DETAILED ACTION
Applicant' s arguments, filed 10/14/2025 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 06/16/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-6, 9-14, and 17-20 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 2022/0287592 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 Objections
Claim 1 objected to because of the following informalities:
Claim 1 it appears that “an calculating processor that calculates a deviation of the subject's active state in each action phase across the multiple instances of the specified behavior task as an evaluation function while comparing the action phases to a reference action pattern established as a baseline” should read “an calculating processor that calculates a deviation of the subject's active state in each action phase across the multiple instances of the specified behavior task as an evaluation function by comparing the action phases to a reference action pattern established as a baseline” to indicate the comparison is how the recited function is being performed rather than a separate task performed alongside the generation of the evaluation function.
Appropriate correction is required.
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.
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 near infrared detection unit of claims 1 and 9
a far infrared detection unit of claims 1 and 9
a flicker test unit of claims 1 and 9
a movement recognition unit in claim 3
a recognition degree detection unit in claim 4
a flicker test unit in claim 9
a second step in claim 9
a fourth step in claim 9
a sixth step in claim 9
a seventh step in claim 9
a tenth step in claim 9
an eleventh step in claim 9
a twelfth step in claim 9
an eighth step in claim 13
a ninth step in claim 14
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 or particular program described in the specification as performing the claimed function, and equivalents thereof.
a near infrared detection unit of claim 1 is described in paragraphs 0064, 0066, 0068-0070 and 0074 as an infrared light source and camera to detect infrared light reflected from the skin. The near infrared detection unit is interpreted as an infrared camera and associated light source and their equivalents for both claims 1 and 9
a far infrared detection unit of claim 1 is described in paragraphs 0096 as an infrared camera to detect infrared light reflected from the skin. The far infrared detection unit is interpreted as an infrared camera and its equivalents for both claims 1 and 9
a flicker test is described in paragraphs 0122-0123 as utilizing a light source which flickers at a controllable speed to determine the flicker speed at which the subject can detect flickering. A flicker test unit is interpreted as a light source whose flicker frequency is controllable, its associated controller, and their equivalents for both claims 1 and 9.
a second step is described in paragraphs 0127-0130 but is not assigned a particular physical structure but is rather a function of a processor. As such the behavior task dividing unit is interpreted as the particular method steps executed by the processor. Paragraphs 0127-0130 describe the functions of the second step and provide an example of its use but fail to describe the particular steps taken to perform the recited function. In particular, the steps taken to divide the “nursing care work” behavior task into the respective action phases is not described. The specification does not appear to describe how the active state data is processed to segment it into the various action phases of a behavior task
a fourth step is described in paragraphs 0131-0133 but is not assigned a particular physical structure but is rather a function of the processor. As such the fourth step is interpreted as the particular method steps executed by the Bayesian clustering processor. Paragraphs 0131-0133 describe the function of the fourth step but does not describe the particular method used to perform the function. In particular, paragraphs 0131-0133 indicate that the step performs cluster analysis using nonparametric Bayesian method using Dirichlet process mixture distribution on a plurality of parameters indicated with multiple values such as attributes, sex, age, and a skill level of the subject but the specification does not appear to describe how these parameters are acquired. The specification does not appear to describe how the data gathered by the active state detection unit is processed into the parameters used for clustering.
a sixth step is described in paragraphs 0139-0142 but is not assigned a particular physical structure but is rather a function of a processor. As such the sixth step is interpreted as the particular method steps executed by the processor. Paragraphs 0139-0142 describe the function of the sixth step but do not appear to describe the method used to carry out the estimation. In particular, the sixth step uses the evaluation function, or difference between current active state data and standard active state data determined by the evaluation function calculation unit, to determine a transition of a health condition but the specification does not appear to describe how this determination is performed or describe what a “health condition” entails. The specification does not appear to describe the particular manner in which a health condition transition is evaluated using the evaluation function.
a seventh step is described in paragraphs 0143-0144 but is not assigned a particular physical structure but is rather a function of a processor. As such the seventh step is interpreted as the particular method steps executed by the processor. Paragraphs 0143-0144 describe the function of the seventh step as using the identified health transition of the sixth step to identify an action phase with an execution efficiency is below a threshold. The specification does not appear to describe the particular method steps to carry out the recited process. The specification does not particularly describe the ”execution efficiency” of a behavior task and how it is evaluated with respect to a health transition.
a tenth step is described in paragraphs 0026, 0028, 0153-0154, and 0158 but is not assigned a particular physical structure but is rather a function of a processor. As such the tenth step is interpreted as the particular method steps executed by the processor. Paragraphs 0026, 0028, 0153-0154, and 0158 describe the tenth step in purely functional language and do not appear to describe how the degradation cause is determined from the gathered information or provide an example as to what the output of this step entails.
an eleventh step is described in paragraphs 0028, 0153, and 0156-0157, but is not assigned a particular physical structure but is rather a function of a processor. As such the eleventh step is interpreted as the particular method steps executed by the processor. Paragraphs 0028, 0153, and 0156-0157 describe that the eleventh step calculates a ratio between the number of subjects with an identified worrying action phase to the total number of subjects performing that action phase. The eleventh step is interpreted as the determination of a ratio between the number of subjects with an identified worrying action phase to the total number of subjects performing that action phase.
