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 .
Status of the Application
Claims 1-20 have been examined in this application. This communication is the first action on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/19/2025 is being considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97. References submitted in the IDS of 02/19/2025 were reviewed and, those were found pertinent, are applied herein.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it is not in accordance with 37 CFR 1.72(b) and MPEP 608.01(b); the submitted sheet includes material other than the abstract, such as bibliographic data, title, applicant/inventor information, IPC classification, a selected figure, etc.
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 non-statutory subject matter.
In determining whether a claim falls within an excluded category, the Examiner is guided by the Court’s two-part framework, described in Mayo and Alice. Id. at 217-18 (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 75-77 (2012)); Bilski v. Kappos, 561 U.S. 593, 611 (2010); 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019); the October 2019 Update of the 2019 Revised Guidance (Oct. 17, 2019); 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (July 17, 2024), and the USPTO’s Paten Subject Matter Eligibility Memorandums of August 4, 2025 and December 5, 2025.
Step 1
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability (i.e., laws of nature, natural phenomena, and abstract ideas). Alice Corp. v. CLS Bank Int'l, 573 U. S. ____ (2014).
Independent claims 1 and 11 are directed to a statutory category, because a series of recited steps satisfies the requirements of a process (a series of acts).
(Step 1: Yes).
Next, the claim is analyzed to determine whether it is directed to a judicial exception.
Step 2A – Prong 1
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more of pain assessment. The claim recites:
1. A method for pain assessment comprising:
obtaining a trained first artificial intelligence (Al) model, a trained second AI model, and a trained third AI model;
obtaining data of a plurality of data modalities corresponding to a time period;
generating a latent feature of the data for each modality of the plurality of data modalities based on the trained first AI model and the data for each modality of the plurality of data modalities;
generating a common latent space based on the trained second AI model and the latent feature space of each modality of the plurality of data modalities;
generating a reconstructed latent feature for a missing modality based on the common latent space and the trained second AI model; and
determining a pain indication based on the latent feature for each modality of the plurality of data modalities, the reconstructed latent feature for the missing modality, and the trained third AI model.
The claim as, currently recited, can be characterized as use unspecified AI models to transform data and make a medical prediction. The recited limitations is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (determining a pain) and mathematical concepts (AI models necessarily perform mathematical operations; the latent-space generation, embedding, reconstruction, and neural network inference all involve mathematical computations c), which may be practically performed in the human mind using observation, evaluation, judgment, and opinion (MPEP 2106.04(a)(2), subsection III).
Specifically, the utilizing statistical tools to process data and to output the estimated values - said functions could be performed by a human using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas (e.g., mental comparison regarding a sample or test subject to a control or target data in Ambry, Myriad CAFC, or the diagnosing an abnormal condition by performing clinical tests and thinking about the results in In re Grams, 888 F.2d 835 (Fed. Cir. 1989) (Grams)). In Grams, the recited functions require obtaining data or patient information (from sensors), and analyze that data to ascertain the existence and identity of an abnormality or estimated responses, and possible causes thereof. While said functions are performed by a computer, they are in essence a mathematical algorithm, in that they represent "[a] procedure for solving a given type of mathematical problem." Gottschalk v. Benson, 409 U.S. 63, 65, 93 S.Ct. 253, 254, 34 L.Ed.2d 273 (1972). Moreover, the Federal Circuit has held, “without additional limitations, a process that employs mathematical algorithms to manipulate existing information to generate additional information is not patent eligible.” Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014).
Regarding the use of artificial intelligence and/or machine learning techniques (AI/ML), said recitation does not make the claim patent eligible, because said tools are utilized merely for data gathering and comparing, and are not utilized in express manipulation and control of functional aspects and/or hardware components/equipment of real-world processes and systems using output of AI models (e.g., manufacturing processes and equipment, medical treatments, communications processes and systems, logistics systems and hardware, interactive smart phone apps, etc.). For instance, the claim recites what the second model produces – a common latent space, but the claim does not explain how the second model produces said result. Also, the claim does not explain how the reconstructed latent feature is achieved. The final result of the claimed method is a medical judgment – determining a pain indication. Thus, said AI/ML recitation is simply using generic AI tools to automate a human decision-making process.
It is similar to other abstract ideas held to be non-statutory by the courts. See, also, Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025), wherein the court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Additionally, applying machine learning to medical judgment, an activity predating computers, does not transform the abstract idea into a patent-eligible invention.
