DETAILED ACTION
Applicant' s arguments, filed 08/25/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 11/17/2022, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-20 are the current claims hereby under examination.
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 .
All reference to Applicant’s specification are made using the paragraph numbers assigned in the publication of the present application US 2024/0127944 A1.
Claim Objections
Claims 9-10 and 19-20 are objected to because of the following informalities:
Claims 9-10 and 19-20, it appears that “the physiological sensor is arranged on a target human body” should read “the physiological sensor is configured to be arranged on a target human body”
Claims 9 and 19, it appears that “wherein the plurality of first correspondences are comprised in the plurality of correlation parameters” should read “wherein the plurality of correlation parameters comprises the plurality of first correspondences” to make clear that the first correspondences are a sub-set of the correlation parameters.
Claims 10 and 20, it appears that “the plurality of second correspondences are comprised in the plurality of correlation parameters” should read “wherein the plurality of correlation parameters comprises the plurality of second correspondences” to make clear that the second correspondences are a sub-set of the correlation parameters.
Appropriate correction is required.
Claim Rejections - 35 USC § 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.
Claim 1 and its dependents are rejected as claim 1 recites “the fatigue analysis model is built by integrating a plurality of reference physiological signals …, a plurality of reference feature data …, a plurality of reference fatigue data … and a plurality of correlation parameters” but it is unclear how each of these elements are used in “building” the model. It is unclear if the model is a machine learning model trained using the various data type or some other type of model that uses the various inputs in some other fashion. For the purposes of this examination, the model will be considered as any model which receives image data and utilizes each of the listed data types in some manner. This rejection is further applied to the similar limitations in claim 11 and its dependents.
Claim 1 recites “obtained by performing a contact-based physiological sensing” but it is unclear what “a contact-based physiological sensing” comprises and what types of reference physiological signals may be obtained therefrom. For the purposes of this examination, the limitation will be interpreted as any type of physiological sensing using a sensor which contacts the body. This rejection is further applied to the similar limitations in claim 11 and its dependents.
Claim 1 recites “a numerical or proportional conversion on the plurality of reference physiological signals” but it is unclear what such a conversion entails. For the purposes of this examination, the limitation will be interpreted as any type of operation to convert physiological signals to fatigue data. This rejection is further applied to the similar limitations in claim 11 and its dependents.
Claim 1 recites “a plurality of correlation parameters indicating correspondence” but it is unclear what these parameters are and how they relate to the other recited reference parameters and the target feature data. It is unclear what “indicating correspondence” between the various reference data types entails. For the purposes of this examination, the correlation parameters will be considered information relating image features to physiological signals and fatigue data. This rejection is further applied to the similar limitations in claim 11.
Claim 1 and its dependents are rejected as claim 1 recites “generating, by the processor using the fatigue analysis model, a target fatigue data according to the target feature data, the plurality of reference feature data, the plurality of reference physiological signal, the plurality of reference fatigue data, and the plurality of correlation parameters” but it is unclear how this generation is carried out using the model. It is unclear how each of the reference feature/data types are related to the correlation parameters and how the correlation parameters are used in conjunction with the target feature data and the reference feature/data types to produce the recited outcome. For the purposes of this examination, the generation step will be considered as any type of fatigue determination using a model and relating to each of the recited data types. This rejection is further applied to the similar limitations in claim 11 and its dependents.
Claim 2 recites “a plurality of first correspondences between the plurality of reference feature data and the reference physiological signals, and a plurality of second correspondences between the plurality of reference physiological signals and the plurality of reference fatigue data” but it is unclear what the “plurality of first/second correspondences” comprise and what relationship they represent between the recited elements. For the purposes of this examination, the correspondences will be considered as any relationship between the recited parameters. This rejection is further applied to the similar limitations in claim 12.
Claim 6 recites “obtaining the target feature data from the target image comprises: calculating at least one target angle according to a plurality of target parts of a target human body in the target image, wherein the plurality of target parts are obtained by performing an image recognition on the target image” but it is unclear what “a plurality of target parts of a target human body” comprises and it is further unclear what “an image recognition” comprises and how it results in the target parts used to calculate an angle. For the purposes of this examination, the target parts will be considered any part of the body and will be considered to be identified using any generic image processing algorithm. This rejection is further applied to the similar limitations in claim 16.
