¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”) to separate or identify entities or measurable characteristics thereof (¶66, “Sleep Promotes Separation of Internal Representations for Different Inputs”, ¶67, “ these correlation graphs suggest that sleep promotes decorrelating the internal representations of the input categories”).
Ren and Bazhenov are analogous art to the claimed invention because they are from a similar field of endeavor of Spiking Neural Network (SNN). Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren resulting in resolutions as disclosed by Bazhenov with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ren-Bazhenov as described above to improve ability of the ANN to process new types of data including but not limited to the data with different statistics, e.g., noisy data; enable ability of the ANN to avoid catastrophic forgetting of the previously learned tasks (Bazhenov, ¶4). This is simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 2,
Ren-Bazhenov teach the method of claim 1, further comprising:
transforming the first additional input signal, by a second processing unit of the processing element, to obtain a second additional input signal (Ren, ¶314, “ each of the concrete information source neurons has one or more (such as 1) matched differential information decoupling neurons 930 … the information decoupling neurons … form unidirectional inhibitory connections with the information source input neurons, or form unidirectional inhibitory synapse-synaptic connections with connections input from the information source neurons to the information input neurons 7, so as to make signal input from the concrete information source neurons to the information input neurons 7 to be subject to inhibitory regulation by the matched differential information decoupling neurons 930”, abstract information path together with the differential information decoupling neuron. It takes the accumulated concrete signal (1st additional input signal) and produce a second, oppositely signed contribution); and
wherein adding, by an addition unit of the processing element, the first additional input signal to the amplified sum signal to obtain an activity potential signal further comprises adding, by the processing element, the second additional input signal to the amplified sum signal to obtain the activity potential signal (Ren, ¶318, “the weight of connection Sconn1 is 5, the weight of connection Sconn2 is −1, and the former accepts the input of the latter. When the upstream neuron connected to Sconn1 is activated, and the upstream neuron connected to Sconn2 is also activated, then the value of the input of connection Sconn2 to connection Sconn1 is −1, and the value of connection Sconn1 transmitted to its downstream neurons is 5-1, i.e., 4”, Bazhenov, ¶44, “Each layer has 2 important parameters that dictates its firing rate: a threshold and a synaptic scaling factor. The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, ¶46, Table 1, ¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 3,
Ren-Bazhenov teach the method of claim 1, further comprising;
transforming the first additional input signal, by a second processing unit of the processing element, to obtain a second additional input signal (Ren, ¶314, “ each of the concrete information source neurons has one or more (such as 1) matched differential information decoupling neurons 930 … the information decoupling neurons … form unidirectional inhibitory connections with the information source input neurons, or form unidirectional inhibitory synapse-synaptic connections with connections input from the information source neurons to the information input neurons 7, so as to make signal input from the concrete information source neurons to the information input neurons 7 to be subject to inhibitory regulation by the matched differential information decoupling neurons 930”, abstract information path together with the differential information decoupling neuron. It takes the accumulated concrete signal (1st additional input signal) and produce a second, oppositely signed contribution), and
wherein the adding further comprises adding, by the processing element, the second additional input signal to the amplified sum signal to obtain the activity potential signal, wherein transforming the first additional input signal, by a second processing unit of the processing element, to obtain a second additional input signal (Ren, ¶318, “the weight of connection Sconn1 is 5, the weight of connection Sconn2 is −1, and the former accepts the input of the latter. When the upstream neuron connected to Sconn1 is activated, and the upstream neuron connected to Sconn2 is also activated, then the value of the input of connection Sconn2 to connection Sconn1 is −1, and the value of connection Sconn1 transmitted to its downstream neurons is 5-1, i.e., 4”, Bazhenov, (¶44, “Each layer has 2 important parameters that dictates its firing rate: a threshold and a synaptic scaling factor. The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, ¶46, Table 1, ¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”) comprises:
providing the first additional input signal to a second accumulator (Ren, ¶107, “ if the neurons are determined to be activated, each downstream neuron is made to accumulate the membrane potential so as to determine whether to activate the neurons”, ¶199, “Each of the concrete information input neurons 7110 forms unidirectional excitatory connections with one or more (such as 1,000 to 10,000) of the concrete memory neurons 830”);
low pass filtering an output of the second accumulator with a low pass filter to create a low-pass filtered version of the output of the second accumulator (¶197, “one or more (such as 40,000) of the concrete memory neurons 820 form unidirectional excitatory connections with one or more (such as 100) of the abstract memory neurons 830”, ¶120, “each neuron of the brain-like neural network adopts spiking neuron and leaky integrate-and-fire neurons (LIF neuron model)”, LIF unit is a first order low pass filter. Because the abstract memory neuron 830 is an LIF unit fed from second accumulator 820, its state is low pass filtered version of the accumulator’s output. This is a property of the disclosed neuron model. Filter time constants are settable ¶255, “By adjusting the leaky time constant of each time encoding neuron 610, as well as the threshold, the time period of that is switched by the each encoding neuron can be adjusted”);
comparing, with a comparator, the output of the second accumulator with the low-pass filtered version to create a negative difference signal (¶319, “a group of perceptual encoding neurons 110A, 110B, and 110C (that is, the concrete information source neurons) are activated …This group of instance encoding neurons 20A, 20B activates the differential information decoupling neurons 910A, 910 B 910C, inhibits the input of the group of perceptual encoding neurons 110A, 110B, 110C to the concrete information input neurons 7110A, 7110B, 7110C”, ¶88, “The inhibitory connection is: when the upstream neurons of the inhibitory connection are activated, non-positive input is provided to the downstream neurons through the inhibitory connection”, abstract version subtracted from concrete accumulator output, and the result is expressly non-positive ¶318, “the weight of connection Sconn1 is 5, the weight of connection Sconn2 is −1, and the former accepts the input of the latter. When the upstream neuron connected to Sconn1 is activated, and the upstream neuron connected to Sconn2 is also activated, then the value of the input of connection Sconn2 to connection Sconn1 is −1, and the value of connection Sconn1 transmitted to its downstream neurons is 5-1, i.e., 4” );
amplifying the negative difference signal with an amplifier, and optionally low pass or high pass filter the amplified negative difference signal, to obtain a second additional input signal (Ren, ¶315, “Each differential information decoupling neuron can have a decoupled control signal input terminal. Degree of information decoupling is adjusted by adjusting magnitude (which can be positive, negative, or 0) of the signal applied on decoupling control signal input”, ¶316, “Weights of unidirectional excitatory connections between the concrete information source neurons/abstract information source neurons and the matched differential information decoupling neurons is constant (such as 0.1), or is dynamically adjusted”, Bazhenov, ¶44, “Each layer has 2 important parameters that dictates its firing rate: a threshold and a synaptic scaling factor. The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, ¶46, Table 1, ¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 4,
Ren-Bazhenov teach the method of claim 1, further comprising:
