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 .
Claim Rejections - 35 USC § 112
Regarding 112(b):
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regard to Claim 1:
Claim 1 recites the limitation "create this test classification model" and “parameter of this test classification model”. There is insufficient antecedent basis for this limitation in the claim. The use of the term “this” in “this test classification model” makes one of ordinary skill in the art unable to ascertain which test classification model is being referred to. The claim introduces multiple test classification models (“creating various test classification models”), thus one cannot tell which of them is the target for “this test classification model”. Updating or amending the claim wording can help indicate which test classification model is the intended target. A possible fix could be replacing “this” with “the” or “the corresponding” should “this test classification model” be intended to refer to a test classification model within the loop of the “each test classification model”.
In regards to claim 7:
Claim 7 recites the limitation "which this group of simulated time-series signals is created”. There is insufficient antecedent basis for this limitation in the claim. The use of the term “this” in “this group of simulated time-series signals” makes one of ordinary skill in the art unable to ascertain which group of simulated time-series signals is being referred to. The claims introduce possibly multiple groups of simulated time series signals (“creating at least one group of simulated time-series signals from the at least one group of initial time-series signals” in claim 1), thus one cannot tell which of them is the target for “this group of simulated time-series signals”. Updating or amending the claim wording can help indicate which group of simulated time-series signals is the intended target. A possible fix could be replacing “this” with “the” or “the corresponding” should “this group of simulated time-series signals” be intended to refer to a group of simulated time-series signals within the loop of the “each group of simulated time-series signals”. An example could be “wherein each group of simulated time-series signals is associated with the class indicated for the group of initial time-series signals used to create the group of simulated time-series signals” where the example alters some wording address the possible wording issues with “from which the group of simulated time-series signals is created” as “is created” gives indication of creating time-series signals in the claim with the newer wording of “the” instead of “this”.
In regards to claim 8:
Claim 8 recites the limitation "extracting the feature values of the initial and simulated time-series signals”. There is insufficient antecedent basis for this limitation in the claim. There is no previous recitation of “feature values of the initial and simulated time-series signals”.
Claim 8 recites the limitation " each test classification model being created from an analysis of the extracted feature values and of the class associated with each group of initial or simulated time-series signals used to create this test classification model”. There is insufficient antecedent basis for this limitation in the claim, in that the claim contains the same issue as claim 1 of unable to determine which test classification model is being referred to by “this test classification model”. However, if claim 8 is attempted to be amended in a similar way given as an example for claim 1 there would be a circular logic issue. The circular logic comes from the limitation reciting that a test classification model is created (“each test classification model being created”) from an analysis of data used to create the classification model (“from an analysis of the extracted feature values and of the class associated with each group of initial or simulated time-series signals used to create this test classification model”), which means the model has to have already have been created to use the values to create it. For prior art rejections, claim 8 is interpreted as creating test classification models using an analysis of extracted feature values and class association for a group of initial or simulated time-series signals.
In regards to claim 9:
Claim 9 recites the limitation "extracting the feature values of the final time-series signals”. There is insufficient antecedent basis for this limitation in the claim. There is no previous recitation of “feature values of the final time-series signals”.
In regards to claim 10:
Claim 10 recites the limitation "results in the latter implementing the classification model”. There is insufficient antecedent basis for this limitation in the claim. There is no previous recitation indicating what is being referred to by “the latter”. The claim notes multiple elements, such as “a computer program product comprising instructions” and “the program is executed by a computer”. Which element is being referred to by “the latter” is unknown. One interpretation can be “the latter” is referring to the instructions in a computer program product, thus “result in the latter implementing the classification model” would be referring to the instructions implementing the classification model. However, a second interpretation is that “when the program is executed by a computer” is what is indicated by “the latter”, thus the program or computer program product resulting in the implementing of the classification model. A fix could be to replace “latter” with whatever element the “latter” was trying to stand in for, thus removing any possible confusion.
In regards to claim 13:
Claim 13 recites the limitation "and an amount of acquired data for this time-series signal”. There is insufficient antecedent basis for this limitation in the claim. The use of the term “this” in “this time-series signal” makes one of ordinary skill in the art unable to ascertain which time-series signal is being referred to. Rewording to claim to indicate which time-series signal is being referred to can alleviate the confusion.
In regards to analogous claims:
Any claims analogous to another claim and/or recite the same limitations rejected in another claim are also rejected under 112(b) for same reasons as the equivalent limitation rejected in the other claim. An example would be claims 11 and 14 contain analogous or similar limitations as claim 1, thus the equivalent limitations are rejected for the same reasons as the limitations were rejected in claim 1.
In regards to dependent claims:
Any claims dependent upon a claim rejected under 112(b) is also rejected under 112(b) for being dependent upon a rejected claim and not resolving what is creating the rejection in the rejected claim depended on.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
In regards to Claim 1:
Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter?
Yes, the claim is directed towards a method, so a process.
