DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-9 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a training data collection unit configured to select and use at least two of… to create multiple training data in claim 1, a prediction model training unit configured to pre-train a prediction mode….in claim 1, a device cooperation cases setting unit configured to decide and store device cooperation cases… by collecting and analyzing sensing data… in claim 1, a device cooperation conducting unit configured to preprocess the sensing data…. in claim 1, a biological condition prediction unit configured to predict and output a biological condition….in claim 1, training data collection unit acquires the data of the device having highest sensitivity… and uses the acquired item representative value…in claim 2, device cooperation cases setting unit further includes a function to acquire sensing data… and additionally decide and store device cooperation cases…in claim 7, device cooperation conducting unit further includes a function to preprocess the sensing data…in claim 8, training data collection unit configured to select and use at least one of… to create multiple training data in claim 9, prediction model training unit configured to pre-train a prediction model…in claim 9, device cooperation cases setting unit configured to acquire sensing data… and decide and store device cooperation cases… in claim 9, device cooperation conducting unit configured to preprocess the sensing data…in claim 9, biological condition prediction unit configured to predict and output a biological condition…in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification, such as paragraph [0029] and Fig. 1, as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 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.
Claim 1 recites the limitation "the training data collection devices" in line 11. There is insufficient antecedent basis for this limitation in the claim.
Claim limitation “a training data collection unit configured to select and use at least two of… to create multiple training data” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the select, use, and create functions. The use of the terms “select”, “use”, and “create” are not adequate structure for performing the select, use, and create functions because it does not describe a particular structure for performing the functions. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “prediction model training unit configured to pre-train a prediction mode….” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the pre-train function. The use of the term “pre-train” is not adequate structure for performing the pre-train function because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “device cooperation cases setting unit configured to decide and store device cooperation cases… by collecting and analyzing sensing data…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the decide, store, collecting, and analyzing functions. The use of the terms “decide”, “store”, “collecting”, and” analyzing” are not adequate structure for performing the decide, store, collecting, and analyzing functions because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “device cooperation conducting unit configured to preprocess the sensing data….” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the preprocess function. The use of the term “preprocess” is not adequate structure for performing the preprocess function because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “biological condition prediction unit configured to predict and output a biological condition….” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the predict and output functions. The use of the terms “predict” and “output” are not adequate structure for performing the predict and output functions because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “training data collection unit acquires the data of the device having highest sensitivity… and uses the acquired item representative value…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the acquires and uses functions. The use of the terms “acquires” and “uses” are not adequate structure for performing the acquires and uses functions because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “device cooperation cases setting unit further includes a function to acquire sensing data… and additionally decide and store device cooperation cases…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the acquire, decide, and store functions. The use of the terms “acquire”, “decide”, and “store” are not adequate structure for performing the acquire, decide, and store functions because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “device cooperation conducting unit further includes a function to preprocess the sensing data…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the preprocess function. The use of the term “preprocess” is not adequate structure for performing the preprocess function because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “training data collection unit configured to select and use at least one of… to create multiple training data” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the select, use, and create functions. The use of the terms “select”, “use”, and “create” are not adequate structure for performing the select, use, and create functions because it does not describe a particular structure for performing the functions. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “prediction model training unit configured to pre-train a prediction model…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the pre-train function. The use of the term “pre-train” is not adequate structure for performing the pre-train function because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “device cooperation cases setting unit configured to acquire sensing data… and decide and store device cooperation cases…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the acquire, decide, and store functions. The use of the terms “acquire”, “decide”, and” store” are not adequate structure for performing the acquire, decide, and store functions because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “device cooperation conducting unit configured to preprocess the sensing data…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the preprocess function. The use of the term “preprocess” is not adequate structure for performing the preprocess function because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Claim limitation “biological condition prediction unit configured to predict and output a biological condition…” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed function. There is no disclosure of any particular structure, either explicitly or inherently, to perform the predict and output functions. The use of the terms “predict” and “output” are not adequate structure for performing the predict and output functions because it does not describe a particular structure for performing the function. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claimed function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structures to perform the claimed functions regarding to select and use at least two of devices, create multiple training data, pre-train a prediction mode, decide and store device cooperation cases, collecting and analyzing sensing data, preprocess the sensing data, and predict and output a biological condition. The specification does not demonstrate that applicant has made an invention that achieves the claimed functions because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim 2 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function regarding to acquires the data, and uses the acquired item representative value. The specification does not demonstrate that applicant has made an invention that achieves the claimed functions because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim 7 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function regarding to acquire sensing data, and decide and store device cooperation cases. The specification does not demonstrate that applicant has made an invention that achieves the claimed functions because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim 8 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function regarding to preprocess the sensing data. The specification does not demonstrate that applicant has made an invention that achieves the claimed functions because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim 9 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structures to perform the claimed functions regarding to select and use at least one of devices, create multiple training data, pre-train a prediction mode, acquire sensing data, decide and store device cooperation cases, preprocess the sensing data, and predict and output a biological condition. The specification does not demonstrate that applicant has made an invention that achieves the claimed functions because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
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.
