DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the amendment filed on Feb. 3rd, 2026. The amendments are linked to the original application filed on Nov. 19th, 2021.
Acknowledgment is made with respect to a claim of priority to KR10-2020-0156897 filed on November 20th, 2020.
Continued Prosecution Application
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Feb. 3rd, 2026 has been entered.
Response to Amendment
The Examiner thanks the applicant for the remarks, edits and arguments.
Regarding Claim Objections
Applicant Remarks:
The applicant has amended claims 1, 9 and 17 and no longer uses the word “losslessly”. Therefore, the applicant requests that the claim objection be withdrawn.
Examiner Response:
The examiner recognizes the amendments made by the applicant and notices that the applicant has removed the word “losslessly” from the independent claims. Therefore, the examiner no longer objects to the claims and the claim objection is withdrawn.
Regarding Claim Rejections – 35 U.S.C. 101
Applicant Remarks:
The applicant has amended the claims to further recite and define a technical process in the claims to reflect the invention and proposed improvements as stated in the specification. Further, the applicant has made amendments to the claims and argues that the technical process disclosed in the claims does not include abstract ideas and the limitations of the claim cannot be performed by a human mentally. As stated in the remarks, the applicant believes the amendments made comply with 35 U.S.C. 101 and integrate the abstract concepts, if any, into a practical application. The applicant has given examples of the claims which they believe cannot be performed by a human mentally and recite technical process of collecting, partitioning, and evaluating firing patterns of neurons. Finally, the applicant states that the claims represent a technical improvement and improves the performance of a computer system. For these reasons and the reasons stated in the remarks, the applicant believes the current amended claims comply with 35 U.S.C. 101 and the request the rejection under this section be withdrawn.
Examiner Response:
The Examiner has recognized the amendments made by the applicant. The applicant argues that the claims no longer recite any abstract concepts and recites a technical process and improvement to computers or technical field. After further consideration of the amended claims, the examiner believes limitations in the independent claims do recite abstract ideas. For example, the independent claims state, “profiling neural firing data based on time series data representing firing timepoint for at least one neural firing within a window defined by a predetermined time length;”. The examiner believes that the process of profiling data, or any data type, is a process a human can perform mentally or using pen and paper. The examiner believes this limitation recites an abstract human process of evaluation. In particular a human is able to observe and evaluate data using a generic computer. Per MPEP 2106.04(a)(2)(III)(C) “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.”. Taking this into consideration the examiner believes that a human is able to profile firing data using a generic computer and therefore the limitation is considered to be an abstract idea. The examiner has evaluated the claims further and has noted other limitations that recite abstract ideas, see 101 rejection below.
Next, the applicant argues that the claims recite a technical process which provides an improvement to the performance of computers. The examiner believes that the claims fail to recite a technical improvement to the performance of computer. The examiner would like to note that the invention itself may provide a technical improvement, however the claims fail to recite the performance improvement as stated. Per MPEP 2106. 04(d)(1), “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.” (emphasis added). The examiner believes the claimed improvements would not be apparent to one of ordinary skill in the art. Therefore, the examiner believes the current amended claims fail to comply with 35 U.S.C. 101 and the rejection under 35 U.S.C. 101 is upheld.
Regarding Claim Rejections – 35 U.S.C. 103
Applicant Remarks:
The applicant has made amendments to the claims and believes the art proposed by the examiner fails to teach or disclose each and every limitation of the amended claims. The applicant argues that Buonomano fails to teach windowing and the use of tuning curves as stated in the claims. Further the applicant argues that the current proposed arts fail to teach Specific Overlapping Topology, an Explicit Computation Process, and a Two-Step Quantified Extraction.
Next, the applicant argues that Zjajo fails to disclose the windowing or partitioning process as disclosed in the claims. Particularly, Zjajo fails to disclose a process which includes overlapping windows and the use of Temporal Tuning Curves. Further the applicant states that Zjajo also fails to disclose the process of summing spikes in a time window using a summation process.
Next, the applicant states that Buonomano also fails to disclose the process of partitioning data and the use of overlapping windows as disclosed in the claims. Further the applicant states that Buonomano also fails to disclose a process of summing spikes in a time window using a summation process.
For the reasons stated above and in the submitted remarks, the applicant believes that the combination of Zjajo and Buonomano fails to teach or disclose the elements of the amended claims and therefore request the rejection under 35 USC 103 be withdrawn.
Examiner Response:
The applicant argues that the proposed arts fail to disclose or teach the amended claims. The applicant argues that Buonomano fails to disclose different elements of the claims. After further evaluation of the claims, the examiner believes that Buonomano does fail to properly teach or disclose the stated elements of the amended claims. Therefore, the examiner no longer relies on Buonomano to disclose the claimed subject matter.
Next, the applicant argues that Zjajo also fails to teach or disclose elements of the amended claims. The applicant argues that Zjajo fails to the use of overlapping windows or bins. The examiner has considered the claims and the argument and the examiner does agree, Zjajo fails to disclose a process of partitioning firing data using overlapping windows as claimed. Next the examiner does also agree with the applicant and Zjajo fails to a process of summing firing values of a window or bin. However, the examiner does believe the claims recite similar subject matter to Zjajo. Zjajo does disclose a process which is able to interpret and decode firing data and evaluate that data to produce useable output data.
