DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see page 11-12, filed 02/06/2026, with respect to the rejection of the claims under 35 USC 101 have been fully considered and are persuasive in light of the amendments to the claim. The 101 rejection of 11/12/2025 has been withdrawn.
Applicant’s arguments, see page 11, filed 02/06/2026, with respect to the rejection of claim 8 under 35 USC 112(b) have been fully considered and are persuasive in light of the amendments to the claim. The 112(b) rejection of 11/12/2025 has been withdrawn.
Applicant’s amendments to the claims overcome the objections of 11/12/2025, which have thus been withdrawn.
Applicant’s arguments, see pages 13, filed 02/06/2026, with respect to the rejection(s) of claims 1 and 8-10 under 35 USC 103 have been fully considered and are persuasive in light of the newly amended recitation of the use of sheet sensors. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Mizoi et al.
Applicant's remaining arguments filed 06/03/2025 with respect to the rejections under 35 USC 103 have been fully considered but they are not persuasive. The applicant argues that the buttocks sensor of Obana et al. merely teaches detection of changes in the position of the response area, but not changes in the size of the response area. However, as discussed with respect to Fig. 1 below, Obana et al. teaches monitoring of the shape of a seat pressure area over time. This shape of a seat pressure, which changes in Fig. 1 as the occupant shifts from a normal to abnormal pressure distribution, thus corresponds to the size of the response area and a change thereof.
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: "gaze data acquiring unit for acquiring gaze data" in claims 1, 9, and 10; "driving-characteristic data acquiring unit for acquiring driving-characteristic data" in claims 1, 9, and 10; "disease-condition assessment unit for being capable of assessing an epileptic condition" in claims 1, 9, 10, 11, 13, and 14; "seat pressure distribution acquiring unit for acquiring data of a seat pressure distribution" in claims 1, 9, 10, 11, 13, and 14; "pressure distribution calculation unit for calculating a seat pressure area" in claims 1, 9, 10, 11, 13, and 14; and "communication unit configured to transmit a control signal" in claims 1, 8-11, 13, and 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 described in the specification as performing the claimed function, and equivalents thereof. Namely, in keeping with paragraphs [0107] and [0275], the aforementioned units will be interpreted as referring to a computer functioning to perform these tasks.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1 and 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Banno et al. (US 10503987, previously cited) in view of Sugano et al. (US 11427205, previously cited), in view of Kruscha et al. (DE 102009041187, previously cited), in view of Coles et al. (US 20210378568, previously cited), in view of Obana et al. (JP2012254745, previously cited), in view of Mizoi et al. (US 20210237620, previously cited).
Claim 1.
Banno et al. teaches:
a seat pressure distribution acquiring unit for acquiring data of a seat pressure distribution on a seating surface on which the subject is seated in the vehicle
(Banno – Col. 4, lines 40-43) “The seat surface sensor 23 is a sensor that detects the pressure distribution of a seat portion 11a of the seat 11 of the driver seat.”
a pressure distribution calculation unit for calculating a seat pressure area as an
(Banno – Col. 4, lines 40-43) “The seat surface sensor 23 is a sensor that detects the pressure distribution of a seat portion 11a of the seat 11 of the driver seat.”
(Banno – Col. 18, lines 26-30) “by using the condition that the high-pressure portion in the pressure distribution of the seat portion 11a concentrates on the edge of the seat portion 11a, the driving incapability state of a driver can be detected with high accuracy.”
wherein the disease-condition assessment unit assesses the epileptic condition of the subject depending on a change in the seat pressure area
(Banno – Col. 1, lines 59-61) “In the apparatus described in Patent Literature 1, posture collapse, caused by factors other than driver’s seizure or the like, are not taken into account.”
[Examiner Note: This section from Banno et al. Column 1 indicates that the improvement to the prior art represented by Banno et al. is in allowing the apparatus to filter out false positives so it can determine the occurrence of a seizure or similar medical emergency.]
(Banno – Col. 18, lines 26-30) “by using the condition that the high-pressure portion in the pressure distribution of the seat portion 11a concentrates on the edge of the seat portion 11a, the driving incapability state of a driver can be detected with high accuracy.”
a communication unit configured to transmit a control signal to an in-vehicle terminal device that
(Banno – Col. 4, lines 10-13) “When the detection apparatus 100 asks a driver that the driver is unable to drive and does not receive a response from the driver, the detection apparatus 100 transmits to a vehicle controller 90 a command to safely stop the vehicle.”
