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 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: “processing device” in claims 11 and 20.
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.
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 § 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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 to 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding independent claims 1, 11, and 20, and similarly for the dependent claims, applicant has apparently not described, in sufficient detail, by what algorithm(s)1, or by what steps or procedure2, he monitored any or all “parameter[s] pertaining to a gait of a person” (e.g., such as perhaps epilepsy, age, tiredness, leg length, knee condition, toddler development, etc.?), as are encompassed and covered by the claim, based on the data from a sensing device in a smart floor tile. Accordingly, the examiner believes applicant has not evidenced, to those skilled in the art, possession of the full scope3 of the claimed invention, but has only described (e.g., in the claims), a desired result.
In this respect, the claimed parameter encompasses and covers (e.g., per the dependent claims, etc.) e.g., “a distance between a head of the person and feet of the person”, “historical information pertaining to whether the person has previously fallen”, “an age of the person”, “medical history of the person”, “fracture history of the person”, “vision impairment of the person”, “neurological condition of the person” (e.g., epilepsy), and yet no algorithm(s) for monitoring the full scope of the parameter as encompassed/covered by the claim based on the data received from a sensing device in the smart floor tile are apparently described, in sufficient detail, in the specification. For examples only, by what algorithm(s) was the head/feet distance monitored based on data received from a sensing device in a smart floor tile, by what algorithm(s) was the historical information or the age or the history of the person monitored based on data received from a sensing device in a smart floor tile, by what algorithm(s) was the vision impairment or neurological condition (e.g., epilepsy) of the person monitored based on data received from a sensing device in a smart floor tile, from the teachings of the specification? Accordingly, the examiner believes applicant has not evidenced, to those skilled in the art, possession of the full scope of the claimed invention, but has only described (e.g., in the claims), a desired result.
Regarding independent claims 1, 11, and 20, applicant has apparently not described, in sufficient detail, by what algorithm(s), or by what steps or procedure, he determined any or all amounts of gait deterioration based on any or all parameters pertaining to a gait covered by the claim. For example only, if the parameter is that the person weighs 190 pounds or is 35 years old or is near-sighted but wears glasses or had a childhood hernia operation or has cerebral palsy, as the claims apparently cover (cf. claims 6 and 16), then by what algorithm(s) or steps/procedure did applicant determine the amount of gait deterioration, from the teachings of the specification, in each instance, based on the parameter? Accordingly, the examiner believes applicant has not evidenced, to those skilled in the art, possession of the full scope of the claimed invention, but has only described (e.g., in the claims), a desired result.
Regarding claims 9 and 19, applicant has apparently not described, in sufficient detail, by what algorithm(s), or by what steps or procedure, he trained the third machine learning model to determine the amount gate deterioration based on the first amount of gait deterioration and the second amount of gait deterioration, and determine whether the propensity for the fall event for the person satisfies the threshold propensity condition based on (i) the amount of gait deterioration satisfying the threshold deterioration condition, (ii) the amount of gait deterioration satisfying the threshold deterioration condition within the threshold time period, or some combination thereof. No algorithm(s) or steps/procedure for training of the third machine learning (so that the model is trained, as claimed) is apparently described, in sufficient detail. Accordingly, the examiner believes applicant has not evidenced, to those skilled in the art, possession of the full scope of the claimed invention, but has only described (e.g., in the claims), a desired result.
For example, paragraph [0141 of the published specification indicates:
[0141] The result machine learning model 154.5 may be trained to analyze the various amounts of gait deterioration for the respective parameters represented by the respective machine learning models 154.1-154.4 and determine a propensity for the fall event. In some embodiments, the amount of gait deterioration for each parameter that is output by the machine learning models 154.1-154.4 may be compared with a respective corresponding gait baseline parameter when determining the propensity for the fall event. Each amount of gait deterioration may be considered a flag if the amount of gait deterioration satisfies a threshold deterioration condition. In some embodiments, the larger the number of flags that are present for the person, the higher the propensity for the fall event to occur for the person. That is, if there are flags present for the amount of gait deterioration determined by the stride variability machine learning model 154.1, the gait speed machine learning model 154.2, the balance machine learning model 154.3, and the normalized activity machine learning model 154.4, then the propensity for the fall event for the person may be high. In contrast, if there is just one flag present for the stride variability machine learning model 154.1, then the propensity for the fall event may be low.
