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 .
Claim Objections
Claim(s) 1-8 is/are objected to because of the following informalities:
The limitations of lines 5-14 of claim 1 should be tabbed over to the right once to indicate the instructions executed by the processor.
The limitations of lines 5-8 of claim 2 should be tabbed over to the right once to indicate the instructions executed by the processor.
The limitations of lines 5-8 of claim 3 should be tabbed over to the right once to indicate the instructions executed by the processor.
The limitations of lines 3-4 of claim 4 should be tabbed over to the right once to indicate the instructions executed by the processor.
The limitation of line 3 of claim 5 should be tabbed over to the right once to indicate the instructions executed by the processor.
Claim 6 should read “a measurement device including: the feature-amount generation device according to claim 1; and a data acquisition device that: is disposed” [lines 3-4].
Claim 6 should read “wherein the measurement device transmits feature-amount data including a feature amount of a gait phase cluster extracted by the feature-amount generation device” [lines 8-9].
Claim 7 should read “the memory of the data processing device” [line 4].
Claim 7 should read “the input feature amount” [line 8].
Claim 7 should read “estimate a physical feature” [line 9].
The limitations of lines 3-5 of claim 7 should be tabbed over to the right once to indicate what is included in the data processing device. The limitations of lines 6-10 of claim 7 should be tabbed over to the right twice to indicate the instructions executed by the processor of the data processing device.
Claim 8 should read “the input feature amount” [line 6].
Claim 8 should read “the estimation model that outputs a degree of hallux valgus” [line 8].
The limitations of lines 4-8 of claim 8 should be tabbed over to the right once to indicate the instructions executed by the processor of the data processing device.
Appropriate correction is required.
Claim Interpretation
Examiner Notes: currently, NO limitation invokes interpretation under § 112(f).
Intended Use: Claim 11 recites the limitation “the information is used for decision making to address the harmonic index” [line 3], which is considered to be an intended use of the claimed system, as the information being used for a certain purpose does not structurally or functionally limit the instant invention [See also Rowe v. Dror, 112 F.3d 473, 478, 42 USPQ2d 1550, 1553 (Fed. Cir. 1997) ("where a patentee defines a structurally complete invention in the claim body and uses the preamble only to state a purpose or intended use for the invention, the preamble is not a claim limitation"); To satisfy an intended use limitation which is limiting, a prior art structure which is capable of performing the intended use as recited in the preamble meets the claim (MPEP § 2111.02(II)); the Examiner notes that the cited portions of the MPEP are directed towards intended use as recited in the preamble, but are still considered to be applicable regarding the instant limitation]. For examination purposes, the Examiner has interpreted any prior art under § 102 or § 103 that is capable of teaching, disclosing, or suggesting, the structure and functionality of the invention of claim 11 to be capable of allowing for information to be “used for decision making to address the harmonic index”.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 11 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 11 recites the limitation “the information is used for decision making to address the harmonic index” [line 3], which the Examiner notes lacks sufficient written description support 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, as the Examiner notes that the Applicant’s Specification is devoid of any disclosure addressing a harmonic index or any relevant decision making regarding a harmonic index.
Examiner’s Note Regarding Machine Learning: the claimed machine learning of claim(s) 11 was considered under § 112(a), wherein the Examiner notes that the disclosure of different machine learning algorithms that may be applied [For example, the training unit 353 performs training unit using a linear regression algorithm. It performs training unit using an algorithm of a support vector machine (SVM). For example, the training unit 353 performs training unit using a Gaussian process regression (GPR) algorithm. For example, the training unit 353 performs training unit using an algorithm such as random forest (RF). For example, the training unit 353 may perform unsupervised training unit that classifies a user who is a generation source of the feature-amount data according to the feature-amount data. The training unit algorithm performed by the training unit 353 is not particularly limited (Applicant’s Specification ¶0085); The table of Fig. 30 includes an RMSE and an ICC related to an estimation value using an estimation model trained by an algorithm such as linear regression (Linear), support vector machine (SVM), and Gaussian process regression (GPR), and random forest (RF). According to the table of Fig. 30, linear regression (Linear) had the smallest RMSE and the largest ICC. That is, in the example of Fig. 30, the estimation model trained by linear regression (Linear) has the best performance (Applicant’s Specification ¶0122)] of the Applicant’s Specification is considered to provide sufficient written description support for the machine learning as presently claimed for one of ordinary skill in the art to understand that the Applicant possessed the instant invention at the time of filing.