While Banno et al. teaches a white eye state detection portion; it does not explicitly teach acquiring gaze data. However, Sugano et al. teaches:
a gaze data acquiring unit for acquiring gaze data indicating a subject’s gazing point measured when the subject is driving a vehicle
(Sugano – Col. 3, lines 49-52) “the abnormality detection part is operable to detect a line-of-sight direction of the driver, and determine whether or not the line-of-sight direction of the driver is coincident with the traveling direction of the vehicle”
a driving-characteristic data acquiring unit for acquiring driving-characteristic data indicating driving characteristics of the subject for the vehicle
(Sugano – Col. 7, lines 36-41) “The abnormality detection part 51 is operable to detect the physical abnormality of the driver, based on signals received by the ECU 5 from the vehicle interior camera 32, the accelerator pedal sensor 34, the brake pedal sensor 35 and the steering sensor 36.”
a disease-condition assessment unit for being capable of assessing an epileptic condition of the subject depending on the relationship between the gaze data and the driving-characteristic data and depending on a comparison of duration time while the gazing point indicated by the gaze data is continuously out of a center part of a visual field in a traveling direction of the vehicle
(Sugano – Col. 7, lines 36-41) “The abnormality detection part 51 is operable to detect the physical abnormality of the driver, based on signals received by the ECU 5 from the vehicle interior camera 32, the accelerator pedal sensor 34, the brake pedal sensor 35 and the steering sensor 36.”
(Sugano – Col. 10, lines 43-46) “The ECU 5 is capable of estimating that one of paralysis, epilepsy, influenza, myocardial infarction and subarachnoid hemorrhage develops.”
(Sugano – Col. 3, lines 49-52) “the abnormality detection part is operable to detect a line-of-sight direction of the driver, and determine whether or not the line-of-sight direction of the driver is coincident with the traveling direction of the vehicle”
wherein the gaze data and the driving-characteristic data are acquired from a device selected from the group consisting of
(Sugano – Col. 7, lines 42-46) “the abnormality detection part 51 is operable to subject the image data acquired by the vehicle interior camera to given processing to identify the upper body, head region, face, eyes, etc., of the driver and acquires information regarding the identified regions”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the driving incapability detection apparatus of Banno et al. with the epilepsy determination system of Sugano et al. As Banno et al. already tracks eye data for use with the white eye state detection portion, a person of ordinary skill in the art would have recognized that this could be accomplished with predictable results. One would have been motivated to combine these teachings in this manner because the use of an additional method of determining an epileptic condition allows for reduction in false positive results, and additionally allows monitoring to continue even if one apparatus fails.
Sugano et al. does not explicitly teach comparing a duration time with a threshold value; however, Kruscha et al. teaches:
a disease-condition assessment unit for being capable of assessing an epileptic condition of the subject depending on the relationship between the gaze data and the driving-characteristic data and depending on a comparison of duration time while the gazing point indicated by the gaze data is continuously out of a center part of a visual field in a traveling direction of the vehicle with a first threshold value
(Kruscha – [0017]) “a unit for detecting the driver’s line of sight, a unit for determining the period of time during which the driver averts his gaze from the road, a comparison unit for comparing the determined period of time with a predetermined critical time value and a signal generation unit for generating a control signal when the determined period exceeds the critical time”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the epilepsy determination system of Sugano et al. with the critical time value of Kruscha. One would have been motivated to do this to reduce the chance of a false positive reading from a briefly distracted driver.
Neither Sugano et al. nor Kruscha explicitly teaches a mobile terminal device carried by the subject; however, Coles et al. teaches:
wherein the gaze data and the driving-characteristic data are acquired from a device selected from the group consisting of a mobile terminal device carried by the subject that collects physiological data of the subject, an in-vehicle terminal device that collects data on the operation and movement of the vehicle, and a combination thereof
(Coles – [0019-0020, 0024]) “the blepharometric data management system receives blepharometric data from one or more of the following sources: wearable configurations including infrared reflectance oculography components; … vehicle passenger configurations in which an image capture device is positioned to capture blepharometric data for a passenger of the vehicle”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the epilepsy determination system of Sugano et al. with the blepharometric data management system of Coles et al. One would have been motivated to do this in order to avoid the expense of installing cameras in a vehicle by instead using wearable devices to collect blepharometric data.
While Banno et al. teaches determining a state of incapacity based on a concentration of a high-pressure portion of the seat pressure distribution, Banno et al. does not explicitly teach calculating an overall size of the seat pressure distribution. However, Obana et al. teaches, with respect to Fig. 1 below:
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Figure 1: Examples of normal (A) and abnormal (B) pressure distributions, according to Obana et al. (originally Obana Fig. 10)
a seat pressure distribution acquiring unit for acquiring data of a seat pressure distribution on a seating surface on which the subject is seated in the vehicle, the data of the seat pressure distribution being measured by a sensor
(Obana – [0004]) “these load sensors detect the weight and center of gravity position of the driver seated in the driver’s seat.”