As per receiving, storing and outputting data limitations, it has been held that “As many cases make clear, even if a process of collecting and analyzing information is ‘limited to particular content’ or a particular ‘source,’ that limitation does not make the collection and analysis other than abstract.” SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018) (citation omitted); see also In re Jobin, 811 F. App’x 633, 637 (Fed. Cir. 2020) (claims to collecting, organizing, grouping, and storing data using techniques such as conducting a survey or crowdsourcing recited a method of organizing human activity, which is a hallmark of abstract ideas).
All these cases describe the significant aspects of the claimed invention, albeit at another level of abstraction. See Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1240-41 (Fed. Cir. 2016) ("An abstract idea can generally be described at different levels of abstraction. As the Board has done, the claimed abstract idea could be described as generating menus on a computer, or generating a second menu from a first menu and sending the second menu to another location. It could be described in other ways, including, as indicated in the specification, taking orders from restaurant customers on a computer.").
Therefore, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” and/or “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A – Prong 1: Yes).
Step 2A – Prong 2
In Prong Two, the Examiner determines whether claim 1, as a whole, recites additional elements that integrate the judicial exception into a practical application of the exception, i.e., whether the additional elements apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is no more than a drafting effort designed to monopolize the judicial exception. See Guidance, 84 Fed. Reg. at 54-55. If the additional elements do not integrate the judicial exception into a practical application, then the claim is directed to the judicial exception. See id., 84 Fed. Reg. at 54. “An additional element [that] reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field” is indicative of integrating a judicial exception into a practical application. See Guidance, 84 Fed. Reg. at 55.
The Examiner determined that this judicial exception is not integrated into a practical application. The additional elements are only generically claimed. These components are described in the Specification at a high level of generality. See Spec. Detailed Description; [0005]; [0006]:
[0006] “…the processor 112 can be any suitable hardware processor or combination of processors, such as a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field- programmable gate array (FPGA), a digital signal processor (DSP), a microcontroller (MCU), etc. In some embodiments, the display 114 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, an infotainment screen, etc. In some embodiments, the input(s) 116 can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, etc.”
The Examiner fails to see how this generic recitation of the most basic computer components and/or of a system so integrates the judicial exception as to "impose[] a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception." Guidance, 84 Fed. Reg. at 53. Merely confining the abstract idea to a particular technological environment does not establish a practical application. See Guidance, 84 Fed. Reg. at 54. "A claim does not cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit." In re Mohapatra, 842 F. App'x 635, 638 (Fed. Cir. 2021).
Thus, while the additional elements have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." The claim only manipulates abstract data elements into another form, and does not set forth improvements to another technological field or the functioning of the computer itself and, instead, uses computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. The recited steps do not control or improve operation of a machine (MPEP 2106.05(a)), do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and do not apply the judicial exception with, or by use a particular machine (MPEP 2106.05(b)), but, instead, require receiving, storing, comparing and outputting data.
Further, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually; there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, their collective functions merely provide conventional computer implementation. None of the additional elements "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
Regarding the use of AI/ML techniques, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for a particular computing application. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations. Therefore, the use of a trained machine learning model does not integrate the abstract idea of limitation into a practical application, because, under its broadest reasonable interpretation when read in light of the specification, the determining a pain indication encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Similar to Recentive Analytics, claim 1 recites conventional machine learning models without specific improvements to the technology itself. The court noted that "iterative training," a claimed feature, was inherent to all machine learning models and thus did not confer eligibility. Claim 1 does not articulate "how" a technological improvement is achieved.
As per receiving, storing and/or outputting data limitations, these recitations amount to mere data gathering and/or outputting, is insignificant post-solution or extra-solution component and represents nominal recitation of technology. Insignificant "post-solution” or “extra-solution" activity means activity that is not central to the purpose of the method invented by the applicant. However, “(c) Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the execution of the claimed method steps. Use of a machine or apparatus 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 weigh against eligibility”. See Bilski, 138 S. Ct. at 3230 (citing Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, ___ (1978)). Thus, claim drafting strategies that attempt to circumvent the basic exceptions to § 101 using, for example, highly stylized language, hollow field-of-use limitations, or the recitation of token post-solution activity should not be credited. See Bilski, 130 S. Ct. at 3230.
Therefore, claim 1 as a whole, outputs only data structure, - everything remains in the form of a code stored in the computer memory. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claim is directed to an abstract idea. (Step 2A – Prong 2: No).
Step 2B
If a claim has been determined to be directed to a judicial exception under revised Step 2A, examiners should then evaluate the additional elements individually and in combination under Step 2B to determine whether the provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). "[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer." Alice, 573 U.S. at 225. They do not.