Finally, the examiner, after each amendment, performs a complete and through search of the claims to ensure the comply with 35 U.S.C. 102/103. While performing this search the examiner has found subject matter that is able to, in combination of Zjajo, disclose or teach the amended claims. The examiner believes the proposed arts of Szucs and Quaglio et al are able to disclose a Specific overlapping strategy as claims and the use of window or bin values. Finally, the examiner believes the proposed arts Zjajo and Quaglio et al are able to disclose a two-step quantified Extraction process as claimed as well. Therefore, with the new subject matter, the examiner believes the rejection under 35 U.S.C. 103 should be upheld, see 103 rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 9, and 17 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 recites, “A method by a computer system, comprising:” therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“profiling neural firing data based on time series data representing firing timepoint for at least one neural firing within a window defined by a predetermined time length; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and profile data using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“extracting content for the neural firing from the profiled neural firing data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and develop opinions or judgements from the evaluation. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the profiling of the neural firing data comprises: dividing the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both end;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe and partition data using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting a section value for said each section based on the firing timepoint within said each section; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe data and detect values in the observed data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the detecting of the section value for said each section comprises: identifying a plurality of firing timepoints of a plurality of neural firings within said each section;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and determine form the record data different marked timestamps. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining individual neural firing values for the plurality of firing timepoints, respectively, based on locations of the firing timepoints relative to the center of said each section; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to identify a firing values and assign a value based on different metrics or values. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate groomed data and apply values, opinions or judgments to that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the extracting of the content comprises: extracting content estimation information from the feature vector by learning the feature vector;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and learn a vector using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“extracting quantified content from the content estimation information based on the detected effects of the identified content type; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and observe changes is groomed data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “generating the profiled neural firing data by combining section values for all of the sections into a feature vector;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“detecting effects of identified content type for the content estimation information; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the extracting of the content estimation information comprises: inputting the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “generating the profiled neural firing data by combining section values for all of the sections into a feature vector;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“detecting effects of identified content type for the content estimation information; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the extracting of the content estimation information comprises: inputting the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 9 recites, “A computer system, comprising: a memory; and a processor connected with the memory and configured to execute at least one instruction stored in the memory,” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
wherein the processor is configured to profile neural firing data based on time series data representing firing point for at least one neural firing within a window defined by a predetermined time length; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and profile data using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“extract content for the neural firing from the profiled neural firing data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and develop opinions or judgements from the evaluation. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the processor is configured to: divide the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both ends;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe and partition data using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detect a section value for said each section based on the firing timepoint within said each section; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe data and detect values in the observed data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the processor is configured to detect the section value for said each section by: identifying a plurality of firing timepoints of a plurality of neural firings within said each section;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and determine form the record data different marked timestamps. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining individual neural firing values for the plurality of firing timepoints, respectively, based on locations of the firing timepoints relative to the center of said each section; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to identify a firing values and assign a value based on different metrics or values. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate groomed data and apply values, opinions or judgments to that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the processor is configured to: extract content estimation information from the feature vector by learning the feature vector;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and learn a vector using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“extract quantified content from the content estimation information based on the detected effects of the identified content type; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and observe changes is groomed data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “generate the profiled neural firing data by combining section values for all of the sections into a feature vector;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“detect effects of identified content type for the content estimation information; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the processor is configured to: input the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extract the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “generate the profiled neural firing data by combining section values for all of the sections into a feature vector;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“detect effects of identified content type for the content estimation information; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the processor is configured to: input the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extract the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 17 recites, “A non-transitory computer-readable medium for storing at least one program, wherein the computer-readable medium is configured to execute:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“profiling neural firing data based on time series data representing firing timepoint for at least one neural firing data within a window defined by a predetermined time length; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and profile data using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“extracting content for the neural firing from the profiled neural firing data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and develop opinions or judgements from the evaluation. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the profiling of the neural firing data comprises: dividing the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both ends;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe and partition data using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting a section value for said each section based on the firing timepoint within said each section; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe data and detect values in the observed data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the detecting of the section value for said each section comprises: identifying a plurality of firing timepoints of a plurality of neural firings within said each section;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and determine form the record data different marked timestamps. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining individual neural firing values for the plurality of firing timepoints, respectively, based on locations of the firing timepoints relative to the center of said each section; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to identify a firing values and assign a value based on different metrics or values. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate groomed data and apply values, opinions or judgments to that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“wherein the extracting of the content comprises: extracting content estimation information from the feature vector by learning the feature vector;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and learn a vector using a generic computer. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“extracting quantified content from the content estimation information based on the detected effects of the identified content type; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and observe changes is groomed data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “generating the profiled neural firing data by combining section values for all of the sections into a feature vector;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“detecting effects of identified content type for the content estimation information; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the extracting of the content estimation information comprises: inputting the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “generating the profiled neural firing data by combining section values for all of the sections into a feature vector;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“detecting effects of identified content type for the content estimation information; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“wherein the extracting of the content estimation information comprises: inputting the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“extracting the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 9, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zjajo et al, (Zjajo et al., "Spiking Neural Network", US20220230051 Al, filed 2019, hereinafter "Zjajo") in view of Szȕcs, (Szȕcs, “Applications of the spike density function in analysis of neuronal firing patterns”, 1998, hereinafter “Szucs”) and Quaglio et al, (Quaglio et al, “Methods for identification of spike patterns in massively parallel spike trains”, 2018, hereinafter “Guaglio”).
Regarding claim 1, Zjajo discloses, “A method by a computer system, comprising:” (Description of Embodiments, pp. 13, [0153]; "In general, the system and method described herein use a network of spiking neurons 1 as a means of generating unique spatio-temporal spike trains in response to a unique stimulus, where the uniqueness of responses is controllable through the operating parameters of the spiking neurons 1 and interconnection network. The system consists of an array of spiking neurons 1 with configurable parameters, interconnected through synaptic elements 2 with configurable parameters. By virtually partitioning the array into multiple different networks, the system can simultaneously realize multiple functionalities." This article discloses a method which is able to intake neural spiking data.)
“profiling neural firing data based on time series data representing firing timepoint for at least one neural firing within a window defined by a predetermined time length; and” (Description of Embodiments, pp. 5, [0061]; "FIG. 3B shows an example of three desired spike responses 14A, 14B, 14C for three different input patterns to a cell of a spiking neural network. Each response in this example takes the form of a set of one or more spatiotemporal spike trains generated by output neurons N 1, N2, N3, N4, NS of the cell. Each response 14A, 14B, 14C is described for a discrete time bin 15A, 15B, 15C that may be selected for each network." This figure shows the different neural spike trains N1, ... , NS. Each of these spike trains contain spikes within a window denoted 15A, ... , 15C. The numbers denoting the time are labeled 1, ... , 9.)
“extracting content for the neural firing from the profiled neural firing data;” (Summary, pp. 3, [0022]; "According to an aspect of the invention, a method is proposed for classifying input pattern signals using a spiking neural network comprising a plurality of spiking neurons implemented in hardware or a combination of hardware and software, and a plurality of synaptic elements interconnecting the spiking neurons to form the network, wherein each of the synaptic elements is adapted to receive a synaptic input signal and apply a weight to the synaptic input signal to generate a synaptic output signal, the synaptic elements being configurable to adjust the weight applied by each synaptic element, and wherein each of the spiking neurons is adapted to receive one or more of the synaptic output signals from one or more of the synaptic elements, and generate a spatio-temporal spike train output signal in response to the received one or more synaptic output signals." This teaches how the content is input into this invention and what is returned. The neural spike trains are input into the system, analyzed and content based on the analysis is extracted.)