(Obana – [0029]) “the shape M(t) of the seat load is calculated, and the average shape Ma(t) of the load within a certain time period is calculated.”
wherein the disease-condition assessment unit assesses the
(Obana – [0029]) “the shape M(t) of the seat load is calculated, and the average shape Ma(t) of the load within a certain time period is calculated. Then it is determined whether M(t) is the same as Ma(t) within the range of
±
α
. If the answer is yes, the posture is determined to be normal, and if the answer is no, an abnormality determination switch is activated”
[Examiner’s Note: As shown in Fig. 1, Obana et al. shows a seat pressure area which varies in both shape and center point. These variations correspond to a change in the seat pressure area and a change in the center position, respectively.]
an in-vehicle terminal device that controls, in response to the control signal, a drive mechanism of the vehicle
(Obana – [0042]) “The determination results by the determination processing ECU are transmitted to the driving assistance HMI and various driving assistance systems, and cause them to operate.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the driving incapability detection apparatus of Banno et al. with the specific method of assessing a condition based on a seat pressure distribution of Obana et al. Banno et al. teaches an improvement to the operation of Obana et al. (Banno – Col. 1, lines 59-63); therefore a person of ordinary skill in the art would have recognized that the two apparatuses could be combined in such a fashion with predictable results.
Banno et al. does not explicitly teach the use of a sheet sensor; however, Mizoi et al. teaches:
the data of the seat pressure distribution being measured by a sensor consisting of a sheet sensor
(Mizoi – [0414]) “the body pressure distribution sensor is a thin sheet shape, and is provided on the rear side of the outer layer of the seat cushion 2”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings by using a sheet sensor to determine a pressure distribution. Such a use is already well known and understood, and the results of using a sheet sensor for such a purpose would have been easily predictable.
Claim 8.
Rejected by the same rationale as claim 1.
Claim 9.
Banno et al. teaches:
non-transitory computer-readable storage medium recording a program for a disease-condition assessment device, for causing a computer to function as:
(Banno – Col. 5, lines 26-30) “In the controller 50, the CPU executes various programs stored in the Rom, thereby achieving the functions of an image analysis portion 60, a learning portion 51, and a state detection portion 70, and thus detecting the driving incapability state of a driver.”
The rest is rejected by the same rationale as claim 1.
Claim 10.
Sugano et al. teaches:
disease-condition assessment system including a terminal device that collects data related to a subject driving a vehicle
(Sugano – Col. 7, lines 36-41) “The abnormality detection part 51 is operable to detect the physical abnormality of the driver, based on signals received by the ECU 5 from the vehicle interior camera 32, the accelerator pedal sensor 34, the brake pedal sensor 35 and the steering sensor 36.”
The rest is rejected by the same rationale as claim 1.
Claim(s) 2-4, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Banno et al., Sugano et al., Kruscha et al., Coles et al., Obana et al., and Mizoi et al. as applied to claim 1 above, and further in view of Kim et al. (US 20200317208, previously cited).
Claim 2.
The combination of Banno et al., Sugano et al., Kruscha et al., Coles et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 1, as discussed above. Sugano et al. does not explicitly teach the value of the relationship; however, Kim et al. teaches:
the disease-condition assessment unit assesses that the disease condition is epilepsy when a correlation coefficient between the gaze data and the driving-characteristic data is below a predetermined value
(Kim – Abstract) “the user drives while drowsy when the slow eye movement of the user occurs and when there is no change in the steering torque for a predetermined period of time.”
[Examiner Note: If eye movement occurs and steering torque does not change, then there is a reduced correlation value between the two variables.]
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the emergency stop system of Sugano et al. with the drowsy driver detection device of Kim et al. One would have been motivated to do this because it allows for the detection of drowsiness with a high degree of accuracy while the danger level is still relatively low (Kim – [0006]).
Claim 3.
The combination of Banno et al., Sugano et al., Kruscha et al., Coles et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 1, as discussed above. Sugano et al. does not explicitly teach that the relationship is a degree of dissociation; however, Kim et al. teaches:
the relationship is a degree of dissociation between the gaze data and the driving-characteristic data
(Kim – Abstract) “the user drives while drowsy when the slow eye movement of the user occurs and when there is no change in the steering torque for a predetermined period of time.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 2.
Claim 4.
The combination of Banno et al., Sugano et al., Kruscha et al., Coles et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 3, as discussed above. Kim et al. further teaches:
the disease-condition assessment unit assesses that the disease condition is epilepsy, when a time for which the degree of dissociation is greater than or equal to a predetermined value is greater than or equal to a predetermined time
(Kim – Abstract) “the user drives while drowsy when the slow eye movement of the user occurs and when there is no change in the steering torque for a predetermined period of time.”
[Examiner Note: The teaching of a disease condition being epilepsy can be found in Sugano et al., as shown above in discussion of claim 1.]
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 2.
Claim 19.