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.
Claim(s) 2, 4, 6, 8, 11, and those dependent therefrom is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation “the generation means” [lines 5-6], which is considered to lack antecedent basis, as claims 1-2 fail to previously define a “generation means”; and wherein the recited limitation is further considered to render claim 2 indefinite, as it is not clear whether the recited generation means is meant to refer to the previously defined processor of claims 1-2 configured to execute instructions, wherein the instructions include steps to “generate…”, or whether the recited generation means is meant to refer to a different element. For examination purposes, the Examiner has interpreted the indefinite limitation to read “the processor”.
Claim 4 recites the limitation “the gait phases constituting the preset gait phase cluster to be extracted” [lines 3-4], wherein each of “the preset gait phase cluster to be extracted” and “the gait phases constituting the preset gait phase cluster to be extracted” are considered to lack antecedent basis, as claims 1 and 4 fail to previously define each of a “preset gait phase cluster to be extracted” and “gait phases constituting the preset gait phase cluster to be extracted”; and wherein the recited limitation is further considered to render claim 4 indefinite, as it is not clear what gait phases and/or what gait phase cluster is being referred to. For examination purposes, the Examiner has interpreted the indefinite limitation to read “from each [[of the]] gait phase[[s]] constituting [[the]] a preset gait phase cluster to be extracted”.
Claim 6 recites the limitation “the feature amount of the gait phase cluster transmitted by the feature-amount generation device” [lines 10-11], which is considered indefinite, as claim 6 previously recites “the measurement device transmitting feature amount data including a feature amount of a gait phase cluster” [lines 8-9], such that it is unclear whether the transmitting is meant to be from the measurement device [which is considered to comprise at least the data acquisition device in addition to the feature-amount generation device] or specifically the feature-amount generation device that comprises the measurement device. For examination purposes, the Examiner has interpreted either identified interpretation to be applicable in light of any prior art applied under § 102 or § 103.
Claim 6 recites the limitation “a measurement device” [line 2], wherein claim 6 further recites that the measurement device includes at least “the feature-amount generation device according to claim 1 and a data acquisition device” [lines 3-4] based on the claim language “including” [line 2] leading directly to the limitations of lines 3-9 of claim 6. However, it is not clear whether the “data processing device” of lines 10-12 of claim 6 is meant to further comprise an element of the measurement device, or whether the “data processing device” is part of the overall system as defined by claim 6 due to the lack of proper formatting [tabbing limitations to the right] to indicate which interpretation is correct [wherein the Examiner notes that “a system comprising a measurement device, wherein the measurement device comprises: the feature-amount generation device according to claim 1, a data acquisition device, and a data processing device” is considered to have a scope that is different from “a system that comprises a measurement device and a data processing device, wherein the measurement device comprises: the feature-amount generation device according to claim 1 and a data acquisition device”]. For examination purposes, the Examiner has interpreted either identified interpretation to be applicable in light of any prior art applied under § 102 or § 103.
Claim 8 recites each of “a feature amount of the gait phase cluster extracted from time-series data of the sensor data” [lines 4-5] and “a gait of the user” [line 5], which are each considered indefinite, as it is not clear whether the recited indefinite limitations are meant to refer to the similarly recited instances of each in claim 7 [“a feature amount of the gait phase cluster extracted from time-series data of the sensor data” (lines 6-7) and “a gait of the user” (line 7) in claim 7] or are meant to refer to any instance of “a feature amount of the gait phase cluster extracted from time-series data of the sensor data” and “a gait of the user”. For examination purposes, the Examiner has interpreted either identified interpretation to be applicable in light of any prior art applied under § 102 or § 103.