Claim 9 recites “obtain and to establish the plurality of reference feature data by performing an image recognition on the reference image” but it is unclear what “an image recognition” comprises and how it results in the reference feature data. For the purposes of this examination, the limitation will be interpreted as any type of image processing technique to extract any type of feature. This rejection is further applied to the similar limitations in claim 19.
Claim 9 recites “establishing a plurality of first correspondences between the plurality of reference feature data and the reference physiological signals according to the plurality of reference physiological signals and the plurality of reference feature data which are received at the same time.” But it is unclear what the plurality of first correspondences comprise. It is unclear relationship the correspondences between the reference physiological signal and reference feature data entails. It is unclear if the correspondence is generated from two signals occurring at the same time or if the correspondence is generated through some other processing technique using signals captured at the same time. For the purposes of this examination, correspondences will be interpreted as a direct correlation between the reference feature data and the reference physiological signal, wherein the correlation is established on the basis of the reference feature data and the reference physiological signal occurring at the same time. This rejection is further applied to the similar limitations in claim 19.
Claim 10 recites “a conversion formula, … wherein the conversion formula converts the plurality of reference physiological signal into the plurality of reference fatigue data according to at least one physiological range” But it unclear what the “conversion formula” entails and how it is applied to each of the different data types. In particular, it is unclear how the reference physiological signal is compared to the corresponding physiological range and how the conversion formula uses these metrics to output a fatigue metric. For the purposes of this examination, the conversion formula will be interpreted as any method of converting physiological signal to a metric of fatigue. This rejection is further applied to the similar limitations in claim 20.
Claim Rejections - 35 USC § 112(a)
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.
Claims 1-2, 9-12, and 19-20 are 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “obtaining, by a processor, a target feature data being an image feature of the target image” but the specification does not support the obtainment of any and all types of “feature data” from an image to perform the recited method. In particular, paragraphs 0037-0039 appear to indicate that the angle between body parts is the desired feature data. This rejection is further applied to the similar limitations in claim 11.
Claim 1 recites “the fatigue analysis model is built by integrating a plurality of reference physiological signals …, a plurality of reference feature data …, a plurality of reference fatigue data …, and a plurality of correlation parameters” but the specification does not provide sufficient support for these data categories to include any and all types of “physiological signals”, “feature data”, “fatigue data”, and “correlation parameters”. Furthermore, the specification does not describe how this combination of data creates the fatigue analysis model capable of carrying out the recited function. In particular, the specification does not appear to describe how these various data types interact to produce the claimed result. This rejection is further applied to the similar limitations in claim 11.
Claim 1 recites “generating, by the processor using the fatigue analysis model a target fatigue data according to the target feature data, the plurality of reference feature data, the plurality of reference physiological signals, the plurality of reference fatigue data, and the plurality of correlation parameters” but the specification does not fully support any method of obtaining fatigue data using the recited inputs. In particular, the specification does not appear to describe how this generation step may be performed using the breadth of provided input parameters. This rejection is further applied to the similar limitations in claim 11.
Claim 2 recites “the plurality of correlation parameters comprise a plurality of first correspondences between the plurality of reference feature data and the reference physiological signals, and a plurality of second correspondences between the plurality of reference physiological signals and the plurality of reference fatigue data” but the specification does not fully support the breadth of the claimed relationships. In particular, the described correlations such as in paragraph 0040 between an angle and a given heart rate are considered insufficient to fully support the claimed breadth. The disclosure of a few “species” of relationships is insufficient to support the claimed “genus” of all relationships. This rejection is further applied to the similar limitations in claim 12.
Claim 9 recites “obtaining, by the camera device, the reference image to obtain and to establish the plurality of reference feature data by performing an image recognition on the reference image; and obtaining and establishing, by a physiological signal sensor, the plurality of reference physiological signals, wherein the physiological signal sensor is arranged on a target human body; and establishing a plurality of first correspondences between the plurality of reference feature data and the reference physiological signals according to the plurality of reference physiological signals and the plurality of reference feature data which are received at the same time” but the specification does not appear to provide sufficient support for the generation of any generic “first correspondences” between data being captured at the same time. In particular, the specification does not provide sufficient support for the claimed breadth of possible physiological data and feature image types and the subsequent generation of correspondences therebetween. The disclosure of certain “species” of correspondences does not fully support the claimed “genus” of all correspondences. This rejection is further applied to the similar limitations of claim 19.
