DETAILED ACTION
Applicant' s arguments, filed 12/08/2025 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 04/03/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1, and 3-11 are the current claims hereby under examination.
All references to Applicant’s specification are made using the paragraph numbers assigned in the US publication of the present application US 2022/0183632 A1.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112(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 and 3-11 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 recites “wherein the data about the biometrical state of the at least one head portion is associated with data about the anatomical structure of the at least one head portion, and wherein the data about the anatomical structure of the at least one head portion includes information about an anatomical layer of the head portion, and the data about the biometrical state of the at least one head portion includes information about at least one of an oxygen saturation of each layer of the head portion, hemoglobin concentration of each layer of the head portion, and moisture content of each layer of the head portion” but it is unclear if “the data about the biometrical state” and/or “the data about the anatomical structure” are meant to refer to “sample data about an anatomical structure” and “sample data about a biometrical state” respectively or if the limitations are meant to refer to subsets of “the biometric information about the head of the person to be measured”. For the purposes of this examination, the limitations will be interpreted as being subsets of the biometric information being measured from the head of a person.
Claims 3-11 are rejected by virtue of their dependence on claim 1.
Claim 10 recites “the data about the anatomical structure of the at least one head portion is pre-processed before input to the simulation model” but it is unclear what this pre-processing entails or what the result is. For the purposes of this examination, the limitation will be interpreted as any type of processing.
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, 4, 6, and 9 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 “wherein the simulation model predicts the simulated data about the optical signal detected from the at least one head portion by individually tracking photons that are irradiated by a virtually provided optical sensor”. The specification paragraph 0066 recites that the simulation model may be based on the Monte-Carlo method and may individually track photons irradiated by the virtually provided optical sensor but such a recitation. This description of the specification is considered insufficient to support the claimed language as the specification does not particularly describe how the photons are tracked and how such tracking results in the simulated optical data. In particular, Monte Carlo methods refer to a broad class of computational algorithms that rely on repeated random sampling. The specification does not recite the particular algorithm or steps taken to generate the simulated data about the optical signal using the sample “biometrical state” and “anatomical structure” data. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement
Claim 1 recites “performing a supervised learning of an estimation model of an artificial neural network by using the simulated data about the about the optical signal as input data and using the sample data about the anatomical structure of at least one head portion and the sample data about the biometrical state of the at least one head portion as output data”. The specification does not appear to describe the particular training methods utilized to train the estimation model. The recitation of “supervised learning” refers to a broad category of possible training techniques. The specification does not recite that supervised learning is utilized. Rather the specification paragraphs 0037, 0054-0055, 0067, and 0085 merely recite that the estimation model is learned. Paragraph 0065 recites the use of labelled training data but does not describe the particular method of training the algorithm. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. In particular, the particular steps taken to train the algorithm are not described.
Claim 1 recites “estimating the biometric information about the head of the person to be measured by inputting the acquired NIRS signal to the learned estimation model” but the specification does not appear to describe how the various data types used for training are combined or otherwise processed to produce the desired result. The specification further does not appear to provide support for the determination of any and all “biometric information”. The specification appears to describe the estimation model as a black box algorithm which transforms input data into output data through an unknown processing means. The particular steps taken to transform the input into the output do not appear to be disclosed nor does the particular training method of the algorithm. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement
Claim 4 recites “the estimated biometric information includes at least one of information about an effective attenuation coefficient of a cerebral cortex, information about an oxygen saturation of the cerebral cortex, information about a volume of a cerebrospinal fluid, information about moisture content of the cerebral cortex, and information about the anatomical structure” but the specification does not appear to disclose how these outputs are generated using the estimation model. The method of operation of the estimation model which would produce these output does not appear to be disclosed. Paragraph 0070 of the specification merely describes that these parameters are determined using the estimation model but does not detail the process as to how they are produced from the given input parameters. The estimation model appears to be described as a black box algorithm which outputs the desired information using the desired input.
Claim 6 recites “the biometric information of the first region and the biometric information of the second region comprise at least one of information about an effective attenuation coefficient of a cerebral cortex and information about an oxygen saturation of the cerebral cortex” but the specification does not appear to disclose how these outputs are generated using the estimation model. The method of operation of the estimation model which would produce these output does not appear to be disclosed. Paragraph 0070 of the specification merely describes that these parameters are determined using the estimation model but does not detail the process as to how they are produced from the given input parameters. The estimation model appears to be described as a black box algorithm which outputs the desired information using the desired input.