a twelfth step is described in paragraphs 0028, 0153, and 0157-0158, but is not assigned a particular physical structure but is rather a function of a processor. As such the twelfth step is interpreted as the particular method steps executed by the processor. Paragraphs 0028, 0153, and 0157-0158 describe the twelfth step in purely functional language and do not appear to describe how the processor determines if the behavior task itself has a problem or the order of the action phases has a problem and what that problem is. It would seem that the ratio is used to identify when the determination should take place but does not appear to be related to the determination of if the behavior task or order of action phases has a problem.
a movement recognition unit is described in paragraphs 0116-0120 which indicate that the movement recognition unit is an inertial measurement unit which may take the form of a camera or accelerometer. The movement recognition unit is interpreted as a camera and/or accelerometer and their equivalents.
a recognition degree detection unit is described in paragraphs 0121-0124 which indicate that the recognition degree detection unit which is a light source. The recognition degree detection unit is interpreted as a light source which flickers at adjustable frequencies and its equivalents.
an eighth step is described in paragraphs 0024-0025, 0091, 0137, and 0145-0151, but is not assigned a particular physical structure but is rather a function of the processor. As such the eight step is interpreted as the particular method steps executed by the processor. Paragraphs 0028, 0153, and 0157-0158 describe the eight step in purely functional language and do not appear to describe how the processor determines if the action phase identified by the seventh step can damage the subject’s health
a nineth step is described in paragraphs 0145-0151, but is not assigned a particular physical structure but is rather a function of the processor. As such the ninth step is interpreted as the particular method steps executed by the processor. Paragraphs 0028, 0153, and 0157-0158 describe the ninth step in purely functional language and do not appear to describe how the processor creates advice data for improving the subject’s health condition or what form this advice may take.
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.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are:
a first step of claim 9
a third step of claim 9
a fifth step in claim 9
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
a first step of claim 9 is not interpreted under 35 USC 112(f) because the claim recites all of the required structure and acts to carry out the recited function.
a third step of claim 9 is not interpreted under 35 USC 112(f) because the claim recites all of the required structure and acts to carry out the recited function.
a fifth step of claim 9 is not interpreted under 35 USC 112(f) because the claim recites all of the required acts to carry out the recited function.
If applicant intends 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 remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-6, 9-14, and 17-20 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.
Claims 9 and 13-14 recite the following limitations that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
a second step in claim 9
a fourth step in claim 9
a sixth step in claim 9
a seventh step in claim 9
a tenth step in claim 9
an eleventh step in claim 9
a twelfth step in claim 9
an eighth step in claim 13
a ninth step in claim 14
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 deficiencies of the specification are described in the above present claim interpretation section for each of the above listed limitations. 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.
Claim 1 recites “a correlation analysis processor configured to, when a subject executes a specified behavior task, divides the behavior task into units of action phases in chronological order according to a correlation of the active states of the subject on a basis of active state data which is a detection result of the active state detection unit” but it is unclear how the behavior task is being divided into action phases. It is unclear what “a correlation of the active states of the subject on a basis of active state data” is intended to convey. For the purposes of this examination, the limitation will be interpreted as dividing a behavior task into action phases based on a correlation of the recorded active state data to predetermined action phases that are associated with certain active state data.
Claim 1 recites “a correlation analysis processor configured to, when a subject executes a specified behavior task, divides the behavior task into units of action phases in chronological order according to a correlation of the active states of the subject on a basis of active state data which is a detection result of the active state detection unit” but it is unclear how the recited “a detection result of the active state detection unit” relates to the various parameters and sensors recited in lines 4-11. For the purposes of this examination, “a detection result” will be interpreted as a combination of all the recited parameters being detected by the active state detection unit’s various sensors.
Claim 1 recites “a health transition processor configured to estimates a transition of a health condition of the subject with respect to each action phase across the multiple instances of the specified behavior task on a basis of the evaluation function calculated by the calculating processor” but it is unclear how the evaluation function is used to estimate a “transition of a health condition” and what such a transition entails. For the purposes of this examination, the limitation will be interpreted as any different between the recorded and reference action phase data indicating some form of change, or transition, in the user’s health.
Claim 1 recites “a efficiency threshold processor configured with comparative analysis circuits” but it is unclear what relationship is intended to be conveyed between the processor and “circuits” it is unclear what function the circuits carry out or how they relate to the function of the processor. For the purposes of this examination the limitation will be interpreted as any type of “circuit” in communication with the processor.
Claim 1 recites “a efficiency threshold processor configured with comparative analysis circuits that applies the transition of the subject's health condition which is estimated by the health transition processor configured to identify an action phase of the action phases regarding which execution efficiency of the behavior task by the subject based on the transition of the subject’s health condition is equal to or lower than a specified level, wherein the behavior task evaluation system is applied to each of a plurality of different subjects” but is it unclear what the “execution efficiency” is and how it is “based on the transition of the subject’s health condition”. It is unclear how the execution efficiency is determined in order to be compared to the specified level. For the purposes of this examination, the execution efficiency is interpreted as any metric of the user’s performance of a task.
Claim 1 recites “a multi-input analysis processor configured to: … analyzes a cause of degradation in execution efficiency of the action phase on a basis of the transition of each subject’s health condition in the action phase” It is unclear how the cause in degradation of execution efficiency is being analyzed and what parameters are utilized in such an analysis. It is further unclear what the output of such an analysis entails. For the purposes of this examination, the limitation will be interpreted as any type of causal analysis related to performance of a task.