Claim 10 recites “obtaining and establishing, by a physiological signal sensor, the plurality of reference physiological signals, wherein the physiological signal sensor is arranged on a target human body; and establishing a plurality of second correspondences between the plurality of reference physiological signals and the plurality of reference fatigue data by a conversion formula, wherein one of the plurality of reference physiological signals comprises at least one of an electromyogram, an electrocardiogram, a heart rate, a muscle strength and a blood pressure wherein the conversion formula converts the plurality of reference physiological signals into the plurality of reference fatigue data according to at least one physiological range corresponding to the at least one of the electromyogram, the electrocardiogram, the heart rate, the muscle strength and the blood pressure, and the plurality of second correspondences are comprised in the plurality of correlation parameters.” But the specification does not appear to describe what the “conversion formula” entails and how it is applied to each of the different data types to produce “second correspondences”. Furthermore, the specification does not provide sufficient support for the generation of any “second correspondences” between any physiological signal and “reference fatigue data”. In particular, the specification does not provide sufficient support for the claimed breadth of possible physiological data types and their corresponding ranges to then generate any type of fatigue data. The disclosure of certain “species” of correspondences and conversion methods do not fully support the claimed “genus” of all correspondences and conversion methods. This rejection is further applied to the similar limitations in claim 20.
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 to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to a method of processing physiological signals using a computational algorithm, which is an abstract idea. Claims 1-20 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process.
Step 2A – Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] obtaining a target feature data being an image feature of the target image
[B1] inputting the target feature data to a fatigue analysis model
[C1] generating , using the fatigue analysis model, a target fatigue data according to the target feature data, the plurality of reference feature data, the plurality of reference physiological signals, the plurality of reference fatigue data, and the plurality of correlation parameters
These elements [A1]-[C1] of claim 1 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] obtaining a target image
[B2] a camera device
[C2] a processor
These elements [A2]-[C2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the elements [A2]-[B2] are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Furthermore, the element [C2] is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “obtaining, a camera device, a target image” does not qualify as significantly more because this limitation is merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the data acquirer is nothing more than a camera. Such cameras are conventional as evidenced by:
U.S. Patent Application Publication No. US 2021/0269046 A1 (Hashimoto) discloses that conventional cameras may be used to capture face images for state determinations (paragraph 0012 of Hashimoto);
U.S. Patent Application Publication No. US 2012/0077163 A1 (Succar) discloses that conventional cameras may be used for body tracking (paragraph 0008 of Succar);
Further, the element [B2] does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Further, the limitation “wherein the fatigue analysis model is built by integrating a plurality of reference physiological signals obtained by performing a contact-based physiological sensing, a plurality of reference feature data being image features of a reference image, a plurality of reference fatigue data obtained by performing a numerical or proportional conversion on the plurality of reference physiological signals, and a plurality of correlation parameters indicating correspondence among the plurality of reference physiological signals, the plurality of reference feature data, and the plurality of reference fatigue data” does not qualify as significantly more because it is merely describing the information used to create the fatigue analysis model and does not integrate the “contact based physiological sensors” themselves into the claimed method. Additionally, the “fatigue analysis model” itself is nothing more than the computer implementation/automation of an abstract mental process of evaluating a subject used a person’s previous experience and pattern recognition.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 2-9 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 4: a display device;
Claim 9: a physiological signal sensor; and
Claim 10: a physiological signal sensor.
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 9-10 are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g).
Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extrasolution activity to the judicial exception, e.g., mere data gathering in conjunction with the abstract idea that uses conventional, routine, and well known elements or simply displaying the results of the algorithm that uses conventional, routine, and well known elements. In particular, the physiological signal sensors may be any generic cardiac sensor such as a PPG sensor. Such sensors are conventional as evidenced by:
U.S. Patent Application Publication No. US 2018/0249964 A1 (Qian) discloses that EKG and PPG device for heart rate determination are conventional (paragraph 0022 of Qian);
U.S. Patent Application Publication No. US 2018/0055364 A1 (Pierro) discloses that conventional PPG systems are used to assess cardiovascular factors such as heart rate, blood oxygen saturation, and peripheral vascular disease based on blood pressure measurements (paragraph 0008 of Pierro);
Also, this limitation from claims 4 is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions (that is, one of display) that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int'l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
The analysis of claim 11 is performed in light of the above analysis of claim 1 and may be abridged where limitations are similar
The analysis of claim 11 is as follows:
Step 1: Claim 11 is drawn to a machine.