Claim 11 recites the limitation “the estimation model” [line 2], which is considered indefinite, as the Examiner notes that claim 7 [from which claim 11 depends from, as claim 11 depends from claim 8] recites “an estimation model that outputs a physical feature” [lines 7-8 in claim 7] and claim 8 [from which claim 11 depends from] recites “an estimation model that outputs a degree of hallux valgus” [lines 5-6 in claim 8], such that it is unclear whether each estimation model of claims 7-8 are meant to be referred to by claim 11 or whether only one or the other are meant to be referred to by claim 11, as the Examiner notes that based on the language of claim 8, it is not explicitly clear that the “estimation model that outputs a degree of hallux valgus” may be intended to further limit the “estimation model that outputs a physical feature” of claim 7. For examination purposes, the Examiner has interpreted either identified interpretation to be applicable in light of any prior art applied under § 102 or § 103.
Claim 11 recites the limitations “the information” [line 3] and “the harmonic index” [line 3], which are each considered to lack antecedent basis, as none of claims 1, 7, 8, or 11 previously define either of “information” and a “harmonic index”. The recitation of “the information” in claim 11 is further considered to render claim 11 indefinite, as it is not clear what is meant to constitute the recited information. For examination purposes, the Examiner has interpreted any data or information extracted, generated, estimated, measured, or output from any of claims 1, 7, 8, and 11 to define “the information”.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 1-3, 5, and 9-10 is/are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1, 2-3, 5, and 9-10 of copending Application No. 18/393,929 in view of Thompson (US-10610131-B1).
Claim 1 of the conflicting Huang reference is considered to anticipate almost each and every limitation of instant claim 1 [see comparison below], except the limitation “generate feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other”.
Thompson discloses a feature-amount generation device, wherein Thompson discloses generating a feature amount of a gait phase cluster using a preset feature-amount constitutive expression [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12), wherein averaging displacements calculated over each stride phase is considered to read on using a preset feature-amount constitutive expression as defined by the Applicant’s Specification ¶0028], wherein feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other is considered to be generated [wherein averaging displacements calculated over each stride phase is considered to “associate” the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Huang to employ instructions for the processor to generate feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other, as this modification would amount to merely applying a known technique [associating calculated data with data used to derive the calculated data] to a known device (method, or product) ready for improvement to yield predictable results [MPEP § 2143(I)(D)].
Conflicting claims 2-3, and 5, as modified by Thompson are further considered to render obvious instant claims 2-3, and 5. Conflicting independent claims 9-10, as modified by Thompson mutatis mutandis to the obviousness rejection above, are similarly considered to render obvious instant claims 9-10.
Claim 1 of the Instant Application
Claim 1 of Conflicting Patent Application 18/393,929
A feature-amount generation device: comprising:
A gait measurement system comprising:
[wherein the system being defined by a memory and processor connected to the memory is considered to define a type of device, as no elements external to the memory and processor are defined]
a memory storing instructions; and
a memory storing instructions
a processor connected to the memory and configured to execute the instructions to:
a processor connected to the memory and configured to execute the instructions to:
generate a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot,
generate a gait waveform for one gait cycle from time-series data of sensor data measured according to a gait of a user
extract a feature amount from the generated gait waveform,
extract a feature amount from the generated gait waveform
extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted;
extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted
generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression, and
generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression
generate feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other.
This is a provisional nonstatutory double patenting rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Representative claim(s) 1 [representing all independent claims] recite(s):
A feature-amount generation device:
comprising:
a memory storing instructions; and
a processor connected to the memory and configured to execute the instructions to:
generate a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot,
extract a feature amount from the generated gait waveform,
extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted;
generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression, and
generate feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other.
(Emphasis added: abstract idea, additional element)
Step 2A Prong 1
Representative claim(s) 1 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper:
“generate a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot” – may be performed by merely applying known mathematical formulas or equations to at least a limited amount of data under no particular time constraints
“extract a feature amount from the generated gait waveform” – may be performed by merely observing at least a limited amount of previously collected or known data under no particular time constraints and drawing mental conclusions therefrom
“extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted” – may be performed by merely applying known mathematical formulas or equations to at least a limited amount of data under no particular time constraints and drawing mental conclusions therefrom
“generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression” – may be performed by merely applying known mathematical formulas or equations to at least a limited amount of data under no particular time constraints
“generate feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other” – may be performed by merely observing at least a limited amount of previously collected or known data under no particular time constraints and drawing mental conclusions therefrom
If a claim, under BRI, covers performance of the limitations in the mind but for the mere recitation of extra-solutionary activity (and otherwise generic computer elements) then the claim falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1 of the Mayo framework as set forth in the 2019 PEG.