However, this paragraph, while describing the “desired result”, apparently does not describe, in sufficient detail, any training algorithm(s) for any model that might somehow be capable of machine learning, as opposed to a machine learning model (154.5) in name only. Accordingly, the examiner believes applicant has not evidenced, to those skilled in the art, possession of the full scope of the claimed invention, but has only described (e.g., in the claims), a desired result.
Claims 1 to 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 1, line 4, in claim 11, line 4, and in claim 20, line 6, “[monitoring] a parameter pertaining to a gait” (e.g., epilepsy, age, tiredness, leg length, knee condition, toddler development, etc.?) is indefinite from the teachings of the specification and in the claim context, with metes and bounds that are not reasonably certain4 and are indeterminate/undeterminable by those skilled in the art based on the disclosure.
In claim 1, line 5, in claim 11, line 5, and in claim 20, line 7, “[determining] an amount of gait deterioration based on the parameter” is facially subjective (MPEP 2173.05(b), IV.) and indefinite from the teachings of the specification (e.g., by what objective standard is gait deterioration defined with reasonable certainty, from the teachings of the specification?)
In this respect, the specification teaches at published paragraph [0118]:
[0118] At block 706, the processing device may determine an amount of gait deterioration based on the parameter. The amount of gait deterioration may be any suitable indication, such as a category (e.g., 1-5), a score (e.g., 1-5), a percentage (0-100%), and the like. In some embodiments, the amount of gait deterioration may be based on the category, score, or percentage for a particular parameter changing a certain amount within a certain time period. For example, the gait deterioration may be determined to be high if the category for a parameter changed from a 1 to a 5 within a short amount of time (e.g., minutes).
However, it is unclear how the categories, scores, or percentages are themselves defined, with reasonable certainty, especially to cover the full scope of parameters encompassed and covered by the claims. For example, what particularly does 50% or Category 3 gait deterioration mean, e.g., when the parameter is e.g., age, weight, or vision impairment?
In claim 1, lines 6ff, in claim 11, lines 6ff, and in claim 20, lines 8ff, “satisfies a threshold propensity condition” is indefinite from the teachings of the specification (e.g., threshold propensity condition defined particularly how so as to not possibly cover every possible condition/threshold in the world?) and is facially subjective (e.g., particularly how is it determined when an indefinite condition is or might be satisfied?)
In claim 1, line 8, in claim 11, line 8, and in claim 20, line 10, satisfying a threshold deterioration condition” is indefinite from the teachings of the specification (e.g., threshold deterioration condition defined particularly how so as to not possibly cover every possible condition/threshold in the world?) and is facially subjective (e.g., particularly how is it determined when an indefinite condition is or might be satisfied?)
In claim 2, line 4, and in claim 12, line 4, “an intervention” is indefinite and not reasonably certain from the teachings of the specification, with indeterminate metes and bounds (e.g., an intervention defined particularly how? is writing a will an intervention? why or why not?)
In claim 3, line 7, and in claim 13, line 7, “care plan” is indefinite and not reasonably certain from the teachings of the specification, with indeterminate metes and bounds (e.g., a care plan defined particularly how? is warning a care giver a care plan? why or why not?)
In claim 5, line 4, and in claim 15, line 4, “a type of intervention” is indefinite (e.g., “type” intending to cover what, particularly how are “types” distinguished and/or defined, etc.?) See MPEP 2173.05(b), III., E.
In claim 5, line 4, and in claim 15, line 4, “severity” in indefinite and facially subjective with no objective standard provided in the specification for allowing the public to determine the scope of the term.
In claim 5, line 6, and in claim 15, line 6, “escalate in severity” in indefinite and facially subjective with no objective standard provided in the specification for allowing the public to determine the scope of the term.
In claim 7, line 1, and in claim 17, line 2, “receiv[ing] the data from a camera” is indefinite and apparently contradicts the independent claim which recites that the data is received from a sensing device in a smart floor tile, and the specification apparently teaches that the camera 50 (see FIG. 1A) is distinct from any sensor(s) that might be included in the smart floor tile 112.