Claim 1 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites an apparatus, which fall within a statutory category.
Step 2A Prong one: claim 1 recites steps of “pre-train a prediction model through the multiple training data”, “decide device cooperation cases by analyzing sensing data acquired”, “preprocess the sensing data of a monitoring device according to the device cooperation cases”, “predict a biological condition corresponding to the sensing data preprocessed through the prediction model”. As is evident from the background, pre-train a prediction model falls into the “mathematical concept” group of abstract ideas, since model training is fundamentally a mathematical/statistical optimization process. And the claimed steps of decide, analyzing, preprocess, and predict, falls into the “mental process” group of abstract ideas, because the recited steps can be practically performed in the human mind. Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A Prong two: Besides the abstract ideas, the claim recites additional limitations “select and use at least two of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; store device cooperation cases by collecting sensing data; output a biological condition”. The additional limitations represent mere data gathering and data outputting that is necessary for use of the recited judicial exception and is recited at a high level of generality. Limitation “select and use at least two of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; store device cooperation cases by collecting sensing data; output a biological condition” in the claim is thus insignificant extra-solution activity. The additional elements “implantable device, a wearable device or an imaging device” in both steps is recited at a high-level of generality (i.e., as a generic component performing a generic function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim as a whole does not amounts to significantly more than the recited exception. The additional limitation of “select and use at least two of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; store device cooperation cases by collecting sensing data; output a biological condition” represent mere data gathering and data outputting recited at a high level of generality, and, as disclosed in the specification, is also well-known. This limitation therefore remains insignificant extra-solution activity even upon reconsideration. Thus, limitations “select and use at least two of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; store device cooperation cases by collecting sensing data; output a biological condition” do not amount to significantly more. The additional elements “implantable device, a wearable device or an imaging device” in both steps is recited at a high-level of generality (i.e., as a generic component performing a generic function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Claim 9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites an apparatus, which fall within a statutory category.