No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) [i.e., “extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted”, “generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression”] to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts [Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)]. The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
The dependent claims merely include limitations that either further define the abstract idea [e.g. limitations relating to the data gathered or particular steps which are entirely embodied in the mental process] and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data [Int. Ventures v. Cap One Financial], collecting information, analyzing it, and displaying certain results of the collection and analysis [Electric Power Group], collection, storage, and recognition of data [Smart Systems Innovations].
Step 2A Prong 2
The judicial exception is not integrated into a practical application.
Representative claim 1 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity [generic computer function] – without further sufficient detail that would tie the abstract portions of the claim into a specific practical application (2019 PEG p. 55 – the instant claim, for example does not tie into a particular machine, a sufficiently particular form of data or signal collection – via the claimed extra-solution activity, or a sufficiently particular form of display or computing architecture/structure).
Dependent claim(s) 2-5 and 11 merely add detail to the abstract portions of the claim but do not otherwise encompass any additional elements which tie the claim(s) into a particular application/integration [the dependent claim(s) recite generic ‘units’ or ‘steps’ which encompass mere computer instructions to carry out an otherwise wholly abstract idea].
Dependent claim(s) 6-8 encounter substantially the same issues as the independent claim(s) from which they depend in that they encompass further generic extra-solutionary activity [generic data gathering] and/or generic computer elements [storage, memory per se].
Accordingly, the claim(s) are not integrated into a practical application under Step 2A Prong 2.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1 and 9-10 as individual wholes fail to amount to significantly more than the judicial exception at Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of extra-solutionary activity [i.e., generic computer function] and generic computer elements cannot amount to significantly more than an abstract idea [MPEP § 2106.05(f)] and is further considered to merely implement an abstract idea on a generic computer [MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality].
For the independent claim portions and dependent claims which provide additional elements of extra-solutionary data gathering, MPEP § 2106.05(g) establishes that mere data gathering for determining a result does not amount to significantly more. The extra-solutionary activity of processor steps [acquiring, storing, outputting signals, etc.] as presently recited, cannot provide an inventive concept which amounts to significantly more than the recited abstract idea.
For the independent claims as well as the dependent claims merely reciting generic computer elements and functions [memory and processor recited at a high level of generality and functions therein], MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality.
Accordingly, the generic computer elements and functions therein, as presently limited, cannot provide an inventive concept since they fall under a generic structure and/or function that does not add a meaningful additional feature to the judicial exception(s) of the claim(s).
Claim 6 recites a “measurement device”, wherein the measurement device comprises “a data acquisition device that is disposed at footwear of a user to be measured, measures a spatial acceleration and a spatial angular velocity according to a gait of the user, generates sensor data based on the measured spatial acceleration and the measured spatial angular velocity”. Such a “measurement device” / “data acquisition device” is considered well-understood, routine, and conventional, as known by at least:
Applicant’s disclosure is not particular regarding the particular structure of the generically claimed data acquisition device of the measurement device, and recites the acceleration sensor and angular velocity sensor that comprise the data acquisition device of the measurement device at a high level of generality [The measurement device 11 includes an acceleration sensor and an angular velocity sensor (Applicant’s Specification ¶0020); The measurement device 11 is achieved by, for example, an inertial measurement device including an acceleration sensor and an angular velocity sensor. An example of the inertial measurement device is an inertial measurement unit (IMU). The IMU includes a three-axis acceleration sensor and a three-axis angular velocity sensor. Examples of the inertial measurement device include a vertical gyro (VG), an attitude heading (AHRS), and a global positioning system/inertial navigation system (GPS/INS) (Applicant’s Specification ¶0024); The acceleration sensor 111 is a sensor that measures acceleration (also referred to as spatial acceleration) in the three axial directions. The acceleration sensor 111 outputs the measured acceleration to the control unit 113. For example, a sensor of a piezoelectric type, a piezoresistive type, a capacitance type, or the like can be used as the acceleration sensor 111. As long as the sensor used for the acceleration sensor 111 can measure an acceleration, the measurement method is not limited (Applicant’s Specification ¶0035); The angular velocity sensor 112 is a sensor that measures angular velocities around the three axes (also referred to as spatial angular velocities). Angular velocity sensor 112 outputs the measured angular velocity to control unit 113. For example, a sensor of a vibration type, a capacitance type, or the like can be used as the angular velocity sensor 112. As long as the sensor used for the angular velocity sensor 112 can measure an angular velocity, the measurement method is not limited (Applicant’s Specification ¶0036)]. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the gait analysis. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional element because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) [see Berkheimer memo from April 19, 2018, Page 3, (III)(A)(1), not attached]. Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible [TLI Communications].