Claim 1 recites “a common ratio calculating processor … a particular action phase identified as worrying” but no such recitation of identifying a “worrying” action phase has been carried out. It is unclear how this limitation relates to the process carried out by the multi-input analysis processor. For the purposes of this examination, the limitation will be interpreted as referring to the action phase identified by the multi-input analysis processor. This rejection is further applied to the similar limitations of claim 9.
Claim 1 recites “a ratio comparison processor configured with threshold detection circuits” but it is unclear what relationship between the processor and circuits is meant to be communicated by “configured with” it is unclear if the processor, the circuit, or both are performing the recited tasks. For the purposes of this examination, the limitation will be interpreted as a processor connected to any circuit.
Claim 1 recites “a ratio comparison processor configured with threshold detection circuits that judges … whether the behavior task itself including each action phase has a problem or a sequential execution order of the respective action phases has a problem” It is unclear what factors are considered when making this judgment. For the purposes of this examination, the limitation will be interpreted as any method of carrying out such a judgment.
Claim 1 recites “wherein a judgment result is outputted to analyze the identified problematic action phases and their sequential relationships to determine specific causes of subject fatigue during chronological task execution” but it is unclear what this limitation is meant to convey. It is unclear what the judgment result entails and where it is generated from. It is unclear what the analysis to determine specific causes of fatigue entails and what element of the system performs this analysis. It is unclear if the recitation “identified problematic action phases” is the same as, related to, or different from the action phases identified by the multi-input analysis processor. For the purposes of this examination, the limitation will be interpreted as a judgment result being output by the processor wherein the judgment result includes the specific causes of subject fatigue during chronological task execution. This rejection is further applied to the similar recitations of claim 9.
Claim 1 recites “wherein advice data is created to improve the subject's health condition including break times, break time timing, and number of break times” but it is unclear how this advice is generated and how they relate to the previously generated metrics such as the specific causes of degradation. For the purposes of this examination, the limitation will be interpreted as any form of recommendation. This rejection is further applied to the similar recitations of claim 9.
Claim 1 recites “an interface provided for the subject to improve the subject's health condition through communicating based on the judgment result for implementing the advice data for modifying the behavior task and the sequential order of action phases”, but the advice data appears to include break times, break time timing, and number of break times which do appear to modify a behavior task but not the sequential order of action phases. It is unclear how the advice data modifies the sequential order of action phases. This rejection is further applied to the similar recitations of claim 9.
Claim 1 recites “wherein the active state detection unit comprises: a near infrared detection unit that irradiates a region of interest of the subject’s face with near infrared light having a wavelength between 650nm and 1350nm … and a far infrared detection unit that detects skin temperature differences between forehead and nose regions by calculating …” but it is unclear if these elements of the active state detect unit are the same as, related to, or intended to further limit the elements described in lines 4-11 of claim 1. For the purposes of this examination, the limitations are interpreted as further limiting the near and far infrared detection units respectively. This rejection is further applied to the similar recitations of claim 9.
Claims 2-6 and 17-20 are rejected by virtue of their dependance on claim 1.
Claims 10-14 are rejected by virtue of their dependance on claim 9.
Claim 2 recites “the active state detection unit comprises motion sensors” but it is unclear if the motion sensors are the same as, related to, or a subset of the sensors listed in claim 1 lines 4-11. For the purposes of this examination, the motion sensors are interpreted as the same component as the inertial measurement unit of claim 1. This rejection is further applied to the similar recitations of claim 10.
Claim 3 recites “the active state detection unit includes a movement recognition unit” but it is unclear if the movement recognition unit is the same as, related to, or a subset of the sensors listed in claim 1 lines 4-11. For the purposes of this examination, the movement recognition unit is interpreted as the same component as the inertial measurement unit of claim 1.
Claim 4 recites “the active state detection unit includes a recognition degree detection unit” but it is unclear if the recognition degree detection unit is the same as, related to, or a subset of the sensors listed in claim 1 lines 4-11. For the purposes of this examination, the recognition degree detection unit is interpreted as the same component as the flicker test unit of claim 1.
Claim 5 recites “a health impact assessment processor that judges, in a heath impact judgement result, whether or not there is a possibility that the action phase identified by efficiency threshold processor configured with comparative analysis circuits may damage the subject's health condition” but it is unclear what factors are considered and how the processor judges whether or not the action phase may damage the subject’s health condition. It is further unclear what the meets and bounds of “damage the subject’s health condition” entails. For the purposes of this examination, the limitation is interpreted as any judgment regarding any affect to the user’s health.
Claim 6 recites “a advice generation processor configured to creates advice data” but it is unclear how these limitations relate to the “interface” and “advice” of claim 1. It is further unclear how the advice is generated and how a processor “feeds back and reports the advice data to the subject”. For the purposes of this examination, these limitations will be interpreted as referring to the same things. This rejection is further applied to the similar recitations of claim 14.
Claim 11 recites “inertia type motion capture” but it is unclear if this limitation is the same as, related to, or different from the variety of sensors and detected parameters of claim 9 lines 4-10. For the purposes of this examination, the limitation will be interpreted as referring to the inertial measurement unit of claim 9.
Claim 12 recites “a flicker test technique” but it is unclear if this limitation is the same as, related to, or different from the variety of sensors and detected parameters of claim 9 lines 4-10. For the purposes of this examination, the limitation will be interpreted as referring to the flicker test unit of claim 9.