Step 2A Prong one: claim 9 recites steps of “pre-train a prediction model through the multiple training data”, “decide device cooperation cases for another type of means by comparative analysis with the sensing data acquired through the training data collection device”, “preprocess the sensing data of another type of device using the device cooperation cases for another type of means”, “predict a biological condition corresponding to the sensing data preprocessed through the prediction model”. As is evident from the background, pre-train a prediction model falls into the “mathematical concept” group of abstract ideas, since model training is fundamentally a mathematical/statistical optimization process. And the claimed steps of decide, preprocess, and predict, falls into the “mental process” group of abstract ideas, because the recited steps can be practically performed in the human mind. Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A Prong two: Besides the abstract ideas, the claim recites additional limitations “select and use at least one of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; acquire sensing data…and store device cooperation cases; output a biological condition”. The additional limitations represent mere data gathering and data outputting that is necessary for use of the recited judicial exception and is recited at a high level of generality. Limitation “select and use at least one of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; acquire sensing data…and store device cooperation cases; output a biological condition” in the claim is thus insignificant extra-solution activity. The additional elements “implantable device, a wearable device or an imaging device” in both steps is recited at a high-level of generality (i.e., as a generic component performing a generic function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim as a whole does not amounts to significantly more than the recited exception. The additional limitation of “select and use at least one of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; acquire sensing data…and store device cooperation cases; output a biological condition” represent mere data gathering and data outputting recited at a high level of generality, and, as disclosed in the specification, is also well-known. This limitation therefore remains insignificant extra-solution activity even upon reconsideration. Thus, limitations “select and use at least one of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data; acquire sensing data…and store device cooperation cases; output a biological condition” do not amount to significantly more. The additional elements “implantable device, a wearable device or an imaging device” in both steps is recited at a high-level of generality (i.e., as a generic component performing a generic function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Claim 2 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 2 recites additional element “when data of a same item is redundantly acquired by the at least two training data collection devices, the training data collection unit acquires the data of the device having highest sensitivity to a condition change of the pregnant breeding cattle as an item representative value, and uses the acquired item representative value as the input condition of the train data”. This judicial exception is not integrated into a practical application because the additional elements is recited at a high-level of generality (i.e., as a generic computing system performing a generic function of displaying data) such that it amounts no more than mere instructions to apply the exception using a generic component and it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim as a whole does not amounts to significantly more than the recited exception, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Claim 3 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 3 recites additional element “in case that the implantable device and the wearable device are used as the training data collection device, and any one of the implantable device and the wearable device is used as the monitoring device, the device cooperation cases define activity level verification using a pulse sensor of the implantable device and an in vitro sensor of the wearable device, correction of a temperature change of the implantable device and an external sensor temperature change of the wearable device, correction of a pressure change of the implantable device and an external sensor pressure change of the wearable device, and detection and training of a change in activity level using a Stage 1 rupture time point value of the implantable device and the in vitro sensor of the wearable device”. This judicial exception is not integrated into a practical application because the additional elements is recited at a high-level of generality (i.e., as a generic computing system performing a generic function of displaying data) such that it amounts no more than mere instructions to apply the exception using a generic component and it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim as a whole does not amounts to significantly more than the recited exception, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Claim 4 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 3 recites additional element “in case that the implantable device and the imaging device are used as the training data collection device, and any one of the implantable device and the imaging device is used as the monitoring device, the device cooperation cases define activity level verification using a pulse sensor of the implantable device and the imaging device, detection and training of a change in activity level using a Stage 1 rupture time point value of the implantable device and the imaging device, and verification of labor and estrus behavior using the imaging device and a change of the in vivo sensor of the implantable device”. This judicial exception is not integrated into a practical application because the additional elements is recited at a high-level of generality (i.e., as a generic computing system performing a generic function of displaying data) such that it amounts no more than mere instructions to apply the exception using a generic component and it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim as a whole does not amounts to significantly more than the recited exception, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Claim 5 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 3 recites additional element “in case that the wearable device and the imaging device are used as the training data collection device, and any one of the wearable device and the imaging device is used as the monitoring device, the device cooperation cases define verification of labor and estrus behavior using the imaging device and a change of the in vitro sensor of the wearable device”. This judicial exception is not integrated into a practical application because the additional elements is recited at a high-level of generality (i.e., as a generic computing system performing a generic function of displaying data) such that it amounts no more than mere instructions to apply the exception using a generic component and it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim as a whole does not amounts to significantly more than the recited exception, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Claim 7 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 7 recites steps of “decide device cooperation cases for another type of means by comparative analysis with the sensing data acquired”, the step cover performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. This judicial exception is not integrated into a practical application because the claim includes the additional limitation “acquire sensing data…, and store device cooperation cases…”. However, the additional limitations represent mere data gathering and data storing that is necessary for use of the recited judicial exception and is recited at a high level of generality. Therefore, the limitation in the claim is thus insignificant extra-solution activity. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim as a whole does not amounts to significantly more than the recited exception, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible
Claim 8 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Dependent Claim 8 recites step of “preprocess the sensing data of the monitoring device according to the device cooperation cases for another type of means in response to a biological condition monitoring request through any one of another type of device and another type of object”, the step cover performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. The claim lacks any additional elements which may serve to integrate it into a practical application and amount to significantly more than the abstract idea itself. The claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo (US 20230057275 A1) in view of ZHANG et al. (hereinafter “ZHANG”) (US 20220122744 A1).