The Examiner determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the recited steps amount to no more than mere instructions to apply the exception using a generic computer component. The claim is now re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
The method would require a processor and memory in order to perform basic computer functions of receiving information, storing the information in a database, retrieving information from the database, comparing data, and outputting said information. These components are not explicitly recited and therefore must be construed at the highest level of generality. Based on the Specification (e.g. [0005]; [0006]), the invention utilizes conventional sensors, communication networks and generic processors, which can be found in mobile devices or desktop computers, conventional memory and display devices, and the functions performed by said generic computer elements are basic functions of a computer - performing a mathematical operation, receiving, storing, comparing and outputting data - have recognized by the courts as routine and conventional activity.
Regarding the use of AI model, and obtaining data via repetitive operation of said model, said steps are nothing more than an attempt to recycle preexisting AI/ML technologies to apply for a particular medical application. There are no improvements in said AI/ML techniques, such as advances in the field of computer science itself, or designing a new neural network, and there is no controlling of a technological process using the outcome of said AI/ML operations. Further, the claim does not specify, e.g., a neural network architecture; an encoder/decoder architecture; dimensionality reduction technique; generative model; and/or reconstruction mechanism. Accordingly, claim 1 neither specifies a specific technical purpose for which the method is used, does not define a specific technical implementation of the method, nor the claimed method is particularly adapted for that implementation in that its design is motivated by technical considerations of the internal functioning of the computer. Said AI/ML algorithms and computations are done inside of a computer, and do not have a real-world impact and are not tied to the functionality of the computer. Further, there is no evidence that the invention lies in the training phase or execution phase or both; said AI/ML recitation represents merely conventionally applying an existing model to an existing data from publicly accessible databases, with the result being not technological, but purely entrepreneurial.
Also, the claim does not involve a non-conventional and non-generic arrangement of known, conventional pieces by receiving information from an external source of data. The receiving of data from an external source over a network, such as via the Internet, can fairly be characterized as insignificant extra-solution activity that does not receive patentable weight. See Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d sub nom Bilski v. Kappos, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity). Similar to Content Extraction, 776 F.3d at 1347; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014): “And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis.” Here, the claims are clearly focused on the combination of those abstract-idea processes. The advance they purport to make is a process of gathering and analyzing information of a specified content, then displaying the results, and not any particular asserted inventive technology for performing those functions. They are therefore directed to an abstract idea. As such, the additional elements, considered individually and in combination with the other claim elements, do not make the claim as a whole significantly more than the abstract idea itself.
Accordingly, a conclusion that the recited steps are well-understood, routine, conventional activity is supported under Berkheimer Option 2. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Further, similar to Electric Power Group v Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016) (Power Group), claim’ invocation of computers, networks, and displays does not transform the claimed subject matter into patent-eligible applications. Claim 1 does not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices. Nothing in the claim, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information. Analogous to Power Group, claim 1 does not even require a new source or type of information, or new techniques for analyzing it. As a result, the claim does not require an arguably inventive set of components or methods, such as measurement devices or techniques that would generate new data. The claim does not invoke any assertedly inventive programming. Merely requiring the selection and manipulation of information - to provide a “humanly comprehensible” amount of information useful for users - by itself does not transform the otherwise-abstract processes of information collection and analysis into patent eligible subject matter. Merely obtaining and selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas. Therefore, the recited steps represent implementing the abstract idea on a generic computer, or “reciting a commonplace business method aimed at processing business information despite being applied on a general purpose computer” Versata, p. 53; Ultramerical, pp. 11-12.
Further, the recited functions do not improve the functioning of computers itself, including of the processor(s) or the network elements. There are no physical improvements in the claim, like a faster processor or more efficient memory, and there is no operational improvement, like mathematical computation that improve the functioning of the computer.
Furthermore, there is no transformation recited in the claim as understood in view of 35 USC 101. The recited steps merely represent abstract ideas which cannot meet the transformation test because they are not physical objects or substances. Bilski, 545 F.3d at 963. Said steps are nothing more than mere manipulation or reorganization of data, which does not satisfy the transformation prong. It is further noted that the underlying idea of the recited steps could be performed via pen and paper or in a person's mind. Moreover, “We agree with the district court that the claimed process manipulates data to organize it in a logical way such that additional fraud tests may be performed. The mere manipulation or reorganization of data, however, does not satisfy the transformation prong.” and “Abele made clear that the basic character of a process claim drawn to an abstract idea is not changed by claiming only its performance by computers, or by claiming the process embodied in program instructions on a computer readable medium. Thus, merely claiming a software implementation of a purely mental process that could otherwise be performed without the use of a computer does not satisfy the machine prong of the machine-or-transformation test”. CyberSource, 659 F.3d 1057, 100 U.S.P.Q.2d 1492 (Fed. Cir. 2011)
Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because, when considered separately and in combination, the claim elements do not add significantly more to the exception. Considered separately and as an ordered combination, the claim elements do not provide an improvement to another technology or technical field; do not provide an improvement to the functioning of the computer itself; do not apply the judicial exception by use of a particular machine that is integral to the claim in non-conventional way; do not effect a transformation or reduce a particular article to a different state or thing; and do not add a specific limitation other than what is well-understood, routine and conventional in the operation of a generic computer, and do not provide an unconventional arrangement of known elements that yields a technological solution to a technological problem. None of the hardware recited "offers a meaningful limitation beyond generally linking 'the use of the [method] t a particular technological environment,' that is, implementation via computers." Id., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). As per “obtaining a trained first artificial intelligence (Al) model” recitations, these limitations do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment, that is, implementation via computers." Id., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)). Limiting the claims to the particular technological environment is, without more, insufficient to transform the claim into patent-eligible applications of the abstract idea at their core.