Claim 17 recites “where the infrared detection unit: extracts … calculates… and creates” which reads as though the infrared detection unit itself, i.e. the camera, carries out the recited function rather than a processor connected to the detection unit. It is unclear whether the camera or a connected processor is carrying out the recited functions. For the purposes of this examination, the limitation will be interpreted as a processor connected to the detection unit carrying out the recited functions.
Claim 18 recites “an inertial measurement unit sensor” but it is unclear if this limitation is the same as, related to, or different from the variety of sensors and detected parameters of claim 1 lines 4-11. For the purposes of this examination, the limitation will be interpreted as referring to the inertial measurement unit of claim 1.
Claim 19 recites “a flicker test unit” but it is unclear if this limitation is the same as, related to, or different from the variety of sensors and detected parameters of claim 1 lines 4-11. For the purposes of this examination, the limitation will be interpreted as referring to the inertial measurement unit of claim 1.
Claim 20 recites “where the infrared detection unit performs coordinate transformation” which reads as though the infrared detection unit itself, i.e. the camera, carries out the recited function rather than a processor connected to the detection unit. It is unclear whether the camera or a connected processor is carrying out the recited function. For the purposes of this examination, the limitation will be interpreted as a processor connected to the detection unit carrying out the recited functions.
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, 5-6, 9, 13-14, 17 and 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.
Claims 9 and 13-14 recite the following limitations that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
a second step in claim 9
a fourth step in claim 9
a sixth step in claim 9
a seventh step in claim 9
a tenth step in claim 9
an eleventh step in claim 9
a twelfth step in claim 9
an eighth step in claim 13
a ninth step in claim 14
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 deficiencies of the specification with respect to each of the above listed elements are described in the above presented claim interpretation section. Therefore, the corresponding claim limitations are not fully supported by the specification.
Claims 1, 5-6, 9, and 13-14 recites a number of different types of processors. Each of the various processors are claimed as their own, distinct processor. The specification does not appear to support a device or method which utilizes the claimed number of different processors and does not appear to describe how each of these distinct processors are configured to communicate with each other to carry out the claimed method. Paragraph 0051 appears to indicate that a single processor is utilized.
Claim 1 recites “a correlation analysis processor configured to, when a subject executes a specified behavior task, divides the behavior task into units of action phases in chronological order according to a correlation of the active states of the subject on a basis of active state data which is a detection result of the active state detection unit” but Paragraphs 0127-0130 describe the recited function and provide an example of its use but fail to describe the particular steps taken to perform the recited function. In particular, the steps taken to divide the “nursing care work” behavior task into the respective action phases is not described. The specification does not appear to describe how the active state data is processed to segment it into the various action phases of a behavior task
Claim 1 recites “a Bayesian clustering processor implementing Dirichlet process mixture distribution that, when the subject executes the behavior task, reads the active state data according to the behavior task executed for a plurality of number of times including at least a last time from the data storage unit, performs cluster analysis of each action phase based on similarity, and forms a set of the action phases across multiple instances of the specified behavior task” but paragraphs 0131-0133 indicate that cluster analysis is performed using nonparametric Bayesian method using Dirichlet process mixture distribution on a plurality of parameters indicated with multiple values such as attributes, sex, age, and a skill level of the subject. The claim does not reflect these parameters being used for the cluster analysis but rather the active state data itself. Such clustering is not seemingly supported by the specification. The specification further does not appear to describe how these parameters are acquired. In particular, it is unclear how the parameters of skill and attributes are generated and what they comprise. The specification does not appear to describe how the data gathered by the active state detection unit is processed into the parameters used for clustering
Claim 1 recites “a health transition processor configured to estimates a transition of a health condition of the subject with respect to each action phase across the multiple instances of the specified behavior task on a basis of the evaluation function calculated by the calculating processor” but Paragraphs 0139-0142 but do not appear to describe the method used to carry out the estimation. In particular, the estimation uses the evaluation function, or difference between current active state data and standard active state data, to determine a transition of a health condition but the specification does not appear to describe how this determination is performed or describe what a “health condition” or “transition of a health condition” entails. The specification does not appear to describe the particular manner in which a health condition transition is evaluated using the evaluation function.
Claim 1 recites “a efficiency threshold processor configured with comparative analysis circuits that applies the transition of the subject's health condition which is estimated by the health transition processor configured to identify an action phase of the action phases regarding which execution efficiency of the behavior task by the subject based on the transition of the subject’s health condition is equal to or lower than a specified level, wherein the behavior task evaluation system is applied to each of a plurality of different subjects” but the specification does not appear to support such an identifications. Paragraphs 0030-0031 and 0143-0144 do not appear to describe how the execution efficiency is determined in order to make the comparison to a threshold value. The specification does not appear to describe the connection between the transition of the subject’s health condition and the execution efficiency.
Claim 1 recites “a multi-input analysis processor configured to: … analyzes a cause of degradation in execution efficiency of the action phase on a basis of the transition of each subject’s health condition in the action phase” but paragraphs 0026, 0028, 0153-0154, and 0158 describe the analysis in purely functional language and do not appear to describe how the degradation cause is determined from the gathered information or provide an example as to what the output of this step entails.
Claim 1 recites “a ratio comparison processor configured with threshold detection circuits that judges … whether the behavior task itself including each action phase has a problem or a sequential execution order of the respective action phases has a problem” but Paragraphs 0028, 0153, and 0157-0158 describe the judgment in purely functional language and do not appear to describe how the processor determines if the behavior task itself has a problem or the order of the action phases has a problem and what that problem is. It would seem that the ratio is used to identify when the determination should take place but does not appear to be related to the determination of if the behavior task or order of action phases has a problem.