As to claim 1, Seo teaches an apparatus for monitoring a biological condition of a pregnant breeding cattle, the apparatus comprising:
a training data collection unit configured to select and use at least two of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data ([0006] a smart livestock management method and system which can manage an animal by using an external device for obtaining bio information from an implant device inserted under the skin of an animal and a gate to which a camera for obtaining image data of the animal is attached; [0015-0016] the bio information and the image data may be transmitted to a server. The server may collect the bio information and the image data as a health condition index, may generate time-series data by accumulating the health condition index for each given time interval, may calculate a health condition index predictive value at future timing by inputting the generated time-series data to a health condition index prediction model);
a prediction model training unit configured to pre-train a prediction model through the multiple training data ([0016] the health condition index prediction model may be trained to receive the time-series data of the health condition indices accumulated over time and to output a predictive value of at least one health condition index at future timing after the time-series data [0063] the cloud server 140 may generate an HCI prediction model by training an artificial intelligence algorithm model through machine learning using learning data);
collecting and analyzing sensing data acquired through the training data collection devices ([0015] The server may collect the bio information and the image data as a health condition index, may generate time-series data by accumulating the health condition index for each given time interval, may calculate a health condition index predictive value at future timing by inputting the generated time-series data to a health condition index prediction model, may compare the calculated health condition index predictive value with a preset threshold, and may generate a danger alert signal when the calculated health condition index predictive value gets out of the threshold; [0057] The cloud server 140 may generate time-series data by accumulating bio information received to predict a health danger of the animal 210. The time-series data may be represented as a two-dimensional array composed of the bio information within a given time section. For example, the cloud server 140 may accumulate and store, as a health condition index (HCI), bio information for each item, such as blood pressure, oxygen saturation, blood glucose, a heart rate, or a body temperature measured with respect to an object, such as the animal 210. In this case, the HCI may be accumulated and stored for each piece of identification information. In this case, image data obtained by the camera 230 may also be considered as one item of the HCI);
a biological condition prediction unit configured to predict and output a biological condition corresponding to the sensing data preprocessed through the prediction model, wherein the prediction model has at least one of activity level, temperature, pressure, pulse, activity type, location information or Stage 1 rupture time point as an input condition and at least one of an estrus index, a parturition index or a health index as an output condition ([0015-0016, 0057, 0059-0061, 0064-0065] The server may collect the bio information and the image data as a health condition index, may generate time-series data by accumulating the health condition index for each given time interval, may calculate a health condition index predictive value at future timing by inputting the generated time-series data to a health condition index prediction model, may compare the calculated health condition index predictive value with a preset threshold… the health condition index prediction model may be trained to receive the time-series data of the health condition indices accumulated over time and to output a predictive value of at least one health condition index at future timing after the time-series data… For example, the cloud server 140 may accumulate and store, as a health condition index (HCI), bio information for each item, such as blood pressure, oxygen saturation, blood glucose, a heart rate, or a body temperature measured with respect to an object, such as the animal 210…. the cloud server 140 may predict an HCI after several minutes to several months by analyzing generated time-series data through an artificial intelligence algorithm… An HCI prediction model 910 may output a predictive value after each time through a calculation process within the HCI prediction model 910 when receiving time-series data 920. A future time (time T1, time T2, . . . , Tn) when an HCI will be predicted may be preset in a model selection process. Learning data may be prepared, if necessary. A model that predicts an HCI in one time (e.g., time T1) may be generated, or a model that predicts HCIs in several times may be generated as in the embodiment of FIG. 9).
