DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Application Status
Present office action is in response to application filed 08/23/2024 Claims 1-20 are currently pending in the application.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: In claims 1 and 11, “a human activity recognition module configured to receive the image or video from the feeding module and to leverages a human pose estimation model to analyze input data” should recite “a human activity recognition module configured to receive the image or video from the feeding module and to leverage a human pose estimation model to analyze input data”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses generic placeholders (module) that is coupled with functional language (configured to …) without reciting sufficient structure/algorithm to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: "… user-end module configured to …" (claims 1, 9, 10, 11, 12, 19 and 20), "… feeding module configured to …" (claims 1 and 11), "… human activity recognition module configured to …", (claims 1, 4, 11 and 14), "… cloud platform module configured to …", "… therapist-end module configured to …" (claims 1, 2, 9, 10, 12 and 19), and "… feedback module … configured to …" (claims 10 and 20). As a result, claims 1, 2, 4, 9, 10, 11, 12, 14, 19 and 20 are interpreted under 112(f). This interpretation is consistent with MPEP § 2181.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure/algorithm described in the specification as performing the claimed function, and equivalents thereof. In particular, the published specification discloses: ¶ 36: In some embodiment, pose estimation and data pre-processing are performed using software, such as MATLAB and Python; ¶¶ 76, 78, 82, 88, 89: The functional; ¶ 93: The functional units and modules of the apparatuses and methods in accordance with the embodiments disclosed herein may be implemented using computing devices, computer processors, or electronic circuitries including but not limited to application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), microcontrollers, and other programmable logic devices configured or programmed according to the teachings of the present disclosure. Computer instructions or software codes executing in the computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure; ¶ 96: Each of the functional units and modules in accordance with various embodiments also may be implemented in distributed computing environments and/or Cloud computing environments, wherein the whole or portions of machine instructions are executed in distributed fashion by one or more processing devices interconnected by a communication network, such as an intranet, Wide Area Network (WAN), Local Area Network (LAN), the Internet, and other forms of data transmission medium. However, the above non-limiting description of each the "user-end module", "feeding module", "human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module" (¶¶ 76, 78, 82, 88, 89, 93, 96) is not a proper indication as to what structure/algorithm the above noted modules are to incorporate or entail. Because of such inadequate corresponding disclosure for § 112(f)/112 6th limitations, each of the noted claim limitations becomes an unbounded purely functional limitation, no limits imposed by structure, material or acts and covers all ways of performing a function – known and unknown. Since § 112(f) or 112 6th has been invoked, and there is no disclosure of corresponding structure/algorithms that performs the claimed functions, therefore, independent claims 1 and 11, and dependents thereof are also rejected under 35 USC 112 (a)/ 35 USC 112 1st paragraph as lacking an adequate written description support and under 35 USC 112 (b)/ 35 USC 112 2nd paragraph as being indefinite for the means-plus-function claim terms "user-end module", "feeding module", "human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module".
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 11 and dependents thereof are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Since § 112(f) or 112 6th has been invoked, and the instant specification discloses no algorithm corresponding to the computer-enabled means-plus-function limitations "user-end module", "feeding module", "human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module", claim 1 and dependents thereof are also rejected under 35 USC 112 (a)/ 35 USC 112 1st paragraph as lacking an adequate written description support. See MPEP § 2163.03 (VI) (If a specification “fails to disclose sufficient corresponding structure, materials, or acts that perform the entire claimed function” of a means-plus-function claim limitation, the limitation “lacks an adequate written description as required by [35 U.S.C. § 112(a)].”).