Claim 11 recites “the estimation model is constructed by machine learning”. Such an estimation model is considered well-understood, routine, and conventional, as known by at least:
Hu (“Intelligent Sensor Networks”, NPL attached) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Hu, Page 5)]
Huang (“Kernel Based Algorithms for Mining Huge Data Sets”, NPL attached) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Huang, Page 1)]
Mitchell (“The Discipline of Machine Learning”, NPL attached) [For example, we now have a variety of algorithms for supervised learning of classification and regression functions; that is, for learning some initially unknown function f : X [Calibri font/0xE0] Y given a set of labeled training examples {xi; yi} of inputs xi and outputs yi = f(xi) (Mitchell, Pages 3-4)]
Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 8 recite subject matter regarding “an estimation model that outputs a degree of hallux valgus” [lines 5-6] and “estimate a degree of hallux valgus of the user” [line 7], which the Examiner notes is not considered to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language that is considered to be a particular treatment or prophylaxis as an additional element to integrate the judicial exception into a practical application or allow the identified claims to amount to significantly more than the judicial exception [MPEP § 2106.04(d)(2)].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5 and 9-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Thompson (US-10610131-B1).
Regarding claim 1, Thompson teaches
A feature-amount generation device: comprising:
a memory storing instructions [In addition and as to computer aspects and each aspect amenable to programming or other electronic automation, the applicant(s) should be understood to have support to claim and make a statement of invention to at least: xv) processes performed with the aid of or on a computer, machine, or computing machine as described throughout the above discussion, xvi) a programmable apparatus as described throughout the above discussion, xvii) a computer readable memory encoded with data to direct a computer comprising means or elements which function as described throughout the above discussion (Thompson Col 13:53-63)]; and
a processor connected to the memory and configured to execute the instructions [Thompson Col 13:53-63] to:
generate a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot [the raw stride inertial data values may be made available for transfer to facilitate further analysis and even sharing between practitioners and researchers (Thompson Col 11:64-67), wherein the raw stride inertial data is considered to be defined by a gait waveform],
extract a feature amount from the generated gait waveform [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform],
extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase Thompson Col 9:37-43, 9:67-10:3), wherein each stride phase is considered to define a gait phase cluster, wherein the data segments combined to define each stride phase are considered to read on temporally continuous gait phases];
generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12), wherein averaging displacements calculated over each stride phase is considered to read on using a preset feature-amount constitutive expression as defined by the Applicant’s Specification ¶0028], and
generate feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other [wherein averaging displacements calculated over each stride phase is considered to “associate” the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster].
Regarding claim 2, Thompson teaches
The feature-amount generation device according to claim 1, wherein
in a case where a feature amount is extracted from a single gait phase that is not temporally continuous,
the processor is configured to execute the instructions to
output a feature amount extracted from the single gait phase to the generation means, and
generate feature-amount data in which the single gait phase and the feature amount of the single gait phase are associated with each other [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… Once the invalid strides (e.g., change in gait, tripping, slipping, forging, turning, or the like) may be detected and even excluded, the usable stride 610 inertial sensor outputs (e.g., three-dimensional angular velocity roll, pitch and yaw components or the like) may be processed, perhaps to extract limb inertial motion metrics and even stride phase duration metrics 612… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase (Thompson Col 9:37-43, 9:59-65, 9:67-10:3), wherein applying the feature amount processes to any non-excluded stride phase or sub-phase is considered to read on being not temporally continuous, wherein applying an average to a displacement defined by a sub-phase is considered to read on the claimed limitations].