Step 2A – Prong One: Claim 11 recites an abstract idea. In particular, claim 11 recites the following limitations:
[A1] receive the target image to obtain a target feature data being an image feature of the target image
[B1] generate, by using the fatigue analysis model, a target fatigue data according to the target feature data, the plurality of reference feature data, the plurality of reference physiological signals, the plurality of reference fatigue data, and the plurality of correlation parameters
These elements [A1]-[B1] of claim 11 are drawn to an abstract idea since they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Step 2A – Prong Two: Claim 11 recites the following limitations that are beyond the judicial exception and have not already been addressed in the above rejection of claim 1:
[A2] a storage unit configured to store a fatigue analysis model
This element [A2] of claim 11 does not integrate the exception into a practical application of the exception. In particular, the element [A2] is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself, as described in the above rejection of claim 1.
Further, the element [A2] does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claims 12-20 depend from claim 11, and recite the same abstract idea as claim 11. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the algorithm), with the following exceptions:
Claim 14: a display device;
Claim 19: a physiological signal sensor; and
Claim 20: a physiological signal sensor.
Each of these claim limitations does not integrate the exception into a practical application. In particular, these additional elements have each been addressed in the above analysis of claim 1 and its dependents.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
All prior art rejections are applied to the claims as best understood in light of the above presented 35 USC 112 rejections.
Claims 1-3, 9-13, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mestha US Patent Application Publication Number US 2018/0186234 A1 hereinafter Mestha
Regarding claim 1, Mestha teaches a fatigue data generation method (Abstract; Paragraph 0031: the model estimates fatigue and drowsiness), comprising:
obtaining, by a camera device, a target image (Paragraphs 0037-0038: the real-time images received of the operator);
obtaining, by a processor, a target feature data being an image feature of the target image (Paragraph 0018: the images and data from images; Paragraphs 0031-0033: the information within the images), and inputting, by the processor, the target feature data to a fatigue analysis model (Paragraphs 0018 and 0035-0038: the 3P model of the operator received the images) which is stored in a storage unit (Paragraph 0022: the control system stores and executes the model), wherein the fatigue analysis model is built by integrating a plurality of reference physiological signals obtained by performing a contact-based physiological sensing, a plurality of reference feature data being image features of a reference image, a plurality of reference fatigue data obtained by performing a numerical or proportional conversion on the plurality of reference physiological signals, and a plurality of correlation parameters indicating correspondence among the plurality of reference physiological signals, the plurality of reference feature data, and the plurality of reference fatigue data (Paragraphs 0031-0036: the 3P model is created by collecting physiological signals, the reference physiological signals, which may be generated from contact based sensors and correlating them to image features, the reference feature data, as described in paragraph 0035 and the physiological signal data is used to determine a user’s fatigue and drowsiness, the reference fatigue data obtained using a conversion from the physiological signals, and the completed model consists of well-correlated features from the imaging system as compared to psychological indicators and a plurality of fitting functions, as described in paragraphs 0031 and 0036, the correlation parameters. Thus, Mestha is considered to at least suggest the fatigue analysis model comprising each of the recited data types); and
generating, by the processor using the fatigue analysis model, a target fatigue data according to the target feature data, the plurality of reference feature data, the plurality of reference physiological signals, the plurality of reference fatigue data and the plurality of correlation parameters (Fig. 4 reference 412; Paragraph 0037-0039: real time data is fed to the model and compared to historical data to indicate fatigue).