“generating the profiled neural firing data by combining section values for all of the sections into a feature vector;” (Summary, pp. 2, [0012]; “The first sub-network is adapted to generate a sub-network output pattern signal from the first sub-set of spiking neurons, in response to a subnetwork input pattern signal applied to the first sub-set of synaptic elements, and the weights of the first sub-set of synaptic elements are configured by training the sub-network on a training set of sub-network input pattern signals, so that the sub-network output pattern signal is different for every unique sub-network input pattern signal of the training set.” The model in this application will evaluate the time bins and generate an output pattern that can be used to evaluate the time bin.) and (Detailed Description, pp. 4, [0051]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.” The output patterns contain information about the signals input into the encoder, this output information contains different forms of data and can be represented as a tuple or vector.)
“wherein the detecting of the section value for said each section comprises: identifying a plurality of firing timepoints of a plurality of neural firings within said each section;” (Summary, pp. 2, [0015]; “In the spiking neural network, each of the spiking neurons may be configurable to adjust the response of the neuron to the received one or more synaptic output signals. Spikes may be generated by the spiking neurons at one or more firing times, and the sub-set of synaptic elements and/or the sub-set of neurons may be configured such that the union of two sets of firing times of the sub-set of neurons that fire for two different sub-network input pattern signals is minimized for all sub-network input pattern signals of the training set.” This system is able to interpret spikes in a spiking neural network or from spike data. This model will input spiking data into a decoder and process the spikes. This will segment the data in to time windows as disclosed in [0065].) and (Detailed Description, pp. 6, [0065]; “The desired spike response 14A, 14B, 14C for each input pattern p can be described as a tuple which specifies the spike response in each time bin where input data 11 derived from an input pattern p is presented. For example, the tuple may specify the population of the cell's output neurons (
A
P
) that fired in response to the input data 11, and their precise firing times (
B
P
).” Each of the time bins is able to identify the spikes contained in the bins as well as the firing times of the spikes in the bin.)
“wherein the feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” (Description of Embodiments, pp. 4, [0051]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.” This model discloses the processing of the input data to produce output data which can be used by the user for classification or further processing. The output spike train generated by the model contains multiple variables including the spike amount and the times of the spikes in the time window.) and (Detailed Description, pp. 6, [0065]; “The desired spike response 14A, 14B, 14C for each input pattern p can be described as a tuple which specifies the spike response in each time bin where input data 11 derived from an input pattern p is presented. For example, the tuple may specify the population of the cell's output neurons (
A
P
) that fired in response to the input data 11, and their precise firing times (
B
P
).” This discloses that the output of the encoder can represent the data in a tuple, which can be interpreted as a feature vector as it contains features data from different categories of data.)
“wherein the extracting of the content comprises: extracting content estimation information from the feature vector by learning the feature vector;” (Detailed Description, pp. 4, [0049]; “The neurons 1 can be configurable in the way they respond to a signal from a synaptic element. For example, in the case of spiking neural networks, the neurons 1 can be configured in the way a certain signal increases or decreases the membrane potential, the time it takes for the membrane potential to naturally decay towards a resting potential, the value of the resting potential, the threshold value that triggers a spike of the spiking neuron 1. The configuration of the neurons 1 can for example be kept constant during training, or be variable and set in a training of the neural network 100 on a particular training set.” After this model is able to input the data and generate an output, the model can use the generated data to train the model. To do this the model would need to evaluate the output and train the model accordingly. This would teach the use of extracting data from a feature vector to train a model.)
“wherein the extracting of the content estimation information comprises: inputting the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” (Detailed Description, pp. 5-6, [0051-0052]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user. Operations such as pattern recognition may be accomplished in a number of steps (e.g. data conversion, feature encoding, classification). The present embodiment enables these steps to be realized using standardized subnetworks of synaptic elements and neurons (cells), and configured using a flexible training methodology for each application domain. The objective of the training methodology is to configure each stage or sub-network of the neural network to produce a unique response for each unique pattern or feature in the input signal.” The model in this article discloses a process of receiving input spike data and evaluating it and producing an output. This data can then be output into a usable form and the model can allow for multiple spike patterns to be classified and further processed.)
“extracting the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” (Description of Embodiments, pp. 4-5, [0051]; "Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and formed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.", This teaches different types can be output and the user can select which of the outputs they want.; and [0136]; "This operation allows the decoding of the output of the classifier stage 404 into the user-space, i.e. user-defined identifier of input patterns. During the configuration phase, the pattern recognizer 400 may be trained with sample features and patterns, and the generated hash codes can be recorded", which discloses extracting content information and identifying a content type ( a user-defined identifier of input patterns) as content estimation information as the information is output from a decoder) and (Detailed Description, pp. [0161]; "Input signals 1104 are submitted to a subset of the synaptic elements 2, and the network 1100 produces a certain output 1105, that can be send to an output decoder")
Zjajo fails to explicitly disclose, “wherein the profiling of the neural firing data comprises: dividing the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both end;”, “detecting a section value for said each section based on the firing timepoint within said each section; and”, “calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” and “detecting effects of identified content type for the content estimation information; and”.
However, Szucs discloses, “wherein the profiling of the neural firing data comprises: dividing the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both end;” (Kernel function and windowing, pp. 160-161; “According to this method the values of the kernel function at the time of spike occurrences (
t
s
) are read, summed and divided by the area of the kernel [See Equation (1)] here g is the Gaussian kernel function cent[e]red at t and represented in some finite interval
t
w
(window width) for practical reasons,
t
s
is the time of spike event. Convolution here simply means the replacement of each spike with unity area kernel function (
∫
t
0
t
1
g
t
*
d
t
*
=
1
) cent[e]red at the actual spike.” This article discloses a process of selecting neural spikes in a segment of timed data. The method will place a window around a section of data with a designated spike at the center of the window. This will then use a sliding window across the data to evaluate and encode it. This teaches that it will use overlapping windows, where the overlap would be at the edges of the windows and potentially a single overlap at the ends of the segment of data.)
“detecting a section value for said each section based on the firing timepoint within said each section; and” (Kernel function and windowing, pp. 161; “The first function resembling the conventional rate histogram is achieved by convolving the spike train with the square-window or boxcar (Fig. 1A). It is quantized and stepwise.” This article discloses a process of evaluating the firing timepoints in a section of data or window. The disclosed method will perform functions to the spikes located in the window to generate a value associated with that time bin.)
“calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” (Correlated firing patterns, pp. 161-162; “The cross correlation function is widely used in the characterization of interaction or mutual dependence between physiological processes. The following equation was used in our calculations (Eq. (2)): [see Equation (2)] where the product of spike density functions of cells i and j, respectively were calculated and the summation was performed in the time window Δt.” The model in this article is able to evaluate the spike density of a time window. As stated, this model is able to sum the spikes in the window and apply values to the spikes in the window.)
“detecting effects of identified content type for the content estimation information; and” (Correlated firing patters, pp. 162; “SDFs of cells functioning in an anticorrelated, precisely tuned mode are shown on Fig. 3A. An increase of the firing rate of the RPeD1 cell is accompanied by the decrease of that of the A-cell and vice versa, as seen more clearly in Fig. 3B with the expanded time scale. The cross correlation function possesses a local minimum at t-0 s, and exhibits overall symmetry and periodicity, clearly indicating the link between the neurons (Fig. 3C).” The method in this article is able to evaluate the firing data and its effects and correlations to other neurons. This article discloses a process of using the identified firing samples for further processing.)
Zjajo and Szucs fail to explicitly disclose, “wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;”, “determining individual neural firing values for the plurality of firing timepoints, respectively, based on locations of the firing timepoints relative to the center of said each section; and” and “extracting quantified content from the content estimation information based on the detected effects of the identified content type; and”.
However, Guaglio discloses, “wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” (Methods to detect population synchronization, pp. 61; “A spike train is fully described by its spike times and, given a time discretization in small temporal bins, we can define the population histogram as the count of spikes that occurred in the same time bin. The maximum possible count of the histogram is thus the number N of neurons. The first three methods presented here are based on statistics derived from the population histogram. They were developed in succession, each to overcome the limitations of the previous one. The first method, the Complexity Distribution (CD) analysis (Grün et al. 2008), proposes a simple statistical approach purely based on the distribution of the entries of the population histogram. It compares such an empirically derived distribution to that expected from neurons firing independently to determine the presence of excess synchronization. The second method, the CUmulant-Based Inference of Correlation (CuBIC, Staude et al. 2010a), derives the null distribution analytically under more specific assumptions about the data, and infers the minimum correlation order existent in the data. The third method, the Population Unitary Event (PUE, Rostami 2017) analysis, works under the same assumptions as CuBIC, but uses a different test statistic which enhances the statistical power of the test, thereby requiring samples of smaller size for a correct identification of excess synchrony and thus also enabling a time-resolved analysis.” The model disclosed in this article is able to evaluate spike pattern data. This will evaluate the data and use histograms to evaluate the relationship of the spikes in a segmented time window. This process uses multiple methods to evaluate the spike pattern data.)
“determining individual neural firing values for the plurality of firing timepoints, respectively, based on locations of the firing timepoints relative to the center of said each section; and” (Methods to detect population synchronization, pp. 61; “Most methods for population synchronization analysis reduce the spike data to the number of active neurons (i.e., spikes) observed at any time bin. A spike train is fully described by its spike times and, given a time discretization in small temporal bins, we can define the population histogram as the count of spikes that occurred in the same time bin. The maximum possible count of the histogram is thus the number N of neurons.” The methods in this article disclose a process which is able to evaluate spikes in segmented time bins. This model uses methods that are able to place the encoded spike data into histograms. This teaches the data in a time bin will be evaluated based using a histogram which is able to place significant spikes or number of spikes at the center of frame and rate the spikes that arrive before and after the selected center of the bin.)
“extracting quantified content from the content estimation information based on the detected effects of the identified content type; and” (Correlation information index (CII), pp. 64; “Maximum entropy models (MEMs) have been introduced to evaluate the occurrence probability of each synchronous spike pattern (seen as a binary sequence of on/off states) given the observed firing rates, pairwise correlations, and possibly higher-order moments of a population of observed neurons. Once a maximum entropy distribution accounting for all and only the observed correlations up to a given order ξ is inferred from data (see Sect. 3.2.1 for more details), the amount of information delivered by such correlations can be quantified as follows.” The methods in this article disclose a process which is able to evaluate spike patterns and extract data from that spike pattern. As stated, this is able to extract quantified data and correlations from input spike data.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Zjajo, Szucs and Guaglio. Zjajo teaches a method and system designed to intake neural firing data and interpret it to produce usable data for various machine learning systems. Szucs teaches methods which uses spike firing data and is able to evaluate spike density of input data. Guaglio teaches different methods to evaluate spike patterns in spike trains. One of ordinary skill would have motivation to combine a system that is able to encode and decode neural firing data with other methods which are able to input firing data and evaluate different features of that data including patterns, density of spikes and other processing data, “In our recent study the choice of Gaussian function was motivated by its advantageous digital filtering properties and by probability considerations. The spike train can be described as the sequence of Dirac-delta functions, inasmuch as the firing probability is equal to 1 at the time of spike occurrence and is 0 in all other points. The delta function can be considered as a Gaussian with unity integral and zero broadness. ‘Expansion’ of single delta functions results in the spike density function alike the convolution procedure does.” (Szucs, Discussion, pp. 166)
Regarding claim 9, Zjajo discloses, “A computer system, comprising: a memory; and a processor connected with the memory and configured to execute at least one instruction stored in the memory,” (Description of Embodiments, pp. 13, [0165]; "One or more embodiments may be implemented as a computer program product for use with a computer system. The program(s) of the program product may define functions of the embodiments (including the methods described herein) and can be contained on a variety of computer readable storage media. The computer-readable storage media may be non-transitory storage media. Illustrative computer-readable storage media include, but are not limited to: (i) nonwritable storage media ( e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, ROM chips or any type of solid-state non-volatile semiconductor memory) on which information may be permanently stored; and (ii) writable storage media, e.g., hard disk drive or any type of solid-state random-access semiconductor memory, flash memory, on which alterable information may be stored." This article discloses an invention that is designed to execute on a computer system. This computer system stores the method disclosed on computer readable memory of some form.)
“wherein the processor is configured to profile neural firing data based on time series data representing firing point for at least one neural firing within a window defined by a predetermined time length; and” (Description of Embodiments, pp. 5, [0061]; "FIG. 3B shows an example of three desired spike responses 14A, 14B, 14C for three different input patterns to a cell of a spiking neural network. Each response in this example takes the form of a set of one or more spatiotemporal spike trains generated by output neurons N 1, N2, N3, N4, NS of the cell. Each response 14A, 14B, 14C is described for a discrete time bin 15A, 15B, 15C that may be selected for each network." This figure shows the different neural spike trains N1, ... , NS. Each of these spike trains contain spikes within a window denoted 15A, ... , 15C. The numbers denoting the time are labeled 1, ... , 9.)