Regarding claim 3, Thompson teaches
The feature-amount generation device according to claim 1, wherein
in a case where a feature amount is extracted from a single gait phase that is not temporally continuous,
the processor is configured to execute the instructions to extract the single gait phase as a gait phase cluster, and
generate feature-amount data in which the single gait phase extracted as the gait phase cluster and the feature amount of the single gait phase are associated with each other [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… Once the invalid strides (e.g., change in gait, tripping, slipping, forging, turning, or the like) may be detected and even excluded, the usable stride 610 inertial sensor outputs (e.g., three-dimensional angular velocity roll, pitch and yaw components or the like) may be processed, perhaps to extract limb inertial motion metrics and even stride phase duration metrics 612… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase (Thompson Col 9:37-43, 9:59-65, 9:67-10:3), wherein applying the feature amount processes to any non-excluded stride phase or sub-phase is considered to read on being not temporally continuous, wherein applying an average to a displacement defined by a sub-phase is considered to read on the claimed limitations].
Regarding claim 4, Thompson teaches
The feature-amount generation device according to claim 1, wherein
the processor is configured to execute the instructions to
extract a feature amount of each of the gait phases constituting the preset gait phase cluster to be extracted [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform].
Regarding claim 5, Thompson teaches
The feature-amount generation device according to claim 1, wherein
the processor is configured to execute the instructions to
extract a feature amount related to a gait affected by a specific physical feature [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform; and wherein the cluster being defined by the stride phase is considered to define the feature amounts as being “affected” by a specific physical feature (the foot of the subject)].
Regarding claim 9, Thompson teaches
A feature-amount generation method comprising:
generating a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot [the raw stride inertial data values may be made available for transfer to facilitate further analysis and even sharing between practitioners and researchers (Thompson Col 11:64-67), wherein the raw stride inertial data is considered to be defined by a gait waveform];
extracting a feature amount from the generated gait waveform [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform];
extracting a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase Thompson Col 9:37-43, 9:67-10:3), wherein each stride phase is considered to define a gait phase cluster, wherein the data segments combined to define each stride phase are considered to read on temporally continuous gait phases];
generating a feature amount of the gait phase cluster using a feature-amount constitutive expression [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12), wherein averaging displacements calculated over each stride phase is considered to read on using a preset feature-amount constitutive expression as defined by the Applicant’s Specification ¶0028]; and
generating feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other [wherein averaging displacements calculated over each stride phase is considered to “associate” the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster].
Regarding claim 10, Thompson teaches
A non-transitory recording medium recording a program for causing a computer [In addition and as to computer aspects and each aspect amenable to programming or other electronic automation, the applicant(s) should be understood to have support to claim and make a statement of invention to at least: xv) processes performed with the aid of or on a computer, machine, or computing machine as described throughout the above discussion, xvi) a programmable apparatus as described throughout the above discussion, xvii) a computer readable memory encoded with data to direct a computer comprising means or elements which function as described throughout the above discussion (Thompson Col 13:53-63)] to execute:
a process of generating a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot [the raw stride inertial data values may be made available for transfer to facilitate further analysis and even sharing between practitioners and researchers (Thompson Col 11:64-67), wherein the raw stride inertial data is considered to be defined by a gait waveform];
a process of extracting a feature amount from the generated gait waveform [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform];
a process of extracting a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase Thompson Col 9:37-43, 9:67-10:3), wherein each stride phase is considered to define a gait phase cluster, wherein the data segments combined to define each stride phase are considered to read on temporally continuous gait phases];
a process of generating a feature amount of the gait phase cluster using a feature- amount constitutive expression [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12), wherein averaging displacements calculated over each stride phase is considered to read on using a preset feature-amount constitutive expression as defined by the Applicant’s Specification ¶0028]; and
a process of generating feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other [wherein averaging displacements calculated over each stride phase is considered to “associate” the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster].
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thompson, as applied to claim 1 above, in view of Selner (US-20170319368-A1).