Seo teaches a smart livestock management method and system using a plurality of detecting devices to collect livestock information and using a machine learning model to predict a future health index of livestock based on the collected information [0015-0016, 0057, 0059-0061, 0064-0065]. Seo does not explicitly teach a device cooperation cases setting unit configured to decide and store device cooperation cases and a device cooperation conducting unit configured to preprocess the sensing data of a monitoring device according to the device cooperation cases when at least one of the training data collection devices is selected as the monitoring device.
However, ZHANG teaches a system for predicting low-frequency sensor signal predictions using a hierarchical prediction model that jointly models the correlation between two sensors’ training data, then using that relationship to derive one sensor’s values from the others. Especially, ZHANG teaches decide and store device cooperation cases by collecting and analyzing sensing data acquired through the training data collection devices and preprocess the sensing data of a monitoring device according to the device cooperation cases when at least one of the training data collection devices is selected as the monitoring device ([0004, 0026, 0066, 0070-0072] a computer-implemented method for predicting unmonitored sensor signals using a prediction model. The computer-implemented method includes receiving a historical dataset of sensor signal data relating to an environment of a sensor monitoring system. The historical dataset includes a first sensor signal data, a second sensor signal data, and input variables relating to the sensor monitoring system. The computer-implemented method also includes generating sensor signal responses relating to the first sensor signal data by applying a Gaussian process regression model to the historical dataset and sensor parameters. The computer-implemented method further includes generating a hierarchical Gaussian process model that jointly considers multi-dimensional covariance structures among the input variables, the first sensor signal data, and the second sensor signal data, and predicting, by the hierarchical Gaussian process model, signal values relating to the second sensor signal data at time periods where the second sensor signal data was not monitored at using the sensor signal responses… the data prediction system can generate a hierarchical prediction model by inputting controllable sensor parameters into a Gaussian process regression model and output sensor predictions for sensors that are monitored at a high-frequency. The sensor predictions being outputted by the Gaussian process regression model can be inputted into a hierarchical Gaussian Prediction model to output sensor predictions for sensors that are monitored at a low-frequency. Through the hierarchical modeling structure, the correlation between sensor parameters and high-frequency prediction data, the correlation between sensor parameters and low-frequency prediction data, and the correlation between high-frequency and low-frequency prediction data are all considered to make a predicted sensor data for the low-frequency monitored sensor data… the hierarchical Gaussian process model predicts sensor signal data relating to the second sensor signal data based on a correlation between the first sensor signal data and the second sensor signal data. Additionally, the hierarchical Gaussian process model jointly considers multi-dimensional covariance structures among the input variables, the first sensor signal data, and the second sensor signal data. A posterior prediction of the hierarchical Gaussian process model can also impute missing sensor data in the sensor monitoring system… The hierarchical Gaussian process model 330 predicts sensor signal values. This is illustrated at step 540. In some embodiments, the hierarchical Gaussian process model 330 predicts the sensor signal values using equation 7 defined above. The predictions can represent low-frequency sensor signals that are missing from the historical dataset and are determined based on the correlation between the high-frequency sensor signals and the low-frequency sensor signals).
Seo and ZHANG are analogous art because they are from the same field of endeavor of analyzing different sensor data using machine learning models to predict output. At the time before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to implement Seo’s dual-sensor would be motivated by ZHANG’s well-known hierarchical/Gaussian-process sensor-relationship technique to reduce reliance on continuous full-device monitoring while preserving prediction accuracy. Therefore, it would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of ZHANG with the teachings of Seo for the purpose of determining correlation between different sensor data and selecting one sensor data as input to predict desired data output by using machine learning model based on the determined correlation information in the claim 1.
As to claim 4, Seo teaches in case that the implantable device and the imaging device are used as the training data collection device, and any one of the implantable device and the imaging device is used as the monitoring device, the device cooperation cases define activity level verification using a pulse sensor of the implantable device and the imaging device, detection and training of a change in activity level using a Stage 1 rupture time point value of the implantable device and the imaging device, and verification of labor and estrus behavior using the imaging device and a change of the in vivo sensor of the implantable device [0015-0016, 0057, 0059-0061, 0064-0065].