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 1 recites the following abstract ideas:
assessing the performances of each test classification model, the performances being associated with the at least one acquisition parameter of this test classification model
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation.
selected from an analysis of the assessed performances of each test classification model
This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation and judgement.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 1 recites the following additional elements:
obtaining at least one group of initial time-series signals, the initial time-series signals being associated with at least one initial acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
creating at least one group of simulated time-series signals from the at least one group of initial time-series signals, the simulated time-series signals of each group being associated with at least one simulated acquisition parameter different from the at least one initial acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
creating various test classification models, each test classification model being created from at least one group of initial time-series signals or from the at least one group of simulated time-series signals, each test classification model being associated with at least one acquisition parameter corresponding to the at least one initial or simulated acquisition parameter associated with the initial or simulated time-series signals used to create this test classification model
At a high level of generality, this is an activity of using time-series signals as an “apply it” use (see MPEP 2106.05(f)).
obtaining at least one group of final time-series signals associated with at least one final acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
creating the classification model from the at least one group of final time-series signals
At a high level of generality, this is an activity of using time-series signals as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 1 recites the following additional elements:
obtaining at least one group of initial time-series signals, the initial time-series signals being associated with at least one initial acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
creating at least one group of simulated time-series signals from the at least one group of initial time-series signals, the simulated time-series signals of each group being associated with at least one simulated acquisition parameter different from the at least one initial acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of sorting data (see MPEP 2106.05(d) example vi for other types of activity). Sorting existing data is an extension of gathering data and does not integrate the abstract idea of the invention into a particular application. Simulated time-series signals are seen as groupings of data within the initial time-series signals, as paragraphs 70-71 of the specification and claims 3-5 indicate the data within the simulated time-series signals is only information that already existed within the initial time-series signals.
creating various test classification models, each test classification model being created from at least one group of initial time-series signals or from the at least one group of simulated time-series signals, each test classification model being associated with at least one acquisition parameter corresponding to the at least one initial or simulated acquisition parameter associated with the initial or simulated time-series signals used to create this test classification model
At a high level of generality, this is an activity of using time-series signals as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “creating various test classification models” using time-series signals does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
obtaining at least one group of final time-series signals associated with at least one final acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
creating the classification model from the at least one group of final time-series signals
At a high level of generality, this is an activity of using time-series signals as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “creating the classification models” using time-series signals does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 2:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 2 recites the following additional elements:
wherein the at least one initial acquisition parameter comprises a combination of an initial sampling frequency and of an initial amount of data from initial time-series signals
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 2 recites the following additional elements:
wherein the at least one initial acquisition parameter comprises a combination of an initial sampling frequency and of an initial amount of data from initial time-series signals
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1. This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
In regards to Claim 3:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 3 recites the following additional elements:
wherein the initial sampling frequency corresponds to a maximum sampling frequency permitted by an acquisition device used to acquire the initial time-series signals.
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 3 recites the following additional elements:
wherein the initial sampling frequency corresponds to a maximum sampling frequency permitted by an acquisition device used to acquire the initial time-series signals.
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1. This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
In regards to Claim 4:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 4 recites the following additional elements:
wherein the initial amount of data corresponds to a maximum amount of data permitted by an acquisition device used to acquire the initial time-series signals
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 4 recites the following additional elements:
wherein the initial amount of data corresponds to a maximum amount of data permitted by an acquisition device used to acquire the initial time-series signals
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1. This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
In regards to Claim 5:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 5 recites the following additional elements:
wherein the at least one simulated acquisition parameter from the simulated time-series signals of each group comprises a simulated sampling frequency less than or equal to the initial sampling frequency and an amount of simulated data less than or equal to the initial amount of data
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 5 recites the following additional elements:
wherein the at least one simulated acquisition parameter from the simulated time-series signals of each group comprises a simulated sampling frequency less than or equal to the initial sampling frequency and an amount of simulated data less than or equal to the initial amount of data
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1. This is a well understood, routine, conventional activity of sorting data (see MPEP 2106.05(d) example vi for other types of activity). Noting the simulated time-series signals are less than or equal to the initial time-series signals in acquisition parameters does not integrate the invention into a practical application.
In regards to Claim 6:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 6 recites the following additional elements:
wherein each group of initial time-series signals is associated with a class indicated during the obtaining of initial time-series signals
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 6 recites the following additional elements:
wherein each group of initial time-series signals is associated with a class indicated during the obtaining of initial time-series signals
This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions).
In regards to Claim 7:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 7 recites the following additional elements:
wherein each group of simulated time-series signals is associated with the class indicated for the group of initial time-series signals from which this group of simulated time-series signals is created
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 7 recites the following additional elements:
wherein each group of simulated time-series signals is associated with the class indicated for the group of initial time-series signals from which this group of simulated time-series signals is created
This limitation is directed towards a continuation of a insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)) in claim 1. This is a well understood, routine, conventional activity of sorting data (see MPEP 2106.05(d) example vi for other types of activity). Noting the simulated time-series signals are associated with the same class as the data used to create the simulated time-series signals does not integrate the invention into a practical application, as the data within the simulated time-series signals was already associated with a class while within the initial time-series signals (meaning the idea of grouping a subset of data (simulated time-series signals) from the initial time-series signals does not appear to alter the data thus the data not changing association makes sense).