Claim 9 recites “wherein the estimated biometric information includes information about a cerebral cortex of the person to be measured” but the specification does not appear to disclose how these outputs are generated using the estimation model. The method of operation of the estimation model which would produce these output does not appear to be disclosed. Paragraph 0070 of the specification merely describes that parameters relating to the cerebral cortex are determined using the estimation model but does not detail the process as to how they are produced from the given input parameters. The estimation model appears to be described as a black box algorithm which outputs the desired information using the desired input.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 7 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. In particular, claim 7 requires the NIRS signal to be acquired using near-infrared spectroscopy, but claim 1, from which it depends, indicates that the acquired signal is an NIRS signal and thus must implicitly be acquired through NIRS techniques. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 and 3-11 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 and 3-11 are directed to a method of processing optical signals using a computational algorithm, which is an abstract idea. Claims 1 and 3-11 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] inputting sample data about an anatomical structure of at least one head portion and sample data about a biometrical state of the at least one head portion to a simulation model to predict simulated data about an optical signal including an intensity of the optical signal, wherein the simulation model predicts the simulated data about the optical signal detected from the at least one head portion by individually tracking photons that are irradiated by a virtually provided optical sensor
[B1] performing a supervised learning of an estimation model of an artificial neural network by using the simulated data about the about the optical signal as input data and using the sample data about the anatomical structure of at least one head portion and the sample data about the biometrical state of the at least one head portion as output data
[C1] estimating the biometric information about the head of the person to be measured by inputting the acquired NIRS signal to the learned estimation model
[D1] wherein the data about the biometrical state of the at least one head portion is associated with data about the anatomical structure of the at least one head portion, and
[E1] wherein the data about the anatomical structure of the at least one head portion includes information about an anatomical layer of the head portion, and the data about the biometrical state of the at least one head portion includes information about at least one of an oxygen saturation of each layer of the head portion, hemoglobin concentration of each layer of the head portion, and moisture content of each layer of the head portion.
These elements [A1]-[E1] 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. In particular, the method of training an algorithm using a given data set and supervised learning is considered a mere computer implementation for the act of training a person using a given amount of information. The recitation of supervised learning is considered equivalent to the user having access to the “correct” answers during their training. The estimation model itself is merely a computer implementation of the decision making and pattern recognition capabilities of the human mind. Additionally, the recitation of “individually tracking photons that are irradiated by a virtually provided optical sensor” is not defined in such a manner to prevent the tracking from being performed by the human mind using pen and paper. There is no recited quantity or accuracy required by the tracking a user may simply guess the pass of a photon on provided model data.
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] acquiring a near-infrared spectroscopy (NIRS) signal detected from a head portion of a person to be measured
[B2] at least one optical sensor disposed on the head portion of the person to be measured
[C2] an estimation model
[D2] a simulation model
These elements [A2]-[D2] 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 elements [C2]-[D2] are 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) and are nothing more than the computer implementation/automation of an abstract mental process of screening a patient as described above.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “acquiring a near-infrared spectroscopy (NIRS) signal detected from a head portion of a person to be measured by at least one optical sensor disposed on the head portion of the person to be measured” 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 an NIRS sensor. Such optical sensors are conventional as evidenced by:
U.S. Patent Application Publication No. US 2018/0028098 A1 (Yamada) discloses that brain-functional near infrared spectroscopy is conventional (paragraph 0011 of Yamada);
U.S. Patent Application Publication No. US 2013/0150687 A1 (Kato) discloses that near infrared spectroscopy (NIRS) brain measurement is conventional (paragraph 0018 of Kato);
Further, the elements [C2]-[D2] do 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 3-11 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 5: a first optical sensor of the at least one optical sensors disposed on a first region of the head portion of the person to be measured and; a second optical sensor of the at least one optical sensors disposed on a second region of the head portion of the person to be measured
Claim 7: the NIRS signal is acquired using near-infrared spectroscopy (NIRS);
Claim 8: the optical sensor comprises at least one light irradiation part and at least one light detection part; and
Claim 11: A non-transitory computer readable recording medium.
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 5 and 7-8 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 presence of two optical sensors disposed on two head regions of claim 5, an optical signal being acquired through NIRS of claim 7 and the at least one light irradiation and at least one light detection part of claim 8 are nothing more than specifying particulars about the generic optical sensor of claim 1. The use of NIRS is conventional as evidenced by Yamada and Kato above and the presence of at least one irradiation and detection parts as well as the use of at least two optical sensors disposed on the head are conventional to optical imaging systems as evidenced by:
U.S. Patent Application Publication No. US 2013/0072804 A1 (Inoue) which discloses that optical brain function imaging devices are conventional (paragraphs 0006-0008 and Fig. 6 of Inoue) and which illustrates a typical optical system comprising two illumination sources and two receiving sensors.