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 11 and dependents thereof are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In particular, the claims are rendered indefinite because the claims invoke 35 U.S.C. 112(f). Therefore, the claims must be interpreted to cover the corresponding structure, materials, or acts described in the specification as performing each entire claimed function and "equivalents thereof." More specifically, the Office has interpreted the "user-end module", "feeding module", "human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module" limitations invoke 35 U.S.C. 112(f) or pre-AIA U.S.C. 112, 6th paragraph. However, it is unclear whether the claim limitations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, 6th paragraph because as per the specification ¶¶ 76, 78, 82, 88, 89, 93, 96: … Each of the functional units and modules in accordance with various embodiments also may be implemented in distributed computing environments and/or Cloud computing environments, wherein the whole or portions of machine instructions are executed in distributed fashion by one or more processing devices interconnected by a communication network, such as an intranet, Wide Area Network (WAN), Local Area Network (LAN), the Internet, and other forms of data transmission medium. The above non-limiting description of each of the "user-end module", "feeding module", "human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module" is not a proper indication as to what structure/algorithm each of the "user-end module", "feeding module", "human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module" is to incorporate or entail. Hence, as per Applicant's published disclosure, the functions associated with the "user-end module", "feeding module", "human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module" are broad enough to encompass all possible ways of performing these functions. Without knowing the exact corresponding structure, materials, or acts, the metes and bounds of the claims are unclear and consequently the scope of the claims are unclear.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: Statutory Category?
Independent claim 1 recites “a system for providing personalized community-based post-stroke rehabilitation, comprising: a user-end module configured to provide schedule information with instructions of at least one specific exercise and to show at least one image or video, wherein the user-end module provides a camera view page to record a target image or video and to record a target user's performance metric, wherein the user-end module comprises: a feeding module configured to acquire the image or video from a consumer-grade smartphone or tablet device; a human activity recognition module configured to receive the image or video from the feeding module and to leverages a human pose estimation model to analyze input data; and a human activity evaluation module configured to receive results transmitted from the human activity recognition module and to evaluate performance of the results based on performance metric information which is rule-based or template-based; a cloud platform module configured to receive and store the target user's performance metric from the user-end module; and a therapist-end module in communication with the user-end module via the cloud platform module, wherein the therapist-end module is permitted to log in the cloud platform module and receive the target user's performance metric from the cloud platform module, and wherein the therapist-end module is further configured to visualize the target user's performance metric so as to show exercise waveform comprising quantitative data for qualitative analysis.”. Each of the above recitations of “module” ("user-end module", "feeding module", "… human activity recognition module", "cloud platform module", "therapist-end module", and "feedback module") appears to be a software component. "Abstract software code is an idea without physical embodiment." Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449 (2007). As the Supreme Court has made clear, "[a]n idea of itself is not patentable." In re Warmerdam, 33 F.3d 1354, 1360 (quoting Rubber-Tip Pencil Co. v. Howard, 87 U.S. (20 Wall.) 498, 507 (1874)). See also MPEP § 2106(I)(vi) (examples of claims that are not directed to one of the statutory categories: "a computer program per se") (citing Gottschalk v. Benson, 409 U.S. 63, 72 (1972)). Because software per se is directed to an abstract idea, it is non-statutory under 35 U.S.C. § 101. (Step 1: NO).
Because independent claim 1 could be amended to fall within a statutory category, the subject matter eligibility analysis continues under that assumption.
Independent claim 11 recites “a method for providing personalized community-based post-stroke rehabilitation” which is a statutory category of invention (i.e. a process) within § 101, i.e., process and machine. (Step 1: YES).
Step 2A – Prong 1: Judicial Exception Recited?
Independent claim 11, analyzed as representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are shown in italics. The additional element(s) recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
A method for providing personalized community-based post-stroke rehabilitation, comprising:
[L1] providing, by a user-end module, schedule information with instructions of at least one specific exercise;
[L2] showing, by the user-end module, at least one image or video, wherein the user-end module provides a camera view page to record a target image or video and to record a target user's performance metric, wherein the user-end module comprises: a [L3] feeding module configured to acquire the image or video from a consumer-grade smartphone or tablet device;
[L4a] a human activity recognition module configured to receive the image or video from the feeding module and
[L4b] to leverages a human pose estimation model to analyze input data; and
[L5a] a human activity evaluation module configured to receive results transmitted from the human activity recognition module and
[L5b] to evaluate performance of the results based on performance metric information which is rule-based or template-based;
[L6] receiving and storing, by a cloud platform module, the target user's performance metric from the user-end module;
[L7] logging, by using a therapist-end module, in the cloud platform module to receive the target user's performance metric from the cloud platform module; and
[L8] visualizing, by using the therapist-end module, the target user's performance metric so as to show exercise waveform comprising quantitative data for qualitative analysis.