Claim 1 recites “wherein a judgment result is outputted to analyze the identified problematic action phases and their sequential relationships to determine specific causes of subject fatigue during chronological task execution” but the specification does not appear to describe the analysis used to identify a specific cause of degradation. A degradation cause analysis unit is referenced in the specification in paragraphs 0153-0154 and 0158 but these paragraphs of the specification do not appear to describe how the recited inputs are transformed into the recited outputs. This rejection is further applied to the similar recitations of claim 9.
Claim 1 recites “wherein advice data is created to improve the subject's health condition including break times, break time timing, and number of break times” but the specification does not appear to describe how the processor generates recommendations. Paragraphs 0147-0148 recite that the advice may include break times and other parameters but does not appear to disclose how the processor generates this advice data and what input parameters are considered in such a generation. This rejection is further applied to the similar recitations of claim 9.
Claim 5 recites “a health impact assessment processor that judges, in a heath impact judgement result, whether or not there is a possibility that the action phase identified by efficiency threshold processor configured with comparative analysis circuits may damage the subject's health condition” but Paragraphs 0028, 0153, and 0157-0158 describe this limitation in purely functional language and do not appear to describe how the processor determines if the action phase can damage the subject’s health. The specification does not appear to describe what factors are considered and what the judgment entails.
Claim 6 recites “a advice generation processor configured to creates advice data to improve the subject’s health condition in accordance with the judgment result of the health impact assessment processor and feeds back and reports advice data to the subject” but the specification does not appear to describe how the processor generates recommendations. Paragraphs 0147-0148 recite that the advice may include break times and other parameters but does not appear to disclose how the processor generates this advice data and what input parameters are considered in such a generation.
Claims 17 and 20 each recites that the near infrared detection unit performs some form of processing. While the recited processing is supported by the specification, it would seem to be performed on an associated processor rather than the near infrared detection unit itself since it would seem that the near infrared detection unit is merely a camera. A generic infrared camera does not appear to be capable of carrying out the recited processing and thus the full scope of the claims are not supported.
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 1-6, 9-14, 17, and 19-20 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 1-6, 9-14, 17, and 19-20 are directed to a method of detecting a change in user action using a computational algorithm, which is an abstract idea. Claims 1-6, 9-14, 17, and 19-20 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 machine.
Step 2A — Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] detects at least one or more active states from among active states of a subject's cognitive system, motor system, nervous system and physiological system
[B1] divides the behavior task into units of action phases in chronological order according to a correlation of the active states of the subject on a basis of active state data
[C1] stores the active state data in the units of the action phases which constitute the behavior task
[D1] when the subject habitually executes the behavior task, reads the active state data according to the behavior task executed for a plurality of number of times including at least a last time
[E1] performs cluster analysis of each action phase based on similarity
[F1] forms a set of the action phases across multiple instances of the specified behavior task
[G1] calculates a deviation of the subject's active state in each action phase across the multiple instances of the specified behavior task as an evaluation function while comparing the action phases to a reference action pattern established as a baseline
[H1] estimates a transition of a health condition of the subject with respect to each action phase across the multiple instances of the specified behavior task on a basis of the evaluation function
[I1] applies the transition of the subject’s health condition to identify an action phase of the action phases regarding which execution efficiency of the behavior task by the subject based on the transition of the subject’s health condition is equal to or lower than a specified level
[J1] when the action phase identified, for at least two of the respective subjects exists in common with the at least the two of the respective subjects, analyzes a cause of degradation in execution efficiency of the action phase on a basis of the transition of each subject's health condition in the action phase
[K1] calculates a ratio representing a number of subjects having a particular action phase identified as worrying divided by a total number of subjects performing that action phase
[L1] judges, when the ratio calculated with respect to each action phase is equal to or more than a specified ratio, whether the behavior task itself including each action phase has a problem or a sequential execution order of the respective action phases has a problem
[M1] wherein a judgment result is outputted to analyze identified problematic action phases and their sequential relationships to determine specific causes of subject fatigue during chronological task execution
[N1] wherein advice data is created to improve the subject’s health condition including break times, break time timing, and number of breaks
[O1] generates pulse rate data by applying a digital filter to remove motion artifacts from reflected light intensity changes
[P1] calculating a relative temperature according to expression Tr = Tn - Tfh, where Tn represents average skin temperature at nose region pixels and Tfh represents average skin temperature at forehead region pixels, to measure autonomic nervous system activity
These elements [A1]-[P1] of claim 1 are drawn to an abstract idea since (1) they involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) 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; and (3) they involve methods of organizing human activity such as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
In particular, the steps [A1]-[P1] appear to recite nothing more than a computer implementation of an observer who observes other humans, recognizes their daily duties, divides their duty into various tasks, and recognizes when the person performs one or more of their tasks unusually. They may then decide that the deviation from typical patterns is indicative of “a transition of a health condition” which may be the patient is tired. The observer may then notice that the same task causes many of their workers to become tired and identify either the specific task, or the order in which tasks are performed, to be the cause of increasing fatigue. The observer may further provide recommendations based on their observations.
Step 2A — Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] various types of processors for carrying out their respectively recited functions
[B2] a data storage unit
[C2] an active state detection unit
[D2] a near infrared detection unit that irradiates a region of interest of the subject's face with near infrared light having a wavelength between 650 nm and 1350 nm
[E2] a far infrared detection unit that detects skin temperature differences between forehead and nose regions
[F2] an inertial measurement unit
[G2] a flicker test unit
[H2] an interface
[I2] comparative analysis circuits
[J2] threshold detection circuits
These elements [A2]-[H2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the elements [C2]-[G2] 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 [A2]-[B2] and [H2]-[J2] 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).