Accordingly, claim 1 is not directed to significantly more than the exception itself, and is not eligible subject matter under § 101. (Step 2B: No).
Because Applicant’s method claims 11 adds nothing of substance to the underlying abstract idea, claim 11 too is patent ineligi-ble under §101.
Further, although the Examiner takes the steps recited in the independent claims as exemplary, the Examiner points out that limitations recited in dependent claims 2-10 and 12-20 further narrow the abstract idea but do not make the claims any less abstract. Dependent claims 2-10 and 12-20 each merely add further details of the abstract steps recited in claims 1 and 11 without including an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. These claims "add nothing of practical significance to the underlying idea," and thus do not transform the claimed abstract idea into patentable subject matter. Ultramercial, 772 F.3d at 716. Therefore, dependent claims 2-10 and 12-20 are also directed to non-statutory subject matter.
Claim Rejections - 35 USC § 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 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6 and 8-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mouton et al. (US 2021/0052215 A1) in view of Regev et al. (US 2022/0180975 A1) (IDS of 02/19/2025).
Claims 1 and 11. Mouton et al. (Mouton) A method for pain assessment comprising:
obtaining a trained first artificial intelligence (Al) model, a trained second AI model, and a trained third AI model; [0016]; [0094]; [0098]
obtaining data of a plurality of data modalities corresponding to a time period; [0014]; [0054]
generating a feature of the data for each modality of the plurality of data modalities based on the trained first AI model and the data for each modality of the plurality of data modalities; Figs. 12A-12B; [0098]; [0099]; [0148]; [0154]; [0175]-[0178]
generating a common space based on the trained second AI model and the latent feature space of each modality of the plurality of data modalities; [0098]; [0099]; [0148]; [0154]; [0175]-[0178]
generating a reconstructed feature for a missing modality based on the common latent space and the trained second AI model; (the face and sound were missing in some videos) [0098];[0099]; [0148]; [0154]; [0175]-[0178]; [0184]; [0185], and
determining a pain indication based on the feature for each modality of the plurality of data modalities, the reconstructed feature for the missing modality, and the trained third AI model. [0098]; [0099]; [0148]; [0154]; [0175]-[0178]; [0184]; [0185]
While Mouton discloses some missing features, Mouton does not specifically teach latent limitation, which is disclosed in Regev et al. (Regev). [0126]; [0759]
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Mouton to include the recited limitations, as disclosed in Regev, for the benefit of enhancing accuracy of data classification and reconstruction. Alternatively, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify Mouton to include the recited limitations, as disclosed in Regev, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The rationale to support a conclusion that the claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR, 550 U.S. at, 82 USPQ2d at 1395; Sakraida v. AG Pro, Inc., 425 U.S. 273, 282, 189 USPQ 449, 453 (1976); Anderson's-Black Rock, Inc. v. Pavement Salvage Co., 396 U.S. 57, 62-63, 163 USPQ 673, 675 (1969); Great Atlantic & P. Tea Co. v. Supermarket Equipment Corp., 340 U.S. 147, 152, 87 USPQ 303, 306 (1950).