In regards to Claim 8:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 8 recites the following additional elements:
extracting the feature values of the initial and simulated time-series signals
At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 8 recites the following additional elements:
extracting the feature values of the initial and simulated time-series signals
At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “apply” or equivalent does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 9:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 9 recites the following additional elements:
extracting the feature values of the final time-series signals
At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 9 recites the following additional elements:
extracting the feature values of the final time-series signals
At a high level of generality, this is an activity of using an element as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “apply” or equivalent does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”.
In regards to Claim 10:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 10 recites the following additional elements:
creating a computer program product comprising instructions which, when the program is executed by a computer, result in the latter implementing the classification model
At a high level of generality, this is an activity of using a computer as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 10 recites the following additional elements:
creating a computer program product comprising instructions which, when the program is executed by a computer, result in the latter implementing the classification model
At a high level of generality, this is an activity of using a computer as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a computer or computer parts appears to be an implementation of the abstract idea on a computer, so merely using a computer as a tool to perform the abstract idea.
In regards to Claim 11:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 11 recites the same abstract ideas as claim 1.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 11 recites the following additional elements in addition to the elements already recited in claim 1:
indicating the performances of each test classification model in relation with the at least one acquisition parameter associated with this test classification model
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)). This is a well understood, routine conventional activity of presenting or transmitting data (see MPEP 2106.05(d)(2)(example iv. Presenting offers and gathering statistics and example i. Receiving or transmitting data over a network)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 11 recites the following additional elements in addition to the elements already recited in claim 1:
indicating the performances of each test classification model in relation with the at least one acquisition parameter associated with this test classification model
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)). This is a well understood, routine conventional activity of presenting or transmitting data (see MPEP 2106.05(d)(2)(example iv. Presenting offers and gathering statistics and example i. Receiving or transmitting data over a network)).
In regards to Claim 12:
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 12 recites the following additional elements:
the indication of performances of each classification model comprises displaying on a screen a performance graph including the performances of each classification model according to the at least one associated acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 12 recites the following additional elements:
the indication of performances of each classification model comprises displaying on a screen a performance graph including the performances of each classification model according to the at least one associated acquisition parameter
This limitation is directed towards the insignificant extra solution activity of mere data outputting (see MPEP 2106.05(g)(Consideration 3)). This is a well understood, routine conventional activity of presenting or transmitting data (see MPEP 2106.05(d)(2)(example iv. Presenting offers and gathering statistics and example i. Receiving or transmitting data over a network)).
In regards to Claim 13:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 13 recites the following abstract ideas:
wherein the assessed performances of each test classification model comprise an accuracy, an acquisition time of a time-series signal, and an amount of acquired data for this time-series signal.
This limitation is directed towards a continuation of the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) in claim 11.
In regards to Claim 14:
Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea?
Yes, the claim does recite a(n) abstract idea.
Claim 14 recites the same abstract ideas as claim 1.
Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception?
No, the application does not recite any additional elements that would integrate the abstract idea into a practical application.
Claim 14 recites the following additional elements in addition to the elements already recited in claim 1:
A computer system comprising: a memory comprising a computer program, the computer program comprising instructions to… and a processor configured to execute the computer program
At a high level of generality, this is an activity of using a computer as an “apply it” use (see MPEP 2106.05(f)).
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception?
No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself.
Claim 14 recites the following additional elements in addition to the elements already recited in claim 1:
A computer system comprising: a memory comprising a computer program, the computer program comprising instructions to… and a processor configured to execute the computer program
At a high level of generality, this is an activity of using a computer as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a computer or computer parts appears to be an implementation of the abstract idea on a computer, so merely using a computer as a tool to perform the abstract idea.
In regards to Claim 15:
This claim recites the same elements as claim 2.
In regards to Claim 16:
This claim recites the same elements as claim 3.
In regards to Claim 17:
This claim recites the same elements as claim 4.
In regards to Claim 18:
This claim recites the same elements as claim 5.
In regards to Claim 19:
This claim recites the same elements as claim 6.
In regards to Claim 20:
This claim recites the same elements as claim 12.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hounslow et al (“Assessing the effects of sampling frequency on behavioral classification of accelerometer data”), referred to as Hounslow in this document, and further in combination with Laput et al (US 20200117889A1), referred to as Laput in this document.
Regarding Claim 1:
Hounslow teaches:
A method for creating a classification model, the method comprising:
obtaining at least one group of initial time-series signals, the initial time-series signals being associated with at least one initial acquisition parameter;
[Hounslow Introduction page 2]: “To achieve this, we used ground-truthed accelerometer data collected [obtaining at least one group of initial time-series signals] from lemon sharks Negaprion brevirostris and presented by Brewster et al. (2018), to develop a supervised ML algorithm for automatic classification [A method for creating a classification model, the method comprising:] of the accelerometer data. We evaluated the effects of sampling frequency [the initial time-series signals being associated with at least one initial acquisition parameter;] on the performance of this algorithm, in order to identify the optimal sampling frequency as a compromise between data resolution and the rate of memory and battery consumption.”