receiving, at a compartment of the processing element, a plurality of compartment input signals from a plurality of sensors and/or from other processing elements (¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”, ¶105, “Samples (images, video streams) can be acquired in real time … a camera mounted on a movable platform”, ¶182, “the perceptual module 1 can also accept audio input or other modal information input. For example, the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons “, ¶183, ¶106, ¶178, ¶89, ¶99);
scaling, by the compartment, each of the plurality of compartment input signals with a respective weight to obtain weighted compartment input signals (¶103, “encodes, stores, and transmits information through the (synaptic) connections (with weights) between the neurons”, ¶174, ¶295, “ this condition can be satisfied by making each of the weights of these connections 0.4 …”, ¶316, “Weights of unidirectional excitatory connections between the concrete information source neurons/abstract information source neurons and the matched differential information decoupling neurons is constant (such as 0.1), or is dynamically adjusted through the synaptic plasticity process”);
calculating, by the compartment, a sum of the weighted compartment input signals to obtain a compartment sum signal (¶126, “step m2: summing all inputs weighted and superimposed to Vm”, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶174, “the signal propagation of the brain-like neural network can be expressed as the dot multiplication operation of the firing rate vector of the neuron and the weight vector of the connection (that is, the weighted sum of the input)”);
processing the compartment sum signal, by a first compartment processing unit, to obtain a first additional compartment input signal (¶120, “each neuron of the brain-like neural network adopts spiking neuron and leaky integrate-and-fire neurons (LIF neuron model) in addition to those with a given specific working process”, ¶119, “one way to implement spiking neurons is to use leaky integrate-and-fire neurons (LIF neuron model)”, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶125, “step m1: letting membrane potential to be Vm=Vm+Vc“, ¶128, “where Vm is the membrane potential, Vc is the cumulative constant, Vrest is the resting potential, and threshold is the threshold”, ¶107, “each downstream neuron is made to accumulate the membrane potential so as to determine whether to activate the neurons”);
optionally transforming the first additional compartment input signal, by a second compartment processing unit, to obtain a second compartment additional input signal;
amplifying the compartment sum signal, by an amplifier of the compartment, to obtain an amplified compartment sum signal (Bazhenov, ¶44, “Each layer has 2 important parameters that dictates its firing rate: a threshold and a synaptic scaling factor. The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, ¶46, Table 1, ¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”);
adding, by the compartment, the first and optionally the second additional compartment input signals to the amplified compartment sum signal to obtain a compartment activity potential signal (Bazhenov, ¶44, “ The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, a is applied to Wx first, and the product is added to the stored potential); and
utilizing the compartment activity potential signal as a third additional compartment input signal to the first compartment processing unit (¶112, “through the unidirectional or bidirectional excitatory connections between the information input neurons 710, when a plurality of the information input neurons are activated, making it easier for other information input neurons 710 connected with said information input neurons 710 to be activated”) and as a compartment output signal to adjust the sum signal based on a transfer function (¶108, “The information synthesis and exchange module 7 controls the information entering and exiting the memory module 8, adjusts the size and proportion of each information component”, ¶317, “the connection Sconn1 accepts the input of one or more other connections (denoted as Sconn2), and when the upstream neurons connected to Sconn1 is fired, the value passed from connection Sconn1 to downstream neurons is the weight of connection Sconn1 plus the input value of each connection Sconn2”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 5,
Ren-Bazhenov teach the method of claim 1, further comprising adjusting, by the processing element), the activity potential signal based on a threshold function (¶127, “step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3”, ¶129, “For example, let Vc=5 mV, Vrest=−70 mV, threshold=−25 mV”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 6,
Ren-Bazhenov teach the method of claim 1, wherein each respective weight is updated based on a combination, such as a correlation, of the activity potential signal and an input activity or a state of each respective weight (¶500, “For example, let DwLTP2=0.01*Fru2*Frd2, DwLTD2=0.01*Fru2*Frd2, and Fru2 and Frd2 are the firing rates of upstream and downstream neurons, respectively”, ¶508, “DwLTP3u=0.01*weight, DwLTD3u=0.01*weight, and weight is the connection weight”, weight change is the product of the downstream firing rate (element activity potential) and the upstream firing rate (input activity)- a combination, and specifically a correlation. “such as correlation” is exemplary; “an input activity or a state of each respective weight” is disjunctive, so the input-activity branch alone satisfies it. The wight-state branch is also disclosed, at ¶508, “DwLTP3u=0.01*weight, DwLTD3u=0.01*weight, and weight is the connection weight”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 7,
Ren-Bazhenov teach the method of claim 1, further comprising:
utilizing the activity potential signal to identify an entity (¶327, “The memory triggering process can be reflected as the recognition process of the sample (images or video streams), … each neuron emitted from the target area can be mapped to one or more labels … as the recognition result”):
comparing over a time period the activity potential signal to known activity potential signals associated with known entities (¶325, “if one or more of neurons in the target area are activated in tenth pre-set period (such as 1s), then representation of each activation neuron in the target area can be taken together with its activation intensity or activation rate as the result of the memory triggering process”, ¶327, “Each readout layer neuron 920 corresponds to a label. The higher the activation intensity or firing rate of the neuron 920, the higher the correlation between the input information and its corresponding label, and vice versa”); and
identifying the entity as the known entity which is associated with the known activity potential signal which is most similar to the activity potential signal (¶328, “the one with the largest activation intensity or firing rate or the one that starts firing first is mapped to the corresponding label through multiple readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F as the recognition result of the instance appearing in the samples (images or videos), and the size of its activation intensity or firing rate is taken as the correlation degree”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 8,
Ren-Bazhenov teach the method of claim 1, wherein the variation of the activity potential signal over time is measured by a post-processing unit (¶349, “the information (denoted as T1 information) of the memory module 8 is encoded by the first group of target neurons. The information (denoted as T2 information) input to the memory module 8 during the T2 time period is encoded by the second group of target neurons. In the time period where the T1 time period and the T2 time period overlap (i.e., T3 time period), the time-series correlation between the T1 information and the T2 information is encoded by the unidirectional or bidirectional excitatory connections between the first group of target neurons and the second group of target neurons”, post-processing unit is the readout layer 92 of ¶327, which receives connections from the target area neurons and reads out their activation),
wherein the post-processing unit is configured to compare the measured variation to known measurable characteristics of entities comprised in a list associated with the post-processing unit (¶327, “the brain-like neural network also includes a readout layer 92, including a plurality of readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F … Each readout layer neuron 920 corresponds to a label … For example, each label could be “Apple,” “car,”, “grassland”, etc”, list of readout layer neurons, each bound to one label, is the list of known entities associated with the post-processing unit). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 21,
Ren-Bazhenov teach the method of claim 1, wherein processing the sum signal, by a first processing unit of the processing element, to obtain a first additional input signal comprises:
checking, by a first checking unit, whether the sum signal is positive or negative (¶87, “The excitatory connection is: when the upstream neurons of the excitatory connection are activated, non-negative input is provided to the downstream neurons through the excitatory connection”, ¶88, “The inhibitory connection is: when the upstream neurons of the inhibitory connection are activated, non-positive input is provided to the downstream neurons through the inhibitory connection”, ¶563, “The symbol “+/−” next to the connection indicates that the connection can conduct an excitatory or inhibitory type or empty (0) signal”);
if the sum signal is negative, feeding, by the first checking unit, the sum signal to a first accumulator which functions as an independent state memory, thereby charging the first accumulator (¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶274, “SDDEN0 accepts excitatory connections from SN0 and inhibitory connections from SN180”);
if the sum signal is positive or zero, feeding, by the first checking unit, the sum signal to a discharge unit connected to the first accumulator to discharge the first accumulator through the discharge unit (¶293, “step b5: when the current membrane potential is greater than or equal to the second pre-set potential, resetting the current membrane potential to the first pre-set potential”, ¶297, “the third pre-set potential interval<the first pre-set potential interval<the second pre-set potential interval. The first pre-set potential interval, the second pre-set potential interval and the third pre-set potential interval are the median values of the first pre-set potential interval, the second pre-set potential interval and the third pre-set potential interval in turn”, ¶298, values -40mV/ 0 mV /+40 mV); and
wherein utilizing the activity potential signal as a third additional input signal to the first processing unit of the processing element comprises:
checking, by a second checking unit, whether the activity potential signal is positive or negative (¶245, “excitatory connections connected back to itself (called self-connection) so that the time encoding neurons 610 can be continuously activated until this time encoding neuron is shut down by inhibitory input of a next time encoding neuron 610”, ¶87, “The excitatory connection is: when the upstream neurons of the excitatory connection are activated, non-negative input is provided to the downstream neurons through the excitatory connection”, ¶88, “The inhibitory connection is: when the upstream neurons of the inhibitory connection are activated, non-positive input is provided to the downstream neurons through the inhibitory connection”);
if the activity potential signal is negative, feeding, by the second checking unit, the activity potential signal to the first accumulator, thereby charging the first accumulator (¶337, “step c4: allowing each activated target neuron to establish the unidirectional or bidirectional excitatory connections with one or more of the other target neurons, or establish self-circulating excitatory connections with itself, adjusting the weights of the unidirectional or bidirectional excitatory connections or the self-circulating excitatory connections through the synaptic plasticity process”); and
if the activity potential signal is positive or zero, feeding, by the second checking unit, the activity potential signal to the discharge unit to discharge the first accumulator (¶294, “step B6: when the current membrane potential is less than or equal to the third pre-set potential interval, resetting the current membrane potential to the first pre-set potential”, activation trigger discharge ¶127, “step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 22,
Ren-Bazhenov teach the method of claim 1, wherein the method is implemented at least partially in hardware (¶176, “the brain-like neural network can also be implemented in the form of firmware (e.g., FPGA) or ASIC (e.g., neuromorphic chip)”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 23,
Ren-Bazhenov teach the method of claim 1, wherein processing the sum signal, by a first processing unit of the processing element, to obtain a first additional input signal comprises:
checking, by a first checking unit, whether the sum signal is positive or negative (¶245, “excitatory connections connected back to itself (called self-connection) so that the time encoding neurons 610 can be continuously activated until this time encoding neuron is shut down by inhibitory input of a next time encoding neuron 610”, ¶87, “The excitatory connection is: when the upstream neurons of the excitatory connection are activated, non-negative input is provided to the downstream neurons through the excitatory connection”, ¶88, “The inhibitory connection is: when the upstream neurons of the inhibitory connection are activated, non-positive input is provided to the downstream neurons through the inhibitory connection”);
if the sum signal is negative, feeding, by the first checking unit, the sum signal to a first accumulator which functions as an independent state memory, thereby charging the first accumulator (¶337, “step c4: allowing each activated target neuron to establish the unidirectional or bidirectional excitatory connections with one or more of the other target neurons, or establish self-circulating excitatory connections with itself, adjusting the weights of the unidirectional or bidirectional excitatory connections or the self-circulating excitatory connections through the synaptic plasticity process”);
if the sum signal is positive or zero, feeding, by the first checking unit, the sum signal to a discharge unit connected to the first accumulator to discharge the first accumulator through the discharge unit (¶294, “step B6: when the current membrane potential is less than or equal to the third pre-set potential interval, resetting the current membrane potential to the first pre-set potential”, activation trigger discharge ¶127, “step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3”);
utilizing an output of the discharge unit as the first additional input signal (¶¶124-127, “step m1: letting membrane potential to be Vm=Vm+Vc, step m2: summing all inputs weighted and superimposed to Vm, step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3“, discharge value Vrest is starting state of next accumulation when the loop return to m1, so the discharge unit’s output is the retained potential identified as the first additional input signal, ¶289, ¶293-294, b5/b6 reset to the first preset potential b1); and
utilizing the activity potential signal as a third additional input signal to the first processing unit of the processing element, wherein the positive feedback loop is formed by (¶245, “Each of the time encoding neurons 610 can also have excitatory connections connected back to itself (called self-connection) so that the time encoding neurons 610 can be continuously activated until this time encoding neuron is shut down by inhibitory input of a next time encoding neuron 610”):
checking, by a second checking unit, whether the activity potential signal is positive or negative (¶245, “excitatory connections connected back to itself (called self-connection) so that the time encoding neurons 610 can be continuously activated until this time encoding neuron is shut down by inhibitory input of a next time encoding neuron 610”, ¶87, “The excitatory connection is: when the upstream neurons of the excitatory connection are activated, non-negative input is provided to the downstream neurons through the excitatory connection”, ¶88, “The inhibitory connection is: when the upstream neurons of the inhibitory connection are activated, non-positive input is provided to the downstream neurons through the inhibitory connection”);
if the activity potential signal is negative, feeding, by the second checking unit, the activity potential signal to the first accumulator, thereby charging the first accumulator (¶337, “step c4: allowing each activated target neuron to establish the unidirectional or bidirectional excitatory connections with one or more of the other target neurons, or establish self-circulating excitatory connections with itself, adjusting the weights of the unidirectional or bidirectional excitatory connections or the self-circulating excitatory connections through the synaptic plasticity process”); and
if the activity potential signal is positive or zero, feeding, by the second checking unit, the activity potential signal to the discharge unit to discharge the first accumulator (¶294, “step B6: when the current membrane potential is less than or equal to the third pre-set potential interval, resetting the current membrane potential to the first pre-set potential”, activation trigger discharge ¶127, “step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 24,