“extract content for the neural firing from the profiled neural firing data;” (Summary, pp. 3, [0022]; "According to an aspect of the invention, a method is proposed for classifying input pattern signals using a spiking neural network comprising a plurality of spiking neurons implemented in hardware or a combination of hardware and software, and a plurality of synaptic elements interconnecting the spiking neurons to form the network, wherein each of the synaptic elements is adapted to receive a synaptic input signal and apply a weight to the synaptic input signal to generate a synaptic output signal, the synaptic elements being configurable to adjust the weight applied by each synaptic element, and wherein each of the spiking neurons is adapted to receive one or more of the synaptic output signals from one or more of the synaptic elements, and generate a spatio-temporal spike train output signal in response to the received one or more synaptic output signals." This teaches how the content is input into this invention and what is returned. The neural spike trains are input into the system, analyzed and content based on the analysis is extracted.)
“generate the profiled neural firing data by combining section values for all of the sections into a feature vector;” (Summary, pp. 2, [0012]; “The first sub-network is adapted to generate a sub-network output pattern signal from the first sub-set of spiking neurons, in response to a subnetwork input pattern signal applied to the first sub-set of synaptic elements, and the weights of the first sub-set of synaptic elements are configured by training the sub-network on a training set of sub-network input pattern signals, so that the sub-network output pattern signal is different for every unique sub-network input pattern signal of the training set.” The model in this application will evaluate the time bins and generate an output pattern that can be used to evaluate the time bin.) and (Detailed Description, pp. 4, [0051]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.” The output patterns contain information about the signals input into the encoder, this output information contains different forms of data and can be represented as a tuple or vector.)
“wherein the processor is configured to detect the section value for said each section by: identifying a plurality of firing timepoints of a plurality of neural firings within said each section;” (Summary, pp. 2, [0015]; “In the spiking neural network, each of the spiking neurons may be configurable to adjust the response of the neuron to the received one or more synaptic output signals. Spikes may be generated by the spiking neurons at one or more firing times, and the sub-set of synaptic elements and/or the sub-set of neurons may be configured such that the union of two sets of firing times of the sub-set of neurons that fire for two different sub-network input pattern signals is minimized for all sub-network input pattern signals of the training set.” This system is able to interpret spikes in a spiking neural network or from spike data. This model will input spiking data into a decoder and process the spikes. This will segment the data in to time windows as disclosed in [0065].) and (Detailed Description, pp. 6, [0065]; “The desired spike response 14A, 14B, 14C for each input pattern p can be described as a tuple which specifies the spike response in each time bin where input data 11 derived from an input pattern p is presented. For example, the tuple may specify the population of the cell's output neurons (
A
P
) that fired in response to the input data 11, and their precise firing times (
B
P
).” Each of the time bins is able to identify the spikes contained in the bins as well as the firing times of the spikes in the bin.)
“wherein feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” (Description of Embodiments, pp. 4, [0051]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.” This model discloses the processing of the input data to produce output data which can be used by the user for classification or further processing. The output spike train generated by the model contains multiple variables including the spike amount and the times of the spikes in the time window.) and (Detailed Description, pp. 6, [0065]; “The desired spike response 14A, 14B, 14C for each input pattern p can be described as a tuple which specifies the spike response in each time bin where input data 11 derived from an input pattern p is presented. For example, the tuple may specify the population of the cell's output neurons (
A
P
) that fired in response to the input data 11, and their precise firing times (
B
P
).” This discloses that the output of the encoder can represent the data in a tuple, which can be interpreted as a feature vector as it contains features data from different categories of data.)
“wherein the processor is configured to: extract content estimation information from the feature vector by learning the feature vector;” (Detailed Description, pp. 4, [0049]; “The neurons 1 can be configurable in the way they respond to a signal from a synaptic element. For example, in the case of spiking neural networks, the neurons 1 can be configured in the way a certain signal increases or decreases the membrane potential, the time it takes for the membrane potential to naturally decay towards a resting potential, the value of the resting potential, the threshold value that triggers a spike of the spiking neuron 1. The configuration of the neurons 1 can for example be kept constant during training, or be variable and set in a training of the neural network 100 on a particular training set.” After this model is able to input the data and generate an output, the model can use the generated data to train the model. To do this the model would need to evaluate the output and train the model accordingly. This would teach the use of extracting data from a feature vector to train a model.)
“wherein the processor is configured to: input the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” (Detailed Description, pp. 5-6, [0051-0052]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user. Operations such as pattern recognition may be accomplished in a number of steps (e.g. data conversion, feature encoding, classification). The present embodiment enables these steps to be realized using standardized subnetworks of synaptic elements and neurons (cells), and configured using a flexible training methodology for each application domain. The objective of the training methodology is to configure each stage or sub-network of the neural network to produce a unique response for each unique pattern or feature in the input signal.” The model in this article discloses a process of receiving input spike data and evaluating it and producing an output. This data can then be output into a usable form and the model can allow for multiple spike patterns to be classified and further processed.)
“extract the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” (Description of Embodiments, pp. 4-5, [0051]; "Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and formed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.", This teaches different types can be output and the user can select which of the outputs they want.; and [0136]; "This operation allows the decoding of the output of the classifier stage 404 into the user-space, i.e. user-defined identifier of input patterns. During the configuration phase, the pattern recognizer 400 may be trained with sample features and patterns, and the generated hash codes can be recorded", which discloses extracting content information and identifying a content type ( a user-defined identifier of input patterns) as content estimation information as the information is output from a decoder) and (Detailed Description, pp. [0161]; "Input signals 1104 are submitted to a subset of the synaptic elements 2, and the network 1100 produces a certain output 1105, that can be send to an output decoder")
Zjajo fails to explicitly disclose, “wherein the processor is configured to: divide the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both ends;”, “detect a section value for said each section based on the firing timepoint within said each section; and”, “calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” and “detect effects of identified content type for the content estimation information; and”.
