DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. This is a nonfinal rejection in response to preliminary amendments filed on 07/22/2024. Claims 1-10 are cancelled. Claims 11-20 are pending and are examined herein.
Priority
3. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to Chinese Patent Application No. CN202310503841.3, filed on 05/06/2023.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 07/22/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
5. Claims 12-19 objected to because of the following informalities:
Claim 12 is recited to be dependent on claim 1, however, in the preliminary amendment, claims 1-9 have been cancelled. It is presumed that the applicant intended for the claim to be dependent on claim 10 instead. Therefore, the applicant should amend the claims such that they are properly dependent on the correct base claim. The examiner will examine the claims as if:
-Claim 12 depends on claim 11,
-Claim 13 depends on claim 11,
-Claim 14 depends on claim 13,
-Claim 15 depends on claim 11,
-Claim 16 depends on claim 15,
-Claim 17 depends on claim 11,
-Claim 18 depends on claim 12,
-Claim 19 depends on claim 11.
This assumptions are based on the dependency of the previous set of claims and the examiners best judgment, however, the applicant is expected to amend the claims to reflect this assumption or correct the dependencies.
Appropriate correction is required.
Claim Interpretation
6. 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.
7. 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. In the following limitations, the generic placeholder is “module” with the subsequent language indicating a recited function. Such claim limitation(s) is/are:
a target neighbourhood detecting module of a quantity of people entering and leaving, used for detecting a quantity of elderly people leaving each housing estate of a target neighbourhood and a quantity of elderly people entering each housing estate of the target neighbourhood in a set period, by face recognition technology of each housing estate of the target neighbourhood; in claim 20
a target neighbourhood analyzing module of neighbourhood traffic suitability, used for analyzing a mobility suitability coefficient of elderly people corresponding to each housing estate of the target neighbourhood, and then analyzing a traffic convenience coefficient JB corresponding to the target neighbourhood accordingly; in claim 20
a target neighbourhood evaluating module of leisure facility perfection, used for obtaining an occupied region of each housing estate of the target neighbourhood, obtaining an occupied area of each leisure district of the target neighbourhood, and analyzing a leisure facility perfection coefficient o corresponding to the target neighbourhood accordingly; in claim 20
a target neighbourhood analyzing module of microenvironment suitability, used for obtaining environmental parameters of each housing estate of the target neighbourhood, and then analyzing a microenvironment suitability coefficient HJ corresponding to the target neighbourhood accordingly; in claim 20
a target neighbourhood evaluating module of aging suitability, used for evaluating an evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; in claim 20
a target neighbourhood processing module, used for displaying the evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; in claim 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. However, while the specification recites the same “modules” as above, the disclosure does not provide any further detail regarding the structure of the modules. For example, there is no indication whether the modules represent software or hardware as neither “software” nor “hardware” is present in the disclosure. Likewise, the specification does not mention any computing component or technology including computers, processors, memory, or any sort of physical structure that can be tied to the modules. Please see the 112(b) rejection below for more details on the lack of written description. Therefore, the claims are interpreted under the broadest reasonable interpretation in view of the specification based on the plain language, therefore a module is interpreted to encapsulate both software and hardware capable of performing the function.
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
8. 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.
9. Claim 20 is 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.
The following claim limitations invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
“a target neighbourhood detecting module of a quantity of people entering and leaving, used for detecting a quantity of elderly people leaving each housing estate of a target neighbourhood and a quantity of elderly people entering each housing estate of the target neighbourhood in a set period, by face recognition technology of each housing estate of the target neighbourhood; in claim 20
a target neighbourhood analyzing module of neighbourhood traffic suitability, used for analyzing a mobility suitability coefficient of elderly people corresponding to each housing estate of the target neighbourhood, and then analyzing a traffic convenience coefficient JB corresponding to the target neighbourhood accordingly; in claim 20
a target neighbourhood evaluating module of leisure facility perfection, used for obtaining an occupied region of each housing estate of the target neighbourhood, obtaining an occupied area of each leisure district of the target neighbourhood, and analyzing a leisure facility perfection coefficient o corresponding to the target neighbourhood accordingly; in claim 20
a target neighbourhood analyzing module of microenvironment suitability, used for obtaining environmental parameters of each housing estate of the target neighbourhood, and then analyzing a microenvironment suitability coefficient HJ corresponding to the target neighbourhood accordingly; in claim 20
a target neighbourhood evaluating module of aging suitability, used for evaluating an evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; in claim 20
a target neighbourhood processing module, used for displaying the evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; in claim 20
However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. For example, there is no indication whether the modules represent software or hardware as neither “software” nor “hardware” is present in the disclosure. Likewise, the specification does not mention any computing component or technology including computers, processors, memory, or any sort of physical structure that can be tied to the modules. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections – 35 USC § 101
10. 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.
11. Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim recites a system...comprising various modules including a “target neighbourhood detecting module” and broadly recites “face recognition technology” without specifically limiting these modules and technology to a specific structure. Given the claim interpretation above under 112(f), which has found that the written description lacks the structure, material or acts performing the entire claimed function. Thus it is indefinite whether the claims are to be interpreted as software or hardware, thus the claims do not fall within processes, machines, manufactures and compositions of matter. MPEP 2106.03(II) states, “A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. Such claims fail the first step (Step 1: NO) and should be rejected under 35 U.S.C. 101, for at least this reason.” Therefore, the claim as a whole does not fall within any statutory category (Step 1: NO) and thus is non-statutory.
However, for purposes of compact prosecution, the claims are reanalyzed under the full 2-step analysis, assuming that the claims were to have passed step 1. The applicant is advised to either cancel claim 20, or amend the claims such that they overcome the issue by no longer claiming modules. Given that the specification lacks any structural recitations that can be brought into the claims, the examiner cautions the applicant that any amendment with the intent of providing structure to the modules must be done without introducing new matter to the claims.
12. Claims 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a Process, Machine, Manufacture, or Composition of Matter?
Claim 11: A method for... Claim 20: A system for...
Therefore, the claims are directed to the potentially eligible subject matter categories, since claims 11 is directed to a process and claim 20 is directed to a system which falls under machine. Therefore the claims are to be further analyzed under step 2 of the 2 step analysis.
Step 2a Prong 1: Is the claim directed to a Judicial Exception(A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claims 11 and 20 are marked up, isolating the abstract idea from additional elements, wherein the abstract idea is in bold and the additional elements have been italicized as follows:
Claim 11: A method for evaluating neighbourhood aging suitability based on multi-source data fusion, comprising:
S1, detecting a quantity of people entering and leaving a target neighbourhood by face recognition technology of each housing estate of the target neighbourhood, and detecting a quantity of elderly people leaving each housing estate of the target neighbourhood and a quantity of elderly people entering each housing estate of the target neighbourhood in a set period;
S2, analyzing traffic suitability of the target neighbourhood: analyzing a mobility suitability coefficient of the elderly people corresponding to each housing estate of the target neighbourhood, and then analyzing a traffic convenience coefficient JB corresponding to the target neighbourhood accordingly;
S3, evaluating perfection of leisure facilities of the target neighbourhood: obtaining an occupied region of each housing estate of the target neighbourhood, obtaining an area of the occupied region of each housing estate of the target neighbourhood, and analyzing a perfection coefficient of the leisure facilities corresponding to the target neighbourhood accordingly;
S4, analyzing microenvironment suitability of the target neighbourhood: obtaining environmental parameters of each housing estate of the target neighbourhood, and then analyzing a microenvironment suitability coefficient HJ corresponding to the target neighbourhood accordingly;
S5, evaluating aging suitability of the target neighbourhood: evaluating an evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; and
S6, processing the target neighbourhood: displaying the evaluation coefficient of aging residential suitability corresponding to the target neighbourhood.
Claim 20: A system for evaluating neighbourhood aging suitability based on multi-source data fusion, comprising:
a target neighbourhood detecting module of a quantity of people entering and leaving, used for detecting a quantity of elderly people leaving each housing estate of a target neighbourhood and a quantity of elderly people entering each housing estate of the target neighbourhood in a set period, by face recognition technology of each housing estate of the target neighbourhood;
a target neighbourhood analyzing module of neighbourhood traffic suitability, used for analyzing a mobility suitability coefficient of elderly people corresponding to each housing estate of the target neighbourhood, and then analyzing a traffic convenience coefficient JB corresponding to the target neighbourhood accordingly;
a target neighbourhood evaluating module of leisure facility perfection, used for obtaining an occupied region of each housing estate of the target neighbourhood, obtaining an occupied area of each leisure district of the target neighbourhood, and analyzing a leisure facility perfection coefficient o corresponding to the target neighbourhood accordingly;
a target neighbourhood analyzing module of microenvironment suitability, used for obtaining environmental parameters of each housing estate of the target neighbourhood, and then analyzing a microenvironment suitability coefficient HJ corresponding to the target neighbourhood accordingly;
a target neighbourhood evaluating module of aging suitability, used for evaluating an evaluation coefficient of aging residential suitability corresponding to the target neighbourhood;
a target neighbourhood processing module, used for displaying the evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; and
a cloud database, used for storing a proportion range of a quantity of elderly people suitable for leaving in each unit time, and storing a total area of leisure facility regions of a standard aging suitable neighbourhood, a total area of the occupied regions of the housing estate of the standard aging suitable neighbourhood, and a quantity of the leisure facility regions of the standard aging suitable neighbourhood.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claims 11 and 20 are directed to at least one abstract idea subcategory in “certain methods of organizing human activity.” This abstract idea grouping found in MPEP 2106.04(a)(2)(II) includes concepts related to “fundamental economic principles or practices,” “commercial or legal interactions,” and “managing personal behavior or relationships or interactions between people.” The present invention falls under managing personal behavior or relationships or interactions between people which include social activities, teaching, and following rules or instructions. When considering the steps in bold, for example, detecting a quantity of people, analyzing traffic suitability, evaluating perfection of leisure facilities, analyzing microenvironment suitability of the target neighborhood, evaluating aging suitability of the target neighborhood, and displaying the evaluation coefficient of aging residential suitability, the steps are no more than “certain methods of organizing human activity” because it describes an advertising, marketing or sales activity or behavior of scoring and displaying the suitability of a particular environment for a particular group of people (elders), which would fall at least under “commercial or legal interactions.” Furthermore, claims recite mere data processing steps towards performing an analysis on human behavior, resulting in an output of the data in the form of displaying a score. This is no more than “managing personal behavior or relationships or interactions between people” because it retries data reflective of social behavior and results in a display of the data. The data processing steps themselves are not recited with sufficient specificity, and are thus a set of rules or instructions for the determining the score. At this point no particular algorithm for determining the scores has been recited, other than broadly “analyzing,” “evaluating” and “displaying” coefficients. Therefore, the claims recite an abstract idea under “certain methods of organizing human activity.”
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 11 and 20 recite the following additional elements:
-face recognition technology in claims 11 and 20
- target neighbourhood detecting module, target neighbourhood analyzing module, target neighbourhood evaluating module, target neighbourhood analyzing module, target neighbourhood evaluating module, target neighbourhood processing module in claim 20
-cloud database in claim 20
The additional elements listed above are no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on a computer on its ordinary capacity. In this case, the abstract idea is being performed on the generic computing devices listed above (various modules). Please see MPEP 2106.05(f) for more information on Mere Instructions to Apply An Exception. Furthermore, the additional elements of using face recognition technology to detect a quantity of individuals is merely a general link to a particular technological environment or field of use. In this case, the claims are generally linked to “face recognition technology” in a way that does not meaningfully limit the claim because it does not suggest a specific form of facial recognition technology to be used, or specifically limit how it is implemented to perform the function. Similarly, the additional element of a “cloud” database does not meaningfully limit the claim because it is merely a data storage step limited to a broad platform, in this case a “cloud” database. Even when considering the additional element individually or as an ordered combination, the additional elements fail to integrate the abstract idea into a practical application because the claims are still so broad such that they are no more than an example of “apply it” or mere instructions to perform the abstract idea on a generic computing device. Furthermore, the combination of elements are not recited with enough specificity to be considered for the “improvements to the functioning of a computer or to any other technology or technical field.” Please refer to MPEP 2106.05(a) for information regarding Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field.
Therefore, claims 11 and 20 are directed to an abstract idea without integration into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 11 and 20 recite the following additional elements:
-face recognition technology in claims 11 and 20
- target neighbourhood detecting module, target neighbourhood analyzing module, target neighbourhood evaluating module, target neighbourhood analyzing module, target neighbourhood evaluating module, target neighbourhood processing module in claim 20
-cloud database in claim 20
These additional elements have not been found to include significantly more for the same reasons set forth in the Prong 2 rejection, specifically, that implementing the abstract idea on generic computing elements (modules) is no more than an example of “apply it” or mere instructions to apply an exception. Furthermore, the additional elements of face recognition technology and cloud database are recited with such generality that they are no more than a general link to particular technological environment. Even when considering the claims as a whole, nothing in the claims meaningfully limits the claims such that it recites significantly more than the abstract idea. Therefore, representative claims 11, and 20 are patent ineligible under 101 for being directed to an abstract idea without significantly more.
Dependent claims 12-19 are also given the full two part analysis both individually and in combination with the claims they depend on herein:
Claim 12 recites more of the same abstract idea because it merely defines the environmental parameters involved in the data processing to include carbon dioxide concentration, sound decibel, and PM 2.5 corresponding to each layout point at each detection time point. However, because this is merely claimed in a way that merely labels the meaning of the inputs, without specifically reciting the steps of sensing the carbon dioxide concentration, sound decibels, and PM 2.5 levels, the claims still fall under “certain methods of organizing human activity.” Furthermore, there are no additional elements to consider, therefore, even when considering individually or in combination, the claims are not integrated into a practical application. Even when viewed as a whole nothing in the claims meaningfully limits the claims such that it recites significantly more than the abstract idea (an inventive concept).
Claim 13 adds steps related to obtaining a face image, and counting the quantity of face images to determine the amount of people entering each housing estate. Without oversimplification of the claims, these claims still recite more of the same abstract idea of “certain methods of organizing human activity” because it merely describes how the quantity of individuals is counted in a generic manner (i.e. counting faces of people by each exit of a property). This is still “managing personal behavior” because it still falls under a set of rules or instructions to an individual to carry out the data collection. Furthermore, there are no additional elements to consider, therefore, even when considering individually or in combination, the claims are not integrated into a practical application. Even when viewed as a whole nothing in the claims meaningfully limits the claims such that it recites significantly more than the abstract idea (an inventive concept).
Claims 14-19 add steps that add mathematical concepts to the existing abstract idea process. MPEP 2106.04(a)(2) defines the mathematical concepts grouping as “mathematical relationships, mathematical formulas or equations, and mathematical calculations.” Since each step of the dependent claim limitations recite either mathematical relationships, formula or equation or calculations, the claims not only merely based on or involving a mathematical concept, but the claims recites a mathematical concept (and fall within the mathematical concept grouping). MPEP 2106.04(a)(2) states, “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula... A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
Therefore, since each and every limitation of claims 14-19 either determine a variable or number that is eventually inputted into a mathematical operation, the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. Some nonlimiting examples from the claims including:
Claim 14:
-obtaining a quantity a...obtaining a quantity b, analyzing a proportion BP.i = Bi/ai (This is a clear example of a mathematical equation).
-comparing the proportion of the quantity of the elderly people in each housing estate with a proportion range...and selecting a quantity of the elderly people suitable for leaving in each time unit, (While this does not directly express in mathematical symbols, the “comparing” is merely determining if the quantity falls within a predetermined range, which is a mathematical relationship. )
Therefore, each of the limitations of claims 14-19 are either outright formulas, or are words used in a claim operating on data to solve a problem that serves the same purpose as a formula. Furthermore, the additional element “cloud database” is repeated in claims 14 and 17, however, similarly to claim 20 it is still a general link to cloud technology because it merely indicates the cloud database a source or destination for the data, without any improvements to cloud databases themselves. Therefore, even when considering individually or in combination, the claims are not integrated into a practical application. Even when viewed as a whole nothing in the claims meaningfully limits the claims such that it recites significantly more than the abstract idea (an inventive concept).
Claim Rejections - 35 USC § 103
13. 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.
14. 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.
15. Claims 11, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over in view of AARP Public Policy Institute (NPL, “AARP Livability Index, Scoring”, April 21, 2022) hereinafter AARP, in view of Monti et al. (US 20220230389 A1) hereinafter Monti, further in view of Harper et al. (US 20120150586 A1) hereinafter Harper.
Regarding Claim 11:
AARP discloses an “AARP Livability Index” which is a scoring criteria designed to draw on multiple, interconnected points to score regions for their quality of life across age groups. AARP teaches:
- A method for evaluating neighbourhood aging suitability based on multi-source data fusion, comprising: (AARP [Scoring Page 1 “How are Livability Scores Determined”] AARP intentionally designed its scoring criteria to draw on multiple, interconnected points to capture the complexity of what produces a high quality of life for a diverse population across many ages. Metric values and policy points are scored for each of the seven livability categories: housing, neighborhood, transportation, environment, health, engagement, and opportunity. A location’s total livability score is an average of those seven category scores)
- S2, analyzing traffic suitability of the target neighbourhood: analyzing a mobility suitability coefficient corresponding to each housing estate of the elderly people corresponding to the target neighbourhood, (AARP [Scoring Page 3, “Zero-step Entrances”] Indicator: Percentage of housing units with a zero-step entrance; higher values are better. Attribute: Housing Accessibility. Rationale: ...Functional, tasteful designs that enable anyone to enter the home – by foot, wheelchair, or walker – constitutes accessible housing. Here, the index assesses the percentage of housing units that can be entered without steps. [Scoring Page 17 State and Local inclusive design laws] Indicator: State and local laws that make housing accessible for people of all abilities. Attribute Housing Accessibility: Housing accessibility. Rationale: As Americans live longer, homes built for easy access are becoming more necessary. At a minimum, a house should be “visitable” for someone in a wheelchair. Visitability requires features such as a zero-step entrance, wide doors and hallways, and a ground-floor bathroom. ) The broadest reasonable interpretation (BRI) of this limitation in view of the specification is any metric that indicates the accessibility of mobility for elderly people for the particular housing estate (home, building, apartment). In the cited sections of AARP above, the metrics analyze the suitability of each housing estate for elderly people and their mobility needs for a target neighborhood, thus satisfying the claims.
- and then analyzing a traffic convenience coefficient JB corresponding to the target neighbourhood accordingly; (AARP [Page 8, Transportation] ADA-accessible stations and vehicles Indicator: Percentage of transit stations and vehicles that are ADA-accessible; higher values are better Attribute: Accessible system design Rationale: People with restricted mobility rely heavily on convenient public transit equipped with accessible features, such as buses with wheelchair ramps and stations with elevators. In addition to allowing wheelchair users to enjoy the transit system, accessible stations and vehicles can also make it easier for people with other mobility issues to use transit. Advanced age alone does not qualify individuals to use paratransit services required by the ADA. [Page 9, Congestion] Indicator: Estimated total hours that the average person spends in traffic each year; lower values are better Attribute: Convenient transportation options Rationale: From a bustling economy to a vibrant culture, some communities have it all, but traffic congestion could make it hard for residents to enjoy these amenities. Not only do clogged roads wreck schedules and make appealing destinations hard to reach, they also increase air pollution. Here, the platform looks at the total amount of time per year that the average person in an urban area spends sitting in traffic.) The BRI of this limitation is any metric associated with the convenience of accessing convenient transportation options.
S3, evaluating perfection of leisure facilities of the target neighbourhood: (AARP [Pages 14-15 Social Involvement Index] Indicator: Extent to which residents belong to groups, organizations, or associations, see or hear from friends and family, do favors for neighbors, or do something positive for their community; higher values are better. [Page 15 Cultural, Arts, and Entertainment Institutions] Indicator: Number of movie theaters and entertainment centers within 5 miles and performing arts and sports venues within 15 miles per 10,000 people; higher values are better ) The BRI of this limitation is any coefficient that corresponds to access to leisurely activities within the neighborhood.
- obtaining an occupied region of each housing estate of the target neighbourhood, (AARP [Page 25 Explore the score] Twenty-three of those metrics evaluate livability at the neighborhood scale (denoted as the census block, block group, tract, or high school district), while the others use data sources at higher levels of geography (metro area, city, or county). [Page 7 Vacancy Rate] Percentage of vacant housing units; lower values are better; Highly livable neighborhoods are vibrant places that nurture a strong sense of community. A neighborhood with many vacant homes can indicate substandard or poorly maintained housing. Here, the Index measures the percentage of vacant housing units in a neighborhood.) The BRI of this limitation is that a certain range is evaluated, which is occupied (meaning people live in it). AARP’s regions are mapped to “occupied regions” as seen in the vacancy measure. The data at higher levels of geography is mapped to “occupied region”.
- obtaining an area of the occupied region of each housing estate of the target neighbourhood, and (AARP [Page 25 Explore the score] Twenty-three of those metrics evaluate livability at the neighborhood scale (denoted as the census block, block group, tract, or high school district), while the others use data sources at higher levels of geography (metro area, city, or county).) The BRI of this limitation is that a certain area within the occupied region is evaluated. The census block, block group, tract or high school district are examples of areas within the occupied region.
- analyzing a perfection coefficient of the leisure facilities corresponding to the target neighbourhood accordingly; (AARP [Pages 14-15 Social Involvement Index] Rationale: Being neighborly isn’t just a matter of etiquette—it’s what creates a community, often with lasting benefits for its residents. In fact, studies show that Americans who socialize regularly live longer. Here, using data at the metro level for the select 260 metropolitan areas and state-level data for other places, we look at how often people interact with their friends and neighbors. Communities where people socialize more frequently than average receive values above 1; those with people who socialize less frequently
receive values below [Page 15 Cultural, Arts, and Entertainment Institutions] Rationale; From sports fanatics to film buffs, a great community helps cultivate the interests of its residents through opportunities to learn, play, and interact with others. Here, the Livability Index measures how many cultural, arts, and entertainment institutions serve community residents. Our data capture only cultural institutions with paid staff, not volunteer-run organizations, such as community theater groups.. )The BRI of this limitation is any coefficient that corresponds to access to leisurely activities within the neighborhood.
- S4, analyzing microenvironment suitability of the target neighbourhood: obtaining environmental parameters of each housing estate of the target neighbourhood, and (AARP [Page 21 State Energy Efficiency Scorecard] Energy efficiency initiatives protect the environment by minimizing pollution from fossil fuels. Green options like energy-optimized homes, appliances, and vehicles can also save people money on gas and electric bills. The American Council for an Energy-Efficient Economy (ACEEE) ranks states based on a range of energy-efficient programs and policies. [Page 10 Regional Air Quality] Indicator: Number of days per year when regional air quality is unhealthy for
sensitive populations; lower values are better. Rationale: Poor air quality not only makes day-to-day life less enjoyable but also poses long-term health threats, especially for young people, older adults, and people who are at risk of developing asthma and other Environment – respiratory diseases. The Air Quality Index (AQI) measures the level of several different air pollutants on a scale of 0 to 500. An AQI of 101 or higher is considered unhealthy for sensitive populations. )
- then analyzing a microenvironment suitability coefficient HJ corresponding to the target neighbourhood accordingly; (AARP [Page 10 Regional Air Quality] Indicator: Number of days per year when regional air quality is unhealthy for sensitive populations; lower values are better. Rationale: Poor air quality not only makes day-to-day life less enjoyable but also poses long-term health threats, especially for young people, older adults, and people who are at risk of developing asthma and other Environment – respiratory diseases. The Air Quality Index (AQI) measures the level of several different air pollutants on a scale of 0 to 500. An AQI of 101 or higher is considered unhealthy for sensitive populations.)
S5, evaluating aging suitability of the target neighbourhood: evaluating an evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; and (AARP [Page 24] General State and local commitment to create age-friendly communities. Indicator: Communities that have taken comprehensive steps to prepare for the aging of the U.S. population. Attribute: Commitment to Livability. Rationale: By 2030, there will be twice as many Americans over the age of 65 as there were in 2000. To help residents live comfortably in all stages of life, communities must provide convenient transportation, walkable neighborhoods, affordable and accessible housing, multigenerational social opportunities, and inclusive business practices—just to name a few.)
S6, processing the target neighbourhood: evaluation coefficient of aging residential suitability corresponding to the target neighbourhood. (AARP [Page 25 Expert Analysis] We conducted a thorough vetting process to determine which metrics and policies best capture the broad nature of livability. We also gave great thought and consideration to the data sources and methodology used in scoring. We did this in partnership with experts from the AARP Public Policy Institute (PPI), our consultant team, and PPI’s technical advisory committee, which includes 30 experts in public policy, community planning, public health, aging, environmental studies, consumer affairs, and economics. The result is a tool that sheds deep insight into what makes a community livable)
AARP fails to teach:
- S1, detecting a quantity of people entering and leaving a target neighbourhood by face recognition technology of each housing estate of the target neighbourhood, and detecting a quantity of elderly people leaving each housing estate of the target neighbourhood and a quantity of elderly people entering each housing estate of the target neighbourhood in a set period;
- displaying the evaluation coefficient of aging residential suitability corresponding to the target neighbourhood.
Alternatively, Monti discloses a system for creating a three-dimensional model of a real estate development site, that comes with scores and indexes that indicate the improved livability of a region based if the development were to occur. Monti teaches:
- displaying the evaluation coefficient of aging residential suitability corresponding to the target neighbourhood.(Monti [[0061] Examples of these final outputs are shown in FIG. 11A is a first view of a populated map, which shows a rendering of a downtown as a three dimensional image with a Social Activity score placed thereon. An example of a Social Activity Score is shown in FIG. 13 as table 1301. [0062] FIG. 11B is a second view of a populated map which has a three-dimensional rendering showing three different scores based upon Social (table 1301), Environmental (table 1401) and Economic Activity (table 1501) shown in greater detail in FIGS. 13-15. In addition, also shown along the left column are listings of entities 1104 in a left hand column.] [0044] the AARP livability index servers,) In Monti, the system outputs various scores based on social, environmental, and economic activity. Since the scores are also based on the AARP livability index measurements, then the display satisfies the limitations.
However, neither AARP nor Monti teach or suggest:
-S1, detecting a quantity of people entering and leaving a target neighbourhood by face recognition technology of each housing estate of the target neighbourhood, and
- detecting a quantity of elderly people leaving each housing estate of the target neighbourhood and
- a quantity of elderly people entering each housing estate of the target neighbourhood in a set period;
Alternatively, Harper discloses an apparatus and method to record customer demographics in a venue or facility using cameras. Harper teaches:
-S1, detecting a quantity of people entering and leaving a target neighbourhood by face recognition technology of each housing estate of the target neighbourhood, and (Harper [0012] The detection subsystem includes a camera and affiliated software programs to identify demographic information of customers entering a venue. The detection subsystem may be positioned such that all customers entering a venue pass through a visual target region of the system. The processing software uses facial detection and/or recognition algorithms to determine, for example, an age, a gender, a race, a height, and/or a weight of a customer. [0091] FIG. 6 shows a demographic recognition or detection analysis performed by local server 114a or the demographic recognition camera 504 of FIG. 5A. In this example, the camera 504 detects customers 508 and 510 who have walked through the door of venue 106 and have entered a zone of interest 600. The zone of interest 600 is created when the camera 504 is setup and is positioned to record customers entering the venue 106. Customers 508 and 510 are counted by traffic flow camera or sensor 502. ) In Harper, the venue is mapped to the target neighborhood limitation, and each housing estate is mapped to each “zone of interest.”
- detecting a quantity of elderly people leaving each housing estate of the target neighbourhood and (Harper [0127] The local processor 114a then updates a demographic profile of the venue 106 with the demographic data associated with the newly entered customer (block 1910). In some examples, the local processor 114a updates the demographic profile by updating a count of different demographic categories. For example, the code blow shows demographic categories that may be tracked for the venue 106. In this example, the demographic categories of "m_age_older_count" and "male_count" listed below can be updated based on the newly entered customer being a 40 year old male. [0128] In one example, local processor 114a determines if any customers have left the venue 106 based upon information provided by the traffic flow camera 502 (block 1912)) Also see Table-US-0001 “f_age_older_count: 0, m_age_older_count: 1.”
- a quantity of elderly people entering each housing estate of the target neighbourhood in a set period; (Harper [0157] In accordance with a twenty-first aspect of the present disclosure, which may be used in combination with any one or more of the preceding aspects, the at least one of a total number of customers at the venue for different time periods or a demographic characteristic of the customers at the venue for different time periods are stored at the central server and made available to an operator of the venue. [0119] The section 1514 can also show trend information for the venue 106. For example, the central server 102 can determine a rate at which customers are entering a venue by comparing count data for subsequent time periods. [0008] In a venue operator context, the example methods and systems compile customer demographic information into history trends and/or provide real-time updates to a venue operator based on analyzed demographic information. For example, history trends may inform venue operators which types of people appeared at their venues at specific times of a day and/or days of a week.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of AARP and Monty by adding Harper’s face recognition technology which can count a quantity of individuals leaving and entering a facility, including counting a tally of older individuals using the face recognition algorithm. Since Harper’s system is for a generalized venue, it would have been obvious to integrate it into AARP and Monty’s housing estates and target neighborhoods to arrive at the predictable outcome of counting the quantity of elderly people entering housing estate of the target neighborhood. One of ordinary skill in the art would have been motivated to perform this combination by the benefit of enabling third parties to gain knowledge and insights about venues across a city of geographic location in real time (Harper [0006] The present disclosure also relates to using the customer demographic information to provide customer data and real-time information to at least three different user groups including: 1) customers, 2) venue operators, and 3) third parties. In this manner, the present disclose enables customers, venue operators, and third parties to gain knowledge about the happenings of venues across a city or other geographic location in real-time.)
Regarding Claim 13:
The combination of AARP, Monti, and Harper teach or suggest: The method for evaluating neighbourhood aging suitability based on multi-source data fusion according to claim 1,
Furthermore, AARP teaches:
-each housing estate of the target neighborhood(see AARP [Scoring Page 3, “Zero-step Entrances”] Indicator: Percentage of housing units with a zero-step entrance; higher values are better. Attribute: Housing Accessibility. Rationale: ...Functional, tasteful designs that enable anyone to enter the home – by foot, wheelchair, or walker – constitutes accessible housing. Here, the index assesses the percentage of housing units that can be entered without steps. [Scoring Page 17 State and Local inclusive design laws] Indicator: State and local laws that make housing accessible for people of all abilities. Attribute Housing Accessibility: Housing accessibility. Rationale: As Americans live longer, homes built for easy access are becoming more necessary. At a minimum, a house should be “visitable” for someone in a wheelchair. Visitability requires features such as a zero-step entrance, wide doors and hallways, and a ground-floor bathroom. )
However, neither AARP nor Monti teach:
-wherein the step of detecting a quantity of elderly people leaving each housing estate of the target neighbourhood and a quantity of elderly people entering each housing estate of the target neighbourhood in a set period comprises: obtaining a face image of each elderly people in each housing estate of the target neighbourhood;
- obtaining a face image collected by each exit of each housing estate of the target neighbourhood in the set period from a target neighbourhood management office, and
- analyzing a face image of each elderly people leaving each housing estate of the target neighbourhood;
- counting a quantity of the face images of the elderly people leaving each housing estate of the target neighbourhood, and
- taking the quantity of the face images as a quantity SLi of the elderly people leaving each housing estate of the target neighbourhood in the set period,
- wherein i is a serial number of each housing estate, and i = 1,2,...,f; and
- in a same way, obtaining a quantity JL, of the elderly people entering each housing estate of the target neighbourhood in the set period.
Alternatively, Harper teaches:
- wherein the step of detecting a quantity of elderly people leaving each area of interest of the target venue and a quantity of elderly people entering each area of interest of the target venue in a set period comprises: obtaining a face image of each elderly people in each area of interest of the target venue; (Harper [0102] In an embodiment, the facial detection software uses algorithms to determine what a customer looks like through physical characteristic analysis or through a matching program that utilizes existing data to match a recorded facial or body image to generic faces or body types stored in a database. The facial detection software determines, for example, that a customer is a twenty-eight year old male. The facial recognition software uses image databases (such as Facebook.TM. or government databases) to match a recorded image to an image in one of these databases to determine an identity of a customer in the image. In this example, the facial recognition determines that a customer is, for example, John Smith. [0096] Parameters, such as age and ethnicity, may be sophisticated guesses that have a certain margin for error. Thus, a recognition software determination that customer 510 is thirty-one years of age can be categorized in a range, such as a three-year, five-year or eleven-year range, e.g., 29.5 to 325, twenty-nine to thirty-three or twenty-six to thirty-six. The ranges have progressively increasing accuracy but large span.)
- obtaining a face image collected by each exit of each area of interest of the target venue in the set period from a target area management office, and (Harper [0100] FIG. 8 shows a side schematic view of the venue 106 with an alternative demographic recognition configuration, using additional camera 804 along with camera 504 for detection subsystem 113a. In this example, the camera 804, mounted via an adjustable mounting member 802, is used to determine demographic or physical characteristics of customers as they exit the venue 106. [0112] In FIG. 14, the registration interface 1400 includes information regarding how the venue operator 122 would prefer to view history and real-time information collected and processed by the central server 102. For example, the venue operator 122 can select different calculation engine options to specify how the central server 102 is to process data collected from the venue 106. The venue operator 122 can also specify times during which the central server 102 is to collect and process data from the venue 106. [0015] The local server may transmit the information at predetermined time periods (e.g., every minute, every five minutes, every fifteen minutes, etc.).) The venue operator is mapped to the management office.
- analyzing a face image of each elderly people leaving each area of interest of the target venue; (Harper [0043] Additionally, facial recognition algorithms implemented by the local servers 114a and 114b analyze video of customers to determine physical characteristics (e.g., age, gender, height, weight, etc.). After determining at least some of these physical characteristics for a number of customers, the local servers 114a and 114b create a record summarizing the information.)
- counting a quantity of the face images of the elderly people leaving each area of interest of the target venue, and (Harper [0042] The local servers 114a and 114b can also maintain records for a number of customers entering and leaving, a number of customers relative to venue capacity and/or a number of customers relative to venue size. [0089] Alternatively, the camera 505 records video images of the customers in venue 106, and local server 114a or the central server 102 includes software that (i) counts a number of customers entering or leaving the venue 106 and (ii) uses physical facial or body recognition algorithms to determine demographics of the customers. [0127] For example, the code blow shows demographic categories that may be tracked for the venue 106. In this example, the demographic categories of "m_age_older_count" and "male_count" listed below can be updated based on the newly entered customer being a 40 year old male.)
- taking the quantity of the face images as a quantity SLi of the elderly people leaving each area of interest of the target venue in the set period, (Harper [0127] TABLE-US-00001 "venue_id":0, "venue_secret":"0", "interval":0, "data":{ "timestamp":"2011-12-06T16:28:43", "count_in":0, "count_out":0, "f_age_unknown_count":0, "f_age_child_count":0, "f_age_teen_count":0, "f_age_young_count":0, "f_age_older_count":0, "f_age_senior_count":0, "m_age_unknown_count":0, "m_age_child_count":0, "m_age_teen_count":0, "m_age_young_count":0, "m_age_older_count":1, "m_age_senior_count":0, "u_age_unknown_count":0, "u_age_child_count":0, "u_age_teen_count":0, "u_age_young_count":0, "u_age_older_count":0, "u_age_senior_count":0, "unknown_count":0, "female_count":0, "male_count":1 } ) See also Fig. 21 for a graph counting the entering and leaving rates, as well as the ages. The table above includes the quantity of older aged people, in a specific time interval, thus satisfying the claims.
- wherein i is a serial number of each area of interest, and i = 1,2,...,f; and (Harper [See 127 Table 00001]) Harper’s table includes a “venue-_id” which satisfies the limitation of i being a serial number for each area of interest.
- in a same way, obtaining a quantity JL, of the elderly people entering each area of interest of the target venue in the set period.(Harper [See 127 Table 00001] “Count_in” indicates the entering count for the time period. [0128] If customers have left, the local processor 114a updates count and/or demographic information based on the customers that have left the venue 106 (block 1914). The local processor 114a then determines if a time period for transmitting data to the central server 102 has elapsed (block 1916). If the time has elapsed, the local processor 114a transmits the customer demographic data to the central server 102 (block 1918). The local server 114a may also transmit real-time venue information including temperature, noise and light levels, humidity, etc. The local server 114a then determines if a time period for monitoring the venue 106 has elapsed (such as when the venue 106 closes). If the time period has not elapsed, the local server 114a returns to detecting if customers have entered the venue 106 (block 1902). If the time period has elapsed, the example process 1900 ends as illustrated.)
Therefore, it would have been obvious to one of ordinary skill in the art to modify the combination of AARP, Monti and Harper, by adding Harper’s demographic counting system to AARP’s index scoring. By merely substituting Harper’s housing estates for the areas of interest in Harper, and the target neighborhoods for the target venues in Harper, one would arrive at the predictable outcome of the limitations above. This is due to the fact that in Harper, the venues can easily be substituted for homes and communities and arrive at the same outcomes. One would have been motivated to perform this combination as it provides an improved technological way to provide demographic information to management to help cater the target age groups. (Harper [0002] Everyone has a different definition an ideal venue. For example, college-aged people may define an ideal venue as one with a lively single younger crowd, while older people may define an ideal venue as one with a more relaxed crowd. [0004] To resolve the above issues, people may compile a few known places and proceed to travel from venue to venue in an attempt to find an ideal or even adequate venue. However, this travel consumes time and resources, especially if the venues are geographically spaced apart.) AARP and Harper share the motivation of indicating to a particular age group whether an environment is suitable for them.
Regarding Claim 20:
A system for evaluating neighbourhood aging suitability based on multi-source data fusion, comprising: (AARP [Scoring Page 1 “How are Livability Scores Determined”] AARP intentionally designed its scoring criteria to draw on multiple, interconnected points to capture the complexity of what produces a high quality of life for a diverse population across many ages. Metric values and policy points are scored for each of the seven livability categories: housing, neighborhood, transportation, environment, health, engagement, and opportunity. A location’s total livability score is an average of those seven category scores)
- a target neighbourhood analyzing module for neighborhoud traffic suitability, user for analyzing a mobility suitability coefficient of elderly people corresponding to each housing estate of the target neighbourhood, and (AARP [Scoring Page 3, “Zero-step Entrances”] Indicator: Percentage of housing units with a zero-step entrance; higher values are better. Attribute: Housing Accessibility. Rationale: ...Functional, tasteful designs that enable anyone to enter the home – by foot, wheelchair, or walker – constitutes accessible housing. Here, the index assesses the percentage of housing units that can be entered without steps. [Scoring Page 17 State and Local inclusive design laws] Indicator: State and local laws that make housing accessible for people of all abilities. Attribute Housing Accessibility: Housing accessibility. Rationale: As Americans live longer, homes built for easy access are becoming more necessary. At a minimum, a house should be “visitable” for someone in a wheelchair. Visitability requires features such as a zero-step entrance, wide doors and hallways, and a ground-floor bathroom. ) The broadest reasonable interpretation (BRI) of this limitation in view of the specification is any metric that indicates the accessibility of mobility for elderly people for the particular housing estate (home, building, apartment). In the cited sections of AARP above, the metrics analyze the suitability of each housing estate for elderly people and their mobility needs for a target neighborhood, thus satisfying the claims. Furthermore, given the 112(f) interpretation, which shows that the present specification does not specify whether the “modules” are hardware or software, the modules are broadly interpreted to be any process, regardless of software or hardware, that performs the accompanying functions. Therefore, any prior art disclosure that teaches the functions, satisfies the claim.
- then analyzing a traffic convenience coefficient JB corresponding to the target neighbourhood accordingly; (AARP [Page 8, Transportation] ADA-accessible stations and vehicles Indicator: Percentage of transit stations and vehicles that are ADA-accessible; higher values are better Attribute: Accessible system design Rationale: People with restricted mobility rely heavily on convenient public transit equipped with accessible features, such as buses with wheelchair ramps and stations with elevators. In addition to allowing wheelchair users to enjoy the transit system, accessible stations and vehicles can also make it easier for people with other mobility issues to use transit. Advanced age alone does not qualify individuals to use paratransit services required by the ADA. [Page 9, Congestion] Indicator: Estimated total hours that the average person spends in traffic each year; lower values are better Attribute: Convenient transportation options Rationale: From a bustling economy to a vibrant culture, some communities have it all, but traffic congestion could make it hard for residents to enjoy these amenities. Not only do clogged roads wreck schedules and make appealing destinations hard to reach, they also increase air pollution. Here, the platform looks at the total amount of time per year that the average person in an urban area spends sitting in traffic.) The BRI of this limitation is any metric associated with the convenience of accessing convenient transportation options.
-a target neighbourhood evaluating module of leisure facility perfection, used for obtaining an occupied region of each housing estate of the target neighbourhood, (AARP [Page 25 Explore the score] Twenty-three of those metrics evaluate livability at the neighborhood scale (denoted as the census block, block group, tract, or high school district), while the others use data sources at higher levels of geography (metro area, city, or county). [Page 7 Vacancy Rate] Percentage of vacant housing units; lower values are better; Highly livable neighborhoods are vibrant places that nurture a strong sense of community. A neighborhood with many vacant homes can indicate substandard or poorly maintained housing. Here, the Index measures the percentage of vacant housing units in a neighborhood.) The BRI of this limitation is that a certain range is evaluated, which is occupied (meaning people live in it). AARP’s regions are mapped to “occupied regions” as seen in the vacancy measure. The data at higher levels of geography is mapped to “occupied region”.
-obtaining an occupied area of each leisure district of the target neighbourhood, and (AARP [Page 25 Explore the score] Twenty-three of those metrics evaluate livability at the neighborhood scale (denoted as the census block, block group, tract, or high school district), while the others use data sources at higher levels of geography (metro area, city, or county).) The BRI of this limitation is that a certain area within the occupied region is evaluated. The census block, block group, tract or high school district are examples of areas within the occupied region.
- analyzing a leisure facility perfection coefficient o corresponding to the target neighbourhood accordingly; (AARP [Pages 14-15 Social Involvement Index] Rationale: Being neighborly isn’t just a matter of etiquette—it’s what creates a community, often with lasting benefits for its residents. In fact, studies show that Americans who socialize regularly live longer. Here, using data at the metro level for the select 260 metropolitan areas and state-level data for other places, we look at how often people interact with their friends and neighbors. Communities where people socialize more frequently than average receive values above 1; those with people who socialize less frequently
receive values below [Page 15 Cultural, Arts, and Entertainment Institutions] Rationale; From sports fanatics to film buffs, a great community helps cultivate the interests of its residents through opportunities to learn, play, and interact with others. Here, the Livability Index measures how many cultural, arts, and entertainment institutions serve community residents. Our data capture only cultural institutions with paid staff, not volunteer-run organizations, such as community theater groups.. )The BRI of this limitation is any coefficient that corresponds to access to leisurely activities within the neighborhood.
- a target neighbourhood analyzing module of microenvironment suitability, used for obtaining environmental parameters of each housing estate of the target neighbourhood, and then (AARP [Page 21 State Energy Efficiency Scorecard] Energy efficiency initiatives protect the environment by minimizing pollution from fossil fuels. Green options like energy-optimized homes, appliances, and vehicles can also save people money on gas and electric bills. The American Council for an Energy-Efficient Economy (ACEEE) ranks states based on a range of energy-efficient programs and policies. [Page 10 Regional Air Quality] Indicator: Number of days per year when regional air quality is unhealthy for
sensitive populations; lower values are better. Rationale: Poor air quality not only makes day-to-day life less enjoyable but also poses long-term health threats, especially for young people, older adults, and people who are at risk of developing asthma and other Environment – respiratory diseases. The Air Quality Index (AQI) measures the level of several different air pollutants on a scale of 0 to 500. An AQI of 101 or higher is considered unhealthy for sensitive populations. )
- analyzing a microenvironment suitability coefficient HJ corresponding to the target neighbourhood accordingly; (AARP [Page 10 Regional Air Quality] Indicator: Number of days per year when regional air quality is unhealthy for sensitive populations; lower values are better. Rationale: Poor air quality not only makes day-to-day life less enjoyable but also poses long-term health threats, especially for young people, older adults, and people who are at risk of developing asthma and other Environment – respiratory diseases. The Air Quality Index (AQI) measures the level of several different air pollutants on a scale of 0 to 500. An AQI of 101 or higher is considered unhealthy for sensitive populations.)
- a target neighbourhood evaluating module of aging suitability, used for evaluating an evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; (AARP [Page 24] General State and local commitment to create age-friendly communities. Indicator: Communities that have taken comprehensive steps to prepare for the aging of the U.S. population. Attribute: Commitment to Livability. Rationale: By 2030, there will be twice as many Americans over the age of 65 as there were in 2000. To help residents live comfortably in all stages of life, communities must provide convenient transportation, walkable neighborhoods, affordable and accessible housing, multigenerational social opportunities, and inclusive business practices—just to name a few.)
- a target neighbourhood processing module, used for displaying the evaluation coefficient of aging residential suitability corresponding to the target neighbourhood; (AARP [Page 25 Expert Analysis] We conducted a thorough vetting process to determine which metrics and policies best capture the broad nature of livability. We also gave great thought and consideration to the data sources and methodology used in scoring. We did this in partnership with experts from the AARP Public Policy Institute (PPI), our consultant team, and PPI’s technical advisory committee, which includes 30 experts in public policy, community planning, public health, aging, environmental studies, consumer affairs, and economics. The result is a tool that sheds deep insight into what makes a community livable)
- storing a total area of leisure facility regions of a standard aging suitable neighbourhood, (AARP [Page 5 Access to Parks] Indicator: Number of parks within a half-mile; higher values are better Attribute: Proximity to destinations Rationale: __HTML__:<p>Parks provide opportunities for people to exercise, gather with friends, or simply enjoy the outdoors. It’s no wonder that most people surveyed by AARP value having a park within walking distance. Communities with multiple nearby parks may provide greater access to trails, athletic facilities, picnic tables, playgrounds, and more.</p>)
- a total area of the occupied regions of the housing estate of the standard aging suitable neighbourhood, and (AARP [Page 7 Vacancy Rate] Indicator: Percentage of vacant housing units; lower values are better Attribute: Neighborhood quality Rationale: __HTML__:<p>Highly livable neighborhoods are vibrant places that nurture a strong sense of community. A neighborhood with many vacant homes can indicate substandard or poorly maintained housing. Here, the Index
measures the percentage of vacant housing units in a neighborhood.</p>)
- a quantity of the leisure facility regions of the standard aging suitable neighbourhood.(AARP [Page 6 Access to Libraries] Indicator: Number of libraries within a half-mile; higher values are better
Attribute: Proximity to destinations Rationale: __HTML__:<p>Libraries promote literacy, provide internet access and other services, and serve as community gathering spaces. They can be particularly important for low-income residents, who may lack the money to Neighborhood – purchase books or internet access at home.</p>)
However, AARP and Monti fails to teach:
- a target neighbourhood detecting module of a quantity of people entering and leaving,
- used for detecting a quantity of elderly people leaving each housing estate of a target neighbourhood and a quantity of elderly people entering each housing estate of the target neighbourhood in a set period,
-by face recognition technology of each housing estate of the target neighbourhood;
-a cloud database, used for storing a proportion range of a quantity of elderly people suitable for leaving in each unit time,
- that the steps of storing a total area of leisure facility regions of a standard aging suitable neighbourhood, a total area of the occupied regions of the housing estate of the standard aging suitable neighbourhood, and a quantity of the leisure facility regions of the standard aging suitable neighbourhood are specifically stored on the cloud database.
Alternatively, Harper teaches:
- a target neighbourhood detecting module of a quantity of people entering and leaving, used for detecting a quantity of elderly people leaving each housing estate of a target neighbourhood and(Harper [0127] The local processor 114a then updates a demographic profile of the venue 106 with the demographic data associated with the newly entered customer (block 1910). In some examples, the local processor 114a updates the demographic profile by updating a count of different demographic categories. For example, the code blow shows demographic categories that may be tracked for the venue 106. In this example, the demographic categories of "m_age_older_count" and "male_count" listed below can be updated based on the newly entered customer being a 40 year old male. [0128] In one example, local processor 114a determines if any customers have left the venue 106 based upon information provided by the traffic flow camera 502 (block 1912)) Also see Table-US-0001 “f_age_older_count: 0, m_age_older_count: 1.”
- a quantity of elderly people entering each housing estate of the target neighbourhood in a set period, (Harper [0012] The detection subsystem includes a camera and affiliated software programs to identify demographic information of customers entering a venue. The detection subsystem may be positioned such that all customers entering a venue pass through a visual target region of the system. The processing software uses facial detection and/or recognition algorithms to determine, for example, an age, a gender, a race, a height, and/or a weight of a customer. [0091] FIG. 6 shows a demographic recognition or detection analysis performed by local server 114a or the demographic recognition camera 504 of FIG. 5A. In this example, the camera 504 detects customers 508 and 510 who have walked through the door of venue 106 and have entered a zone of interest 600. The zone of interest 600 is created when the camera 504 is setup and is positioned to record customers entering the venue 106. Customers 508 and 510 are counted by traffic flow camera or sensor 502. ) (Harper [0157] In accordance with a twenty-first aspect of the present disclosure, which may be used in combination with any one or more of the preceding aspects, the at least one of a total number of customers at the venue for different time periods or a demographic characteristic of the customers at the venue for different time periods are stored at the central server and made available to an operator of the venue. [0119] The section 1514 can also show trend information for the venue 106. For example, the central server 102 can determine a rate at which customers are entering a venue by comparing count data for subsequent time periods. [0008] For example, history trends may inform venue operators which types of people appeared at their venues at specific times of a day and/or days of a week.) In Harper, the venue is mapped to the target neighborhood limitation, and each housing estate is mapped to each “zone of interest.”
-by face recognition technology of each housing estate of the target neighbourhood; Harper [0102] In an embodiment, the facial detection software uses algorithms to determine what a customer looks like through physical characteristic analysis or through a matching program that utilizes existing data to match a recorded facial or body image to generic faces or body types stored in a database. The facial detection software determines, for example, that a customer is a twenty-eight year old male. The facial recognition software uses image databases (such as Facebook.TM. or government databases) to match a recorded image to an image in one of these databases to determine an identity of a customer in the image. In this example, the facial recognition determines that a customer is, for example, John Smith. [0096] Parameters, such as age and ethnicity, may be sophisticated guesses that have a certain margin for error. Thus, a recognition software determination that customer 510 is thirty-one years of age can be categorized in a range, such as a three-year, five-year or eleven-year range, e.g., 29.5 to 325, twenty-nine to thirty-three or twenty-six to thirty-six. The ranges have progressively increasing accuracy but large span.)
-a cloud database, used for storing a proportion range of a quantity of elderly people suitable for leaving in each unit time, (Harper [0011] Any of these locations can include or use a system according to the present disclosure, which may include a detection subsystem (e.g., facial or demographic detection and recognition), a traffic flow subsystem, and a local server communicatively coupled to a centrally located monitoring server (described in detail herein). In other examples, functionality of a local server and/or a monitoring server (described in detail herein) may be combined and located at a central location or, alternatively, may be implemented in a cloud computing environment. [0045] The example central server 102 of FIG. 1 analyzes the demographic data and real-time information received in the reports from the venues 106 and 108. Additionally, the central server 102 analyzes demographic data and real-time information from venue 110 based on its own stored demographic recognition and/or detection algorithms. Central server 102 routes the reports into appropriate databases corresponding to the venues 106 to 110. For example, a report received from the venue 106 is routed to a database designated for venue 106. Additionally, a general database for an attribute, e.g., six feet or taller, can be kept for multiple ones on all of the venues of system 100. [0075] The local server 114a or the central server 102 can access these attributes from a government or commercial database. [0108] In yet other instances, cameras 502 and 504 may be communicatively coupled to application programming interfaces ("APIs") via the network 112. In these instances, the APIs are hosted in a cloud platform that provides central processing for facial or demographic identification from one or more venues. Here, the cloud computing may replace the functionality provided by the local server 114a and the central server 102.) See TABLE-US-00001 for the proportion range of quantity of elderly people suitable for leaving in each unit time.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of AARP and Monty by adding Harper’s face recognition technology which can count a quantity of individuals leaving and entering a facility, including counting a tally of older individuals using the face recognition algorithm. Since Harper’s system is for a generalized venue, it would have been obvious to integrate it into AARP and Monty’s housing estates and target neighborhoods to arrive at the predictable outcome of counting the quantity of elderly people entering housing estate of the target neighborhood. One of ordinary skill in the art would have been motivated to perform this combination by the benefit of enabling third parties to gain knowledge and insights about venues across a city of geographic location in real time (Harper [0006] The present disclosure also relates to using the customer demographic information to provide customer data and real-time information to at least three different user groups including: 1) customers, 2) venue operators, and 3) third parties. In this manner, the present disclose enables customers, venue operators, and third parties to gain knowledge about the happenings of venues across a city or other geographic location in real-time.)
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify the combination of AARP and Monty by adding the cloud database of Harper, used for storing a proportion range of a quantity of elderly people suitable for leaving in each unit time. Furthermore, though Harper does not teach that the steps of storing a total area of leisure facility regions of a standard aging suitable neighbourhood, a total area of the occupied regions of the housing estate of the standard aging suitable neighbourhood, and a quantity of the leisure facility regions of the standard aging suitable neighbourhood are specifically stored on the cloud database, it would have been obvious to try to store this information (as taught by AARP) on the cloud database of Harper. One of ordinary skill in the art would have been motivated by the benefit of storing demographic information a cloud database to combine all of the information from different locations onto a single centralized location. (Harper [0011] To illustrate the systems and methods disclosed herein, reference is made to restaurants and bars. However, the example systems and methods can be applied to any venue location that caters to customers (e.g., restaurants, bowling allies, movie theaters, clubs, parks, retail stores, malls, grocery stores, cafes, gas stations, stadiums, schools, museums, etc.). Any of these locations can include or use a system according to the present disclosure, which may include a detection subsystem (e.g., facial or demographic detection and recognition), a traffic flow subsystem, and a local server communicatively coupled to a centrally located monitoring server (described in detail herein). In other examples, functionality of a local server and/or a monitoring server (described in detail herein) may be combined and located at a central location or, alternatively, may be implemented in a cloud computing environment.)
16. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over in view of AARP Public Policy Institute (NPL, “AARP Livability Index, Scoring”, April 21, 2022), in view of Monti et al. (US 20220230389 A1), further in view of Harper et al. (US 20120150586 A1), further in view of Granger et al. (US 20200200416 A1) hereinafter Granger.
The combination of AARP, Monti, and Harper teach, The method of The method for evaluating neighbourhood aging suitability based on multi-source data fusion according to claim 1,
Furthermore, AARP teaches:
Environmental parameters (AARP [Page 11 Local Industrial Pollution] Indicator: Toxicity of airborne chemicals released from nearby industrial facilities; lower values are better Attribute: Air quality Rationale: __HTML__:<p>Industrial facilities emit a wide range of pollutants that taint the local air and damage the health and quality of life for nearby residents, especially older adults, children, and those who have difficulty breathing)
However, AARP fails to teach:
-wherein the environmental parameters comprise a carbon dioxide concentration, sound decibel, and a PM2.5 value corresponding to each layout point at each detection time point.
However, Monti teaches:
-wherein the environmental parameters comprise a carbon footprint, noise pollution, and pollution level corresponding to each layout point at each detection time point. (Monti [0034] Next, once the map is constructed, in step S8, the system would geotag locations on the map. This geotagging includes importing information about places of interest, pinpointing particular locations, regions or GPS coordinates for monuments, stores, or areas of interest. In addition, during this step of geotagging, the system can import geo specific data into the system via a geotag device such as geotag device 63. The geotag device 63 can be any form of suitable geotag device such as a system to record foot traffic, automobile traffic, noise pollution, demographics etc. One example for a geotagging device 63 would be a camera connected to a sensor which feeds foot traffic information to a server for recording of such foot traffic information for later storage. [0028] Another would be Roadway Pollution, which can be in the form of air pollution from automobiles, or vehicles, or oil or gasoline or other solvents from the autos, or other materials left on the roadways. Another criteria would be based upon the Carbon Footprint of the buildings, including the footprint based upon cement, oil or petrochemical use, such as heating or other carbon production.) In Monty, the geotag device at each area of interest corresponds to each layout point at each detection time point.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of AARP, Monti, and Harper by adding the features of Monty in which carbon footprint, noise pollution, and pollution level measurements are detected at particular detection points through sensors. One of ordinary skill in the art would have been motivated by the benefit of predicting environmental implications. (Monti [0132] [0066] FIG. 14 is a view of a chart or table 1401 for the environmental implications for development. The criteria listed were described above and include but are not limited to the criteria listed above.)
However, neither AARP, Monti, nor Harper specifically teach or suggest:
-wherein the environmental parameters comprise a carbon dioxide concentration, sound decibel, and a PM2.5 value
Alternatively, Granger discloses a method for assessing and improving public health or well-being. Granger teaches:
-wherein the environmental parameters comprise a carbon dioxide concentration, sound decibel, and a PM2.5 value (Granger [0214] As used herein, the problem may relate to, for example, one or more of the following: PM2.5 level, plant-based particulate, animal-based particulate, pest-based particulate, bacteria, virus, fungi, mold, PM10 level, ozone level, radon level, benzene level, carbon dioxide level, [0063] and ambient decibel level in at least a part of the built environment.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify the combination of AARP, Monti, and Harper by adding the features of Granger of specifically measuring carbon dioxide concentration, sound decibel, and PM 2.5 as these are viable measures for particular metrics such as carbon footprint, noise pollution and pollution levels. One of ordinary skill would have been motivated to substitute these specific measurements from Granger into Monti’s broader metrics as it would provide the benefit of more precisely quantifying metrics that may be required in certain regulatory settings. (Granger [0054] In some embodiments, the disclosed systems and methods may be useful to a diverse group of stakeholders, including, but not limited to, architects, interior designers, health scientists, insurance personnel, and regulatory agencies, for identifying and comparing interventions based on their top concerns (e.g., health needs, preference, efficacy, efficiency, cost, benefit provided, duration or scope of benefit provided, cost, cost-effectiveness, etc.).
Subject Matter Distinguished Over the Prior Art
17. Claims 14-19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding Claim 14:
The reasons for indicating subject matter distinguished over prior art for claim 14 are as follows: Claim 14 recites a specific sequence of mathematical calculations for analyzing mobility suitability that are not taught or suggested in the prior art of record. Neither AARP, Monti, nor Harper teach or suggest at least the limitations of (but not limited to):
- analyzing a suitability coefficient
ε
i
=
(
e
+
1
)
(
S
Y
i
1
+
|
S
L
i
-
S
Y
i
|
)
- comprehensively analyzing the mobility suitability coefficient
u
i
=
…
Regarding Claims 15 and 16:
Similarly, neither AARP, Monti, nor Harper teach or suggest at least the claim 15 limitations of (but not limited to):
-analyzing the traffic convenience coefficient
J
B
=
l
n
(
1
+
1
n
…
)
, corresponding to the target neighborhood wherein
σ
is a traffice line patency coefficient of the target neighborhood, and y1, y2, y3 are present proportion factors respectively corresponding to mobility suitability of elderly people, traffic distance suitability of elderly people, and traffic line patency of elderly people.
Furthermore, given that claim 16 is dependent on claim 15 which distinguished over the prior art, by virtue of its dependency, claim 15 also distinguishes over the prior art.
Regarding Claim 17:
Similarly, neither AARP, Monti, nor Harper teach or suggest at least the claim 17 limitations of (but not limited to):
-analyzing the leisure facility perfection coefficient
ω
=
…
(
s
e
e
e
q
u
a
t
i
o
n
i
n
c
l
a
i
m
17
)
corresponding to the target neighborhood, wherein SF’’ is a present allowable error of an area ratio of the leisure facility regions, and o1 and o2 are present weight coefficients respectively corresponding to the area ratio of the leisure facility regions and the quantity of the leisure facility regions.
Regarding Claim 18:
Similarly, neither AARP, Monti, nor Harper teach or suggest at least the claim 18 limitations of (but not limited to):
-analyzing the microenvironment suitability coefficient HJ = (see attached equation in the claim 18 filed 07/22/2024), corresponding to the target neighborhood.
Regarding Claim 19:
Similarly, neither AARP, Monti, nor Harper teach or suggest at least the claim 18 limitations of (but not limited to):
-wherein the evaluation coefficient of the aging residential suitability corresponding to the target neighborhood is calculated by a equation: (see attached equation in the claim 19 filed 07/22/2024).
Because the prior art of record fails to teach or suggest the claim limitations above, whether individually or as an obvious combination, the claims distinguish over the prior art of record and would be allowable if amended to overcome the 101 rejections. Furthermore, due to the specificity of the equations and the particularity of the inputs into the equations that require very specific forms of data, the equations define over the prior art and were not found in a prior art search.
Conclusion
18. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
- Kim et al., (Gerontologist, 2022, Vol. 62, No. 1, e17-e27, "Measurement Indicators of Age-Friendly Communities: Findings from the AARP Age-Friendly Community Survey")
- Harper et al., (AARP Public Policy Institute, "Is This a Good Place to Live?" Measuring Community Quality of Life for All Ages)
- Jagannathan et al. (US 20230039222 A1) discloses a system for determining the livableness of real estate assets by a livability index based on local measures.
- Choi Jae Hong (KR 20140113755 A) discloses a system for calculating the accessibility score for disabled, to measure the convenience of people with disabilities for traveling in certain areas such as facilities or walkways.
19. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICO LAUREN PADUA whose telephone number is (703)756-1978. The examiner can normally be reached Mon to Fri: 8:30 to 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at (571) 270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NICO L PADUA/ Junior Patent Examiner, Art Unit 3626
/SANGEETA BAHL/ Primary Examiner, Art Unit 3626