In claim 9, line 8ff, and in claim 19, lines 8ff, “a third machine learning model trained to: determine . . .” is indefinite from the teachings of the specification that does not apparently clarify e.g., any third model that would perform actual machine learning in the manner claimed, or clarify how the Result ML Model 154.5 might process the inputs from the other models using actual machine learning. See published specification paragraphs [0140], [0141], and [0144].
In claim 11, line 6, “the propensity for the fall event” apparently lacks antecedent basis in two respects, and is unclear.
In claim 12, line 3, in claim 14, line 3, in claim 15, line 3, in claim 17, lines 1ff, in claim 18, lines 4ff, “is further to” is grammatically incorrect/ambiguous and indefinite (e.g., does this mean “is further configured to”, “is further adapted to”, “if further utilized to”, or something else entirely?) See MPEP 2173.02, I. (“For example, if the language of a claim, given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph is appropriate.”)
Claim(s) depending from claims expressly noted above are also rejected under 35 U.S.C. 112 by/for reason of their dependency from a noted claim that is rejected under 35 U.S.C. 112, for the reasons given.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 to 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 and Step 2A, Prong I:
Claim(s) 1 to 20, while (each) reciting a statutory category of invention defined in 35 U.S.C. 101 (a useful process, machine, manufacture, or composition of matter), is/are directed to an abstract idea, which is a judicial exception, the recited abstract idea being that of monitoring a parameter pertaining to a gait of a person based on the data; determining an amount of gait deterioration based on the parameter; and determining whether the propensity for the fall event for the person satisfies a threshold propensity condition based on (i) the amount of gait deterioration satisfying a threshold deterioration condition, or (ii) the amount of gait deterioration satisfying the threshold deterioration condition within a threshold time period, e.g., by receiving data from a sensing device in a smart floor tile; monitoring a parameter pertaining to a gait of a person based on the data; determining an amount of gait deterioration based on the parameter; and determining whether the propensity for the fall event for the person satisfies a threshold propensity condition based on (i) the amount of gait deterioration satisfying a threshold deterioration condition, or (ii) the amount of gait deterioration satisfying the threshold deterioration condition within a threshold time period; wherein responsive to determining the propensity for the fall event for the person satisfies the threshold propensity condition, the method further comprises: determining an intervention to perform based on the propensity for the fall event, and performing the intervention; wherein the intervention comprises: transmitting a first message to a computing device of the person, transmitting a second message to a computing device of a medical personnel, causing an alarm to be triggered in a facility in which the person is located, changing a property of an electronic device located in a physical space with the person, changing a care plan for the person, changing an intensity of a directional indicator in the physical space in which the person is located, or some combination thereof; wherein responsive to determining the propensity for the fall event for the person does not satisfy the threshold propensity condition, the method further comprises: receiving subsequent data from the sensing device; monitoring the parameter pertaining to the gait of the person based on the subsequent data; determining a second amount of gait deterioration based on the parameter; and determining whether the propensity for the fall event for the person satisfies the threshold propensity condition based on (i) the second amount of gait deterioration satisfying the threshold deterioration condition, (ii) the second amount of gait deterioration satisfying the threshold deterioration condition within the threshold time period, or some combination thereof; wherein responsive to determining the propensity for the fall event for the person satisfies the threshold propensity condition, the method further comprises: performing a type of intervention that has a severity that corresponds to the propensity for the fall event, the intervention included in a plurality of interventions that escalate in severity based on the propensity for the fall event; wherein the parameter comprises at least one of: a speed of the gait of the person, a distance between a head of the person and feet of the person, a distance between the feet during the gait of the person, historical information pertaining to whether the person has previously fallen, a weight of the person, an age of the person, medical history of the person, fracture history of the person, vision impairment of the person, activity level of the person, balance distribution of weight while stationary, during gait, or both, neurological condition of the person, change in stride of the person, results of a calibration test, or some combination thereof; further comprising receiving the data from a camera, and wherein the parameter is monitored using computer vision, object recognition, measured pressure, location of feet of the person, or some combination thereof; wherein the monitoring the parameter pertaining to the gait of the person based on the data, the determining the amount of gait deterioration based on the parameter, and the determining whether the propensity for the fall event for the person satisfies the threshold propensity condition further comprises: inputting the data into one or more machine learning models trained to determine the amount of gait deterioration based on the parameter and to determine whether the propensity for the fall event for the person satisfies the threshold propensity condition; wherein the one or more machine learning models comprise: a first machine learning model trained to identify a change in the parameter and determine a first amount of gait deterioration, a second machine learning model trained to identify a change in a second parameter pertaining to the gait of the person based on the data and determine a second amount of gait deterioration, and a third machine learning model trained to: determine the amount gate deterioration based on the first amount of gait deterioration and the second amount of gait deterioration, and determine whether the propensity for the fall event for the person satisfies the threshold propensity condition based on (i) the amount of gait deterioration satisfying the threshold deterioration condition, (ii) the amount of gait deterioration satisfying the threshold deterioration condition within the threshold time period, or some combination thereof; further comprising: calibrating one or more gait baseline parameters for the person; and determining the amount of gait deterioration based on comparing the parameter to at least one of the one or more gait baseline parameters.