Regarding claim 11, Mestha teaches a fatigue data generation system (Abstract; Paragraph 0031: the model estimates fatigue and drowsiness), comprising:
a camera device configured to obtain a target image (Paragraphs 0037-0038: the real-time images received of the operator);
a storage unit configured to store a fatigue analysis model (Paragraph 0022: the control system stores and executes the model), wherein the fatigue analysis model is built by integrating a plurality of reference physiological signals obtained by performing a contact-based physiological sensing, a plurality of reference feature data being image features of a reference image, a plurality of reference fatigue data obtained by performing a numerical or proportional conversion on the plurality of reference physiological signals, and a plurality of correlation parameters indicating correspondence among the plurality of reference physiological signals, the plurality of reference feature data, and the plurality of reference fatigue data (Paragraphs 0031-0036: the 3P model is created by collecting physiological signals, the reference physiological signals, which may be generated from contact based sensors and correlating them to image features, the reference feature data, as described in paragraph 0035 and the physiological signal data is used to determine a user’s fatigue and drowsiness, the reference fatigue data obtained using a conversion from the physiological signals, and the completed model consists of well-correlated features from the imaging system as compared to psychological indicators and a plurality of fitting functions, as described in paragraphs 0031 and 0036, the correlation parameters. Thus, Mestha is considered to at least suggest the fatigue analysis model comprising each of the recited data types); and
a processor communicatively connected to the camera device and the storage unit (Paragraphs 0022-0023: the control system stores and executes the model and the imaging system), and configured to:
receive the target image to obtain a target feature data being an image feature of from the target image (Paragraph 0018: the images and data from images; Paragraphs 0031-0033: the information within the images); and
generate, by using the fatigue analysis model, a target fatigue data according to the target feature data, the plurality of reference feature data, the plurality of reference physiological signals, the plurality of reference fatigue data and the plurality of correlation parameters (Fig. 4 reference 412; Paragraph 0037-0039: real time data is fed to the model and compared to historical data to indicate fatigue).
Regarding claims 2 and 12, Mestha teaches the fatigue data generation method and system of claims 1 and 11 respectively. Mestha further teaches the method and system wherein the plurality of correlation parameters comprise a plurality of first correspondences between the plurality of reference feature data and the reference physiological signals (Paragraphs 0031-0035: the imaging and body-worn sensors are used to generate features, the body worn sensors can be used to correlate the output of the imaging devices. The imaging device output features such as pulse rate. The conversion of the imaging data into these features are considered the first correspondences), and a plurality of second correspondences between the plurality of reference physiological signals and the plurality of reference fatigue data (Paragraphs 0031 and 0035-0036: the features from the imaging system, the output features such as pulse rate are analogous to the reference physiological signals, are correlated with psychological indicators of fatigue and drowsiness), and generating the target fatigue data comprises:
by the processor using the fatigued analysis model, comparing the target feature data with the plurality of reference feature data to select at least one similar feature data among the plurality of reference feature data (Fig. 4 references 408 and 410; Paragraphs 0032, 0035-0036, and 0037-0038: the control system receives real-time information from the imaging system and determines features. The determination of features is performed by the model and thus is considered to at least suggest comparing the image to reference images since the model is correlates the image to certain “ physical, physiological and psychological features or parameters” which have been determined as well-correlated from the model training);
by the processor using the fatigue analysis model, generating a control physiological data according to the at least one similar feature data, the plurality of correlation parameters and a part of the plurality of reference physiological signals (Fig. 4 reference 410; Paragraphs 0032, 0035-0036, and 0037-0038: the control system receives real-time information from the imaging system and determines features. The determination of features is performed by the model and thus is considered to implicitly discloses that the training data and thus the correlated parameters and/or fitting functions are sued) ; and
by the processor using the fatigue analysis model, generating the target fatigue data according to the control physiological data, the plurality of correlation parameters and a part of the plurality of reference fatigue data (Fig. 4 reference 412; Paragraphs 0036-0038: the model uses the features to compute psychological conditions such as fatigue. The determination of fatigue of performed by the model and thus implicitly involves the training data and the correlations determined from the features).
Regarding claims 3 and 13, Mestha teaches the fatigue data generation method and system of claims 2 and 12 respectively. Mestha further teaches the method and system further comprising: calculating a relative ratio between a first target fatigue data and a second target fatigue data in the target fatigue data to obtain a target fatigue index, wherein the first target fatigue data corresponds to a first time point, and the second target fatigue data corresponds to a second time point after the first time point (Fig. 4 references 414, 416, and 420; Paragraph 0039: the output of the model is compared to historical data, or data collected at a different time point. The “relative ratio” between the two is considered to be the range into which the output falls into).