Support for the initial time-series signals being associated with an initial acquisition parameter is provided in [Hounslow 2.2 data analysis page 2]: “the original 30 Hz data” indicating a parameter of the recording such as the sampling frequency being 30 Hz.
creating at least one group of simulated time-series signals from the at least one group of initial time-series signals, the simulated time-series signals of each group being associated with at least one simulated acquisition parameter different from the at least one initial acquisition parameter
[Hounslow 2.2 data analysis page 2]: “To determine the optimal sampling frequency for classifying the behaviours, the ground-truthed, raw acceleration data collected by Brewster et al. (2018), were re-sampled for each axis, using the ‘resample’ function in IGOR Pro v7.06 (WaveMetrics Inc., Lake Oswego, Oregon, USA). Data were ‘down-sampled’ using decimation by omission, whereby every ‘nth’ data point of the original 30 Hz data set was systematically deleted according to the new sampling frequency [creating at least one group of simulated time-series signals from the at least one group of initial time-series signals, the simulated time-series signals of each group being associated with at least one simulated acquisition parameter different from the at least one initial acquisition parameter] (Broell et al., 2013; Sur et al., 2017). For example, when resampling raw 30 Hz data down to 15 Hz, every other data point was omitted from the original data set using a decimation rate of 2. Resampling by decimation requires the resampled frequencies to be a factor of the raw sampling frequency (30 Hz), precluding certain sampling frequencies from analysis (e.g., 20 Hz and 25 Hz). Alternative resampling functions use a less systematic combination of both interpolation (up-sampling) and decimation, but such methods reflect estimations, rather than real data, and are not representative of data that would be produced from a tag with a lower sampling rate.”
creating various test classification models, each test classification model being created from at least one group of initial time-series signals or from the at least one group of simulated time-series signals, each test classification model being associated with at least one acquisition parameter corresponding to the at least one initial or simulated acquisition parameter associated with the initial or simulated time-series signals used to create this test classification model
assessing the performances of each test classification model, the performances being associated with the at least one acquisition parameter of this test classification model
[Hounslow 2.4 Evaluating classifier performance pages 3-4]: “To allow for comparison of classifier performance between each of the individual behaviour classes, recall, precision and an F1 score were calculated within each RF. Accuracy and the macro F1 score (FM), allow comparison of overall classifier performance between sampling frequencies [creating various test classification models, each test classification model being created from at least one group of initial time-series signals or from the at least one group of simulated time-series signals, each test classification model being associated with at least one acquisition parameter corresponding to the at least one initial or simulated acquisition parameter associated with the initial or simulated time-series signals used to create this test classification model as this quote indicates a classifier was created for each sampling frequency thus each of the resampled signals/simulated signals], encompassing all behaviour classes. These methods (Fig. 1) were repeated for each sampling frequency to allow for direct comparison in classification performance as sampling frequency [assessing the performances of each test classification model, the performances being associated with the at least one acquisition parameter of this test classification model] was reduced from 30 Hz to 1 Hz.”
Further support on aspects related to training machine learning models is provided in Hounslow in section 2.3 Machine learning classification algorithm pages 2-3.
obtaining at least one group of final time-series signals associated with at least one final acquisition parameter selected from an analysis of the assessed performances of each test classification model
and creating the classification model from the at least one group of final time-series signals
[Hounslow 4.2 Recommendations and Implications page 8]: “While classification was poor for chafe, burst and headshake behaviours, there was no significant decrease in classifier performance for these behaviours until sampling frequency was <5 Hz. Therefore, for best determination of both basic and fine-scale fast movement behaviours in animals of similar size and kinematics, accelerometers should be programmed at a sampling frequency of 5 Hz [obtaining at least one group of final time-series signals associated with at least one final acquisition parameter selected from an analysis of the assessed performances of each test classification model as this notes the signals from the simulated signal of 5 Hz is considered the chosen option where reasoning for the decision are gone over further in this quote]. These findings have major positive implications for the practical aspects of future studies classifying behaviours from accelerometer data. Programming accelerometer devices at the lowest frequency possible could drastically reduce the rate at which the available memory and battery capacity of devices is consumed. For this study, the total available memory capacity of the data logger used was 56 MB, and maximum recording duration at 30 Hz was ≈5 days. Based on our recommendation to programme devices with a sampling frequency of 5 Hz, study durations using this device could last as long as 30 days (depending on digital storage programming e.g., number of bits) without sacrificing classification performance [and creating the classification model from the at least one group of final time-series signals as here Hounslow is indicating that a classification model should be made with the signals with 5 Hz as that was the result Hounslow deemed best for the application (as details on the decision and motivation for the decision are gown over in this quote such as battery life and memory/storage usage)] (see Supplementary Material, Fig. S1). With regards to reducing device battery consumption, there can be a trade-off between improving classification performance and extending device battery life. Battery life was halved when sampling frequencies were increased from 16Hz to 32Hz for human activity (Khan et al., 2016), yet the same increase in sampling frequency only achieved a 5% increase in classification accuracy in sheep (Walton et al., 2018). The minimal improvement in classifier performance at higher sampling frequencies is therefore not worth the increased battery consumption, further demonstrating the logistical benefits of programming devices with 5 Hz.”