This abstract idea falls within the grouping(s) of mathematical concepts, mental processes, and/or certain methods of organizing human activity, distilled from case law, because the monitoring and determining, identifying, etc. is a mental process that could be practically performed in the human mind.
Step 2A, Prong II and Step 2B:
Additionally, applying a preponderance of the evidence standard, the abstract idea is not integrated (e.g., at Step 2A, Prong II) by the recitation of additional elements/limitations into a practical application (using the considerations set forth in MPEP §§ 2106.04(a)-(h)) because merely using a computer as a tool to perform an abstract idea or adding the words "apply it" (e.g., determining and performing the intervention) is not integrating the idea into a practical application of the idea, and e.g., looking at the claim as a whole and considering any additional elements/limitations individually and in combination, no (additional) particular machine, transformation, improvement to the functioning of a computer or an existing technological process or technical field, or meaningful application of the idea, beyond generally linking the idea to a technological environment (e.g., "implementation via computers", Alice) or adding insignificant extra-solution activity (e.g., receiving data from a smart floor tile as mere data gathering, performing an intervention that is not recited as a particular treatment or prophylaxis for a disease or medical condition, etc.), is recited in or encompassed by the claims.
Moreover, applying a preponderance of the evidence standard, the claim(s) does/do not include additional elements/limitations/steps (e.g., at Step 2B) that are, individually or in ordered combination, sufficient to amount to significantly more than the judicial exception because the elements/limitations/steps are recited at a high level of generality (e.g., a smart floor tile, performing an intervention, etc.) so as to not favor eligibility (MPEP § 2106.05(d)) and/or are used e.g., for data/information gathering only or for other activities that were well-understood, routine, and conventional activity in the industry (see e.g., the literature cited with this Office action regarding using smart floor tiles for data gathering), for example as indicated in applicant's specification at published paragraph [0003], and moreover, the generically recited computer elements (e.g., a medium, a memory, a processing device, etc.; see e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 110 USPQ2d 1984 (2014); buySAFE, Inc. v. Google, Inc., 765 F.3d. 1350, 112 USPQ2d 1093 (Fed. Cir. 2014); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 115 USPQ2d 1090 (Fed. Cir. 2015); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362; Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-1355, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter.”); Mobile Acuity, Ltd. v. Blippar Ltd., Case No. 22-2216 (Fed. Cir. Aug. 6, 2024); see also the 2019 PEG Advanced Module at pages 89, 145, etc.) do not add a meaningful limitation to the abstract idea because their use would be routine (and conventional) in any computer implementation of the idea.
Moreover, limiting or linking the use of the idea to a particular technological environment (e.g., having a medium, a processing device, etc.) is not enough to transform the abstract idea into a patent-eligible invention (Flook[5]) e.g., because the preemptive effect of the claims on the idea within the field of use would be broad.
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.
Claims 1 to 4, 6 to 8, 10 to 14, 16 to 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cole et al.6 (2015/0282766) in view of MacKinnon et al. (2014/0307118) and Zheng et al. (2020/0205697).
Cole et al. (‘766), assigned to Tactonic Technologies, LLC, reveals:
per claim 1, a method for determining a propensity for a fall event to occur [e.g., paragraph [0003], “predict the propensity to fall”], the method comprising:
receiving data [e.g., from the sensor 21 in FIG. 2, at 42 in FIG. 5, at 51 in FIG. 6, etc.] from a sensing device in a [e.g., the plurality of sensor tiles forming a walkway (FIG. 2), such as Tactonic Technology LLC's pressure sensing floor tiles (paragraph [0053])7; see also paragraphs [0087], [0090], [0106], etc.];
monitoring a parameter pertaining to a gait of a person based on the data [e.g., paragraph [0003], “changes in gait parameters can monitor the progression of certain diseases. The measurements of gait, balance, and activity can be used to monitor the person's ergonomic performance and provide a means to give feedback information to the worker or to a system in order to reduce risk or increase the overall performance and efficiency of the work and the system”; and e.g., in the data processing & analytics unit (as described at paragraphs [0083], [0103], etc., for using/monitoring gait velocity and balance measurements) in Cole et al. (‘766)];
determining an amount of gait deterioration based on the parameter [e.g., paragraph [0058], “Use of statistical techniques to compare of patterns of gait and/or balance over time for an individual can indicate physical deterioration and can be projected to allow prediction of the probability of falling in some future time period”; see also paragraphs [0033], [0083], [0101], [0103] (“declines in observed gait velocity and deteriorating balance”), etc.]; and
determining whether the propensity for the fall event for the person satisfies a threshold propensity condition [e.g., his/her predicted/future risk of falling, at paragraphs [0083], [0103], etc. in Cole et al. (‘766)] based on (i) the amount of gait deterioration satisfying a threshold deterioration condition [e.g., paragraph [0103], “A specific utility of such predictive capacity of the invention is the calculation of the future probability of falling, which has been correlated with declines in observed gait velocity and deteriorating balance.”], or (ii) the amount of gait deterioration satisfying the threshold deterioration condition within a threshold time period;
It may be alleged that the floor tiles in Cole et al. (‘766) are not explicitly described as being “smart”, although the examiner understands that these tiles must have aspects that are intelligent or smart, since their outputs are sent to a signal processing unit, and e.g., dumb floor tiles would apparently not have any outputs that might be signal processed/processable.
It may also be alleged that Cole et al. (‘766) does not explicitly reveal details related to the threshold propensity condition or the threshold deterioration condition.
However, in the context/field of improved sensor tiles used for gait analysis and, for example, detecting whether someone has fallen, MacKinnon et al. (‘118) teaches the use of smart tiles having both sensors and microprocessors, for allowing the sensors to communicate with a host computer.
Moreover, in the context/field of an improved fall risk assessment system, Zheng et al. (‘697) teaches in conjunction with FIG. 13 and at paragraphs [0170] to [0175], etc. that gait features extracted (e.g., from video) over a predetermined time period (e.g., hourly, weekly, etc.) with the assessment system 1300 may be used by a gait analysis module 1312 to estimate a fall risk. For example, determined temporal variations over time in the gait features may be compared to predefined threshold values to identify abnormal behavior or mobility behavior (e.g., the number of steps during a day) or balance ability linked to a high fall risk. When the assessment system determines the high fall risk, a high-fall-risk warning 1340 may be output to a mobile app installed on one or more mobile devices (e.g., of a caregiver).
It would have been obvious before the effective filing date of the claimed invention to implement or modify the Cole et al. (‘766) method and apparatus to infer object and agent properties, including the risk of falling, so that the sensor floor tiles used for gait analysis and, for example, detecting whether someone has fallen, would have been smart tiles as taught by MacKinnon et al. (‘118) having both sensors and microprocessors, in order to allow the sensors to communicate with the computer (19), as taught by MacKinnon et al. (‘118), with a reasonable expectation of success, and e.g., as a use of a known technique to improve similar devices (methods, or products) in the same way.