Ren-Bazhenov teach the method of claim 1, wherein the variation of the activity potential signal over time is measured by a post-processing unit (¶349, “the information (denoted as T1 information) of the memory module 8 is encoded by the first group of target neurons. The information (denoted as T2 information) input to the memory module 8 during the T2 time period is encoded by the second group of target neurons. In the time period where the T1 time period and the T2 time period overlap (i.e., T3 time period), the time-series correlation between the T1 information and the T2 information is encoded by the unidirectional or bidirectional excitatory connections between the first group of target neurons and the second group of target neurons”, post-processing unit is the readout layer 92 of ¶327, which receives connections from the target area neurons and reads out their activation), wherein the post-processing unit is configured to compare the measured variation to known measurable characteristics of entities comprised in a list associated with the post-processing unit (¶327, “the brain-like neural network also includes a readout layer 92, including a plurality of readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F … Each readout layer neuron 920 corresponds to a label … For example, each label could be “Apple,” “car,”, “grassland”, etc”, list of readout layer neurons, each bound to one label, is the list of known entities associated with the post-processing unit), and wherein the post-processing unit is configured to identify an entity based on the comparison (¶328, “the one with the largest activation intensity or firing rate or the one that starts firing first is mapped to the corresponding label through multiple readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F as the recognition result of the instance appearing in the samples (images or videos)”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 25,
Ren-Bazhenov teach the method of claim 1, wherein each processing element of the network has a global network clock independent memory (¶288, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶123, “The unconditionally spontaneous firing neurons automatically gradually accumulate the membrane potential without external input, when the membrane potential reaches the threshold, the unconditionally spontaneous firing neurons activate, and restore the membrane potential to resting potential to restart accumulation process”, ¶300, “different initial membrane potential values are used for each unidirectional integral distance displacement encoding neuron of the same relative displacement encoding unit”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 10,
Claim 10 is similar in scope to claim 1 and 23; therefore, it is rejected under similar rationale. Ren-Bazhenov further teach controlling circuitry configured to cause, at a processing element of the network of processing elements See at least Ren, ¶176, “In another embodiment, the brain-like neural network can also be implemented in the form of firmware (e.g., FPGA) or ASIC (e.g., neuromorphic chip)”, Bazhenov Fig. 6, ¶73, “FIG. 6 illustrates example computing component 600, which may in some instances include a processor on a computer system (e.g., control circuit)”, ¶77.
With regard to Claim 11,
Ren-Bazhenov teach the apparatus of claim 10, wherein at least one of the processing elements in the network of processing elements comprises a transfer function unit for adjusting the dynamics of a signal (Ren, ¶¶290-292, “when the current membrane potential is within the interval of a first pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the first pre-set potential, the greater the deviation between the current membrane potential and the first pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0, step b3: when the current membrane potential is within the interval of a second pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the second pre-set potential, the greater the deviation between the current membrane potential and the second pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0”), where the transfer function unit comprises:
a reception unit configured to receive an input signal (Ren, ¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”, ¶105, “Samples (images, video streams) can be acquired in real time … a camera mounted on a movable platform”, ¶182, “the perceptual module 1 can also accept audio input or other modal information input. For example, the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons “, ¶183, ¶106, ¶178, ¶89, ¶99, Bazhenov, Fig. 2, ¶44, “he input layer in the SNN is represented as a Poisson-distributed spike train with mean firing rate given by the average value of that unit in the ANN for all tasks seen so far”);
an amplifier configured to amplify the input signal to obtain an amplified input signal (Bazhenov, ¶44, “Each layer has 2 important parameters that dictates its firing rate: a threshold and a synaptic scaling factor. The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, ¶46, Table 1, ¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”);
a first processing unit comprising a first checking unit, wherein the first checking unit is configured to check whether the input signal is positive or negative (Ren, ¶87, “The excitatory connection is: when the upstream neurons of the excitatory connection are activated, non-negative input is provided to the downstream neurons through the excitatory connection”, ¶88, “The inhibitory connection is: when the upstream neurons of the inhibitory connection are activated, non-positive input is provided to the downstream neurons through the inhibitory connection”, ¶563, “The symbol “+/−” next to the connection indicates that the connection can conduct an excitatory or inhibitory type or empty (0) signal”), wherein the first checking unit is configured to feed the input signal to a first accumulator if the input signal is negative (Ren, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶274, “SDDEN0 accepts excitatory connections from SN0 and inhibitory connections from SN180”) and wherein the first checking unit is configured to feed the input signal to a discharge unit connected to the first accumulator if the input signal is positive or zero (Ren, ¶293, “step b5: when the current membrane potential is greater than or equal to the second pre-set potential, resetting the current membrane potential to the first pre-set potential”, ¶297, “the third pre-set potential interval<the first pre-set potential interval<the second pre-set potential interval. The first pre-set potential interval, the second pre-set potential interval and the third pre-set potential interval are the median values of the first pre-set potential interval, the second pre-set potential interval and the third pre-set potential interval in turn”, ¶298, values -40mV/ 0 mV /+40 mV), and wherein the first processing unit is configured to process the input signal to obtain a first additional input signal by utilizing an output of the discharge unit as the first additional input signal (Ren, ¶¶124-127, “step m1: letting membrane potential to be Vm=Vm+Vc, step m2: summing all inputs weighted and superimposed to Vm, step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3“, discharge value Vrest is starting state of next accumulation when the loop return to m1, so the discharge unit’s output is the retained potential identified as the first additional input signal, ¶289, ¶293-294, b5/b6 reset to the first preset potential b1);
an addition unit configured to add the first additional input signal to the amplified input signal to obtain an activity potential signal (Ren, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential” Bazhenov, ¶44, “ The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, a is applied to Wx first, and the product is added to the stored potential); and
an output unit configured to provide the activity potential signal as a third additional input signal to the first processing unit and as an output signal (Ren, ¶117, “One or more of the neurons are mapped to corresponding labels as output. For example, 10,000 instance encoding neurons 20 are mapped to 1 label as output”, Bazhenov, (¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”) to separate or identify entities or measurable characteristics thereof (¶66, “Sleep Promotes Separation of Internal Representations for Different Inputs”, ¶67, “ these correlation graphs suggest that sleep promotes decorrelating the internal representations of the input categories”), the dynamics of the output signal being different from the dynamics of the input signal (Ren, ¶¶290-292, “when the current membrane potential is within the interval of a first pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the first pre-set potential, the greater the deviation between the current membrane potential and the first pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0, step b3: when the current membrane potential is within the interval of a second pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the second pre-set potential, the greater the deviation between the current membrane potential and the second pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0”, ¶127, “step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3”). The same motivation to combine for claim 10 equally applies for current claim.