As per the published Specification (¶¶ 1, 4, 14, 16), “the present invention generally relates to validation of digital human activity evaluation … validation of consumer-grade digital camera-based human activity evaluation for upper limb exercises and for development of a therapist-guided, automated telerehabilitation framework and platform for stroke rehabilitation”; “[O]ther than conventional in-person rehabilitation services …. adoption of telemedicine and telerehabilitation … significant increase in real-world adoption of telemedicine and telerehabilitation globally …”; “the SmartRehab system can be delivered as an application on a smartphone and smart device platform, making it accessible and scalable for community use. It provides a unique and innovative solution for stroke patients in need of rehabilitation, especially during the COVID-19 era, when rehabilitation services have been severely affected globally … During the exercise, SmartRehab system provides real-time visual and audio feedback to correct any inaccuracies in the exercise, simulating a therapist providing comments during in-person rehabilitation sessions, using metrics and rubrics”. It is apparent that, other than reciting the “consumer-grade smartphone” and “tablet device” in representative independent claim 11 above, under the broadest reasonable interpretation, at least the italicized claim limitations may be performed in the human mind, including observations, evaluations, and judgments and may also be characterized as a certain method of organizing human activity, i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea under Step 2A: Prong 1. (Step 2A – Prong 1: YES).
Step 2A – Prong 2: Integrated into a Practical Application?
The computer component(s), namely the “consumer-grade smartphone” and “tablet device” are each recited at a high level of generality, as evident in the published Specification: at least ¶ 6: … consumer-grade products (i.e., smartphones and tablet devices).…; ¶ 12: a telerehabilitation platform accessible via smartphones or tablets …; ¶ 13: … use of mobile devices such as smartphones, tablets, and wearable devices in healthcare delivery and management …; ¶¶ 17, 18: … consumer-grade smartphone or tablet device; ¶ 33: … use of mobile devices such as smartphones, tablets, and wearables to deliver health services and information …; ¶ 43: … accelerometers, gyroscopes, and magnetometers, which are commonly used in smartphones and wearable devices …; ¶ 68: … SmartRehab system utilizes the built-in RGB camera of the tablet or smartphone device …; ¶ 79: … acquire photos or videos (e.g., RGB-digital camera input) from a consumer-grade smartphone or tablet device, which serve as the primary data sources…; ¶ 89: … RGB-digital camera input from a consumer-grade smartphone or tablet device…; ¶ 95: … embodiments may be executed in one or more computing devices including server computers, personal computers, laptop computers, mobile computing devices such as smartphones and tablet computers … The lack of details about the “consumer-grade smartphone” and “tablet device” indicates that the additional element(s) is/are generic, or part of generic computer elements performing or being used in performing the generic functions claimed. The additional elements [L2]: “showing … at least one image or video” (data presentation), [L3]: “acquire the image or video” (data gathering), [L4]: “receive the image or video” (data gathering), [L5a]: “receive results transmitted” (data transmission and gathering), [L6]: “receiving and storing … the target user's performance metric” (data gathering), [L7]: “acquire the image or video” (data gathering), and [L8]: “visualizing the target user's performance metric” (data presentation), simply add insignificant extra-solution activity to the judicial exception, i.e., mere data gathering, data transmission, and data presentation, each of which is generic and conventional. No technological implementation details are recited. The claim limitations do not purport to improve the functioning of any implied additional element, do not improve the technology of the technical field, and do not require a “particular machine.” Rather, they are performed using generic computer components. Further, the claim fails to effect any particular transformation of an article to a different state. The recited steps in the claim fail to provide meaningful limitations to limit the judicial exception. In this case, the claim merely uses the claimed computer elements as a tool to perform the abstract idea.