Moreover, it would have been obvious before the effective filing date of the claimed invention to implement or further modify the Cole et al. (‘766) method and apparatus to infer object and agent properties, including the risk of falling, so that in order to infer or estimate the risk of falling, in addition to or in place of using the pattern of changes inputted to the trained classifier to make predictions that supplement or replace expert judgments as taught by Zheng et al. (‘697) at paragraphs [0103], etc., gait features extracted (e.g., from video) over a predetermined time period (e.g., hourly, weekly, etc.) would have been used by a gait analysis module (1312), as taught by Zheng et al. (‘697), in order to estimate a fall risk, with determined temporal variations (obviously deteriorations) over time in the gait features obviously being compared to predefined threshold values in order to identify abnormal behavior or mobility behavior (e.g., the number of steps during a day) or balance ability that is linked to a high fall risk, and so that when the high fall risk is determined, a high-fall-risk warning 1340 would have been output, as taught by Zheng et al. (‘697), e.g., to a mobile app installed on one or more mobile devices (e.g., of a caregiver), as taught by Zheng et al. (‘697), in order to additionally estimate fall risks using predefined thresholds for comparison and predetermined time periods for gait feature extraction and output warnings, as taught by Zheng et al. (‘697), with a reasonable expectation of success, and e.g., as a use of a known technique to improve similar devices (methods, or products) in the same way.
As such, the implemented or modified Cole et al. (‘766) method and apparatus to infer object and agent properties, including the risk of falling, would have rendered obvious:
per claim 1, . . . receiving data from a sensing device [e.g., in Cole et al. (‘766), from the sensor portion 21 in FIG. 2, at 42 in FIG. 5, at 51 in FIG. 6, etc.] in a smart floor tile [e.g., the smart tiles in MacKinnon et al. (‘118), implementing in Cole et al. (‘766) the plurality of sensor tiles forming a walkway (FIG. 2), such as Tactonic Technology LLC's pressure sensing floor tiles (paragraph [0053])8; see also paragraphs [0087], [0090], [0106], etc. in Cole et al. (‘766)];
monitoring a parameter pertaining to a gait of a person based on the data [e.g., the extracted gait features (1330) at paragraph [0170] and FIG. 13 in Zheng et al. (‘697); and paragraph [0003] in Cole et al. (‘766), “changes in gait parameters can monitor the progression of certain diseases. The measurements of gait, balance, and activity can be used to monitor the person's ergonomic performance and provide a means to give feedback information to the worker or to a system in order to reduce risk or increase the overall performance and efficiency of the work and the system”; and e.g., in the data processing & analytics unit (as described at paragraphs [0083], [0103], etc., for using/monitoring gait velocity and balance measurements) in Cole et al. (‘766)];
determining an amount of gait deterioration based on the parameter [e.g., the temporal variations in the extracted gait feature(s) at paragraph [0170] in Zheng et al. (‘697), with the variations obviously constituting a deterioration (e.g., “dropped down”) when the monitored person becomes at risk (or at high risk) of falling, such as (but not limited to) gait features related to body sway and/or number of steps in a day; and paragraph [0058] in Cole et al. (‘766), “Use of statistical techniques to compare of patterns of gait and/or balance over time for an individual can indicate physical deterioration and can be projected to allow prediction of the probability of falling in some future time period”; see also paragraphs [0033], [0083], [0101], [0103] (“declines in observed gait velocity and deteriorating balance”), etc.]; and
determining whether the propensity for the fall event [e.g., the propensity or risk of (the person) falling in Cole et al. (‘766) or Zheng et al. (‘697)] for the person satisfies a threshold propensity condition [e.g., that the person exhibits the high fall risk at paragraphs [0170] to [0175] in Zheng et al. (‘697); or alternately, the absence of the person exhibiting the high fall risk in Zheng et al. (‘697); and/or the person’s predicted/future risk of falling, at paragraphs [0083], [0103], etc. in Cole et al. (‘766), based on gait velocity and/or balance deterioration] based on (i) the amount of gait deterioration satisfying a threshold deterioration condition [e.g., paragraphs [0170] to [0175] in Zheng et al. (‘697), when the temporal variations in the extracted gait feature(s) reaches/drops down to/deteriorates to the “predefined threshold values”; and at paragraph [0103] in Cole et al. (‘766), “A specific utility of such predictive capacity of the invention is the calculation of the future probability of falling, which has been correlated with declines in observed gait velocity and deteriorating balance”, e.g., when the gait velocity in Cole et al. (‘766) deteriorates to a predefined threshold value as taught by Zheng et al. (‘697)], or (ii) the amount of gait deterioration satisfying the threshold deterioration condition within a threshold time period [e.g., as mapped above by the examiner, within an hour, within a day, or weekly, as taught at paragraphs [0017], [0169], [0170], [0173], etc. in Zheng et al. (697)];