As to claim 7, ZHANG teaches the device cooperation cases setting unit further includes a function to acquire sensing data based on another type of means which is any one of another type of device and another type of object, and additionally decide and store device cooperation cases for another type of means by comparative analysis with the sensing data acquired through the training data collection device [0004, 0026, 0066, 0070-0072].
As to claim 8, ZHANG teaches the device cooperation conducting unit further includes a function to preprocess the sensing data of the monitoring device according to the device cooperation cases for another type of means in response to a biological condition monitoring request through any one of another type of device and another type of object [0004, 0026, 0066, 0070-0072].
As to claim 9, Seo teaches an apparatus for monitoring a biological condition of a pregnant breeding cattle, the apparatus comprising:
a training data collection unit configured to select and use at least one of an implantable device, a wearable device or an imaging device as a training data collection device to create multiple training data ([0006] a smart livestock management method and system which can manage an animal by using an external device for obtaining bio information from an implant device inserted under the skin of an animal and a gate to which a camera for obtaining image data of the animal is attached; [0015-0016] the bio information and the image data may be transmitted to a server. The server may collect the bio information and the image data as a health condition index, may generate time-series data by accumulating the health condition index for each given time interval, may calculate a health condition index predictive value at future timing by inputting the generated time-series data to a health condition index prediction model);
a prediction model training unit configured to pre-train a prediction model through the multiple training data ([0016] the health condition index prediction model may be trained to receive the time-series data of the health condition indices accumulated over time and to output a predictive value of at least one health condition index at future timing after the time-series data [0063] the cloud server 140 may generate an HCI prediction model by training an artificial intelligence algorithm model through machine learning using learning data);
a biological condition prediction unit configured to predict and output a biological condition corresponding to acquired sensing data preprocessed through the prediction model, wherein the prediction model has at least one of activity level, temperature, pressure, pulse, activity type, location information or Stage 1 rupture time point as an input condition and at least one of an estrus index, a parturition index or a health index as an output condition ([0015-0016, 0057, 0059-0061, 0064-0065] The server may collect the bio information and the image data as a health condition index, may generate time-series data by accumulating the health condition index for each given time interval, may calculate a health condition index predictive value at future timing by inputting the generated time-series data to a health condition index prediction model, may compare the calculated health condition index predictive value with a preset threshold… the health condition index prediction model may be trained to receive the time-series data of the health condition indices accumulated over time and to output a predictive value of at least one health condition index at future timing after the time-series data… For example, the cloud server 140 may accumulate and store, as a health condition index (HCI), bio information for each item, such as blood pressure, oxygen saturation, blood glucose, a heart rate, or a body temperature measured with respect to an object, such as the animal 210…. the cloud server 140 may predict an HCI after several minutes to several months by analyzing generated time-series data through an artificial intelligence algorithm… An HCI prediction model 910 may output a predictive value after each time through a calculation process within the HCI prediction model 910 when receiving time-series data 920. A future time (time T1, time T2, . . . , Tn) when an HCI will be predicted may be preset in a model selection process. Learning data may be prepared, if necessary. A model that predicts an HCI in one time (e.g., time T1) may be generated, or a model that predicts HCIs in several times may be generated as in the embodiment of FIG. 9).
Seo teaches a smart livestock management method and system using a plurality of detecting devices to collect livestock information and using a machine learning model to predict a future health index of livestock based on the collected information [0015-0016, 0057, 0059-0061, 0064-0065]. Seo does not explicitly teach a device cooperation cases setting unit configured to acquire sensing data based on another type of means which is any one of another type of device and another type of object, and decide and store device cooperation cases for another type of means by comparative analysis with the sensing data acquired through the training data collection device, and a device cooperation conducting unit configured to preprocess the sensing data of another type of device using the device cooperation cases for another type of means.