With regard to Claim 12,
Claim 12 is similar in scope to claims 11 and 21; therefore, it is rejected under similar rationale.
With regard to Claim 13,
Ren teach a system for separating or identifying entities using a network of processing elements, each processing element of the network being independent of global control signals (¶327, “Each neuron of the target area forms a unidirectional excitatory or inhibitory connection with one or more neurons of the readout layer 920A, 920B, 920C, 920D, 920E and 920F. … For example, each label could be “Apple,” “car,”, “grassland”, etc “, ¶103, ¶174, “each neuron and each connection (including neuron-neuron connection and synapse-synapse connection) can be represented by vector or matrix”, ¶176, “ the brain-like neural network can also be implemented in the form of firmware …”, ¶565, “present invention has been verified by software simulation, and its source code has been registered and obtained the software copyright”, ¶5, “The present invention is used to improve the effectiveness and accuracy of the intelligent agent's ability of object recognition, spatial navigation, reasoning and autonomous decision-making”, ¶107, “For one or more of the neurons, membrane potential is calculated to determine whether to activate the neurons, and if the neurons are determined to be activated, each downstream neuron is made to accumulate the membrane potential so as to determine whether to activate the neurons, such that the activation of the neurons will propagate in the brain-like neural network”, ¶122, “If the conditionally spontaneous firing neurons are not activated by external input in a first pre-set time interval, the conditionally spontaneous firing neurons are self-activated according to probability P”, ¶123, “The unconditionally spontaneous firing neurons automatically gradually accumulate the membrane potential without external input”), the system comprising:
a plurality of processing elements, each processing element having an independent memory (¶288, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶123, “The unconditionally spontaneous firing neurons automatically gradually accumulate the membrane potential without external input, when the membrane potential reaches the threshold, the unconditionally spontaneous firing neurons activate, and restore the membrane potential to resting potential to restart accumulation process”, ¶300, “different initial membrane potential values are used for each unidirectional integral distance displacement encoding neuron of the same relative displacement encoding unit”) and comprising:
an input unit, configured to receive a plurality of input signals from a plurality of sensors and/or from other processing elements (¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”, ¶105, “Samples (images, video streams) can be acquired in real time … a camera mounted on a movable platform”, ¶182, “the perceptual module 1 can also accept audio input or other modal information input. For example, the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons “, ¶183, ¶106, ¶178, ¶89, ¶99);
a scaling unit, configured to scale each of the plurality of input signals with a respective weight to obtain weighted input signals (¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”, ¶103, “encodes, stores, and transmits information through the (synaptic) connections (with weights) between the neurons”, ¶174, ¶295, “ this condition can be satisfied by making each of the weights of these connections 0.4 …”, ¶316, “Weights of unidirectional excitatory connections between the concrete information source neurons/abstract information source neurons and the matched differential information decoupling neurons is constant (such as 0.1), or is dynamically adjusted through the synaptic plasticity process”);
a summing unit, configured to calculate a sum of the weighted input signals to obtain a sum signal (¶126, “step m2: summing all inputs weighted and superimposed to Vm”, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶174, “the signal propagation of the brain-like neural network can be expressed as the dot multiplication operation of the firing rate vector of the neuron and the weight vector of the connection (that is, the weighted sum of the input)”); and
a transfer function unit for adjusting the dynamics of a signal (Ren, ¶¶290-292, “when the current membrane potential is within the interval of a first pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the first pre-set potential, the greater the deviation between the current membrane potential and the first pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0, step b3: when the current membrane potential is within the interval of a second pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the second pre-set potential, the greater the deviation between the current membrane potential and the second pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0”), the transfer function unit comprising:
a reception unit configured to receive an input signal (Ren, ¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”, ¶105, “Samples (images, video streams) can be acquired in real time … a camera mounted on a movable platform”, ¶182, “the perceptual module 1 can also accept audio input or other modal information input. For example, the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons “, ¶183, ¶106, ¶178, ¶89, ¶99, Bazhenov, Fig. 2, ¶44, “he input layer in the SNN is represented as a Poisson-distributed spike train with mean firing rate given by the average value of that unit in the ANN for all tasks seen so far”);
a first processing unit comprising a first checking unit, wherein the first checking unit is configured to check whether the input signal is positive or negative (Ren, ¶87, “The excitatory connection is: when the upstream neurons of the excitatory connection are activated, non-negative input is provided to the downstream neurons through the excitatory connection”, ¶88, “The inhibitory connection is: when the upstream neurons of the inhibitory connection are activated, non-positive input is provided to the downstream neurons through the inhibitory connection”, ¶563, “The symbol “+/−” next to the connection indicates that the connection can conduct an excitatory or inhibitory type or empty (0) signal”), wherein the first checking unit is configured to feed the input signal to a first accumulator if the input signal is negative (Ren, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶274, “SDDEN0 accepts excitatory connections from SN0 and inhibitory connections from SN180”) and wherein the first checking unit is configured to feed the input signal to a discharge unit connected to the first accumulator if the input signal is positive or zero (Ren, ¶293, “step b5: when the current membrane potential is greater than or equal to the second pre-set potential, resetting the current membrane potential to the first pre-set potential”, ¶297, “the third pre-set potential interval<the first pre-set potential interval<the second pre-set potential interval. The first pre-set potential interval, the second pre-set potential interval and the third pre-set potential interval are the median values of the first pre-set potential interval, the second pre-set potential interval and the third pre-set potential interval in turn”, ¶298, values -40mV/ 0 mV /+40 mV), and