Considering the elements of the claim both individually and as “an ordered combination” the functions performed at each step of the method are purely conventional. Each step performed in the claim does no more than require a generic computer to perform a generic computer function. Thus, the claimed elements have not been shown to integrate the judicial exception into a practical application as set forth in the Revised Guidance which references the Manual of Patent Examining Procedure (“MPEP”) §§ 2106.04(d) and 2106.05(a)–(c) and (e)–(h). Reviewing courts have concluded that mental processes include similar concepts of collecting, manipulating, and providing data. See Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340 (Fed. Cir. 2017) (the Federal Circuit held “the concept of . . . collecting data, . . . recognizing certain data within the collected data set, and . . . storing that recognized data in a memory” ineligible); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes); CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011) (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson [409 U.S. 63 (1972)].”). Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong Two: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the “consumer-grade smartphone” and “tablet device” in the claim amounts to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (for example, ¶¶ 6, 12, 123, 17, 18, 33, 43, 68, 79, 89, 95) describes the “consumer-grade smartphone” and “tablet device” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.” The generic description of the ““consumer-grade smartphone” and “tablet device” indicates the steps are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, the recited additional elements [L2]: “showing … at least one image or video” (data presentation), [L3]: “acquire the image or video” (data gathering), [L4]: “receive the image or video” (data gathering), [L5a]: “receive results transmitted” (data transmission and gathering), [L6]: “receiving and storing … the target user's performance metric” (data gathering), [L7]: “acquire the image or video” (data gathering), and [L8]: “visualizing the target user's performance metric” (data presentation), simply add insignificant extra-solution activity to the judicial exception, i.e., mere data gathering, data transmission, and data presentation, each of which amounts to nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering, data transmission, and data presentation with a computer.
Considered as an ordered combination, the computer components of representative independent claim 11 add nothing that is not already present when the steps are considered separately. The sequence of the steps is equally generic and conventional. See Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission). Hence, the additional element(s) is/are generic, well-known, and conventional computing element(s). The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to the dependent claims:
Dependent claims 2-10 and 12-20 include all the limitations of corresponding independent claims 1 and 11 from which they depend and, as such, recite the same abstract idea(s) noted above for corresponding independent claims 1 and 11. The dependent claims do not appear to remedy the issues noted above. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-10 and 12-20 integrates the judicial exception into a practical application. Additionally, while dependent claims 2-10 and 12-20 may have a narrower scope than corresponding independent claims 1 and 11, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-10 and 12-20 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Rejections - 35 USC § 102/103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-2 and 11-12 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over SALSABILI et al. (US 20240355467 A1) (SALSABILI).