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation “an active state detection unit that detects at least one or more active states from among active states of a subject's cognitive system, motor system, nervous system and physiological system, wherein the active state detection unit comprises: a near infrared detection unit that irradiates a region of interest of the subject's face with near infrared light and generates pulse rate data from reflected light intensity changes; a far infrared detection unit that detects skin temperature differences between forehead and nose regions to measure autonomic nervous system activity; an inertial measurement unit sensor that captures triaxial acceleration and angular velocity data for motion recognition; and a flicker test unit that measures cognitive recognition thresholds using adjustable frequency light sources” 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 near and far infrared detection units are nothing more than an infrared camera and light source for detecting temperature and infrared reflection of the skin. Such sensors and the recited ranges of operations are conventional as evidenced by:
U.S. Patent Application Publication No. 2 US 2019/0221060 A1 (Liu) discloses that infrared cameras capable of imaging a face are conventional (paragraph 0020 of Liu);
U.S. Patent Application Publication No. US 2011/0188712 A1 (Yoo) discloses that conventional face recognition systems utilize infrared cameras and vein maps of the face (paragraph 0005 of Yoo);
U.S. Patent Application Publication No. US 2004/0155781 A1 (DeOme) discloses that infrared cameras are conventional (paragraph 0028 of DeOme); and
U.S. Patent Application Publication No. US 2017/0017941 A1 (Takahashi) discloses that infrared cameras are well-known (paragraphs 0046 of Takahashi).
U.S. Patent Application Publication Nos. US 2014/0375785 A1 (Kogut) teaches an infrared light between 700nm and 1mm in wavelength applied to a face to determine heart rate (Paragraphs 0009 and 0023-0024)
U.S. Patent Application Publication Nos. US 2019/0350471 A1 (Marks) teaches an infrared light and camera system using a 940nm wavelength applied to a face to determine heart rate (Paragraphs 0022-0024 and 0081)
U.S. Patent Application Publication Nos. US 2018/0333102 A1 (Haan) teaches an infrared light illumination system using 300-1000nm wavelengths and camera system to determine heart rate from a face and the use of filtering in the desired bpm frequency range to remove noise (Paragraphs 0032-0033 and 0039-0040)
U.S. Patent Application Publication Nos. US 2017/0143272 A1 (Brouse) teaches an infrared light illumination system and camera for determining heart rate and using a bandpass frequency envelope to remove noise (Paragraphs 0043, 0060, and 0070)
U.S. Patent Application Publication Nos. US 2013/0077823 A1 (Mestha) teaches an infrared light illumination system using 680-1000nm light and camera for determining heart rate and using a bandpass filter to remove motion and breathing noise (Paragraphs 0023 and 0030)
U.S. Patent Application Publication Nos. US 2016/0332549 A1 (Marquette) teaches an infrared camera system which detects a temperature difference between a nose and forehead and/or cheeks (Paragraph 0062)
U.S. Patent Application Publication Nos. US 2017/0095157 A1 (Tzvieli) teaches the use of infrared sensors to detect temperature differences between forehead and nose and that these differences can be used to determine stress, emotional state, pain, and other physiological responses (Paragraphs 0003, 0025, and 0079)
Additionally, a flicker test unit is nothing more than a controllable light source which is well-known, routine, and conventional as evidenced by:
U.S. Patent Application Publication Nos. US 2015/0257659 A1 (Broers) teaches that LEDs, Laser diodes, conventional light bulbs, and neon lights are controllable (Paragraph 0018).
U.S. Patent Application Publication Nos. US 2017/0127992 A1 (Eizo) teaches that LEDs, may be used to carry out a flicker frequency test and that flicker frequency tests may be used as fatigue indicators (Paragraphs 0068-0071).
Additionally, an inertial measurement unit is nothing more than a tri-axial accelerometer which is well-known, routine, and conventional as evidenced by:
U.S. Patent Application Publication Nos. US 2011/0054272 A1 (Derchak) teaches that there are various conventional means for monitoring orientation and movement including accelerometers and gyroscopes (Paragraph 0127).
U.S. Patent Application Publication Nos. US 2007/0272599 A1 (Miyashita) teaches that accelerometers and well-known (Paragraph 0165).
U.S. Patent Application Publication Nos. US 2016/0161281 A1 (Schuijers) teaches that three dimensional accelerometers and conventional (Paragraph 0031).
As evidenced above, each of the recited sensors are well-known, routine, and/or conventional in the art.
Further, the elements [A2]-[B2] and [H2]-[J2] do not qualify as significantly more because these limitations are 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’I, 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 2-6 17, and 19-20 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 2: motion sensors; an environment data acquisition unit;
Claim 3: a movement recognition unit; and
Claim 4: a recognition degree detection unit.