Claims 2 and 12. The method of claim 1, wherein the data of the plurality of data modalities comprises a plurality of intermediate features corresponding to the plurality of data modalities, the plurality of intermediate features produced from a plurality of sub-AI models, the plurality of intermediate features corresponding to the plurality of sub-AI models. Mouton; [0152]-[0159]; [0164]-[0169]
Claims 3 and 13. The method of claim 2, further comprising: obtaining sensor data of the plurality of data modalities corresponding to the time period, wherein the data of the plurality of data modalities is determined based on the sensor data of the plurality of data modalities and the plurality of sub-AI models. Mouton; [0061]; [0152]-[0159]; [0164]-[0169]
Claim 4. The method of claim 3, wherein the plurality of data modalities comprises at least one of: a face modality, a body modality, or an auditory modality. Mouton; [0061]; [0152]-[0159]; [0164]-[0169]
Claims 5 and 15. The method of claim 4, wherein the sensor data of the plurality of data modalities comprises at least one of: a plurality of facial images for the face modality, a plurality of body images for the body modality, or audio data for the auditory modality. [0061]; [0152]-[0159]; [0164]-[0169]
Claim 6. The method of claim 1, wherein the trained first AI model comprises an autoencoder neural network, and wherein the latent feature for each modality of the plurality of data modalities comprises an output of an encoder in the autoencoder neural network. Regev; [0012]; [0015]; [0018]; [0184]. Same rationale as applied to claim 1.
Claim 8. The method of claim 1, wherein the trained third AI model comprises a transformer encoder neural network, wherein the method further comprises: stacking the latent feature for each modality of the plurality of data modalities and the reconstructed latent feature of the missing modality; generating a plurality of attentive features based on the trained third AI model and the stacked latent feature for each modality of the plurality of data modalities with the reconstructed latent feature; and concatenating the plurality of attentive features for a final feature vector, wherein the pain indication is determined based on the final feature vector. Mouton; [0056]; [0148]; [0154]; [0157]; Regev; [0233]; [0255]; [0359]. Same rationale as applied to claim 1.
Claim 9. The method of claim 1, wherein the pain indication comprises at least one of: a pain classification or a level of pain intensity. Muton; [0016]; [0031]; [0056]; [0062]; [0071]; [0087]; [0131]; [0174]
Claim 10. The method of claim 1, further comprising: obtaining a vital sign; and producing a sepsis indication based on the pain indication and the vital sign. Muton; cl.1
Claim 14. The method of claim 13, wherein the time period comprises a plurality of segments, each segment having a same time duration, wherein the method further comprises: selecting a same amount of the sensor data for each segment. Mouton; 0061]; Regev; [0161]. Same rationale as applied to claim 1.
Claim 16. The method of claim 11, wherein the first AI model compris7es an autoencoder neural network, wherein the latent feature for each modality of the plurality of data modalities comprises an encoder output of an encoder in the autoencoder neural network, and wherein the first AI model is trained based on a difference between the encoder output of the encoder and a decoder output of a decoder in the autoencoder neural network. Regev; [0012]; [0015]; [0018]; [0184]. Same rationale as applied to claim 1.
Claim 17. The method of claim 11, wherein the second AI model comprises a variational autoencoder neural network, and wherein the second AI model is trained based on an inference output of an inference model in the variational autoencoder neural network and a generative output of a generative model in the variational autoencoder neural network. Regev; [0122]; [0125]; [0250] Same rationale as applied to claim 11.
Distinguishable Subject Matter
Claims 7 and 18-20 have been examined with respect to the prior art, and may be allowable if amended to overcome the rejection under 35 U.S.C 101, and if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The prior art of record neither anticipates, nor, alone or combined, renders obvious as a whole the specific combination of the inventive features as recited in said claims.
Citations of pertinent art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Thiam et al. “Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective” DOI: 10.3389/fphys.2021.720464; pp. 1-21, discloses pain assessment technique utilizing multimodal deep learning, latent representations and reconstructions. The technique employes multimodal deep learning architecture for pain intensity assessment from diverse physiological channels. The encoder maps each input into the latent space, and the decoder reconstructs the input from the latent representation.
Zambelli et al. “Multimodal representation models for prediction and control from partial information” arXiv:1910.03854v1 [cs.RO]; 2019; pp. 1-16, discloses missing modality latent space model technique; the model learn representation from multiple modalities; works with diverse signals; learns a coherent representation when parts of the input are missing; reconstructs missing sensory modalities.
Salekin et al. “Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment” Comput Biol Med. 2020 Nov 28;129:104150. doi: 10.1016/j.compbiomed.2020.104150; pp. 1-29, discloses multimodal pain assessment and fusion of multimodal pain related modalities employing temporal information.
Wu et al. “Multimodal Generative Models for Scalable Weakly-Supervised Learning” arXiv:1802.05335v3 [cs.LG]: pp. 1-19, discloses multimodal autoencoder trained to learn a joint representation and to operate with missing modalities.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Igor Borissov whose telephone number is 571-272-6801. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor Kambiz Abdi can be reached on 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/IGOR N BORISSOV/ Primary Examiner, Art Unit 3685 8/28/2026
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