Laput teaches:
Computer elements related to the implementation of the method and systems related to “A method for creating a classification model, the method comprising:”
[Laput 0005]: “According to an embodiment, a system comprises a memory [memory] that stores computer executable components [instructions or computer programs]. The system also comprises a processor [processor] that executes the computer executable components stored in the memory.”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Hounslow and Laput. Hounslow and Laput are in the same field of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Hounslow and Laput in order to be able to implement aspects of the invention into a computer ([Laput 0005]: “According to an embodiment, a system comprises a memory that stores computer executable components. The system also comprises a processor that executes the computer executable components stored in the memory.”).
Regarding Claim 2:
The method of claim 1 is taught by Hounslow and Laput.
Hounslow teaches:
wherein the at least one initial acquisition parameter comprises a combination of an initial sampling frequency
[Hounslow 2.2 data analysis page 2]: “To determine the optimal sampling frequency for classifying the behaviours, the ground-truthed, raw acceleration data collected by Brewster et al. (2018), were re-sampled for each axis, using the ‘resample’ function in IGOR Pro v7.06 (WaveMetrics Inc., Lake Oswego, Oregon, USA). Data were ‘down-sampled’ using decimation by omission, whereby every ‘nth’ data point of the original 30 Hz data [wherein the at least one initial acquisition parameter comprises a combination of an initial sampling frequency where the number of data points is noted as changing here in the quote but the initial amount is noted later] set was systematically deleted according to the new sampling frequency (Broell et al., 2013; Sur et al., 2017). For example, when resampling raw 30 Hz data down to 15 Hz, every other data point was omitted from the original data set using a decimation rate of 2. Resampling by decimation requires the resampled frequencies to be a factor of the raw sampling frequency (30 Hz), precluding certain sampling frequencies from analysis (e.g., 20 Hz and 25 Hz). Alternative resampling functions use a less systematic combination of both interpolation (up-sampling) and decimation, but such methods reflect estimations, rather than real data, and are not representative of data that would be produced from a tag with a lower sampling rate.”
and of an initial amount of data from initial time-series signals
[Hounslow 3 Results page 4]: “Accelerometer data were collected for four lemon sharks during captive trials (Brewster et al., 2018). From these data sets, over 35,000 s of data [and of an initial amount of data from initial time-series signals] were ground-truthed and labelled as one of five distinct behaviours: swim, rest, burst, chafe, and headshake (Brewster et al., 2018).”
Support in the number of data points is kept track of in [Hounslow Introduction page 2]: “When recording at sub-second frequencies (> 1 Hz) in three axes, accelerometers quickly produce millions of data points, making analysis of acceleration data a time-consuming task”
Regarding Claim 3:
The method of claim 2 is taught by Hounslow and Laput.
Hounslow teaches:
wherein the initial sampling frequency corresponds to a maximum sampling frequency permitted by an acquisition device used to acquire the initial time-series signals
[Hounslow 2.2 data analysis page 2]: “To determine the optimal sampling frequency for classifying the behaviours, the ground-truthed, raw acceleration data collected by Brewster et al. (2018), were re-sampled for each axis, using the ‘resample’ function in IGOR Pro v7.06 (WaveMetrics Inc., Lake Oswego, Oregon, USA). Data were ‘down-sampled’ using decimation by omission, whereby every ‘nth’ data point of the original 30 Hz data set was systematically deleted according to the new sampling frequency (Broell et al., 2013; Sur et al., 2017). For example, when resampling raw 30 Hz data down to 15 Hz, every other data point was omitted from the original data set using a decimation rate of 2. Resampling by decimation requires the resampled frequencies to be a factor of the raw sampling frequency (30 Hz) [wherein the initial sampling frequency corresponds to a maximum sampling frequency permitted by an acquisition device used to acquire the initial time-series signals], precluding certain sampling frequencies from analysis (e.g., 20 Hz and 25 Hz). Alternative resampling functions use a less systematic combination of both interpolation (up-sampling) and decimation, but such methods reflect estimations, rather than real data, and are not representative of data that would be produced from a tag with a lower sampling rate.”
Regarding Claim 4:
The method of claim 2 is taught by Hounslow and Laput.
Hounslow teaches:
wherein the initial amount of data corresponds to a maximum amount of data permitted by an acquisition device used to acquire the initial time-series signals
[Hounslow 2.2 data analysis page 2]: “To determine the optimal sampling frequency for classifying the behaviours, the ground-truthed, raw acceleration data collected [wherein the initial amount of data corresponds to a maximum amount of data permitted by an acquisition device used to acquire the initial time-series signals as the data collected is the raw data and further support in regards to maximum amount permitted is given below] by Brewster et al. (2018), were re-sampled for each axis, using the ‘resample’ function in IGOR Pro v7.06 (WaveMetrics Inc., Lake Oswego, Oregon, USA). Data were ‘down-sampled’ using decimation by omission, whereby every ‘nth’ data point of the original 30 Hz data set was systematically deleted according to the new sampling frequency (Broell et al., 2013; Sur et al., 2017). For example, when resampling raw 30 Hz data down to 15 Hz, every other data point was omitted from the original data set using a decimation rate of 2. Resampling by decimation requires the resampled frequencies to be a factor of the raw sampling frequency (30 Hz), precluding certain sampling frequencies from analysis (e.g., 20 Hz and 25 Hz). Alternative resampling functions use a less systematic combination of both interpolation (up-sampling) and decimation, but such methods reflect estimations, rather than real data, and are not representative of data that would be produced from a tag with a lower sampling rate.”