Claim 5: a health impact assessment processor
Claim 6: a advice generation processor
Claim 19: a flicker test unit
Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 2 and 3 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, an environment data acquisition unit and a movement recognition unit are nothing more than a temperature sensor detecting environmental or ambient temperature and an accelerometer. Such sensors are conventional as evidenced by Derchak, Miyashita, and Schuijers above and further evidenced by:
U.S. Patent Application Publication No. US 2003/0152133 A1 (Ellenz) discloses that conventional temperature sensors may detect environmental temperature (paragraph 0018 of Ellenz);
U.S. Patent Application Publication No. US 2016/0287087 A1 (Abreu) discloses ambient temperature may be sensed using a conventional temperature sensor (paragraph 0333 of Abreu);
U.S. Patent Application Publication No. US 2018/0190385 A1 (Huynh) discloses a variety of conventional sensors including temperature and movement sensors (paragraphs 0215 of Huynh).
Additionally the flicker test unit of claim 19 is well-known in the art as evidenced by Broers and Eizo above.
Also, the limitations from claims 4-6 are 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 and processing) 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.
Claims 9-14 recite only limitations addressed in the above rejections of claim 1-6, 17, and 19-20 and are thus rejected on the same basis of claims 1-6, 17, and 19-20.
Claim 18 is not rejected under 35 USC 101 because the limitation of “a sensor module comprising an IMU sensor, triaxial acceleration sensor, and monoaxial angular velocity sensor arranged perpendicularly to each other, wherein the sensor module switches between respective sensors according to measurement range to maintain linear output value relationships for motion recognition during behavior task execution” is not considered to be a well-known, routine, and conventional arrangement of sensors.
Prior Art
The closest prior art of record is described below.
US Patent Application Publication Number US 2019/0038133 A1 hereinafter Tran teaches a monitoring system a user activity sensor to determine patterns of activity based upon the user activity occurring over time (Abstract). Tran teaches a monitoring system including various mesh network sensors provided with a number of activity detectors which monitor various activities of daily living (Paragraph 0060). Tran teaches that a variety of different sensor types may be utilized by the system including various physiological sensors, positioning and motion detection systems, and environmental monitoring systems (Paragraphs 0005 and 0033-0059). Tran further teaches that the daily activity pattern of the user, or a behavior task, is monitored and divided into various daily activities. When the user varies from a pattern or when a dangerous condition occurs, the system detects the event and may notify appropriate personnel. The system may further monitor such activity patterns for a plurality of patients and provide the monitoring information a case monitor which may be a human or machine learning network which can determine what action is most appropriate for a given event. Abnormal user events are interpreted on both an individual and aggregate level using trend analysis software to detect larger than statistically normal deviations in event patterns. The analysis software may further detect certain event classes and compare them with baseline task performance to detect changes trends in regards to certain events such as an increasing frequency of the patient leaving the stove on or failing to comply with medication instructions which may be indicative of a decline in health (Paragraphs 0006 and 0064). The trend analysis may include clustering operations to detect patterns in the data and or the use of different neural networks (Paragraph 0101). Various events outside of pattern deviations may also be detected such as falls. These events as well as other types of pattern deviations may generate alarms to alert caretakers that the patient is in need an assistance (Paragraph 0109).
US Patent Application Publication Number US 2011/0066383 A1 hereinafter Jangle teaches methods, systems and apparatuses for identifying an activity of an animate or inanimate object. The method includes identifying each elemental motion of a sequence of elemental motions of a device attached to the animate or inanimate object. The activity of the animate or inanimate object can be identified by matching the sequence of identified elemental motions of the device with a library of stored sequences of elemental motions, wherein each stored sequence of elemental motions corresponds with an activity (Abstract). Jangle teaches a system and method for tracking the motions of an object such as a user using motion sensors like accelerometers (Paragraphs 0029-0030). The system compares the received motion signals to a library of signatures where each stored signature corresponds to a type of motion. The received signals are then matched to a particular motion within the library (Paragraph 0031). A controller then identifies what activity a user of performing based on a sequence of identified motions. The identification may be a comparison of a particular motion sequence to a library of motion sequences with corresponding activity designations. A sequence of identified activities can then be matched to a particular behavior (Paragraphs 0035 and 0038). The identified behaviors can be tracked over time to create patterns of the tracked object or user and the system may generate an alert when the tracked pattern changes (Paragraph 0042). Each level of identification may be more intelligently identified by using other sources of data such as time, location, and/or age of the user. The additional information may be used to generate daily patterns of the user (Paragraphs 0043-0044; Fig. 3). The system may further identify critical conditions by detecting early signs of the particular conditions and alerting experts (Paragraph 0045).