Re claims 1-2 and 11-12:
[Claim 11] SALSABILI discloses a method for providing personalized community-based post-stroke rehabilitation (at least ¶ 48: An instructor 10 can create an exercise or rehabilitation program/module in the exercise database 120 that can then be assigned or prescribed to a user 20 – the “post-stroke rehabilitation” intended use of the preamble is not given any patentable weight), comprising: providing, by a user-end module, schedule information with instructions of at least one specific exercise (at least ¶ 19: FIG. 4 is an illustration of a user interface for a patient … for viewing schedule classes); showing, by the user-end module, at least one image or video, wherein the user-end module provides a camera view page to record a target image or video and to record a target user's performance metric, wherein the user-end module comprises: a feeding module configured to acquire the image or video from a consumer-grade smartphone or tablet device; a human activity recognition module configured to receive the image or video from the feeding module and to leverages a human pose estimation model to analyze input data (at least ¶ 34: The system 1 can communicate with a manufactured hardware/or patient personal user device 110 such as cellphone, tablet or laptop. An integrated graphical display or separate graphical display can be communicatively coupled to the user device 110 can display the exercises and a video monitoring system 118 can collect video content including real-time biomechanical/physiological data as a user performs an exercise. The method and system of the present disclosure can provide real-time feedback 40 to a user and data through a movement analysis engine that can include a human pose estimation model 140 and image analysis … ; ¶ 40); and a human activity evaluation module configured to receive results transmitted from the human activity recognition module and to evaluate performance of the results based on performance metric information which is rule-based or template-based (at least ¶ 34: An integrated graphical display or separate graphical display can be communicatively coupled to the user device 110 can display the exercises and a video monitoring system 118 can collect video content including real-time biomechanical/physiological data as a user performs an exercise. The method and system … can provide real-time feedback 40 to a user and data through a movement analysis engine that can include a human pose estimation model 140 and image analysis by artificial intelligence/machine learning module 190. The system can then provide real-time auditory, visual, and vibrating tactile feedback during exercises to correct pattern of exercise performance; ¶ 41: The system 1 can additionally measure and compare the movement measurements in real-time of the user to the prescribed goal movement parameters for the prescribed movement. This comparison can be utilized by the system to generate the real-time feedback to the user 20 and similarly can be used to rate the performance of the movement by the user 20 …); receiving and storing, by a cloud platform module, the target user's performance metric from the user-end module (at least ¶¶ 35, 36: The system can utilize any computing means such as cloud computing, server, 106, or computing through a user device 110 … ; ¶¶ 42, 43: … the movement feedback system 1 … can provide biomechanical and physiological data back to an instructor/therapy professional and/or the user for further analysis and for real-time feedback. The system can additionally provide a score/rating to the evaluator and/or the user … The rating can be based on one or more metrics including number of repetitions completed, number of repetitions completely correctly, assessment of measured movement parameter versus the goal movement parameter, etc. Real-time feedback can be generated and provided to a user and/or group of users in the class using artificial intelligence and/or machine learning during the performance of the one or more prescribed movement … ; ¶ 58: The user interface may present a variety of screens to the user 20, which the user 20 can move among to manage their experience, including selecting prescribed exercise routines, group classes or sessions, historical performance data and report …); logging, by using a therapist-end module, in the cloud platform module to receive the target user's performance metric from the cloud platform module (at least ¶ 47: the system can provide real-time tele-communication with a virtually located instructor 10, which can include but is not limited to a movement evaluator, trainer, physical therapist, occupational therapist, or any other third party for supervision and feedback as well based upon the movement execution by the patient/user 20; ¶ 53: the data obtained of the remote users prescribed routine can be transmitted to a third party, such as a trainer or physical therapist for future review, further analysis, and or archived. The system can record and store the video and/or communicate the video in real-time to a third party); and visualizing, by using the therapist-end module, the target user's performance metric so as to show exercise waveform comprising quantitative data for qualitative analysis (at least ¶ 12: the video and/or audiovisual content obtained/recorded from the camera and/or device can be communicated to a third-party monitoring the one or more users performing the prescribed exercise routine. Real-time feedback can be provided to one or more of the users based upon the analysis of the system and/or by a third party instructor/therapist that obtains the recorded/live-stream video from the one or more users. Post routine/performance feedback can be provided and transmitted to one or more of the users that participated in the group session upon completing the exercise routine. The post performance feedback can additionally be communicated to a third-party; ¶ 49: the report can include biomechanical analysis of exercise movement parameters, including but not limited to joint angles, displacement of body joints, the speed of joints' movement for each exercise among other biomechanical data metrics to which the system can provide a rating … the report can include physiological analysis of each exercise performance includes but not limited to heart rate, number of steps, blood pressure, blood oxygen level, energy exertion, energy consumption among other physiological data metrics; ¶ 64: FIG. 8 … a display illustrating progress by a user over time based upon each of the prescribed exercises and the baseline or initial movement measurement and the current movement parameter or measurement related to the prescribed movement/routine. The baseline measurements can be stored utilizing the memory module and accessed through the network. The baseline measurements can be established on a per user per exercise basis … the baseline measurements can be established using a contour base calibration based upon the individual user's personal information including but not limited to height and other body measurements. The calibration/baseline can be set for each of the prescribed exercises/movements to be performed by the user. The progress data can be displayed in any suitable manner including graphical illustrations of the improvements in movement over a period of time).