Support for maximum amount of data permitted is provided by Hounslow noting the memory capacity and such for the data logging/sampling is kept in mind for the collection process and notes how long and thus how many data samples was able to be gathered at the raw sampling frequency of 30 Hz ([Hounslow 4.2 Recommendations and implications page 8]: “For this study, the total available memory capacity of the data logger used was 56MB, and maximum recording duration at 30 Hz was ≈5days.”)
Regarding Claim 5:
The method of claim 2 is taught by Hounslow and Laput.
Hounslow teaches:
wherein the at least one simulated acquisition parameter from the simulated time-series signals of each group comprises a simulated sampling frequency less than or equal to the initial sampling frequency and an amount of simulated data less than or equal to the initial amount of data
[Hounslow 2.2 data analysis page 2]: “To determine the optimal sampling frequency for classifying the behaviours, the ground-truthed, raw acceleration data collected by Brewster et al. (2018), were re-sampled for each axis, using the ‘resample’ function in IGOR Pro v7.06 (WaveMetrics Inc., Lake Oswego, Oregon, USA). Data were ‘down-sampled’ using decimation by omission, whereby every ‘nth’ data point of the original 30 Hz data set was systematically deleted according to the new sampling frequency (Broell et al., 2013; Sur et al., 2017). For example, when resampling raw 30 Hz data down to 15 Hz, every other data point was omitted from the original data set using a decimation rate of 2 [wherein the at least one simulated acquisition parameter from the simulated time-series signals of each group comprises a simulated sampling frequency less than or equal to the initial sampling frequency and an amount of simulated data less than or equal to the initial amount of data]. Resampling by decimation requires the resampled frequencies to be a factor of the raw sampling frequency (30 Hz), precluding certain sampling frequencies from analysis (e.g., 20 Hz and 25 Hz). Alternative resampling functions use a less systematic combination of both interpolation (up-sampling) and decimation, but such methods reflect estimations, rather than real data, and are not representative of data that would be produced from a tag with a lower sampling rate.”
Regarding Claim 6:
The method of claim 1 is taught by Hounslow and Laput.
Hounslow teaches:
wherein each group of initial time-series signals is associated with a class indicated during the obtaining of initial time-series signals
[Hounslow 3 Results page 4]: “Accelerometer data were collected for four lemon sharks during captive trials (Brewsteretal.,2018). From these datasets, over 35,000s of data were ground-truthed and labelled [wherein each group of initial time-series signals is associated with a class indicated during the obtaining of initial time-series signals] as one of five distinct behaviours: swim, rest, burst, chafe, and headshake (Brewsteretal.,2018). Swimming and resting behaviours were performed most frequently (97.57 and 1.23%, Table2), whilst burst, chafe and headshake behaviours were infrequent in comparison(0.13, 0.76 and 0.31% respectively, Table2).”
Regarding Claim 7:
The method of claim 6 is taught by Hounslow and Laput.
Hounslow teaches:
wherein each group of simulated time-series signals is associated with the class indicated for the group of initial time-series signals from which this group of simulated time-series signals is created
[Hounslow Figure 2 page 5]: "Example time-series plots for dynamic sway (Z) acceleration for observed behaviours for semi-captive juvenile lemon sharks (N. brevirostris)(n=4). Raw acceleration data (30 Hz) were resampled to show the representative change in acceleration waveform signal amplitude and frequency as sampling frequency was reduced for five observed behaviours [wherein each group of simulated time-series signals is associated with the class indicated for the group of initial time-series signals from which this group of simulated time-series signals is created as the simulated signals (reduced sampling frequency signals) are still being judged to the class the initial signal data was for thus the class association has not changed] (A) swim, (B) rest, (C) chafe, (D) burst and (E) headshake. Note different scales for acceleration (g) to aid visualisation of behaviours."
Figure 2 of Hounslow provides a visual indication of the change of a signal associated with a class and how the signal appears for the resampled/simulated signal.
Regarding Claim 8:
The method of claim 7 is taught by Hounslow and Laput.