However, Szucs discloses, “wherein the processor is configured to: divide the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both ends;” (Kernel function and windowing, pp. 160-161; “According to this method the values of the kernel function at the time of spike occurrences (
t
s
) are read, summed and divided by the area of the kernel [See Equation (1)] here g is the Gaussian kernel function cent[e]red at t and represented in some finite interval
t
w
(window width) for practical reasons,
t
s
is the time of spike event. Convolution here simply means the replacement of each spike with unity area kernel function (
∫
t
0
t
1
g
t
*
d
t
*
=
1
) cent[e]red at the actual spike.” This article discloses a process of selecting neural spikes in a segment of timed data. The method will place a window around a section of data with a designated spike at the center of the window. This will then use a sliding window across the data to evaluate and encode it. This teaches that it will use overlapping windows, where the overlap would be at the edges of the windows and potentially a single overlap at the ends of the segment of data.)
“detect a section value for said each section based on the firing timepoint within said each section; and” (Kernel function and windowing, pp. 161; “The first function resembling the conventional rate histogram is achieved by convolving the spike train with the square-window or boxcar (Fig. 1A). It is quantized and stepwise.” This article discloses a process of evaluating the firing timepoints in a section of data or window. The disclosed method will perform functions to the spikes located in the window to generate a value associated with that time bin.)
“calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” (Correlated firing patterns, pp. 161-162; “The cross correlation function is widely used in the characterization of interaction or mutual dependence between physiological processes. The following equation was used in our calculations (Eq. (2)): [see Equation (2)] where the product of spike density functions of cells i and j, respectively were calculated and the summation was performed in the time window Δt.” The model in this article is able to evaluate the spike density of a time window. As stated, this model is able to sum the spikes in the window and apply values to the spikes in the window.)
“detect effects of identified content type for the content estimation information; and”(Correlated firing patters, pp. 162; “SDFs of cells functioning in an anticorrelated, precisely tuned mode are shown on Fig. 3A. An increase of the firing rate of the RPeD1 cell is accompanied by the decrease of that of the A-cell and vice versa, as seen more clearly in Fig. 3B with the expanded time scale. The cross correlation function possesses a local minimum at t-0 s, and exhibits overall symmetry and periodicity, clearly indicating the link between the neurons (Fig. 3C).” The method in this article is able to evaluate the firing data and its effects and correlations to other neurons. This article discloses a process of using the identified firing samples for further processing.)
Zjajo and Szucs fail to explicitly disclose, “wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” and “extract quantified content from the content estimation information based on the detected effects of the identified content type; and”.
However, Guaglio discloses, “wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” (Methods to detect population synchronization, pp. 61; “A spike train is fully described by its spike times and, given a time discretization in small temporal bins, we can define the population histogram as the count of spikes that occurred in the same time bin. The maximum possible count of the histogram is thus the number N of neurons. The first three methods presented here are based on statistics derived from the population histogram. They were developed in succession, each to overcome the limitations of the previous one. The first method, the Complexity Distribution (CD) analysis (Grün et al. 2008), proposes a simple statistical approach purely based on the distribution of the entries of the population histogram. It compares such an empirically derived distribution to that expected from neurons firing independently to determine the presence of excess synchronization. The second method, the CUmulant-Based Inference of Correlation (CuBIC, Staude et al. 2010a), derives the null distribution analytically under more specific assumptions about the data, and infers the minimum correlation order existent in the data. The third method, the Population Unitary Event (PUE, Rostami 2017) analysis, works under the same assumptions as CuBIC, but uses a different test statistic which enhances the statistical power of the test, thereby requiring samples of smaller size for a correct identification of excess synchrony and thus also enabling a time-resolved analysis.” The model disclosed in this article is able to evaluate spike pattern data. This will evaluate the data and use histograms to evaluate the relationship of the spikes in a segmented time window. This process uses multiple methods to evaluate the spike pattern data.)
“determining individual neural firing values for the plurality of firing timepoints, respectively, based on locations of the firing timepoints relative to the center of said each section; and” (Methods to detect population synchronization, pp. 61; “Most methods for population synchronization analysis reduce the spike data to the number of active neurons (i.e., spikes) observed at any time bin. A spike train is fully described by its spike times and, given a time discretization in small temporal bins, we can define the population histogram as the count of spikes that occurred in the same time bin. The maximum possible count of the histogram is thus the number N of neurons.” The methods in this article disclose a process which is able to evaluate spikes in segmented time bins. This model uses methods that are able to place the encoded spike data into histograms. This teaches the data in a time bin will be evaluated based using a histogram which is able to place significant spikes or number of spikes at the center of frame and rate the spikes that arrive before and after the selected center of the bin.)
“extract quantified content from the content estimation information based on the detected effects of the identified content type; and” (Correlation information index (CII), pp. 64; “Maximum entropy models (MEMs) have been introduced to evaluate the occurrence probability of each synchronous spike pattern (seen as a binary sequence of on/off states) given the observed firing rates, pairwise correlations, and possibly higher-order moments of a population of observed neurons. Once a maximum entropy distribution accounting for all and only the observed correlations up to a given order ξ is inferred from data (see Sect. 3.2.1 for more details), the amount of information delivered by such correlations can be quantified as follows.” The methods in this article disclose a process which is able to evaluate spike patterns and extract data from that spike pattern. As stated, this is able to extract quantified data and correlations from input spike data.)
Regarding claim 17, Zjajo discloses, “A non-transitory computer-readable medium for storing at least one program, wherein the computer-readable medium is configured to execute:” (Description of Embodiments, pp. 13, [0165]; "One or more embodiments may be implemented as a computer program product for use with a computer system. The program(s) of the program product may define functions of the embodiments (including the methods described herein) and can be contained on a variety of computer readable storage media. The computer-readable storage media may be non-transitory storage media. Illustrative computer readable storage media include, but are not limited to: (i) non-writable storage media ( e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, ROM chips or any type of solid-state non-volatile semiconductor memory) on which information may be permanently stored; and (ii) writable storage media, e.g., hard disk drive or any type of solid-state random-access semiconductor memory, flash memory, on which alterable information may be stored." This article discloses an invention that is designed to execute on a computer system. This computer system stores the method disclosed on computer readable memory of some form.)
“profiling neural firing data based on time series data representing firing timepoint for at least one neural firing data within a window defined by a predetermined time length; and” (Description of Embodiments, pp. 5, [0061]; "FIG. 3B shows an example of three desired spike responses 14A, 14B, 14C for three different input patterns to a cell of a spiking neural network. Each response in this example takes the form of a set of one or more spatiotemporal spike trains generated by output neurons N1, N2, N3, N4, NS of the cell. Each response 14A, 14B, 14C is described for a discrete time bin 15A, 15B, 15C that may be selected for each network." This figure shows the different neural spike trains N1, ... , NS. Each of these spike trains contain spikes within a window denoted 15A, ... , 15C. The numbers denoting the time are labeled 1, ... , 9.)