However, ZHANG teaches a system for predicting low-frequency sensor signal predictions using a hierarchical prediction model that jointly models the correlation between two sensors’ training data, then using that relationship to derive one sensor’s values from the others. Especially, ZHANG teaches acquire sensing data based on another type of means which is any one of another type of device and another type of object, and decide and store device cooperation cases for another type of means by comparative analysis with the sensing data acquired through the training data collection device, and preprocess the sensing data of another type of device using the device cooperation cases for another type of means ([0004, 0026, 0066, 0070-0072] a computer-implemented method for predicting unmonitored sensor signals using a prediction model. The computer-implemented method includes receiving a historical dataset of sensor signal data relating to an environment of a sensor monitoring system. The historical dataset includes a first sensor signal data, a second sensor signal data, and input variables relating to the sensor monitoring system. The computer-implemented method also includes generating sensor signal responses relating to the first sensor signal data by applying a Gaussian process regression model to the historical dataset and sensor parameters. The computer-implemented method further includes generating a hierarchical Gaussian process model that jointly considers multi-dimensional covariance structures among the input variables, the first sensor signal data, and the second sensor signal data, and predicting, by the hierarchical Gaussian process model, signal values relating to the second sensor signal data at time periods where the second sensor signal data was not monitored at using the sensor signal responses… the data prediction system can generate a hierarchical prediction model by inputting controllable sensor parameters into a Gaussian process regression model and output sensor predictions for sensors that are monitored at a high-frequency. The sensor predictions being outputted by the Gaussian process regression model can be inputted into a hierarchical Gaussian Prediction model to output sensor predictions for sensors that are monitored at a low-frequency. Through the hierarchical modeling structure, the correlation between sensor parameters and high-frequency prediction data, the correlation between sensor parameters and low-frequency prediction data, and the correlation between high-frequency and low-frequency prediction data are all considered to make a predicted sensor data for the low-frequency monitored sensor data… the hierarchical Gaussian process model predicts sensor signal data relating to the second sensor signal data based on a correlation between the first sensor signal data and the second sensor signal data. Additionally, the hierarchical Gaussian process model jointly considers multi-dimensional covariance structures among the input variables, the first sensor signal data, and the second sensor signal data. A posterior prediction of the hierarchical Gaussian process model can also impute missing sensor data in the sensor monitoring system… The hierarchical Gaussian process model 330 predicts sensor signal values. This is illustrated at step 540. In some embodiments, the hierarchical Gaussian process model 330 predicts the sensor signal values using equation 7 defined above. The predictions can represent low-frequency sensor signals that are missing from the historical dataset and are determined based on the correlation between the high-frequency sensor signals and the low-frequency sensor signals).
Seo and ZHANG are analogous art because they are from the same field of endeavor of analyzing different sensor data using machine learning models to predict output of a sensor device using output of another device. At the time before the effective filing date of the invention it would have been obvious to a person of ordinary skill in the art to implement Seo’s dual-sensor would be motivated by ZHANG’s well-known hierarchical/Gaussian-process sensor-relationship technique to reduce reliance on continuous full-device monitoring while preserving prediction accuracy. Therefore, it would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of ZHANG with the teachings of Seo for the purpose of determining correlation between different sensor data and selecting another sensor data as input to predict desired data output by using machine learning model based on the determined correlation information in the claim 9.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of ZHANG, and further in view of Taware et al. (hereinafter “Taware”) (US 20060074496 A1).
As to claim 2, Seo teaches continually acquires sensor data from the implantable device and the imaging device and uses the acquired item representative value as the input condition of the train data [0015]. ZHANG teaches data of a same item is redundantly acquired by the at least two training data collection devices and uses the acquired item representative value as the input condition of the train data [0048]. Seo and ZHANG do not explicitly teach the training data collection unit acquires the data of the device having highest sensitivity to a condition change of the pregnant breeding cattle as an item representative value.
However, Taware teaches a system and method for sensor validation from a plurality of sensors. Especially, Taware teaches the training data collection unit acquires the data of the device having highest sensitivity as an item representative value [0031, 0039, 0050].