Hounslow teaches:
further comprising extracting the feature values of the initial and simulated time-series signals, each test classification model being created from an analysis of the extracted feature values and of the class associated with each group of initial or simulated time-series signals used to create this test
[Hounslow 2.2 Data Analysis page 2]: "Descriptive statistics were extracted [further comprising extracting the feature values of the initial and simulated time-series signals,] as per Brewster et al. (2018) and used as predictor variables (n =44; Table 1). These predictor variables included the static and dynamic acceleration from each axis and their derivatives, including ODBA, waveform amplitude and frequency of the dominant cycle for each sampling frequency. To enable time matching of the ground truthed data [and of the class associated with each group of initial or simulated time-series signals used to create this test classification model as this is indicating the ground truth (labels for the classes) is being used. Table 3 indicates being able to match the outputs of the models predictions (thus proving being able to compare the class associated with signals with the prediction) and [2.3 Machine learning classification algorithm page 2] notes “Random forest (RF) is a supervised ML algorithm that has been used to classify behaviour from acceleration data (Nathan et al., 2012; Wang et al., 2015; Sur et al., 2017; Walton et al., 2018).” thus showing classes relevance to training (as supervised means using labeled data)] to observed behaviours recorded on a per second basis, these acceleration-derived predictors were calculated for one-second segments. This procedure was repeated for each sampling frequency to create a set of predictor variables for each dataset."
Continued to the note with table 1: “Predictor variables extracted from each of the acceleration axes for one second segments, used to train [each test classification model being created from an analysis of the extracted feature values… as this is indicating the extracted feature values are used to train the models] the RF ML algorithm for each sampling frequency.”
Regarding Claim 9:
The method of claim 8 is taught by Hounslow and Laput.
further comprising extracting the feature values of the final time-series signals,
the classification model being created from an analysis of the extracted feature values and of the class associated with each group of final time-series signals
[Hounslow 2.2 Data Analysis page 2]: "Descriptive statistics were extracted [further comprising extracting the feature values of the final time-series signals] as per Brewster et al. (2018) and used as predictor variables (n =44; Table 1). These predictor variables included the static and dynamic acceleration from each axis and their derivatives, including ODBA, waveform amplitude and frequency of the dominant cycle for each sampling frequency. To enable time matching of the ground truthed data to observed behaviours recorded on a per second basis, these acceleration-derived predictors were calculated for one-second segments. This procedure was repeated for each sampling frequency to create a set of predictor variables for each dataset."
Continued to the note with table 1: “Predictor variables extracted from each of the acceleration axes for one second segments, used to train [the classification model being created from an analysis of the extracted feature values and of the class associated with each group of final time-series signals where further explanation regarding the use of extracted feature values and class associated is shown in claim 8 mapping] the RF ML algorithm for each sampling frequency.”
Claim 8 already indicates the creation of the test models, thus claim 8 already teaches the creation of models from the data. This means the creation of the classification model using the final time-series signal data is apparent, for if the final time-series signal data is one of the simulated time series signal data, then a model would have already been made using that data to create the test classification models. Creating a model utilizing the final signal data or the recommended acquisition parameters is noted in claim 1.
Regarding Claim 10:
The method of claim 1 is taught by Hounslow and Laput.
Laput teaches:
further comprising creating a computer program product comprising instructions which, when the program is executed by a computer, result in the latter implementing the classification model
Aspects of the classification model is taught in earlier claim mappings such as the mapping for claim 1. Computer elements are taught in Claim 1 by Laput. Laput paragraph 25 notes computer program products (“To address these and/or other issues associated with conventional activity sensing techniques, embodiments described herein include systems, computer-implemented methods, and computer program products [further comprising creating a computer program product comprising instructions which, when the program is executed by a computer, result in the latter implementing the classification model] for hand activity sensing.”)
The motivation to combine with Laput is the same as the motivation provided in claim 1.
Regarding Claim 11:
Claim 11 recites the same limitation as claim 1 (which those element’s teachings are already indicated in claim 1) except what is indicated below.
Hounslow teaches:
and indicating the performances of each test classification model in relation with the at least one acquisition parameter associated with this test classification model
[Hounslow Figure 3 page 6]
PNG
media_image1.png
834
796
media_image1.png
Greyscale
[Hounslow Figure 3 page 6]: “Overall random forest classification performance [and indicating the performances of each test classification model in relation with the at least one acquisition parameter associated with this test classification model where aspects of the indication are provided by the figure] for classification of behaviours in juvenile lemon sharks (N. brevirostris)(n=4). As sampling frequency decreases from 30 to 1Hz, overall classification performance (described by macro averaged F1 Score [FM]) decreases. Performance decreases more significantly when sampling frequency is reduced to <5Hz.”
Regarding Claim 12:
The method of claim 11 is taught by Hounslow and Laput.
Hounslow teaches:
wherein the indication of performances of each classification model comprises displaying on a screen a performance graph including the performances of each classification model according to the at least one associated acquisition parameter
[Hounslow Figure 3 page 6] as shown in claim 8 shows performance of the models [wherein the indication of performances of each classification model comprises displaying on a screen a performance graph including the performances of each classification model according to the at least one associated acquisition parameter] in association with an acquisition parameter (sampling frequency).
Hounslow does not explicitly teach:
Displaying on a screen
Laput teaches:
Displaying on a screen
[Laput 0069]: “A monitor 1246 or other type of display device [Displaying on a screen] can be also connected to the system bus 1208 via an interface, such as a video adapter 1248. In addition to the monitor 1246, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.”