“extracting content for the neural firing from the profiled neural firing data;” (Summary, pp. 3, [0022]; "According to an aspect of the invention, a method is proposed for classifying input pattern signals using a spiking neural network comprising a plurality of spiking neurons implemented in hardware or a combination of hardware and software, and a plurality of synaptic elements interconnecting the spiking neurons to form the network, wherein each of the synaptic elements is adapted to receive a synaptic input signal and apply a weight to the synaptic input signal to generate a synaptic output signal, the synaptic elements being configurable to adjust the weight applied by each synaptic element, and wherein each of the spiking neurons is adapted to receive one or more of the synaptic output signals from one or more of the synaptic elements, and generate a spatio-temporal spike train output signal in response to the received one or more synaptic output signals." This teaches how the content is input into this invention and what is returned. The neural spike trains are input into the system, analyzed and content based on the analysis is extracted.)
“generating the profiled neural firing data by combining section values for all of the sections into a feature vector;” (Summary, pp. 2, [0012]; “The first sub-network is adapted to generate a sub-network output pattern signal from the first sub-set of spiking neurons, in response to a subnetwork input pattern signal applied to the first sub-set of synaptic elements, and the weights of the first sub-set of synaptic elements are configured by training the sub-network on a training set of sub-network input pattern signals, so that the sub-network output pattern signal is different for every unique sub-network input pattern signal of the training set.” The model in this application will evaluate the time bins and generate an output pattern that can be used to evaluate the time bin.) and (Detailed Description, pp. 4, [0051]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.” The output patterns contain information about the signals input into the encoder, this output information contains different forms of data and can be represented as a tuple or vector.)
“wherein the detecting of the section value for said each section comprises: identifying a plurality of firing timepoints of a plurality of neural firings within said each section;” (Summary, pp. 2, [0015]; “In the spiking neural network, each of the spiking neurons may be configurable to adjust the response of the neuron to the received one or more synaptic output signals. Spikes may be generated by the spiking neurons at one or more firing times, and the sub-set of synaptic elements and/or the sub-set of neurons may be configured such that the union of two sets of firing times of the sub-set of neurons that fire for two different sub-network input pattern signals is minimized for all sub-network input pattern signals of the training set.” This system is able to interpret spikes in a spiking neural network or from spike data. This model will input spiking data into a decoder and process the spikes. This will segment the data in to time windows as disclosed in [0065].) and (Detailed Description, pp. 6, [0065]; “The desired spike response 14A, 14B, 14C for each input pattern p can be described as a tuple which specifies the spike response in each time bin where input data 11 derived from an input pattern p is presented. For example, the tuple may specify the population of the cell's output neurons (
A
P
) that fired in response to the input data 11, and their precise firing times (
B
P
).” Each of the time bins is able to identify the spikes contained in the bins as well as the firing times of the spikes in the bin.)
“wherein the feature vector maintains information on the firing timepoints of the neural firings and the frequency of the neural firings;” (Description of Embodiments, pp. 4, [0051]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.” This model discloses the processing of the input data to produce output data which can be used by the user for classification or further processing. The output spike train generated by the model contains multiple variables including the spike amount and the times of the spikes in the time window.) and (Detailed Description, pp. 6, [0065]; “The desired spike response 14A, 14B, 14C for each input pattern p can be described as a tuple which specifies the spike response in each time bin where input data 11 derived from an input pattern p is presented. For example, the tuple may specify the population of the cell's output neurons (
A
P
) that fired in response to the input data 11, and their precise firing times (
B
P
).” This discloses that the output of the encoder can represent the data in a tuple, which can be interpreted as a feature vector as it contains features data from different categories of data.)
“wherein the extracting of the content comprises: extracting content estimation information from the feature vector by learning the feature vector;” (Detailed Description, pp. 4, [0049]; “The neurons 1 can be configurable in the way they respond to a signal from a synaptic element. For example, in the case of spiking neural networks, the neurons 1 can be configured in the way a certain signal increases or decreases the membrane potential, the time it takes for the membrane potential to naturally decay towards a resting potential, the value of the resting potential, the threshold value that triggers a spike of the spiking neuron 1. The configuration of the neurons 1 can for example be kept constant during training, or be variable and set in a training of the neural network 100 on a particular training set.” After this model is able to input the data and generate an output, the model can use the generated data to train the model. To do this the model would need to evaluate the output and train the model accordingly. This would teach the use of extracting data from a feature vector to train a model.)
“wherein the extracting of the content estimation information comprises: inputting the feature vector, which simultaneously represents information on the firing timepoints of the neural firings and the frequency of the neural firings without loss, respectively to a plurality of decoders to each of which each of different content types is assigned; and” (Detailed Description, pp. 5-6, [0051-0052]; “Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and transformed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user. Operations such as pattern recognition may be accomplished in a number of steps (e.g. data conversion, feature encoding, classification). The present embodiment enables these steps to be realized using standardized subnetworks of synaptic elements and neurons (cells), and configured using a flexible training methodology for each application domain. The objective of the training methodology is to configure each stage or sub-network of the neural network to produce a unique response for each unique pattern or feature in the input signal.” The model in this article discloses a process of receiving input spike data and evaluating it and producing an output. This data can then be output into a usable form and the model can allow for multiple spike patterns to be classified and further processed.)
“extracting the content estimation information while identifying the content type for the content estimation information as the content estimation information is output from at least one of the decoders.” (Description of Embodiments, pp. 4-5, [0051]; "Output signals 12 of the neural network 100 are for example spatio-temporal spike trains, which can be read out from the output neurons 1 and further classified and formed by an output transformation stage into a set of digital values corresponding to the type of output code selected by the user.", This teaches different types can be output and the user can select which of the outputs they want.; and [0136]; "This operation allows the decoding of the output of the classifier stage 404 into the user-space, i.e. user-defined identifier of input patterns. During the configuration phase, the pattern recognizer 400 may be trained with sample features and patterns, and the generated hash codes can be recorded", which discloses extracting content information and identifying a content type ( a user-defined identifier of input patterns) as content estimation information as the information is output from a decoder) and (Detailed Description, pp. [0161]; "Input signals 1104 are submitted to a subset of the synaptic elements 2, and the network 1100 produces a certain output 1105, that can be send to an output decoder")
Zjajo fails to explicitly disclose, “wherein the profiling of the neural firing data comprises: dividing the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both ends;”, “detecting a section value for said each section based on the firing timepoint within said each section; and”, “calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” and “detecting effects of identified content type for the content estimation information; and”.