Regarding claims 9 and 19, Mestha teaches the fatigue data generation method and system of claims 1 and 11 respectively. Mestha further teaches the method and system further comprising: obtaining, by the camera device, the reference image to obtain and to establish the plurality of reference feature data by performing an image recognition on the reference image (Paragraphs 0031-0033: the RBG and thermal information of the images ); and
obtaining and establishing, by a physiological signal sensor, the plurality of reference physiological signals, wherein the physiological signal sensor is arranged on a target human body (Paragraphs 0031 and 0035: the contact sensors); and
establishing a plurality of first correspondences between the plurality of reference feature data and the reference physiological signals according to the plurality of reference physiological signals and the plurality of reference feature data which are received at the corresponding to a same time, wherein the plurality of first correspondences are comprised in the plurality of correlation parameters (Paragraph 0035: the contact sensor readings are correlated to those derived from the image. The correlation and the method of converting the raw image data into physiological features are considered the first correspondences).
Regarding claims 10 and 20, Mestha teaches the fatigue data generation method and system of claims 1 and 11 respectively. Mestha further teaches the method and system further comprising:
obtaining and establishing, by a physiological signal sensor, the plurality of reference physiological signals, wherein the physiological signal sensor is arranged on a target human body (Paragraphs 0031 and 0035: the contact sensors); and
establishing a plurality of second correspondences between the plurality of reference physiological signals and the plurality of reference fatigue data by a conversion formula, wherein one of the plurality of reference physiological signals comprises at least one of an electromyogram, an electrocardiogram, a heart rate, a muscle strength and a blood pressure, wherein the conversion formula converts the plurality of reference physiological signals into the plurality of reference fatigue data according to at least one physiological range corresponding to the at least one of the electromyogram, the electrocardiogram, the heart rate, the muscle strength and the blood pressure, and the plurality of second correspondences are comprised in the plurality of correlation parameters (Paragraphs 0031 and 0036: the well correlated features from the imaging system used to determine the psychological indicators of fatigue. The physiological signals may include pulse rate which can be correlated with the power spectrum in different frequency bands which indicates fatigue).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mestha US Patent Application Publication Number US 2018/0186234 A1 hereinafter Mestha as applied to claims 1 and 11 respectively above and further in view of Pavelka international Patent Application Publication Number WO 2006/000166 A1 hereinafter Pavelka.
Regarding claims 4 and 14, Mestha teaches the fatigue data generation method of claim 3 and system of claim 13. Mestha further discloses the method further comprising: wherein the relative ratio corresponds to one of a plurality of fatigue intervals (Fig. 4 references 414, 416, and 420; Paragraph 0039: the various ranges).
Mestha fails to further disclose the method wherein the plurality of fatigue intervals correspond to a plurality of different colors, and the fatigue data generation method further comprises: obtaining one of the plurality of different colors according to the relative ratio; and displaying the target fatigue index in the one of the plurality of different colors by a display device.
Pavelka teaches a method of monitoring operator fatigue (Abstract). Thus Pavelka falls within the same field of endeavor as Applicant’s invention.
Pavelka teaches a method wherein the extend of the operator’s fatigue may be displayed to the operator in any suitable manner including by signaling the extend of fatigue using display color or digital digits which change color when a critical level of fatigue is exceeded (Page 54 lines 23-31).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the invention to combine the display for displaying of fatigue extent using different colors as taught by Pavelka into the method of Mestha because notifying the operator of Mestha of their fatigue extend and signaling when they cross a critical threshold may prevent operator accidents by alerting them that they need to stop and rest or otherwise reduce their fatigue for safety.
Claims 5-7 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mestha US Patent Application Publication Number US 2018/0186234 A1 hereinafter Mestha as applied to claims 1 and 11 respectively above and further in view of Nishimura US Patent Application Publication Number US 2015/0086110 A1 hereinafter Nishimura.
Regarding claims 5 and 15, Metha teaches the fatigue data generation method of claim 1 and system of claim 11. Mestha fails to further disclose the method wherein each of the plurality of reference feature data comprises a plurality of reference angles, the plurality of reference angles are calculated according to a plurality of reference parts of a target human body in the reference image.
Nishimura teaches an attribute estimation system which estimates an attribute of a target person using an estimation model which receives image data (Abstract). Thus, Nishimura falls within the same field of endeavor as Applicant’s invention.
Nishimura