Alternatively, in the event SALSABILI is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment and/or the claim elements are not disclosed as claimed, for example, “visualizing … the target user's performance metric so as to show exercise waveform comprising quantitative data for qualitative analysis”, because the claim limitations are at least suggested, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified SALSABILI as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 1] The claim is a system counterpart to representative method claim 11
and is, as a result, rejected for reasons similar to those previously explained when
addressing representative independent claim 11.
[Claims 2 and 12] Using claim 12 as representative, SALSABILI discloses wherein the cloud platform module is further configured to store the target image or video from the user-end module, and wherein the therapist-end module is permitted to receive and store the target image or video from the cloud platform module (at least ¶ 9: Information/data about prescribed individualized exercises and performance by the user of the exercises can be transmitted to an instructor and/or physical therapist that can be accessed via a digital communication network by a user at a remote location, sending digital video and audio content comprising prescribed individualized exercises; ¶ 53: the data obtained of the remote users prescribed routine can be transmitted to a third party, such as a trainer or physical therapist for future review, further analysis, and or archived. The system can record and store the video and/or communicate the video in real-time to a third party).
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as obvious over SALSABILI, as applied to claims 1 and 11 above.
Re claims 3-4 and 13-14:
[Claims 3 and 13] Using claim 13 as representative, SALSABILI discloses wherein the target user's performance metric comprises joint angles, speed, key point distance, and combinations thereof (at least ¶ 49: … the report can include biomechanical analysis of exercise movement parameters, including but not limited to joint angles, displacement of body joints, the speed of joints' movement for each exercise among other biomechanical data metrics to which the system can provide a rating ... ; ¶ 57: … the feedback can include any suitable information, including but not limited to performance rating, audiovisual correction feedback, number of repetitions, performance rating, heart rate, calories burned, range of motion rating, lift height, depth, speed of movement, and joint displacement among others …).
SALSABILI appears to be silent on wherein the target user's performance metric comprises acceleration, movement smoothness. However, SALSABILI discloses “feedback report … can include … the speed of joints' movement for each exercise among other biomechanical data metrics to which the system can provide a rating”. See ¶ 49. Speed tells how fast something is going and acceleration tells how motion is changing which establishes that the claimed “acceleration” limitation(s) would have been (an) obvious variation(s) to SALSABILI’s speed disclosure. Additionally, SALSABILI discloses “system and method that allows physical therapist/trainer to monitor and engage the patients/clients in a private and/or class and social environment to motivate the exercise performance at remote locations”, that “the video and/or audiovisual content obtained/recorded from the camera and/or device can be communicated to a third-party monitoring the one or more users performing the prescribed exercise routine”, that “real-time feedback can be provided by one or more of a remote telecommuting instructor”, that “[T]he computing system can further be communicatively coupled to one or more other devices such as a tablet, smart phone, camera, personal computers, laptops, sensors, or wearable device”, and that “[A]s the user performs the movements remotely, a video monitoring device 118 that is communicatively coupled to a user device 110 can obtain and analyze the user performing the prescribed movements”. See ¶¶ 6, 12, 13, 37, 45. It is apparent that the “remote telecommuting instructor” could provide any desired feedback based on the monitoring. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified SALSABILI as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claims 4 and 14] Using claim 14 as representative, SALSABILI further discloses wherein the human pose estimation model of the human activity recognition module is implemented using artificial intelligence/machine learning to analyze the input data, and wherein the human pose estimation model is configured to identify and predict a position and orientation of various body joints according to the input data, providing detailed information about user's movements (at least ¶ 3: real-time feedback 40 to a user and data through a movement analysis engine that can include a human pose estimation model 140 and image analysis by artificial intelligence/machine learning module 190; ¶ 49: the report can include biomechanical analysis of exercise movement parameters, including but not limited to joint angles, displacement of body joints, the speed of joints' movement for each exercise among other biomechanical data metrics to which the system can provide a rating; ¶ 57: The feedback can include any suitable information, including but not limited to performance rating, audiovisual correction feedback, number of repetitions, performance rating, heart rate, calories burned, range of motion rating, lift height, depth, speed of movement, and joint displacement among others). SALSABILI appears to be silent on the artificial intelligence/machine learning being “convolutional neural networks (CNNs)”. However, “convolutional neural networks (CNNs)” is a “type of feedforward neural network” and “some applications of CNNs include: image and video recognition”1. Hence, when faced with the issue of human pose estimation, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified SALSABILI as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 5-10 and 15-20 are rejected under 35 U.S.C. 103 as obvious over SALSABILI, as applied to claims 1 and 11 above, in view of Wang et al. (US 20230298204 A1) (Wang).
Re claims 5-8 and 15-18:
[Claims 5-8 and 15-18] Using claim 13 as representative, SALSABILI discloses target user's performance metric comprises joint angles, speed, key point distance, and combinations thereof (at least ¶ 49: … the report can include biomechanical analysis of exercise movement parameters, including but not limited to joint angles, displacement of body joints, the speed of joints' movement for each exercise among other biomechanical data metrics to which the system can provide a rating ... ; ¶ 57: … the feedback can include any suitable information, including but not limited to performance rating, audiovisual correction feedback, number of repetitions, performance rating, heart rate, calories burned, range of motion rating, lift height, depth, speed of movement, and joint displacement among others …). Additionally, speed tells how fast something is going and acceleration tells how motion is changing which establishes that modifying SALSABILI so that the target user's performance metric comprises acceleration would have been (an) obvious variation(s) to SALSABILI’s speed disclosure.
Additionally, SALSABILI discloses “system and method that allows physical therapist/trainer to monitor and engage the patients/clients in a private and/or class and social environment to motivate the exercise performance at remote locations”, that “the video and/or audiovisual content obtained/recorded from the camera and/or device can be communicated to a third-party monitoring the one or more users performing the prescribed exercise routine”, that “real-time feedback can be provided by one or more of a remote telecommuting instructor”, that “[T]he computing system can further be communicatively coupled to one or more other devices such as a tablet, smart phone, camera, personal computers, laptops, sensors, or wearable device”, and that “[A]s the user performs the movements remotely, a video monitoring device 118 that is communicatively coupled to a user device 110 can obtain and analyze the user performing the prescribed movements”. See ¶¶ 6, 12, 13, 37, 45. It is apparent that the “remote telecommuting instructor” could provide any desired feedback based on the monitoring. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified SALSABILI so that the target user's performance metric comprises movement smoothness because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
SALSABILI appears to be silent on but Wang teaches or at least suggests extracting, by the human pose estimation model, relevant information from each input frame of the target image or video; and predicting, by the human pose estimation model, key points of a body appearing in the target image or video, ([Claim 16]) wherein the human pose estimation model is configured to use the predicted key points to infer skeleton, biometrics, and movement data (at least ¶ 11: FIG. 2 … train a neural network to predict positions of keypoints of a subject in image data; ¶ 12: FIG. 13 … train a neural network to perform a mapping of 2D skeleton data to a joint depth offset map; ¶ 20: Pose estimation determines a pose (e.g., a position and orientation) of a subject (e.g., a human) or an object using image data; ¶ 57: the 2D pose detector 216 executes the keypoint prediction model 234 for each synchronized image), ([Claim 17]) conducting, by the human pose