However, Szucs discloses, “wherein the profiling of the neural firing data comprises: dividing the window into a plurality of sections, each section of the sections corresponding to a temporal tuning curve of a neuron having a peak sensitivity at its center, wherein a first section and a last section among the plurality of sections are configured to overlap with an adjacent section at only one end, and remaining sections among the plurality of sections are configured to overlap with adjacent sections at both ends;” (Kernel function and windowing, pp. 160-161; “According to this method the values of the kernel function at the time of spike occurrences (
t
s
) are read, summed and divided by the area of the kernel [See Equation (1)] here g is the Gaussian kernel function cent[e]red at t and represented in some finite interval
t
w
(window width) for practical reasons,
t
s
is the time of spike event. Convolution here simply means the replacement of each spike with unity area kernel function (
∫
t
0
t
1
g
t
*
d
t
*
=
1
) cent[e]red at the actual spike.” This article discloses a process of selecting neural spikes in a segment of timed data. The method will place a window around a section of data with a designated spike at the center of the window. This will then use a sliding window across the data to evaluate and encode it. This teaches that it will use overlapping windows, where the overlap would be at the edges of the windows and potentially a single overlap at the ends of the segment of data.)
“detecting a section value for said each section based on the firing timepoint within said each section; and” (Kernel function and windowing, pp. 161; “The first function resembling the conventional rate histogram is achieved by convolving the spike train with the square-window or boxcar (Fig. 1A). It is quantized and stepwise.” This article discloses a process of evaluating the firing timepoints in a section of data or window. The disclosed method will perform functions to the spikes located in the window to generate a value associated with that time bin.)
“calculating the section value by determining a sum of the individual neural firing values determined for the plurality of firing timepoints in said each section, the sum representing both a frequency of the neural firings and temporal information of the neural firings in said each section;” (Correlated firing patterns, pp. 161-162; “The cross correlation function is widely used in the characterization of interaction or mutual dependence between physiological processes. The following equation was used in our calculations (Eq. (2)): [see Equation (2)] where the product of spike density functions of cells i and j, respectively were calculated and the summation was performed in the time window Δt.” The model in this article is able to evaluate the spike density of a time window. As stated, this model is able to sum the spikes in the window and apply values to the spikes in the window.)
“detecting effects of identified content type for the content estimation information; and” (Correlated firing patters, pp. 162; “SDFs of cells functioning in an anticorrelated, precisely tuned mode are shown on Fig. 3A. An increase of the firing rate of the RPeD1 cell is accompanied by the decrease of that of the A-cell and vice versa, as seen more clearly in Fig. 3B with the expanded time scale. The cross correlation function possesses a local minimum at t-0 s, and exhibits overall symmetry and periodicity, clearly indicating the link between the neurons (Fig. 3C).” The method in this article is able to evaluate the firing data and its effects and correlations to other neurons. This article discloses a process of using the identified firing samples for further processing.)
Zjajo and Szucs fail to explicitly disclose, “wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” and “extract quantified content from the content estimation information based on the detected effects of the identified content type; and”.
However, Guaglio discloses, “wherein, for implementing the tuning curve of the neuron in said each section, a neural firing value is configured to be determined as a value within a predetermined range assigned to said each section, the maximum value of the range is assigned to the center in said each section and the minimum value of the range is assigned to both endpoints in said each section, respectively, and the neural firing value is determined as a smaller value as the firing timepoint in said each section is further away from the center;” (Methods to detect population synchronization, pp. 61; “A spike train is fully described by its spike times and, given a time discretization in small temporal bins, we can define the population histogram as the count of spikes that occurred in the same time bin. The maximum possible count of the histogram is thus the number N of neurons. The first three methods presented here are based on statistics derived from the population histogram. They were developed in succession, each to overcome the limitations of the previous one. The first method, the Complexity Distribution (CD) analysis (Grün et al. 2008), proposes a simple statistical approach purely based on the distribution of the entries of the population histogram. It compares such an empirically derived distribution to that expected from neurons firing independently to determine the presence of excess synchronization. The second method, the CUmulant-Based Inference of Correlation (CuBIC, Staude et al. 2010a), derives the null distribution analytically under more specific assumptions about the data, and infers the minimum correlation order existent in the data. The third method, the Population Unitary Event (PUE, Rostami 2017) analysis, works under the same assumptions as CuBIC, but uses a different test statistic which enhances the statistical power of the test, thereby requiring samples of smaller size for a correct identification of excess synchrony and thus also enabling a time-resolved analysis.” The model disclosed in this article is able to evaluate spike pattern data. This will evaluate the data and use histograms to evaluate the relationship of the spikes in a segmented time window. This process uses multiple methods to evaluate the spike pattern data.)
“determining individual neural firing values for the plurality of firing timepoints, respectively, based on locations of the firing timepoints relative to the center of said each section; and” (Methods to detect population synchronization, pp. 61; “Most methods for population synchronization analysis reduce the spike data to the number of active neurons (i.e., spikes) observed at any time bin. A spike train is fully described by its spike times and, given a time discretization in small temporal bins, we can define the population histogram as the count of spikes that occurred in the same time bin. The maximum possible count of the histogram is thus the number N of neurons.” The methods in this article disclose a process which is able to evaluate spikes in segmented time bins. This model uses methods that are able to place the encoded spike data into histograms. This teaches the data in a time bin will be evaluated based using a histogram which is able to place significant spikes or number of spikes at the center of frame and rate the spikes that arrive before and after the selected center of the bin.)
“extracting quantified content from the content estimation information based on the detected effects of the identified content type; and” (Correlation information index (CII), pp. 64; “Maximum entropy models (MEMs) have been introduced to evaluate the occurrence probability of each synchronous spike pattern (seen as a binary sequence of on/off states) given the observed firing rates, pairwise correlations, and possibly higher-order moments of a population of observed neurons. Once a maximum entropy distribution accounting for all and only the observed correlations up to a given order ξ is inferred from data (see Sect. 3.2.1 for more details), the amount of information delivered by such correlations can be quantified as follows.” The methods in this article disclose a process which is able to evaluate spike patterns and extract data from that spike pattern. As stated, this is able to extract quantified data and correlations from input spike data.)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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