Also, the limitation from claim 11 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 memory) 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.
Prior Art
The claims are not rejected over the prior art as they contain one or more distinguishing features in the limitation “wherein the data about the anatomical structure of the at least one head portion includes information about an anatomical layer of the head portion, and the data about the biometrical state of the at least one head portion includes information about at least one of an oxygen saturation of each layer of the head portion, hemoglobin concentration of each layer of the head portion, and moisture content of each layer of the head portion” in claims 1 and 12. The closest prior art of record is noted below.
US Patent Application Publication Number US 2008/0316488 A1 hereinafter Mao teaches a device with two near detectors, two far detectors, and two sources. The two near detectors are arranged closer to the two sources than the two far detectors. A light -diffusing layer covers the two near detectors. The device may be part of a medical device that is used to monitor or measure oxygen saturation levels in a tissue. Light is transmitted into the tissue and received by the detectors. An attenuation coefficient is first calculated for a shallow layer of tissue. The attenuation coefficient is then used to calculate an attenuation coefficient for a deep layer of tissue (Abstract).
US Patent Application Publication Number US 2020/0253479 A1 hereinafter Nurmikko teaches an ultra-high resolution near infrared brain imaging system (Paragraph 0007). The system includes a light source and detector array (Paragraph 0008). The system is configured to generate a composite hologram of the cortical target by summing the slightly different optical pathways reflecting from the cortical target at different angles for each individual laser-detector pair (Paragraph 0009). Nurmikko teaches a near infrared imaging system which utilizes machine learning to extract neurovascular signals from the detected imagery (Paragraph 0041). Nurmikko further teaches that the system may be utilized to monitor disease progression and drug-efficacy, which are both related to the state of the patient (Paragraphs 0031-0032).
US Patent Application Publication Number US 2016/0360966 A1 hereinafter Ishii teaches a method of performing an optical examination on a test object. The method comprises obtaining a first light quantity distribution for each of a plurality of optical models that simulate the test object, obtaining, using an optical sensor, a second detection light quantity distribution from the test object, and selecting the optical model best suited for the test object based on the first and second light quantity distributions (Abstract). Ishii teaches a method wherein a light quantity distribution is collected from a test object which may be a living body (ParagraphO122). The test object is not limited to a living body and may further be a prism or water tank filed with gel (Paragraphs 0143-0144). A probe is utilized on the test object to collect data on the optical properties. The optical properties are stored and labeled (Paragraphs 0145-0150) The light quantity distribution is used to calculate the optical properties of the test object (ParagraphOO151). The system then performs a simulation for eight kinds of optical models that simulate the test object. The simulated model of the test object, such as a human head, maybe a multi-layer simulation. The simulation of a human head may have the layers of gray matter, bone marrow fluid, skull, skin, and hair that are laminated in order. The simulation models also include a virtual optical sensor. (Paragraph 0153-0154). The simulation is conducted and the sensitivity distribution is obtained for each optical model (Paragraph 0156). The sensitivity distribution of the test object is then calculated and obtained by performing the measurement using the same layout of probes used in the simulation. The optical model whose sensitivity distribution best matches the test object is selected (Paragraph 0157-0158). The selected model can then be used to calculate and estimate other parameters (Paragraphs 0160-0163). Ishii further teaches that the light used to stimulate the test object may be 780 or 900 nanometers as these wavelengths have widely varying absorption coefficients dependent on oxygen concentration (Paragraph 0227). Thus, the optical properties may include information on oxygen concentration within the test object.
Response to Arguments
Applicant's arguments filed 12/08/2025 have been fully considered but they are not persuasive.
Applicant’s amendments have overcome some of the previously presented grounds of rejection but necessitated new grounds of rejection.
In particular, the thrust of the rejections issued under 35 USC 112(a) remains as the specification does not appear to describe the particular method of determining the recited parameters using the recited input data. The machine learning model used to perform this determination is described as a “black box” algorithm where the internal process is unknown. Additionally, the training method of the algorithm is not described in sufficient detail. A mere statement of training using labelled data is not considered sufficient.
Applicant’s arguments drawn towards the 35 USC 101 rejection have been fully considered but are not found to be persuasive. In particular, applicant argues that the claims are not drawn towards a mental process since the steps are not practically performed on the human mind. This argument is not found to be persuasive because it is not commensurate in scope with the recited steps of claim 1. In particular, none of the steps are recited with a sufficient level of detail or complexity to preclude the process being performed in the human mind. Additionally, the step of acquiring NIRS signal data is not considered part of the abstract idea and is addressed in step 2B as being drawn to mere data gathering and not amounting to significantly more than the abstract idea.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MATTHEW ERIC OGLES/ Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791