wherein the first processing unit is configured to process the input signal to obtain a first additional input signal by utilizing an output of the discharge unit as the first additional input signal (Ren, ¶¶124-127, “step m1: letting membrane potential to be Vm=Vm+Vc, step m2: summing all inputs weighted and superimposed to Vm, step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3“, discharge value Vrest is starting state of next accumulation when the loop return to m1, so the discharge unit’s output is the retained potential identified as the first additional input signal, ¶289, ¶293-294, b5/b6 reset to the first preset potential b1);
an addition unit configured to add the first additional input signal to the amplified input signal to obtain an activity potential signal (Ren, ¶289, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”); and
an output unit configured to provide the activity potential signal as a third additional input signal to the first processing unit and as an output signal (Ren, ¶117, “One or more of the neurons are mapped to corresponding labels as output. For example, 10,000 instance encoding neurons 20 are mapped to 1 label as output”, the dynamics of the output signal being different from the dynamics of the input signal (Ren, ¶¶290-292, “when the current membrane potential is within the interval of a first pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the first pre-set potential, the greater the deviation between the current membrane potential and the first pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0, step b3: when the current membrane potential is within the interval of a second pre-set potential, the activation rate of the unidirectional integral distance displacement encoding neuron is the maximum when the current membrane potential is equal to the second pre-set potential, the greater the deviation between the current membrane potential and the second pre-set potential, the lower the activation rate of the unidirectional integral distance displacement encoding neuron is until it reaches 0”, ¶127, “step m3: if Vm>=threshold, then letting the unconditionally spontaneous firing neuron activates and letting Vm=Vrest, and repeating steps m1 to m3”); and
wherein the sum signal is utilized as the input signal for the transfer function unit (¶288, “step b1: adding weighted sum of all inputs to the membrane potential at the previous moment to obtain current membrane potential”, ¶125, “step m2: summing all inputs weighted and superimposed to Vm”); and
wherein the output signals of the transfer function units of the plurality of processing elements are utilized to separate or identify entities (¶327, “Each neuron of the target area forms a unidirectional excitatory or inhibitory connection with one or more neurons of the readout layer 920A, 920B, 920C, 920D, 920E and 920F. … For example, each label could be “Apple,” “car,”, “grassland”, etc “).
Ren does not explicitly teach an amplifier configured to amplify the input signal to obtain an amplified input signal, wherein the range of the output signal of the processing element is dynamically adapted to separate or identify entities or measurable characteristics thereof.
Bazhenov teach a system for separating or identifying entities using a network of processing elements, each processing element of the network being independent of global control signals (¶¶5-6, “the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶66, “Sleep Promotes Separation of Internal Representations for Different Inputs”, ¶67, “ these correlation graphs suggest that sleep promotes decorrelating the internal representations of the input categories”), the system comprising:
a plurality of processing elements, each processing element having an independent memory and comprising:
an input unit, configured to receive a plurality of input signals from a plurality of sensors and/or from other processing elements (Fig. 2, ¶44, “he input layer in the SNN is represented as a Poisson-distributed spike train with mean firing rate given by the average value of that unit in the ANN for all tasks seen so far”);
a scaling unit, configured to scale each of the plurality of input signals with a respective weight to obtain weighted input signals (¶44, “The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer.”);
a summing unit, configured to calculate a sum of the weighted input signals to obtain a sum signal (¶44, “ The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer.”); and
an amplifier configured to amplify the input signal to obtain an amplified input signal (¶44, “Each layer has 2 important parameters that dictates its firing rate: a threshold and a synaptic scaling factor. The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, ¶46, Table 1, ¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”);
an addition unit configured to add the first additional input signal to the amplified input signal to obtain an activity potential signal (¶44, “ The input to a neuron is computed as aW{dot over (x)}, where a is the layer-specific synaptic scaling factor, W is the weight matrix, and x is the spiking activity (binary) of the previous layer”, a is applied to Wx first, and the product is added to the stored potential); and
an output unit configured to provide the activity potential signal as a third additional input signal to the first processing unit and as an output signal (¶6, “mapping weights from the first ANN to SNN, the SNN comprises a network of integrate-fire units, and applying weight normalization and returning scale for each layer of the SNN”, ¶43, “the weights from an ANN with ReLU activation units are transferred directly to the SNN, which consists of leaky integrate-and-fire neurons and the weights are scaled by the maximum activation in each layer during training”) to separate or identify entities or measurable characteristics thereof (¶66, “Sleep Promotes Separation of Internal Representations for Different Inputs”, ¶67, “ these correlation graphs suggest that sleep promotes decorrelating the internal representations of the input categories”); and
wherein the output signals of the transfer function units of the plurality of processing elements are utilized to separate or identify entities (¶66, “Sleep Promotes Separation of Internal Representations for Different Inputs”, ¶67, “ these correlation graphs suggest that sleep promotes decorrelating the internal representations of the input categories”).
Ren and Bazhenov are analogous art to the claimed invention because they are from a similar field of endeavor of Spiking Neural Network (SNN). Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren resulting in resolutions as disclosed by Bazhenov with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ren-Bazhenov as described above to improve ability of the ANN to process new types of data including but not limited to the data with different statistics, e.g., noisy data; enable ability of the ANN to avoid catastrophic forgetting of the previously learned tasks (Bazhenov, ¶4). This is simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 14,
Claim 14 is similar in scope to claim 21; therefore, it is rejected under similar rationale.