US Patent Application Publication Number US 2017/0103178 A1 hereinafter Heinrich teaches a device for detecting a health condition of a subject, comprising a data interface for receiving sensor data of said subject and one or more disease classification parameters for characterizing one or more diseases, a user interface for receiving a user input related to a disease activity, an analysis unit for extracting one or more physiological and/or behavioral features from said received sensor data, an optimizer unit for optimizing said one or more disease classification parameters based on a correlation analysis between said one or more physiological and/or behavioral features and said user input, and a detection unit for detecting a health condition of said subject by applying said one or more disease classification parameters to said one or more physiological and/or behavioral features (Abstract). Heinrich further teaches a system with sensor for detecting physiological and/or behavioral conditions and extracting features from said sensor data. The features may include behavioral features such as tossing and turning, scratching, changes in body potion, or other body motions. The physiological parameters including body temperature, blood pressure, aspiratory features, sounds, or other physiological parameters (Paragraphs 0031-0033). The detected physiological and/or behavioral features are compared to temporal pattern baselines of diseases which are characteristic of a given severity. The tracking of patient parameters in comparison with these baselines can be used to identify remission or flare-ups of one or more diseases in the subject and may help identify early onset of flare-ups (Paragraph 0038)
None of Tran, Jangle, and/or Heinrich either alone or in combination teach or reasonably suggest the system comprising “a degradation cause analysis unit that, when the action phase identified, for at least two of the respective subjects, by the worrying action phase identifying unit exists in common with the at least the two of the respective subjects, analyzes a cause of degradation in the execution efficiency of the action phase on a basis of the transition of each subject's health condition in the action phase; a common ratio calculation unit that calculates a ratio of the action phase, identified by the worrying action phase identifying unit existing in common with the respective subjects, with respect to each of the action phases; and a problem part judgment unit that judges, when the ratio calculated by the common ratio calculation unit with respect to each action phase is equal to or more than a specified ratio, whether the behavior task itself including each action phase has a problem or a sequential execution order of the respective action phases has a problem, wherein the degradation cause analysis unit analyzes a cause of degradation in the execution efficiency of each of the action phases including a judgment result of the problem part judgment unit, and wherein the judgment result is outputted to a processer to evaluate fatigue cause on a chronological action phase basis” in combination with the other claimed elements. In particular, while Tran teaches that pattern tracking and deviation detection method being applied to a plurality of patients and detecting trends in aggregate level data from the plurality of patients (Paragraph 0064), Tran does not teach or suggest determining a common cause of degradation across the plurality of patients and determining if a particular activity, or a chronological order of activity, is the cause of the degradation.
Response to Arguments
Applicant's arguments filed 010/14/2025 have been fully considered but they are not persuasive.
In particular, Applicant’s amendments are sufficient to overcome some of the previously presented rejections issued under 35 USC 112 but do not address the 112(a) rejections directed towards the execution efficiency and judgment result or a number of the clarity rejections. Applicant’s amendments have necessitated new grounds of rejection.
In particular, the amendment stating that execution efficiency is “based on” the subject’s health transition does not define the term “execution efficiency” or particularly describe how it is calculated. While the specification may repeat the claim language verbatim, this is not considered sufficient to describe how the execution efficiency is calculated based on the health transition and what exactly execution efficiency entails.
Similarly, Applicant’s amendments to the limitations of the multi-input analysis processor and generation of advice data, may reflect the language of the specification but the thrust of the rejection remains as the specification does not appear to explicitly describe how an analysis of degradation in execution is carried out and what factors are considered to generate advice data. The recitation of what the advice data is, while helpful for clarity, does not satisfy the written description requirement since the specification does not describe how it comes up with the particular advice data to display to the subject.
Applicant’s amendments to claims 1 and 5-6 are sufficient to eliminate the 35 USC 112(f) interpretation of these claims but claims 9 and 13-14 are still interpreted under 35 USC 112(f) as they recite “a step of” and the newly recited processor is insufficient to carry out the recited functions, rather the specific algorithm used by the processor is what the steps are interpreted as.
In particular, Applicant’s amendments and arguments directed towards the rejections previously issued under 35 USC 101 are insufficient to overcome the previously presented grounds of rejection.
Applicant argues that the amendments integrate specific hardware elements and automated control actions across multiple subjects and outputs results to enable evaluation of fatigue causes over time which represents a technological improvement that allows for automation of fatigue analysis tasks that previously could not be automated.
This argument is not found to be persuasive because the claims do not require specific hardware components, the claims require only a generic computer capable of executing an algorithm and generic sensors for collecting data. The analysis of the collected data, which is the abstract idea itself, occurs on the computer which then outputs some form of recommendation which is not particularly limited by the claims. The analysis taking place across multiple subjects is merely a duplication of the algorithm alongside comparisons of similar users who perform similar behavior tasks. The determination of fatigue cause is not reasonably limited by the present claims and thus applicant’s arguments are not found to be persuasive because the “cause” of the fatigue may simply be the determination that the users have been performing the behavior task for a given time.
In particular, the newly added detailed hardware components are well-known in the art as described in the above presented 35 USC 101 rejection. The particular wavelengths utilized and temperature differentials measured are routine in the art. As such, these components are not considered to qualify and particular hardware components and thus do not serve to integrate the abstract idea into a practical application.
Applicant’s arguments that the amended language including Bayesian clustering and Dirichlet process mixture distribution combined with the health transition and comparative analysis circuits represents concrete technological improvements over conventional monitoring approaches.
This argument is not found to be persuasive because the Bayesian clustering and Dirichlet process mixture distribution are simply incorporating well-known mathematical analysis into the abstract idea. Mathematical principles themselves are considered abstract ideas and thus the incorporation of these processes merely serves to integrate an abstract idea with another abstract idea and thus does not amount to significantly more. Additionally, the circuit components are not meaningfully limited by the claim language and thus are not considered to amount to significantly more than the abstract idea.
New claims 17 and 19-20 serve to further limit the abstract idea or incorporate well-known, routine, and conventional sensors for mere data gathering and thus do not amount to significantly more than the abstract idea.
Claim 18 is considered to amount to significantly more than the abstract idea because while each sensor individually is well known, they are recited in a particular combination and arrangement for carrying out a particular function. The particular combination and arrangement of the sensors together is considered to amount to significantly more than the abstract idea itself.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW ERIC OGLES whose telephone number is (571)272-7313. The examiner can normally be reached M-F 8:00AM - 5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Sims can be reached on Monday-Friday from 9:00AM – 4:00PM at (571) 272 – 7540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW ERIC OGLES/ Examiner, Art Unit 3791
/JASON M SIMS/ Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights