Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 2/2/2026, Applicant responded on 4/29/2026. Amended claims 1, 10, 16. Claims 2-4, 7- 8, 11-12, 15, 17-18, 21-22 and 24 were previously cancelled.
Claims 1, 5-6, 9-10, 13-14, 16, 19-20, 23 and 25-28 are pending in this application have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/29/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 10, 16 are sufficient to overcome the 35 USC 112(a) rejections set forth in the previous action. The 35 USC 112(a) rejections are hereby withdrawn.
Applicant's amendments to claims 1, 10, 16 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…Applicant respectfully traverses on the ground that the claims are not directed to an abstract idea. Notwithstanding the foregoing traversal, Applicant has amended the claims without prejudice and solely in order to expedite prosecution. More particularly, independent claims 1, 10 and 16 have been amended to clarify the following limitations:…This specific architectural arrangement, with defined series and parallel connections between identified artificial intelligence components, and explicit input/output coupling at multiple stages, does not represent a mathematical concept, a method of organizing human activity, or a mental process. The amended limitations represent, rather, a specific, concrete artificial intelligence system architecture implemented in a specific way to achieve a specific technical result. Such limitations accordingly do not recite an abstract idea within the meaning of the 2019 Revised Guidance…the present claims are analogous to the claims found eligible in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016), wherein the Federal Circuit held that claims directed to "a specific type of data structure designed to improve the way a computer stores and retrieves data" were not abstract. The court in Enfish emphasized that the proper inquiry is whether the claims are directed to "a specific implementation of a solution to a problem in the software arts," not merely whether the claims involve a computer. Id. at 1339. With respect to the amended independent claims herein, the specific artificial intelligence architectural arrangement, with named components, defined data flows, and parallel and series connections, is analogously directed to a specific implementation of an artificial intelligence system designed to solve the concrete technical problem of automated ergonomic assessment in remote work environments, not a generic application of artificial intelligence or mathematical processing…in McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1316 (Fed. Cir. 2016), the Federal Circuit held claims eligible where they required "specific, limited" rules that "improved" the relevant technology rather than "just the use of any rules." The court observed that the claims did not "merely recite the abstract concept ... along with the requirement to apply it, but instead recited a specific technique that produced a result not previously achievable by conventional methods. Id. at 1313-16. Similarly, the amended independent claims do not recite the use of artificial intelligence models generically, but rather a particular arrangement of named artificial intelligence components with specific input/output coupling and a parallel connection topology that together achieve automated ergonomic parameter computation not achievable by conventional workplace monitoring techniques….the Federal Circuit's recent decision in Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. 2024), further supports this conclusion. In Recentive, the court found claims ineligible where they "apply[ied] machine learning" in a "straightforward" and "generic" manner to generate schedules and maps, without reciting any specific machine learning architecture, model structure, or technical improvement to the machine learning technology itself. By contrast, the amended independent claims expressly recite (i) the names and functional roles of the specific artificial intelligence components in the system, (ii) specific inputs of the models and specific outputs of the models, (iii) specific topological relationships between the components, including the parallel connection of the aggregator model and the threshold filter model, and (iv) the specific computational technique employed by the posture model (e.g., detection from camera frames). This level of architectural specificity takes the amended independent claims well beyond the generic artificial intelligence application found ineligible in Recentive and squarely within the specific implementation framework affirmed as eligible in Enfish and McRO…Applicant respectfully submits that the amended independent claims do not recite an abstract idea within any of the categories enumerated in the 2019 Revised Guidance, and is patent-eligible on this basis alone without the need to reach Step 2A, Prong 2…Applicant points, for example, to page 5, lines 7- 9 of the specification, which states as follows regarding non-limiting illustrative embodiments of the claimed arrangements: The automated ergonomics determination system 105 further comprises position-related ergonomic model 112, illumination-related ergonomic model 114, posture-related ergonomic model 116, and automated action generator 118. Additionally, Applicant notes that page 6, lines 22-23 of the specification explicitly describes the use of "one or more trained artificial intelligence techniques." Also, by way of further support for the amendments, Applicant points, for example, to page 12, line 19 through page 13, line 12 of the specification, which states as follows regarding non-limiting illustrative embodiments of the claimed arrangements: By way of illustration, FIG. 2 depicts inputs 220, which includes distance and brightness inputs captured by webcam 221, and screen size and screen resolution inputs provided by the user as a user configuration 222. At least a portion of inputs 220 are provided to and/or processed by automated ergonomics determination system 205, which includes position-related ergonomic model 212, illumination-related ergonomic model 214, and posture-related ergonomic model 216. As illustrated in FIG. 2, each of model 212, model 214, and model 216 generates at least one parameter score (e.g., by processing at least a portion of inputs 220) and provides such score(s) to ergonomics aggregator 224 and ergonomics threshold filter 226. As detailed above and herein, with respect to ergonomics threshold filter 226, the individual parameter scores (generated by model 212, model 214, and model 216) are each compared to a respective parameter-related predetermined threshold value, and if the given parameter score passes a given predetermined threshold in step 230 (e.g., exceeds the threshold value or falls below the threshold value), then at least one corresponding alert is generated and output to the user in step 232. Alternatively, if the given parameter score does not pass a given predetermined threshold in step, then at least one notification is generated and output to the user in step 234 to indicate that there are no issues. With respect to ergonomics aggregator 224, the individual parameter scores (generated by model 212, model 214, and model 216) are combined and/or aggregated via at least one model or formula to generate an ergonomics index 228 (comprising, for example, a numerical value or score based on a weighted combination of the individual parameter scores). If the ergonomics index 228 passes a given predetermined threshold in step 230 (e.g., exceeds the threshold value or falls below the threshold value), then at least one alert is generated and output to the user in step 232...”. The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, unlike Enfish and McRO, and very much like Recentive Analytics, Inc. v. Fox Corp, the claims do not recite any specific AI models. The claims generically recite “…first artificial intelligence model…”, “…second artificial intelligence model…”, “…third artificial intelligence model…”.
To which, according to Applicant’s own remarks and specifications, “…in connection with input R for a full HD resolution screen (e.g., a screen having 1920 pixels horizontally across the screen and 1080 pixels vertically across the screen), a visual acuity distance formula can be given as dva=mva_fhdx+c; a minimum distance formula can be given as dmin=mminx+c; and a maximum distance formula can be given as dmax=mmaxx+c….”, “…in connection with input R for a Quad HD resolution screen (e.g., a screen having 2560 pixels horizontally across the screen and 1440 pixels vertically across the screen), a visual acuity distance formula can be given as dva=mva_qhdx+c; a minimum distance formula can be given as dmin=mminx+c; and a maximum distance formula can be given as dmax=mmaxx+c….”, “…in connection with input R for an ultra HD 4K resolution screen (e.g., a screen having 3840 pixels horizontally across the screen and 2160 pixels vertically across the screen), a visual acuity distance formula can be given as dva=mva_uhdx+c; a minimum distance formula can be given as dmin=mminx+c; and a maximum distance formula can be given as dmax=mmaxx+c. Accordingly, in one or more embodiments, across the different screen resolution categories, the same minimum distance formula and the same maximum distance formula are used. The different R inputs do not make a difference for mmin and mmax coefficients, but the different R inputs do make a difference for the mva (visual acuity) coefficient….”, “…to determine an ergonomic score e associated with user position from a given screen, input for such a computation can include the actual distance (da) that the user is from screen, which can be determined and/or retrieved using, for example, a webcam associated with the given screen. In such an embodiment, input for the computation can also include a recommended minimum distance value, a recommended maximum distance value, and a visual acuity distance value, such as calculated as detailed above. Accordingly, in at least one embodiment, a decision-based condition can be determined based at least in part on the actual distance value and the visual acuity distance value, and the corresponding formulas for computing the score can be defined as follows. Given a condition wherein dmin<da<dva, the ergonomic score formula, with respect to user position/distance from the given screen, is given as follows:…”, “…the ergonomic score formula, with respect to user position/distance from the given screen, is given as follows:..”, “…a model can process inputs such as screen brightness values L, which can be determined and/or indicated (e.g., as a percentage value) via the corresponding operating system (OS). Based at least in part on the determined screen brightness value, at least one embodiment includes computing a recommended environmental illuminance value (iR) using, for example, a formula such as follows: iR=0.36*L2. In such an embodiment, 0.36 is a coefficient value (and it is to be appreciated that one or more other embodiments can include one or more different coefficient values)…..”, “…To determine the ergonomic score for lighting in room, such an embodiment can include implementing a formula which uses a recommended environmental illuminance (iR) and an actual environmental illuminance value (ia). Additionally, in such an embodiment, let Lmax be 100(%) which is the maximum screen brightness, in connection with a formula such as follows: iR=k*Lmax 2….”, “…Given a condition wherein ia<iR, the ergonomic score formula, with respect to illumination values for a given user environment, is given as follows: e=ia/iR. Also, given a condition wherein iR≤ia<imax, the ergonomic score formula, with respect to illumination values for a given user environment, is given as follows:…”, “…an ergonomic score related to user posture can be computed using the following formula: e=(da−doptimal)/doptimal….”, “…each of the above-noted parameters (e.g., position-related parameter(s), illumination-related parameter(s), and posture-related parameter(s)) can be determined and output to the user and/or one or more automated systems individually or in one or more combinations. For example, scenarios may occur wherein two or more parameters are correlated to each other, such as, for instance, the lighting of the room being related to how close the user is positioned from the given computer screen. Accordingly, in such a scenario, at least one embodiment can include normalizing the multiple parameters (also referred to herein as ergonomic scores) and aggregating the parameters to create an index….”, “…With respect to ergonomics aggregator 224, the individual parameter scores (generated by model 212, model 214, and model 216) are combined and/or aggregated via at least one model or formula to generate an ergonomics index 228 (comprising, for example, a numerical value or score based on a weighted combination of the individual parameter scores)….”, the models are mathematical models and mathematical formulas.
Further, Applicant’s specification only generically discloses and generically supports the recited “…artificial intelligence…” element.
To expand on Applicant’s remarks, Applicant’s specification actually only generically discloses, “…a position-related ergonomic model determines a recommended distance based at least in part on a visual acuity distance, which is the distance that the human eye can read and/or process one or more details with a given level of precision. Additionally or alternatively, a position-related ergonomic model can determine a recommended distance based at least in part on predetermined minimum distances and predetermined maximum distances determined in connection with historical data and one or more trained artificial intelligence techniques. Accordingly, in one or more embodiments, a position-related ergonomic model generates a position-related ergonomic score for a given individual based at least in part on comparing one or more recommended distance values with the actual distance of the individual from the given screen (e.g., as determined and/or calculated using input data from the webcam associated with the given screen)…”. Applicant’s specification does not disclose any specific types of “…trained artificial intelligence techniques…”, nor is “…artificial intelligence…” mentioned anywhere else in Applicant’s specification. Thus, Applicant’s specification does not support any specific types of “…artificial intelligence model…”, and “…artificial intelligence…” is being interpretated to be a generic additional computing element. And, the “…position-related ergonomic model…” is being interpreted to be mathematical model and mathematical formula.
Thus, the claims and the argued elements, recite and direct to, …automatically determining work environment-related ergonomic data…one or more recommendations automatically generated and output to the user…recommendations for improving one or more aspects of the provided ergonomic data (e.g., one or more suggestions to improve lighting in the room, one or more suggestions to improve the user's sitting posture, etc.). In one or more embodiments, such recommendations are determined based at least in part on the individual ergonomics…, is a problem directed to organizing human activity (i.e. human recommending and instructing human workers on proper ergonomic postures and changing lighting conditions of work environment based on human observation) and a mental process (i.e. human observing human posture and work environment, human evaluating human posture and work environment, human providing recommendation on observed posture and work environment) and mathematical concepts (i.e. human observing human posture and work environment, human evaluating human posture and work environment using mathematical models and mathematical formulas, human providing recommendation on observed posture and work environment based on mathematical models and mathematical calculations), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, performing extra solution activities, according to Applicant’s specification, …in FIG. 1 and FIG. 2, and further detailed herein, one or more embodiments includes implementing at least one illumination-related ergonomic model, which can process inputs in the form of, for example, illumination values captured and/or obtained using one or more ambient light sensors, one or more cameras (e.g., webcam(s)) associated with a given screen and/or work environment, etc. Such a model can be implemented to determine one or more parameters related to illumination, such as, for example, lighting brightness in a given space and/or room within a work environment. In one or more embodiments, such a model can process inputs such as screen brightness values L, which can be determined and/or indicated (e.g., as a percentage value) via the corresponding operating system (OS). Based at least in part on the determined screen brightness value, at least one embodiment includes computing a recommended environmental illuminance value (iR) using, for example, a formula such as follows: iR= 0.36 * L2. In such an embodiment, 0.36 is a coefficient value (and it is to be appreciated that one or more other embodiments can include one or more different coefficient values). In at least one embodiment, the recommended environmental illuminance (iR) value can represent a room lighting metric that is recommended for the user's eyes, and la can represent actual illumination in the room. Accordingly, in such an embodiment, if la<iR, the user can be notified to turn the room lights up or on, and if la>iR, the user can be notified to turn the room lights down or off… an alert can be generated and output to the user indicating the ergonomic index and/or one or more of the individual parameter scores, as well as one or more recommendations automatically generated and output to the user and/or one or more external and/or automated systems, wherein such recommendations include recommendations for improving one or more aspects of the provided ergonomic data…such recommendations are determined based at least in part on the individual ergonomics. For example, if the ergonomics index is poor, but within it, the posture and the screen-to-user distance is good while the brightness of the room is poor, a recommendation can be generated to adjust the brightness of the room, wherein such a recommendation is delivered and/or output to the user, e.g., via text on the screen…. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2.
Further, according to, https://web.archive.org/web/20220910022112/http://en.wikipedia.org/wiki/Human_factors_and_ergonomics, 9/10/2022, “…Some have stated that human ergonomics began with Australopithecus prometheus (also known as “little foot”), a primate who created handheld tools out of different types of stone, clearly distinguishing between tools based on their ability to perform designated tasks.[30] The foundations of the science of ergonomics appear to have been laid within the context of the culture of Ancient Greece. A good deal of evidence indicates that Greek civilization in the 5th century BC used ergonomic principles in the design of their tools, jobs, and workplaces. One outstanding example of this can be found in the description Hippocrates gave of how a surgeon's workplace should be designed and how the tools he uses should be arranged.[31] The archaeological record also shows that the early Egyptian dynasties made tools and household equipment that illustrated ergonomic principles…Bernardino Ramazzini was one of the first people to systematically study the illness that resulted from work earning himself the nickname “father of occupational medicine”. In the late 1600s and early 1700s Ramazzini visited many worksites where he documented the movements of laborers and spoke to them about their ailments. He then published “De Morbis Artificum Diatriba” (Latin for Diseases of Workers) which detailed occupations, common illnesses, remedies.[32] In the 19th century, Frederick Winslow Taylor pioneered the "scientific management" method, which proposed a way to find the optimum method of carrying out a given task. Taylor found that he could, for example, triple the amount of coal that workers were shoveling by incrementally reducing the size and weight of coal shovels until the fastest shoveling rate was reached.[33] Frank and Lillian Gilbreth expanded Taylor's methods in the early 1900s to develop the "time and motion study". They aimed to improve efficiency by eliminating unnecessary steps and actions. By applying this approach, the Gilbreths reduced the number of motions in bricklaying from 18 to 4.5,[clarification needed] allowing bricklayers to increase their productivity from 120 to 350 bricks per hour.[33] However, this approach was rejected by Russian researchers who focused on the well-being of the worker. At the First Conference on Scientific Organization of Labour (1921) Vladimir Bekhterev and Vladimir Nikolayevich Myasishchev criticised Taylorism. Bekhterev argued that "The ultimate ideal of the labour problem is not in it [Taylorism], but is in such organisation of the labour process that would yield a maximum of efficiency coupled with a minimum of health hazards, absence of fatigue and a guarantee of the sound health and all round personal development of the working people."[34] Myasishchev rejected Frederick Taylor's proposal to turn man into a machine. Dull monotonous work was a temporary necessity until a corresponding machine can be developed. He also went on to suggest a new discipline of "ergology" to study work as an integral part of the re-organisation of work. The concept was taken up by Myasishchev's mentor, Bekhterev, in his final report on the conference, merely changing the name to "ergonology"[34]…The dawn of the Information Age has resulted in the related field of human–computer interaction (HCI). Likewise, the growing demand for and competition among consumer goods and electronics has resulted in more companies and industries including human factors in their product design. Using advanced technologies in human kinetics, body-mapping, movement patterns and heat zones, companies are able to manufacture purpose-specific garments, including full body suits, jerseys, shorts, shoes, and even underwear…”
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process, organizing human activities, mathematical concepts, applied with generic computing components and generally linked to a technical environment. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Further, “the transformation is extra-solution activity or a field-of-use (i.e., the extent to which (or how) the transformation imposes meaningful limits on the execution of the claimed method steps). A transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application).” MPEP 2106.05(c). Thus, Applicant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes, organizing human activities, and mathematical concepts implemented using or applying generic computer components.
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.” In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a ‘‘process of organizing information through mathematical correlations’’ are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of ‘‘managing a stable value protected life insurance policy by performing calculations and manipulating the results’’ as an abstract idea).
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. See MPEP 2106.04(a)(2).
Further, the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).
And, the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 26, 27, 28 are rejected under is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant(s) regard as their invention.
Claim 26, 27, 28 recites “…automatically initiating, by transmitting action instructions to one or more automated systems associated with the work environment, modification of one or more physical components within the work environment comprises…”, it is unclear to what these elements refer. Appropriate correction is required.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 5-6, 9, 10, 13-14, 16, 19-20, 23, 25-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (similarly 10 and 16) recites, “A … method comprising:
obtaining ergonomic-related data pertaining to an individual within a work environment, at least one computing device being used by the individual within the work environment, and the work environment, wherein obtaining the ergonomic-related data comprises:
capturing the ergonomic-related data pertaining to the individual within the work environment by determining a distance of the individual from a screen associated with the at least one computing device, using at least one … positioned within the work environment;
capturing the ergonomic-related data pertaining to the work environment by automatically determining one or more illuminance values, …, attributed to at least a portion of the work environment, using at least one … positioned within the work environment; and
capturing the ergonomic-related data pertaining to the at least one computing device by automatically accessing … and processing one or more screen brightness values, attributed to the screen associated with the at least one computing device, from the …;
determining ergonomic parameter values by processing at least a portion of the obtained ergonomic-related data using a … having an architectural arrangement comprising a first … model, a second … model, and a third … model, wherein an output of each of the first … model, the second … model, and the third … model is coupled to respective inputs of an ergonomics aggregator model and an ergonomics threshold filter model, wherein the ergonomics threshold filter model is connected in parallel with the ergonomics aggregator model, wherein determining ergonomic parameter values comprises:
computing at least one ergonomic parameter value pertaining to the individual's distance from the screen associated with the at least one computing device in the work environment using the first … model, wherein the first … model receives at an input, the distance of the individual from the screen and at least one of a screen size value and a screen resolution value, and has as an output providing a position-related ergonomic score based at least in part on applying resolution-dependent linear distance equations and piecewise condition-based scoring formulas to the input;
computing at least one ergonomic parameter value pertaining to illumination in the work environment using the second … model, wherein the second … model receives at an input, the one or more illuminance values from the at least one … and the one or more screen brightness values from the …, and has as an output providing an illumination-related ergonomic score based at least in part on applying a quadratic illuminance formula to the input to compute a recommended environmental illuminance value; and
computing at least one ergonomic parameter value pertaining to the individual's posture using the third … model, wherein the third … model receives at an inputs, image frames …, and has as an output providing a posture-related ergonomic score, wherein computing the at least one ergonomic parameter value pertaining to the individual's posture is based at least in part on: (i) calculating one or more elbow angles attributed to the individual using the third … model by identifying a shoulder joint landmark, an elbow joint landmark, and a wrist joint landmark of the individual from the image frames, (ii) calculating one or more leg angles attributed to the individual using the third … model by identifying a hip joint landmark, a knee joint landmark, and an ankle joint landmark of the individual from the image frames, and (iii) calculating one or more back angles attributed to the individual using the third … model by identifying a neck landmark, a hip landmark, and a knee landmark of the individual from the image frames;
generating at least one ergonomic index at an output of the ergonomics aggregator model by aggregating the ergonomic parameter values in conjunction with weights applied to the ergonomic parameter values, and performing an independent comparing, at an output of the ergonomics threshold filter model connected in parallel with the ergonomics aggregator model, each of the ergonomic parameter values to a respective predetermined threshold value;
generating and outputting at least one notification based at least in part on the at least one ergonomic index and at least one result of the independent comparison performed by the ergonomics threshold filter model; and
performing one or more automated actions based at least in part on one or more of the at least one ergonomic index and the at least one notification, wherein performing one or more automated actions comprises automatically transmitting at least one recommendation to one or more … associated with the work environment, the at least one recommendation pertaining to adjustment of at least one screen brightness value in the operating system of the at least one computing device in the work environment;
wherein the method is performed by at least ….”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …obtaining ergonomic-related data pertaining to an individual within a work environment, at least one computing device being used by the individual within the work environment, and the work environment, wherein obtaining the ergonomic-related data comprises: capturing the ergonomic-related data pertaining to the individual within the work environment by determining a distance of the individual from a screen associated with the at least one computing device, using at least one … positioned within the work environment; capturing the ergonomic-related data pertaining to the work environment by automatically determining one or more illuminance values, …, attributed to at least a portion of the work environment, using at least one … positioned within the work environment; and capturing the ergonomic-related data pertaining to the at least one computing device by automatically accessing … and processing one or more screen brightness values, attributed to the screen associated with the at least one computing device, from the …; determining ergonomic parameter values by processing at least a portion of the obtained ergonomic-related data using a … having an architectural arrangement comprising a first … model, a second … model, and a third … model, wherein an output of each of the first … model, the second … model, and the third … model is coupled to respective inputs of an ergonomics aggregator model and an ergonomics threshold filter model, wherein the ergonomics threshold filter model is connected in parallel with the ergonomics aggregator model, wherein determining ergonomic parameter values comprises: computing at least one ergonomic parameter value pertaining to the individual's distance from the screen associated with the at least one computing device in the work environment using the first … model, wherein the first … model receives at an input, the distance of the individual from the screen and at least one of a screen size value and a screen resolution value, and has as an output providing a position-related ergonomic score based at least in part on applying resolution-dependent linear distance equations and piecewise condition-based scoring formulas to the input; computing at least one ergonomic parameter value pertaining to illumination in the work environment using the second … model, wherein the second … model receives at an input, the one or more illuminance values from the at least one … and the one or more screen brightness values from the …, and has as an output providing an illumination-related ergonomic score based at least in part on applying a quadratic illuminance formula to the input to compute a recommended environmental illuminance value; and computing at least one ergonomic parameter value pertaining to the individual's posture using the third … model, wherein the third … model receives at an inputs, image frames …, and has as an output providing a posture-related ergonomic score, wherein computing the at least one ergonomic parameter value pertaining to the individual's posture is based at least in part on: (i) calculating one or more elbow angles attributed to the individual using the third … model by identifying a shoulder joint landmark, an elbow joint landmark, and a wrist joint landmark of the individual from the image frames, (ii) calculating one or more leg angles attributed to the individual using the third … model by identifying a hip joint landmark, a knee joint landmark, and an ankle joint landmark of the individual from the image frames, and (iii) calculating one or more back angles attributed to the individual using the third … model by identifying a neck landmark, a hip landmark, and a knee landmark of the individual from the image frames; generating at least one ergonomic index at an output of the ergonomics aggregator model by aggregating the ergonomic parameter values in conjunction with weights applied to the ergonomic parameter values, and performing an independent comparing, at an output of the ergonomics threshold filter model connected in parallel with the ergonomics aggregator model, each of the ergonomic parameter values to a respective predetermined threshold value; generating and outputting at least one notification based at least in part on the at least one ergonomic index and at least one result of the independent comparison performed by the ergonomics threshold filter model; and performing one or more automated actions based at least in part on one or more of the at least one ergonomic index and the at least one notification, wherein performing one or more automated actions comprises automatically transmitting at least one recommendation to one or more … associated with the work environment, the at least one recommendation pertaining to adjustment of at least one screen brightness value in the operating system of the at least one computing device in the work environment;…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations; therefore, the claims recite a mental process.
Further, …obtaining ergonomic-related data pertaining to an individual within a work environment, at least one computing device being used by the individual within the work environment, and the work environment, wherein obtaining the ergonomic-related data comprises: capturing the ergonomic-related data pertaining to the individual within the work environment by determining a distance of the individual from a screen associated with the at least one computing device, using at least one … positioned within the work environment; capturing the ergonomic-related data pertaining to the work environment by automatically determining one or more illuminance values, …, attributed to at least a portion of the work environment, using at least one … positioned within the work environment; and capturing the ergonomic-related data pertaining to the at least one computing device by automatically accessing … and processing one or more screen brightness values, attributed to the screen associated with the at least one computing device, from the …; determining ergonomic parameter values by processing at least a portion of the obtained ergonomic-related data using a … having an architectural arrangement comprising a first … model, a second … model, and a third … model, wherein an output of each of the first … model, the second … model, and the third … model is coupled to respective inputs of an ergonomics aggregator model and an ergonomics threshold filter model, wherein the ergonomics threshold filter model is connected in parallel with the ergonomics aggregator model, wherein determining ergonomic parameter values comprises: computing at least one ergonomic parameter value pertaining to the individual's distance from the screen associated with the at least one computing device in the work environment using the first … model, wherein the first … model receives at an input, the distance of the individual from the screen and at least one of a screen size value and a screen resolution value, and has as an output providing a position-related ergonomic score based at least in part on applying resolution-dependent linear distance equations and piecewise condition-based scoring formulas to the input; computing at least one ergonomic parameter value pertaining to illumination in the work environment using the second … model, wherein the second … model receives at an input, the one or more illuminance values from the at least one … and the one or more screen brightness values from the …, and has as an output providing an illumination-related ergonomic score based at least in part on applying a quadratic illuminance formula to the input to compute a recommended environmental illuminance value; and computing at least one ergonomic parameter value pertaining to the individual's posture using the third … model, wherein the third … model receives at an inputs, image frames …, and has as an output providing a posture-related ergonomic score, wherein computing the at least one ergonomic parameter value pertaining to the individual's posture is based at least in part on: (i) calculating one or more elbow angles attributed to the individual using the third … model by identifying a shoulder joint landmark, an elbow joint landmark, and a wrist joint landmark of the individual from the image frames, (ii) calculating one or more leg angles attributed to the individual using the third … model by identifying a hip joint landmark, a knee joint landmark, and an ankle joint landmark of the individual from the image frames, and (iii) calculating one or more back angles attributed to the individual using the third … model by identifying a neck landmark, a hip landmark, and a knee landmark of the individual from the image frames; generating at least one ergonomic index at an output of the ergonomics aggregator model by aggregating the ergonomic parameter values in conjunction with weights applied to the ergonomic parameter values, and performing an independent comparing, at an output of the ergonomics threshold filter model connected in parallel with the ergonomics aggregator model, each of the ergonomic parameter values to a respective predetermined threshold value; generating and outputting at least one notification based at least in part on the at least one ergonomic index and at least one result of the independent comparison performed by the ergonomics threshold filter model; and performing one or more automated actions based at least in part on one or more of the at least one ergonomic index and the at least one notification, wherein performing one or more automated actions comprises automatically transmitting at least one recommendation to one or more … associated with the work environment, the at least one recommendation pertaining to adjustment of at least one screen brightness value in the operating system of the at least one computing device in the work environment;…, under the broadest reasonable interpretation, are human observing human workspace posture and ergonomics to make recommendations to improve human work posture and ergonomics, therefore it is, managing personal behavior or relationships or interactions between people. Thus, the claims recite certain methods of organizing human activity.
Additionally, …obtaining ergonomic-related data pertaining to an individual within a work environment, at least one computing device being used by the individual within the work environment, and the work environment, wherein obtaining the ergonomic-related data comprises: capturing the ergonomic-related data pertaining to the individual within the work environment by determining a distance of the individual from a screen associated with the at least one computing device, using at least one … positioned within the work environment; capturing the ergonomic-related data pertaining to the work environment by automatically determining one or more illuminance values, …, attributed to at least a portion of the work environment, using at least one … positioned within the work environment; and capturing the ergonomic-related data pertaining to the at least one computing device by automatically accessing … and processing one or more screen brightness values, attributed to the screen associated with the at least one computing device, from the …; determining ergonomic parameter values by processing at least a portion of the obtained ergonomic-related data using a … having an architectural arrangement comprising a first … model, a second … model, and a third … model, wherein an output of each of the first … model, the second … model, and the third … model is coupled to respective inputs of an ergonomics aggregator model and an ergonomics threshold filter model, wherein the ergonomics threshold filter model is connected in parallel with the ergonomics aggregator model, wherein determining ergonomic parameter values comprises: computing at least one ergonomic parameter value pertaining to the individual's distance from the screen associated with the at least one computing device in the work environment using the first … model, wherein the first … model receives at an input, the distance of the individual from the screen and at least one of a screen size value and a screen resolution value, and has as an output providing a position-related ergonomic score based at least in part on applying resolution-dependent linear distance equations and piecewise condition-based scoring formulas to the input; computing at least one ergonomic parameter value pertaining to illumination in the work environment using the second … model, wherein the second … model receives at an input, the one or more illuminance values from the at least one … and the one or more screen brightness values from the …, and has as an output providing an illumination-related ergonomic score based at least in part on applying a quadratic illuminance formula to the input to compute a recommended environmental illuminance value; and computing at least one ergonomic parameter value pertaining to the individual's posture using the third … model, wherein the third … model receives at an inputs, image frames …, and has as an output providing a posture-related ergonomic score, wherein computing the at least one ergonomic parameter value pertaining to the individual's posture is based at least in part on: (i) calculating one or more elbow angles attributed to the individual using the third … model by identifying a shoulder joint landmark, an elbow joint landmark, and a wrist joint landmark of the individual from the image frames, (ii) calculating one or more leg angles attributed to the individual using the third … model by identifying a hip joint landmark, a knee joint landmark, and an ankle joint landmark of the individual from the image frames, and (iii) calculating one or more back angles attributed to the individual using the third … model by identifying a neck landmark, a hip landmark, and a knee landmark of the individual from the image frames; generating at least one ergonomic index at an output of the ergonomics aggregator model by aggregating the ergonomic parameter values in conjunction with weights applied to the ergonomic parameter values, and performing an independent comparing, at an output of the ergonomics threshold filter model connected in parallel with the ergonomics aggregator model, each of the ergonomic parameter values to a respective predetermined threshold value; generating and outputting at least one notification based at least in part on the at least one ergonomic index and at least one result of the independent comparison performed by the ergonomics threshold filter model; and performing one or more automated actions based at least in part on one or more of the at least one ergonomic index and the at least one notification, wherein performing one or more automated actions comprises automatically transmitting at least one recommendation to one or more … associated with the work environment, the at least one recommendation pertaining to adjustment of at least one screen brightness value in the operating system of the at least one computing device in the work environment;…, recite mathematical concepts.