It would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of Taware with the teachings of Seo and ZHANG for the purpose of determining and applying the more reliable sensed data as sensor output to be used as an item representative value for further processing by using sensor validation from a plurality of sensors.
Claim(s) 3, 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seo in view of ZHANG, and further in view of Choo et al. (hereinafter “Choo”) (US 20240136067 A1).
As to claim 3, Seo teaches continually acquires sensor data from the implantable device and the imaging device and uses the acquired item representative value as the input condition of the train data, and any one of the implantable device is used as the monitoring device, the device cooperation cases define activity level verification using a pulse sensor of the implantable device, correction of a temperature change of the implantable device, correction of a pressure change of the implantable device, and detection and training of a change in activity level using a Stage 1 rupture time point value of the implantable device [0015]. Seo and ZHANG do not explicitly teach using wearable device are used as the training data collection device.
However, Choo teaches a system and method for monitoring an object such that in response to information on a behavior of a domestic animal being estimated from sensor data measured by a sensor for the domestic animal using a machine learning-based behavior recognition model, estimating health status of the domestic animal with reference to the information on the behavior of the domestic animal and a health criterion for the domestic animal; and determining first breeding information on the domestic animal to be provided to a user on the basis of the health status. Especially, Choo teaches using the implantable device and the wearable device are used as the training data collection device [0039, 0054-0059].
It would have been obvious to an ordinary person skilled in the art before the effective filing date of the invention to incorporate the teachings of Choo with the teachings of Seo and ZHANG for the purpose of using different types of sensors to detect status of domestic animal to perform machine learning modeling using correlation information between the different types of sensors to predict desired output information for the domestic animal.
As to claim 5, Seo and Choo combined to teach in case that the wearable device and the imaging device are used as the training data collection device, and any one of the wearable device and the imaging device is used as the monitoring device, the device cooperation cases define verification of labor and estrus behavior using the imaging device and a change of the in vitro sensor of the wearable device (Seo [0015-0016, 0057, 0059-0061, 0064-0065]; Choo [0039, 0054-0059]).
As to claim 6, Seo and Choo combined to teach in case that the implantable device, the wearable device and the imaging device are used as the training data collection device, and at least one of the implantable device, the wearable device or the imaging device is used as the monitoring device, the device cooperation cases define correction of an actual behavior in the imaging device through an external sensor pressure change of the wearable device based on an in vivo pressure change of the implantable device, correction of accurate Stage 1 rupture time point of the wearable device through the Stage 1 rupture time point value of the implantable device and verification through the imaging device, verification of the labor and estrus behavior monitored by the imaging device and a change of the in vivo sensor of the implantable device and the in vitro sensor of the wearable device, correction of the activity level information of the in vivo activity sensor of the implantable device and the in vitro activity sensor of the wearable device and the activity type and activity level in an input image of the imaging device, data correction of the in vitro change of the wearable device based on the in vivo change of the implantable device and training of the imaging device with the activity type and activity level, correction of the imaging device with regard to the labor and estrus detected by the change of the in vivo sensor of the implantable device and the in vitro sensor of the wearable device and correction of the results, cross correction of the activity level of the in vivo activity sensor of the implantable device after activity level analysis and correction of the in vitro activity sensor of the wearable device and the imaging device, clustering correction of a candidate result group of analysis results of the in vitro change of the wearable device into the change of the in vivo sensor of the implantable device by analysis of a relationship with the activity level through the imaging device, training of the in vivo sensor change of the implantable device based on the in vitro sensor activity level information of the wearable device and the activity type verified by the imaging device, and training of the in vivo sensor change of the implantable device based on the in vitro sensor change of the wearable device and the labor and estrus detected by activity level analysis of the imaging device (Seo [0015-0016, 0057, 0059-0061, 0064-0065]; Choo [0039, 0054-0059]).
Conclusion
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/ZHIPENG WANG/Primary Examiner, Art Unit 2115
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