With regard to Claim 15,
Ren-Bazhenov teach the system of claim 13, further comprising a classifier comprising a list of known entities, such as objects (Ren, ¶327, “the brain-like neural network also includes a readout layer 92, including a plurality of readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F … Each readout layer neuron 920 corresponds to a label … For example, each label could be “Apple,” “car,”, “grassland”, etc”, list of readout layer neurons, each bound to one label, is the list of known entities associated with the post-processing unit), wherein each known entity is mapped to a respective distribution of activity potential signals of each processing element (Ren, ¶327, “Each neuron of the target area forms a unidirectional excitatory or inhibitory connection with one or more neurons of the readout layer 920A, 920B, 920C, 920D, 920E and 920F. Each readout layer neuron 920 corresponds to a label”, ¶103, “encodes, stores, and transmits information through the (synaptic) connections (with weights) between the neurons”) and wherein the classifier is configured to receive the activity potential signal of each processing element wherein the classifier is configured to compare the activity potential signal of each processing element to the distributions of activity potential signals of the known entities over a time period (Ren, ¶325, “if one or more of neurons in the target area are activated in tenth pre-set period (such as 1s), then representation of each activation neuron in the target area can be taken together with its activation intensity or activation rate as the result of the memory triggering process”, ¶327, “Each readout layer neuron 920 corresponds to a label. The higher the activation intensity or firing rate of the neuron 920, the higher the correlation between the input information and its corresponding label, and vice versa”), and configured to identify the entity as one of the entities of the list based on the comparison (Ren, ¶328, “the one with the largest activation intensity or firing rate or the one that starts firing first is mapped to the corresponding label through multiple readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F as the recognition result of the instance appearing in the samples (images or videos), and the size of its activation intensity or firing rate is taken as the correlation degree”, ¶328, “the one with the largest activation intensity or firing rate or the one that starts firing first is mapped to the corresponding label through multiple readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F as the recognition result of the instance appearing in the samples (images or videos)”). The same motivation to combine for claim 13 equally applies for current claim.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Ren Hualong [US 2023/0087722 A1] in view of Bazhenov et al. [US 2022/0374679 A1, hereinafter Bazhenov] in view of “Event-driven visual attention for the humanoid robot iCub” [hereinafter D1] Published 2013.
With regard to Claim 17,
Ren-Bazhenov teach the system of claim 13, wherein the plurality of input signals changes dynamically over time and follows a sensor input trajectory (Ren, ¶103, “Samples (images, video streams) can be acquired in real time using recorded images or video streams, using monocular, binocular, or multi-view cameras that can be rotated, or using camera gimbal, or a camera mounted on a movable platform”, ¶3, “Autonomous robots (intelligent agents) need to be able to integrate their motion trajectories and multi-modal perceptual information into episodic memory including temporal and spatial sequences”, ¶351, “the motion orientation information of the intelligent agent to be input to the memory module 8 through the firing of a series of the information input neurons 710 (such as 710A, 710B, 710C, and 710D in FIG. 2 ), and is encoded as spatial memory”), and wherein the plurality of input signals comprises pixel values, such as intensity, of images captured by a camera (¶104, “Image or video stream are input such that one or more pixel values R, G, B of multiple pixels of each frame image are respectively multiplied by a weight of 1 and fed into a plurality (such as 100) of the perceptual encoding neurons 110 so as to activate the plurality of the perceptual encoding neurons 110”), wherein the entity identified is an object or a feature of an object present in one or more images of the captured images (Ren, ¶327, “the brain-like neural network also includes a readout layer 92, including a plurality of readout layer neurons 920A, 920B, 920C, 920D, 920E, 920F … Each readout layer neuron 920 corresponds to a label … For example, each label could be “Apple,” “car,”, “grassland”, etc”). The same motivation to combine for claim 13 equally applies for current claim.
Ren-Bazhenov does not explicitly teach the activity potential signal of each processing element is further utilized to control a position of the camera by rotational and/or translational movement of the camera, thereby controlling the sensor input trajectory.
D1 teach the activity potential signal of each processing element is further utilized to control a position of the camera by rotational and/or translational movement of the camera (P. 5, 3, PERFORMANCE AND BENCHMARK, Col. 1-2, “The selected attended location can be communicated to the oculomotor controllers to direct the robot’s gaze toward salient regions with a saccade command”, P. 5, 2.3.3. Ocular movements, “A dedicated module implements saccades or gaze shifts toward salient regions selected by EVA. Tremor and microsaccades are used to generate motion of static visual stimuli on the DVS sensor focal plane, to elicit activity of the pixels that only respond to stimulus changes. This approach is similar to the mammals visual system, where small eye movements counteract photoreceptors bleaching adaptation (Kowler, 2011). Tremor is implemented as an omnidirectional movement of 0.45◦ amplitude with frequency of 500 Hz and random direction, superimposed on microsaccades of amplitude 0.75◦ and frequency 2.5 Hz in exclusively horizontal direction”, P. 2, 2.1, “… It features three degrees of freedom in the eyes to realize the tilt, vergence and version movements required for the implementation of active vision”), thereby controlling the sensor input trajectory (P. 5, 2.3.3. Ocular movements, “A dedicated module implements saccades or gaze shifts toward salient regions selected by EVA. Tremor and microsaccades are used to generate motion of static visual stimuli on the DVS sensor focal plane, to elicit activity of the pixels that only respond to stimulus changes. This approach is similar to the mammals visual system, where small eye movements counteract photoreceptors bleaching adaptation (Kowler, 2011). Tremor is implemented as an omnidirectional movement of 0.45◦ amplitude with frequency of 500 Hz”, Fig. 3, Fig. 4) and wherein the entity identified is an object or a feature of an object present in one or more images of the captured images (P. 3, 2.3.1. “In EVA a number of features are extracted from the DVS output to populate diverse feature maps. As the DVS does not convey information about color or absolute intensity, we implemented a subset of feature maps from Itti and Koch(2001): contrast, orientation(0◦, 45◦, 90◦,−45◦) and flicker map”, P. 5, 2.3.2., “Finally, a WTA module selects the most conspicuous location of the saliency map, defining the current focus of attention”).
Ren-Bazhenov and D1 are analogous art to the claimed invention because they are from a similar field of endeavor of developing a biologically inspired attention system to efficiently recognize objects, perform spatial navigation, reasoning and autonomous decision-making. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren-Bazhenov resulting in resolutions as disclosed by D1 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ren-Bazhenov as described above to provide a system with low-latency and fast determination of the location of the focus of attention, allowing for fast reaction to unexpected, dynamic events and for a more natural interaction of robots with the environment (D1, P. 1, Abstract, P. 5, 3). This is simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Ren Hualong [US 2023/0087722 A1] in view of Bazhenov et al. [US 2022/0374679 A1, hereinafter Bazhenov] in view of “Discrimination of Dynamic Tactile Contact by Temporally Precise Event Sensing in Spiking Neuromorphic Networks” [hereinafter D2] Published 2017.
With regard to Claim 18,
Ren-Bazhenov teach the system of claim 13. The same motivation to combine for claim 13 equally applies for current claim.