per claim 2, depending from claim 1, wherein responsive to determining the propensity for the fall event for the person satisfies the threshold propensity condition, the method further comprises:
determining an intervention to perform based on the propensity for the fall event [e.g., the outputting of the high-risk-warning 1340 in FIG. 13 of Zheng et al. (‘697); and obviously to one of ordinary skill in the art paragraph [0043] in Cole et al. (‘766), “Yet another object of the invention is to use inferences from various of the invention objects as evidence for systems that communicate or signal the agent for some purpose, for example by making a commercial offer, providing information, making a warning, or changing some aspect of the environment, such as lighting”, with the inference obviously being for the risk of falling (FIG. 5)], and
performing the intervention [e.g., outputting the warning in (FIG. 13 of) Zheng et al. (‘697), or making the warning or changing some aspect of the environment, in (paragraph [0043]) of Cole et al. (‘766)];
per claim 3, depending from claim 2, wherein the intervention comprises:
transmitting a first message to a computing device of the person,
transmitting a second message to a computing device of a medical personnel [e.g., the mobile devices 206 – 208 in Zheng et al. (‘697), monitored by medical personnel, etc. (paragraphs [0047], [0170], etc.)],
causing an alarm to be triggered in a facility in which the person is located [e.g., at the mobile devices 206 – 208 in Zheng et al. (‘697), monitored by medical personnel, etc. (paragraphs [0047], [0170], etc.)],
changing a property of an electronic device located in a physical space with the person [e.g., of the mobile devices 206 – 208 in Zheng et al. (‘697), monitored by medical personnel, etc. (paragraphs [0047], [0170], etc.)],
changing a care plan for the person [e.g., by outputting the high-fall-risk warning 1340 at the mobile devices 206 – 208 in Zheng et al. (‘697), monitored by medical personnel, etc. (paragraphs [0047], [0170], etc.), to obviously change how they will act/respond],
changing an intensity of a directional indicator in the physical space in which the person is located, or
some combination thereof;
per claim 4, depending from claim 1, wherein responsive to determining the propensity for the fall event for the person does not satisfy the threshold propensity condition [e.g., in the fall risk assessment in FIG. 13 of Zheng et al. (‘697), obviously used (in Cole et al. (‘766)) for “continuously assessing the fall risk for the person” (paragraph [0180]) and at times obviously not assessing the high fall risk; and for estimating the future/predicted risk of falling, obviously continuously, in Cole et al. (‘766)], the method further comprises:
receiving subsequent data from the sensing device [e.g., from the (smart) floor tiles in Cole et al. (‘766) and/or from the video, etc. in Zheng et al. (‘697)];
monitoring the parameter pertaining to the gait of the person based on the subsequent data [e.g., in the gait analysis module 1312 (e.g., as described at paragraphs [0170] to [0175]) of Zheng et al. (‘697)]; and e.g., in the data processing & analytics unit (as described at paragraphs [0083], [0103], etc., for using/monitoring gait velocity and balance measurements) in Cole et al. (‘766)];
determining a second amount of gait deterioration based on the parameter [e.g., the temporal variations in the extracted gait feature(s) at paragraph [0170] in Zheng et al. (‘697), with the variations obviously constituting a deterioration (e.g., “dropped down”) when the monitored person becomes at risk (or at high risk) of falling, such as (but not limited to) gait features related to body sway and/or number of steps in a day; and paragraph [0058] in Cole et al. (‘766), “Use of statistical techniques to compare of patterns of gait and/or balance over time for an individual can indicate physical deterioration and can be projected to allow prediction of the probability of falling in some future time period”; see also paragraphs [0083], [0101], [0103] (“declines in observed gait velocity and deteriorating balance”), etc.]; and