estimation model, a human pose estimation process that utilizes the predicted key points to infer relevant biometric data for analysis and evaluation, wherein information applied to the human pose estimation process by the model includes joint angles, speed, acceleration, movement smoothness, and key point distances (at least ¶ 21: estimate a 2D pose of a subject captured in the image data based on joint or keypoint recognition; ¶ 33: predict positions of keypoints, or joints (e.g., elbow, wrist, pelvis), of the subject 102 in the images and to estimate a 2D pose of the subject based on the keypoints positions; 45: implementing a ML/AI system involves two phases, a learning/training phase and an inference phase; ¶ 51: Once trained, the deployed model may be operated in an inference phase to process data. In the inference phase, data to be analyzed (e.g., live data) is input to the model, and the model executes to create an output. This inference phase can be thought of as the AI “thinking” to generate the output based on what it learned from the training (e.g., by executing the model to apply the learned patterns and/or associations to the live data; ¶ 64: the variable λ is a scale factor to account for distances between, for instance, a root joint (e.g., a pelvis joint) of the subject and other joints of the subject. The scale factor λ can be defined based on reference data defining distances between the pelvis joint (e.g., the root joint) and other joints; ¶ 85: an axis-angle representation of a relative rotation of a bone part k with respect to a parent of the particular bone part in the kinematic tree. For example, a skeletal joint rotation vector θ can define the rotation of the bone extending between joints 5 and 6 relative to the bone extending between joints 4 and 5 as labeled in FIGS. 9 and 10 (e.g., rotation at joint 5); ¶ 87: measures the distance between the projected joint locations and corresponding estimated joint locations; E.sub.init measures a difference between the optimized parameters and the initial parameter …), ([Claim 18]) wherein the human pose estimation model comprises computer vision models for predicting the body and key point locations in space as two-dimensional coordinates at a given time (at least ¶ 111: The 2D pose detector 216 predicts the positions (e.g., (X, Y) coordinates) of keypoints or joints of each subject in the images using the neural-network generated keypoint prediction model 234 (e.g., as disclosed in the flowchart of FIG. 12)). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have used Wang’s pose estimation’s features and to have modified SALSABILI as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claims 9-10 and 19-20] Using claim 19 as representative, SALSABILI in view of Wang teaches or at least suggests transmitting, by the human activity evaluation module, evaluation results to the therapist-end module through the cloud platform module; and allowing, by using a web-based portal system of the therapist-end module, an external accessor to manage a user's profile remotely according to the evaluation results, thereby ensuring that therapist-instruction information is updated for the user-end module, ([Claim 20]) sending, by using a feedback module of the therapist-end module, a session instruction to the user-end module so as to amend the schedule information and the instructions of the specific exercise of the user-end module (at least SALSABILI: ¶ 42: an evaluator can determine to revise or generate a new routine for a user based upon the report; ¶ 45: as a user progresses, the instructor and/or system can revise or reassess the user's goal movement parameter. As the user performs the movements remotely; ¶ 50: archive and/or transmit or provide a summary feedback report to an instructor 10 to access prior to the next session to aid in ensuring that the user 20 makes particular changes to their movements or to ensure that the user is properly performing the movements/programs when at their house or remote outside of a rehabilitation or training facility. The instructor 10 may then use the feedback reports to assign or change the prescribed program in the exercise database 120 for the user or determine what movements may need to be directed and/or monitored during an in-person therapy/instruction session; ¶ 53: A Tele-PT/instructors can also modify and prescribe exercises for the future or current session of exercising … the data obtained of the remote users prescribed routine can be transmitted to a third party, such as a trainer or physical therapist for future review, further analysis, and or archived).
Conclusion
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
1 https://en.wikipedia.org/wiki/Convolutional_neural_network
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