Regarding claim 6, Thompson teaches
A gait measurement system comprising:
the feature-amount generation device according to claim 1 and
a data acquisition device that is disposed at a foot of a user to be measured, measures a spatial angular velocity according to a gait of the user, generates sensor data based on the measured spatial angular velocity, and outputs the generated sensor data to the feature-amount generation device [this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3); FIG. 1 illustrates an example of an equine limb inertial sensing system 100 according to embodiments of the present invention. A pair of inertial sensing devices 102 (left L and right R) may be located on the lower limb segment that may include the third metacarpal and/or metatarsal bone, perhaps depending on whether either one or both of the front or hind limb pairs are evaluated. The biosensor devices may include casings and inertial sensors, such as gyroscopes or the like, perhaps for measuring the three-dimensional motion of the lower limb (Thompson Col 5:61-6:3)], the data acquisition device transmitting data [As shown in FIG. 1, a portable controller and analytics unit 104 may be used to wirelessly collect gait data from each device pair using the device controller module 106 in addition to analyzing the data into limb motion metrics, perhaps using the gait analytics reporting module 108 (Thompson Col 6:41-45)]; and
a data processing device that receives the data and performs data processing using the received data [Transmission of the information for further evaluation or review can be performed using the mobile device controller (104) which can send the information electronically (e.g., email, electronic communications, or the like) or from data downloaded to a personal computer (Thompson Col 8:51-56)].
However, Thompson fails to explicitly disclose wherein the data acquisition device is disposed at footwear of a user to be measured; further measures spatial acceleration according to the gait of the user, and generates sensor data based on the spatial acceleration; wherein the feature-amount generation device and the data acquisition device are employed as a single measurement device, such that the data transmitted by the measurement device and received/processed by the data processing device comprises feature-amount data including a feature amount of a gait phase cluster extracted by the feature-amount generation device.
Selner discloses systems for monitoring foot movement of a subject during a gait cycle, wherein Selner discloses a measurement device including a processing system for processing data, and a data acquisition device, disposed at footwear of the subject to be measured, configured to measure spatial acceleration and spatial angular velocity [An electronic system, according to this block diagram, includes a microprocessor 104, a forefoot 3-axis accelerometer/3-axis gyroscope 101, an arch region 3-axis accelerometer/3-axis gyroscope 106, and a wireless transmitter 111. The components are embedded in the orthotic. Suitable components can include the Intel® Quark™ SE microcontroller, said to be the heart of the Intel Curie. The Quark™ SE CPU would be connected to a 3-axis accelerometer, and 3-axis gyroscope, and 3-axis magnetometer IC, also embedded in the orthotic. The STMicroelectronics LSM9DS0 9DOF IMU IC would be a suitable component for this purpose… The electronic system, overall, will measure and record raw sensor data, pre-process it for external analysis and analyze the data (Selner ¶0027)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Thompson to employ a measurement device including the feature amount generation device and the data acquisition device, wherein the data acquisition device also measures spatial acceleration, such that the data transmitted by the measurement device and received/processed by the data processing device comprises feature-amount data including a feature amount of a gait phase cluster extracted by the feature-amount generation device, so as to provide additional context regarding foot movement during the gait cycle [with respect to measuring spatial acceleration], as well as amount to mere simple substitution of one known element [device for processing data as disclosed by Thompson and device for measuring motion] for another [single device for processing data and measuring motion as disclosed by Selner] with similar expected results [MPEP § 2143(I)(B)].
Claim(s) 7-8 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thompson in view of Selner, as applied to claim 6 above, in further view of Najafi (WO-2019213399-A1, foreign reference attached).
Regarding claim 7, Thompson in view of Selner teaches
The gait measurement system according to claim 6, wherein
the data processing device includes
a memory storing instructions [Thompson Col 8:51-56, wherein a computer is considered be defined by a processor and memory], and
a processor connected to the memory [Thompson Col 8:51-56].