determining whether the propensity for the fall event [e.g., the propensity or risk of falling in Cole et al. (‘766) or Zheng et al. (‘697)] for the person satisfies the threshold propensity condition [e.g., that the person exhibits the high fall risk at paragraphs [0170] to [0175] in Zheng et al. (‘697); or alternately, the absence of the person exhibiting the high fall risk in Zheng et al. (‘697); and/or the person’s predicted/future risk of falling, at paragraphs [0083], [0103], etc. in Cole et al. (‘766), based on gait velocity and/or balance deterioration] based on (i) the second amount of gait deterioration satisfying the threshold deterioration condition [e.g., paragraphs [0170] to [0175] in Zheng et al. (‘697), when the temporal variations in the extracted gait feature(s) reaches/drops down to/deteriorates to the “predefined threshold values”; and at paragraph [0103] in Cole et al. (‘766), “A specific utility of such predictive capacity of the invention is the calculation of the future probability of falling, which has been correlated with declines in observed gait velocity and deteriorating balance”, e.g., when the gait velocity in Cole et al. (‘766) deteriorates to a predefined threshold value as taught by Zheng et al. (‘697)], (ii) the second amount of gait deterioration satisfying the threshold deterioration condition within the threshold time period [e.g., as above mapped above by the examiner, within an hour, within a day, or weekly, as taught at paragraphs [0017], [0169], [0170], [0173], etc. in Zheng et al. (697)], or some combination thereof;
per claim 6, depending from claim 1, wherein the parameter comprises at least one of:
a speed of the gait of the person [e.g., the gait velocity at paragraph [0033], [0083], etc. in Cole et al. (‘766)],
a distance between a head of the person and feet of the person,
a distance between the feet during the gait of the person [e.g., paragraphs [0101] in Cole et al. (‘766)],
historical information pertaining to whether the person has previously fallen,
a weight of the person,
an age of the person,
medical history of the person [e.g., paragraph [0103] in Cole et al. (‘766), “Changes in such pattern properties over time can be used as inputs to calculate historical trends and make predictions about the future value of properties and features of an object. One example is prediction of the expected gait velocity and balance of a person. A specific utility of such predictive capacity of the invention is the calculation of the future probability of falling, which has been correlated with declines in observed gait velocity and deteriorating balance.”],
fracture history of the person,
vision impairment of the person,
activity level of the person [e.g., the steps per day at paragraph [0170] in Zhang et al. (‘697)],
balance distribution of weight while stationary, during gait, or both [e.g., the balance in both Cole et al. (‘766) at paragraphs [0033], [0083], etc. and Zheng et al. (‘697) at paragraphs [0170], etc. used to estimate/predict fall risk],
neurological condition of the person,
change in stride of the person [e.g., paragraphs [0101] in Cole et al. (‘766)],
results of a calibration test, or
some combination thereof;
per claim 7, depending from claim 1, further comprising receiving the data from a camera [e.g., the camera 102 for obtaining video data in Zheng et al. (‘697)], and wherein the parameter is monitored using computer vision [e.g., FIG. 1 in Zheng et al. (‘697)], object recognition [e.g., in the face recognition module 118 in FIG. 1 of Zheng et al. (‘697)], measured pressure [e.g., as taught in conjunction with FIGS. 1 to 5 of Cole et al. (‘766)], location of feet of the person [e.g., as taught in conjunction with FIGS. 7 to 11 of Cole et al. (‘766)], or some combination thereof;
per claim 8, depending from claim 1, wherein the monitoring the parameter pertaining to the gait of the person based on the data, the determining the amount of gait deterioration based on the parameter, and the determining whether the propensity for the fall event for the person satisfies the threshold propensity condition further comprises:
inputting the data into one or more machine learning models trained to determine the amount of gait deterioration based on the parameter [e.g., the trained classifier(s) as taught by Cole et al. (‘766) e.g., in conjunction with FIGS. 2, 4, 5, paragraphs [0083], [0103], claim 12, etc.; and the fall prediction using the CNN (convolutional neural network) and other neural network (classifier) techniques used in Zheng et al. (‘697), e.g., at paragraphs [0073], [0167], etc.] and to determine whether the propensity for the fall event [e.g., based on the gait velocity and balance in Cole et al. (‘766); and based on other gait features 1330 in Zheng et al. (‘697)] for the person satisfies the threshold propensity condition [e.g., the risk/propensity in Cole et al. (‘766); and/or the high risk in (FIG. 13 of) Zheng et al. (‘697)];
per claim 10, depending from claim 1, further comprising:
calibrating one or more gait baseline parameters for the person [e.g., paragraph [0103] in Cole et al. (‘766), “For all object properties and features, patterns of statistical properties and sequenc