Ren-Bazhenov does not explicitly teach the plurality of input signals changes dynamically over time and follows a sensor input trajectory, and wherein the plurality of sensors are touch sensors and the input from each of the plurality of sensors comprises a touch event signal with a force dependent value and wherein the activity potential signal of each processing element is utilized to identify the sensor input trajectory as a new contact event, the end of a contact event, a gesture or as an applied pressure.
D2 teach plurality of input signals changes dynamically over time and follows a sensor input trajectory (Abstract, “touch signals are characterized as patterns of millisecond precise binary events to denote pressure changes. This approach is amenable to a sparse signal representation and enables the extraction of relevant features from thousands of sensing elements with sub-millisecond temporal precision”), and wherein the plurality of sensors are touch sensors and the input from each of the plurality of sensors comprises a touch event signal with a force dependent value (Abstract, “Implemented on a state-of-the-art 4096 element tactile sensor array with 5.2kHz sampling frequency, we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations”, P. 2, Col. 1, “Spatiotemporal patterns were generated by detecting the time each sensor element crossed a pre-defined pressure threshold for a given stimulus “, Col. 2, “we propose the use of spatiotemporal representations of tactile pressure changes to resolve dynamic contact events that are common during daily interactions with our environment”) and wherein the activity potential signal of each processing element is utilized to identify the sensor input trajectory as a new contact event, the end of a contact event, a gesture or as an applied pressure (Abstract, “we demonstrate the classification of transient impact events while utilizing 20 times less communication bandwidth compared to frame based representations. Spiking sensor responses to a large library of contact conditions were also synthesized using finite element simulations, illustrating an 8-fold improvement in information content and a 4-fold reduction in classification latency when millisecond-precise temporal structures are available. Our research represents a significant advance, demonstrating that a neuromorphic spatiotemporal representation of touch is well suited to rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications”).
Ren-Bazhenov and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of developing a biologically inspired artificial intelligence and neurorobotics. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren-Bazhenov resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ren-Bazhenov as described above to provide the ability for rapid identification of critical contact events, making it suitable for dynamic tactile sensing in robotic and prosthetic applications (D2, Abstract). This is simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ren Hualong [US 2023/0087722 A1] in view of Bazhenov et al. [US 2022/0374679 A1, hereinafter Bazhenov] in view of KRISHNAN et al. [US 2015/0235125 A1, hereinafter KRISHNAN].
With regard to Claim 19,
Ren-Bazhenov teach the system of claim 13 wherein the plurality of input signals changes dynamically over time and follows a sensor input trajectory (Ren, ¶182, “the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons 110”), and wherein each sensor of the plurality of sensors is associated with a different frequency band of an audio signal (Ren, ¶182, “the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons 110”), and wherein the combined input from the plurality of sensors follows a sensor input trajectory signal (Ren, ¶182, “the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons 110”). The same motivation to combine for claim 13 equally applies for current claim.
Ren-Bazhenov does not explicitly teach wherein each sensor reports an energy present in the associated frequency band, and wherein the activity potential signal of each processing element is utilized to identify a speaker and/or a spoken letter, a syllable, a phoneme, a word or a phrase present in the audio signal.
KRISHNAN teach plurality of input signals changes dynamically over time and follows a sensor input trajectory (Fig. 9, 902), and wherein each sensor of the plurality of sensors is associated with a different frequency band of an audio signal (¶76, “ … audio processor 904 may comprise a bank of band pass filters with center frequencies spanning a predetermined range. The audio processor 904 may identify a pitch (i.e., an audio attribute) that is dominated by a target source …”, each filter channel is a sensor (source of one of the input signals)), wherein each sensor reports an energy present in the associated frequency band (¶¶76-77, “A pitch track may be produced based on a collection of raw power values corresponding to the indicated channel of the audio signal”, ¶85, “audio attribute is identified as band pass filter output that is highest in a specified range (e.g., with center frequencies between 180-200 Hz) for a time period (e.g., 25 ms) For each identified pitch channel, raw power values can be collected from the corresponding channel in a spectrogram and at the corresponding time, yielding a one dimensional array …”), and wherein the activity potential signal of each processing element is utilized to identify a speaker and/or a spoken letter, a syllable, a phoneme, a word or a phrase present in the audio signal (¶91, “These applications include, but are not limited to, speech activity detection, speech recognition, speech coding, and audio enhancements”).
Ren-Bazhenov and KRISHNAN are analogous art to the claimed invention because they are from a similar field of endeavor of using artificial neural networks for audio detection. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren-Bazhenov resulting in resolutions as disclosed by KRISHNAN with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ren-Bazhenov as described above to provide innovative and useful computational techniques for certain applications in which traditional computational techniques are cumbersome, impractical, or inadequate, especially in applications where the complexity of the task or data makes the design of the function by conventional techniques burdensome (KRISHNAN, ¶5). This is simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 20,
Ren-Bazhenov teach the system of claim 13, wherein the plurality of input signals changes dynamically over time and follows a sensor input trajectory (Ren, ¶182, “the audio information is decomposed into a number of (e.g., 32) frequency bands of signals, and each frequency band of signals is fed to one or more perceptual encoding neurons 110”), , and wherein the plurality of sensors comprise a plurality of sensors related to a speaker, such as microphones (¶182, “accept audio input”, ¶183, “ accepting audio stream input”). The same motivation to combine for claim 13 equally applies for current claim.
Ren-Bazhenov does not explicitly teach the output signal for the processing element is utilized to separate or identify one or more speakers.
KRISHNAN teach wherein the plurality of sensors comprise a plurality of sensors related to a speaker, such as microphones, and wherein the output signal for the processing element is utilized to separate or identify one or more speakers (KRISHNAN, ¶5). Ren-Bazhenov and KRISHNAN are analogous art to the claimed invention because they are from a similar field of endeavor of using artificial neural networks for audio detection. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Ren-Bazhenov resulting in resolutions as disclosed by KRISHNAN with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify Ren-Bazhenov as described above to provide innovative and useful computational techniques for certain applications in which traditional computational techniques are cumbersome, impractical, or inadequate, especially in applications where the complexity of the task or data makes the design of the function by conventional techniques burdensome (KRISHNAN, ¶5). This is simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20080258767 filed by Sinder et al. that disclose the usage of Neuron-like computational node for use by a computer scientist, hardware designer, researcher's designer, researcher's focused on artificial intelligence and biological intelligence, to construct a parallel, distributed, dynamical computational-node network (claimed) e.g. perceptron network and neural network, for pattern recognition, diagnosis of the causes of complex phenomena, signal processing and signal denoising application
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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