Accordingly, the claims recite and directed to a mental process, certain methods of organizing human activity, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 10, 16: computer-implemented, web-based camera, measured in lumen per square meter, ambient illumination sensor, an operating system of the at least one computing device, processor-based artificial intelligence system, artificial intelligence, captured by the at least one web-based camera, automated systems, one processing device comprising a processor coupled to a memory, A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device, An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device, one or more systems,
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…obtaining…”, “…capturing…”, “…receives at an input…”, “…output…”, “…generating…”, “…generating and outputting…”, “….performing one or more automated actions comprises automatically transmitting at least one recommendation…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…obtaining…”, “…capturing…”, “…receives at an input…”, “…output…”, data output –“…output…”, “…generating…”, “…generating and outputting…”, “….performing one or more automated actions comprises automatically transmitting at least one recommendation…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
assessing work environments of individuals working at home and/or remotely and generating and outputting one or more suggestive values in real-time to evaluate ergonomics associated with the individuals. Such an embodiment includes implementing an evaluation index referred to herein as a human computer ergonomics index, which includes one or more weightages of one or more parameters. The parameters can include real-time input data from one or more work environment-related sources (e.g., computer webcams, light sensors, microphones, etc.) and/or user feedback (e.g., user inputs pertaining to computer screen size, screen resolution, screen brightness, etc.).
Step 306 includes performing one or more automated actions based at least in part on one or more of the one or more ergonomic values and the at least one notification. In one or more embodiments, performing one or more automated actions includes generating and outputting, to the individual via one or more automated systems, one or more recommendations for improving at least one of the one or more ergonomic parameter values.
in FIG. 1 and FIG. 2, and further detailed herein, one or more embodiments includes implementing at least one illumination-related ergonomic model, which can process inputs in the form of, for example, illumination values captured and/or obtained using one or more ambient light sensors, one or more cameras (e.g., webcam(s)) associated with a given screen and/or work environment, etc. Such a model can be implemented to determine one or more parameters related to illumination, such as, for example, lighting brightness in a given space and/or room within a work environment. In one or more embodiments, such a model can process inputs such as screen brightness values L, which can be determined and/or indicated (e.g., as a percentage value) via the corresponding operating system (OS). Based at least in part on the determined screen brightness value, at least one embodiment includes computing a recommended environmental illuminance value (iR) using, for example, a formula such as follows: iR= 0.36 * L2. In such an embodiment, 0.36 is a coefficient value (and it is to be appreciated that one or more other embodiments can include one or more different coefficient values). In at least one embodiment, the recommended environmental illuminance (iR) value can represent a room lighting metric that is recommended for the user's eyes, and la can represent actual illumination in the room. Accordingly, in such an embodiment, if la<iR, the user can be notified to turn the room lights up or on, and if la>iR, the user can be notified to turn the room lights down or off
an alert can be generated and output to the user indicating the ergonomic index and/or one or more of the individual parameter scores, as well as one or more recommendations automatically generated and output to the user and/or one or more external and/or automated systems, wherein such recommendations include recommendations for improving one or more aspects of the provided ergonomic data
In one or more embodiments, such recommendations are determined based at least in part on the individual ergonomics. For example, if the ergonomics index is poor, but within it, the posture and the screen-to-user distance is good while the brightness of the room is poor, a recommendation can be generated to adjust the brightness of the room, wherein such a recommendation is delivered and/or output to the user, e.g., via text on the screen.
However, as the day becomes evening, and the natural light diminishes, the illumination in the room reaches a particular threshold (e.g., crosses below a predetermined level of brightness), and an alert is generated and output to the user, as well as an automatically generated recommendation that the user can then switch on one or more supplemental light sources in the room to improve the ergonomics index.
As is apparent from the above, one or more of the processing modules or other components of system 100 may each run on a computer, server, storage device or other processing platform element. A given such element is viewed as an example of what is more generally referred to herein as a "processing device." The cloud infrastructure 400 shown in FIG. 4 may represent at least a portion of one processing platform. Another example of such a processing platform is processing platform 500 shown in FIG. 5.
Also, numerous other arrangements of computers, servers, storage products or devices, or other components are possible in the information processing system 100. Such components can communicate with other elements of the information processing system 100 over any type of network or other communication media. For example, particular types of storage products that can be used in implementing a given storage system of an information processing system in an illustrative embodiment include all-flash and hybrid flash storage arrays, scale-out all-flash storage arrays, scale-out NAS clusters, or other types of storage arrays. Combinations of multiple ones of these and other storage products can also be used in implementing a given storage system in an illustrative embodiment.
It should again be emphasized that the above-described embodiments are presented for purposes of illustration only. Many variations and other alternative embodiments may be used. Also, the particular configurations of system and device elements and associated processing operations illustratively shown in the drawings can be varied in other embodiments. Thus, for example, the particular types of processing devices, modules, systems and resources deployed in a given embodiment and their respective configurations may be varied. Moreover, the various assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the disclosure. Numerous other alternative embodiments within the scope of the appended claims will be readily apparent to those skilled in the art.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 5-6, 9, 10, 13-14, 16, 19-20, 23, 25-28 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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