The combination of Banno et al., Sugano et al., Kruscha et al., Coles et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 2, as discussed above. Kim et al. further teaches:
wherein the gaze data indicates a value of a gazing point in a horizontal direction relative to a traveling direction of the vehicle and the driving characteristic data is characteristic data selected from a group consisting of steering wheel angle, steering wheel torque, and vehicle lateral acceleration
(Kim – [0029]) “The determining whether the slow eye movement of the user occurs and whether there is no change in the steering torque may include calculating an eye movement distance during the predetermined period of time using a value obtained by adding the eye movement yaw angle with respect to the external environment and determining that the slow eye movement occurs, when the eye movement distance is less than a predetermined reference value”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 2.
Claim 20.
Rejected by the same rationale as claim 19.
Claim(s) 11-14 and 21-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Banno et al. and Obana et al. as applied to claim 1 above, and further in view of Mizoi et al.
Claim 11.
Claim 1 above recites a disease-condition assessment device consisting of units for making a determination based on gaze data and units for making a determination based on seat pressure data. The combination of Banno et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 11, which only recites the units for making a determination based on seat pressure (as discussed in further detail in the rejection of claim 1).
Claim 12.
Rejected by the same rationale as claim 11.
Claim 13.
Banno et al. teaches:
non-transitory computer-readable storage medium recording a program for a disease-condition assessment device, for causing a computer to function as:
(Banno – Col. 5, lines 26-30) “In the controller 50, the CPU executes various programs stored in the Rom, thereby achieving the functions of an image analysis portion 60, a learning portion 51, and a state detection portion 70, and thus detecting the driving incapability state of a driver.”
The rest is rejected by the same rationale as claim 11.
Claim 14.
Banno et al. teaches:
disease-condition assessment system including a terminal device that collects data related to a subject driving a vehicle
(Banno – Col. 4, lines 44-46) “The vehicle information recognition device includes a vehicle speed sensor 31, a steering angle sensor 32, an acceleration sensor 33, and a brake sensor 34.”
The rest is rejected by the same rationale as claim 11.
Claim 21.
The combination of Banno et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 11, as discussed above. Banno et al. further teaches:
wherein the seat pressure area is calculated as a rate of the area where pressure is applied in the entire seating surface above a predetermined value
(Banno – Col. 18, lines 26-30) “by using the condition that the high-pressure portion in the pressure distribution of the seat portion 11a concentrates on the edge of the seat portion 11a, the driving incapability state of a driver can be detected with high accuracy.”
[Examiner Note: The comparison of “high-pressure” to another pressure value necessarily entails some benchmark by which the pressure is considered to be “high”.]
Claim 22.
The combination of Banno et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 11, as discussed above. Banno et al. further teaches:
a database storing data required to assess the epileptic condition including a threshold value for the seat pressure area, and wherein the disease-condition assessment unit assesses the epileptic condition depending on whether the seat pressure area is below the threshold value
(Banno – Col. 1, lines 59-61) “In the apparatus described in Patent Literature 1, posture collapse, caused by factors other than driver’s seizure or the like, are not taken into account.”
[Examiner Note: This section from Banno et al. Column 1 indicates that the improvement to the prior art represented by Banno et al. is in allowing the apparatus to filter out false positives so it can determine the occurrence of a seizure or similar medical emergency.]
(Banno – Col. 11, lines 27-28) “Each threshold and each determination value used by each state detection portion are stored in the storage device 52”
(Banno – Col. 18, lines 26-30) “by using the condition that the high-pressure portion in the pressure distribution of the seat portion 11a concentrates on the edge of the seat portion 11a, the driving incapability state of a driver can be detected with high accuracy.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
Claim 23.
The combination of Banno et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 22, as discussed above. Obana et al. further teaches:
wherein the threshold value is based on average values of the seat pressure area during a non-seizure period or a seizure period
(Obana – [0029]) “the shape M(t) of the seat load is calculated, and the average shape Ma(t) of the load within a certain time period is calculated. Then it is determined whether M(t) is the same as Ma(t) within the range of ±α.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
Claim 24.
The combination of Banno et al., Obana et al., and Mizoi et al. teaches all the limitations of claim 22, as discussed above. Obana et al. further teaches:
wherein the data required to assess the epileptic condition further includes road classifications and the disease-condition assessment unit classifies the data of the seat of pressure distribution depending on a vehicle position at a time of measurement
(Obana – [0037]) “In order to prevent the buttocks sensor from erroneously determining that the bias in pressure between the left and right areas of the seat caused by the vehicle turning left or right or cornering is an abnormality in the driver’s posture, the seat sensor may be configured to interrupt the determination of the driver’s posture when the vehicle is turning left or right or cornering, based on car navigation information or information combining this with steering information.”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings for the reasons given in discussion of claim 1.
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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/S.A.M./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669