However, while Thompson discloses analyzing the feature amount based on historical data to assess a physical feature of the user [differences between each of the angular displacement and angular velocity curves can be compared either for the same limb (unilateral) or between limbs (bilateral) on a point by point basis (perhaps for each data collection time point over the course of a stride). These differences can be averaged over multiple strides (aggregated across each run within a session for intra-session comparison or runs among sessions for multi-session comparison), and the results may be displayed, perhaps with animation and/or graphically, as an average difference curve (e.g., over the entire stride and/or for each stride phase or the like). These difference-curves can highlight non-uniform motion signatures and/or patterns that can be visualized and even recognized at a glance as indicative of the type and/or source of lameness (Thompson Col 10:21-35); The goal of the uniformity analysis process may be to quantify the difference in the motion of one limb compared to itself (unilateral consistency) or to compare the difference between contralateral limbs (bilateral symmetry) (refer to FIG. 8). With both approaches, the horse may serve as their own reference for sensitive detection of limb motion that may be considered abnormal for that particular horse (Thompson Col 10:37-44); wherein based on Thompson Col 5:1-3, the analysis is considered to be similarly applicable to a human], Thompson in view of Selner fails to explicitly disclose wherein the processor is configured to execute the instructions to input a feature amount of the gait phase cluster extracted from time-series data of the sensor data measured along with a gait of the user to an estimation model that outputs a physical feature according to input feature amount, and estimate physical feature of the user based on an estimation value output from the estimation model.
Najafi discloses systems and methods for assessing user gait, wherein Najafi discloses inputting a feature amount of a user’s gait and a gait of the user to an estimation model that outputs a physical feature according to input feature amount, and estimate physical feature of the user based on an estimation value output from the estimation model that uses angular velocity information as an input [A neural network model 900, as shown in FIGURE 9, may be used to analyze lower extremity motion data and establish reliability of a predictive relationship between gait characteristics and a frailty level. For example, a neural network model may determine whether it is possible to accurately predict a frailty level based on a set of gait characteristics. The neural network 900 may analyze a discriminating power of gait characteristics 912-922 to differentiate between non-frail, pre-frail, and frail individuals. The neural network model may receive input data from a right leg sensor 902 and/or a left leg sensor 904, such as angular velocity data from gyroscopes of the left and right leg sensors 902, 904 at a sensor configuration 906. Sensed data, such as angular velocity data, may be input into a neural network processing algorithm 908 (Najafi ¶0047); For example, if a propulsion efficiency calculated based on one or more gait parameters falls below a predetermined threshold, a determination may be made that a foot is at risk of diabetic foot ulcers or deformity, such as bunions, hammer toes, overlapping toes, and other deformities (Najafi ¶0052)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Thompson in view of Selner to employ the processor to execute the instructions to input a feature amount of the gait phase cluster extracted from time-series data of the sensor data measured along with a gait of the user to an estimation model that outputs a physical feature according to input feature amount, and estimate physical feature of the user based on an estimation value output from the estimation model, so as to allow for correlation and determination of user frailty based on measured parameters.
Regarding claim 8, Thompson in view of Selner and Najafi teaches
The gait measurement system according to claim 7, wherein
the processor of the data processing device is configured to execute the instructions to
input a feature amount of the gait phase cluster extracted from time-series data of the sensor data measured along with a gait of the user to an estimation model that outputs a degree of hallux valgus according to input feature amount [See § 103 rejection of claim 7 above; wherein based on Najafi ¶0052, one of the risks being a bunion, which is considered to be equivalent to hallux valgus], and
estimate a degree of hallux valgus of the user based on an estimation value output from the estimation model [Najafi ¶¶0047, 0052, wherein a risk level for developing a bunion may be considered to read on a “degree” of hallux valgus due to how broadly the limitation is recited].
Regarding claim 11, Thompson in view of Selner and Najafi teaches
The gait measurement system according to claim 8, wherein
the estimation model is constructed by machine learning [Najafi ¶0047], and
the information is used for decision making to address the harmonic index [wherein this limitation is considered to be intended use and is not structurally required; wherein the Examiner notes that since the system as taught by the prior art is considered to “generate” any information, such information generated by the system may be used as a part of any type of decision making process, such that the system as taught by the prior art is considered to be capable of performing the intended use limitation].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEVERO ANTONIO P LOPEZ whose telephone number is (571)272-7378. The examiner can normally be reached M-F 9-6 EST.
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, Charles Marmor II can be reached at (571) 272-4730. 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.
/SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791