One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Hounslow and Laput. Hounslow and Laput are in the same field of endeavor of machine learning. One of ordinary skill in the art would have been motivated to combine Hounslow and Laput in order to be able to display graphs and thus help show analysis of performance ([Laput 0046]: “FIG. 10 illustrates an example, non-limiting graph 1000 in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. The graph 1000 provides analysis of sampling frequency (Hz) vs. classification accuracy (%).”).
Regarding Claim 13:
The method of claim 11 is taught by Hounslow and Laput.
Hounslow teaches:
wherein the assessed performances of each test classification model comprise an accuracy,
[Hounslow 2.4 Evaluating classifier performance page 3]: “The trained RF model was applied to the unseen test data set (30%) to produce predictions and evaluate model performance. Evaluation metrics—accuracy [wherein the assessed performances of each test classification model comprise an accuracy], precision, recall and F1 (described below)— were calculated from a confusion matrix in the ‘caret’ package in R (Kuhn, 2015). In this confusion matrix, rows are actual observed values and columns are predicted values, represented by true positive (TP), false positive (FP), and false negative (FN) values (Breiman, 1999, Breiman, 2001). TP values occur when the predicted behavioural class has been correctly identified. Conversely, FP values are those which have been incorrectly attributed to a behavioural class. FN predictions are observations which have been incorrectly assigned to a different class.”
an acquisition time of a time-series signal
[Hounslow Abstract page 1]: "In this study we assess the effect of sampling frequency on a ML algorithm's ability to correctly classify behaviours from accelerometer data and present a framework for programming bio-logging devices that maintains classifier performance while optimizing data collection duration [an acquisition time of a time-series signal]"
Motivation for why acquisition time would be considered as a metric in [Hounslow 5 Conclusion page 8]: "Sampling frequencies as low as 5 Hz are suitable for classifying behaviours in addition to dramatically reducing demand on archival device memory and battery. The benefits of lengthening study duration include extending insight to ecologically meaningful time scales (e.g., tidal, lunar), reduced study costs and tagging fewer animals."
and an amount of acquired data for this time-series signal
[Hounslow Introduction page 2]: “To achieve this, we used ground-truthed accelerometer data collected from lemon sharks Negaprion brevirostris and presented by Brewster et al. (2018), to develop a supervised ML algorithm for automatic classification of the accelerometer data. We evaluated the effects of sampling frequency on the performance of this algorithm, in order to identify the optimal sampling frequency as a compromise between data resolution and the rate of memory [and an amount of acquired data for this time-series signal as noting that memory consumption is considered for the optimal sampling frequency means the accessed performance involves the amount of data acquired as the data acquired is what fills up the memory] and battery consumption.”
Support is given in Hounslow ([Hounslow 4.2 Recommendations and implications page 8]: “For this study, the total available memory capacity of the data logger used was 56MB, and maximum recording duration at 30 Hz was ≈5days.”) indicating that memory size with a sampling frequency gives you a limitation on how long you can record and ([Hounslow 5 Conclusion page 8]: “Sampling frequencies as low as 5 Hz are suitable for classifying behaviours in addition to dramatically reducing demand on archival device memory and battery. The benefits of lengthening study duration include extending insight to ecologically meaningful time scales (e.g., tidal, lunar), reduced study costs and tagging fewer animals.”) notes the motivation for recording longer by recording less frequently. Meaning Hounslow provides an indication and motivation for keeping track of the amount of data for a signal.
Data amount is noted to change with the sampling frequency in [Hounslow 2.2 data analysis page 2]. Thus data amount is being assessed when sampling frequency is being assessed.
Hounslow also indicates managing the data amount for a class (which is related to the signals as the amount of data for a class is the amount of signals classified for the class) to reduce computing time in [Hounslow 2.3 page 3]: “The majority classes were reduced in frequency to be closer to the minority class (burst) by a factor of ten. Despite some loss of training data from the majority classes, this method drastically reduces computational time (Chen et al., 2004).”
Without a limitation indicating what is meant by “wherein the assessed performances of each test classification model comprise… an amount of acquired data for this time-series signal” the BRI for the limitation is very broad. The multiple mappings above for the limitation are to show how different or broad interpretations of what the limitation could mean.
Regarding Claim 14:
This claim is analogous to claim 1.
Regarding Claim 15:
This claim is analogous to claim 2.
Regarding Claim 16:
This claim is analogous to claim 3.
Regarding Claim 17:
This claim is analogous to claim 4.
Regarding Claim 18:
This claim is analogous to claim 5.
Regarding Claim 19:
This claim is analogous to claim 6.
Regarding Claim 20:
This claim is analogous limitations recited in claim 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Junker et al (“Sampling Frequency, Signal Resolution and the Accuracy of Wearable Context Recognition Systems”) is relevant art that notes sampling frequency and signal resolution (bits used by data) can be reduced while still retaining good detection performance.
Santoyo-Ramon et al (“A study of the influence of the sensor sampling frequency on the performance of wearable fall detectors”) is relevant art that discusses optimal sampling frequency when balancing for things such as battery usage.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER D DEVORE whose telephone number is (703)756-1234. The examiner can normally be reached Monday-Friday 7:30 am - 5 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.D.D./Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
Prosecution Insights