DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“Blood pressure sensor module” in claim 1, is referring to plurality of body pressure sensors configured in a three-dimensional structure (¶ [0027]).
“Sample body pressure distribution data generation module,” “posture discrimination module,” “storage module,” and “communication module,” recited in claims 1 and 4-6, are modules “that process at least one function or operation, which may be implemented as . . . a combination of hardware and software.” (¶ [0046]).
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 described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2 and 9-10 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.
Claims 1 and 9 recite inputting data to a generative adversarial network (GAN), convolutional neural network (CNN), long short-term memory neural network model (LSTM), and outputting data based on the inputs. However, the specification is devoid of the governing equations and structure of the models to reasonably convey possession of the claimed invention. As such, the subject matter related to the above models is not adequately described in the specification.
Claims 2 and 10 recite a loss equation comprising “p denotes prediction result, t denotes actual data value, i denotes data number, and j denotes class,” but lacks detail in the specification. The specification merely recites the claim language, but does not provide or suggest what those variables are referring to (¶ [0066-71]). As such, the claims lack proper written description and do not reasonably convey possession of the claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 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.
Claims 1 and 9 recite a GAN, CNN and LSTM, but is indefinite because the structure or governing equations corresponding to the models are unknown. The models appear to merely receive an input and provide an output, without describing how the input is manipulated to arrive at the output.
Claims 1 and 9 recite “generate the corresponding user’s sample body pressure distribution data” in lines 6-7, but lacks proper antecedent basis. A user has been previously recited in line 4, thus, “the corresponding user’s” is interpreted to refer to the same user in line 4. The term “corresponding” can be removed to maintain claim language consistency or added to precede “user” in line 4. Additionally, “sample body pressure distribution data” appears to lack proper antecedent basis because “the corresponding use’s” precedes the limitation. As such, it is unclear if the limitation is interpreted as “the . . . sample body pressure distribution data” or “a . . . sample body pressure distribution data”. Therefore, clarification is required. For example, a possible amendment can be -- generate sample body pressure distribution data of the user--.
The same issue is seen in Claims 1 and 9, in line 8 regarding the recitation of “the corresponding user’s actual body pressure measurement,” in lines 10-11, “the corresponding user’s actual body pressure distribution data,” and in lines 12-13, “the corresponding user’s lying postures.”
Claim 1 recites “on the basis of receiving the provision” in line 11, but instead should recite --based on the received provision-- to establish proper antecedent basis.
Claims 1 and 9 recite “the basis of learned and analyzed time series body pressure distribution data” in line 13, but is indefinite. The term “the” indicates a previous recitation, however it is unclear if the body pressure distribution data is of the generated sample data or the actual data of the user. Additionally, while an analysis is implied when generating the sample data, learning the sample data, and using the actual user’s data, it has not been previously recited, which adds to the indefiniteness. As such, it is unclear if the limitation is referring to a previous limitation or is attempting to establish a new limitation. Clarification required.
Claims 2 and 10 recite “p denotes prediction result, t denotes actual data value, i denotes data number, and j denotes class” in line 4, but is indefinite. What is the value or corresponding data of the prediction result? What is the data number? What is the class? It is unclear what the variables for the equation are. As such, one of ordinary skill in the art cannot examine the claim without knowing what the variables are referring to.
Claims 4 and 12 recite “the plurality of body pressure sensors configured in a three-dimensional structure having time (t),” but is indefinite. How can a physical structure be configured with time? Clarification required.
Claim 5 recites “a storage module configured to convert, into a database (DB) for each user, store, and manage user information data” in line 2-3, but is indefinite. Is the storage module converting into a database, does it comprise a database, or something else? For examination purposes, it will be interpreted that the storage module comprises a database and configured to convert, store, and manage user information for each user. Additionally, independent claim only recites one user, how can there be more than 1? Further clarification required. The same issue is seen in claim 8 regarding “the external terminal or the server.” As such, the terminal/server will be interpreted to comprise the database.
Claim 13 recites “converting, into a database (DB) for each user, by a separate storage module after. . . storing . . .” in line 2, but is indefinite. What is being converted into a database? The user information? It appears the data is transferring from one storage module to another that comprises a database and will be interpreted as such. Further clarification required. The same issue is seen in claim 16 regarding “the external terminal or the server.” As such, the terminal/server will be interpreted to comprise the database.
Claims not listed are rejected by virtue of claim dependency.
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 (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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-6, 8-11, 13-14, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh et al. (US 20230145268), hereinafter Ghosh, further in view of and Amer et al. (US 20190304157), hereinafter Amer.
Regarding claims 1, 9 and 17, Ghosh teaches a smart bed using pressure sensors to determine user posture (abstract and ¶[0059,0078], “ present invention may be a system, a method . . . computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.”), comprising:
a body pressure sensor module installed on an upper part of a frame of a bed or inside a mattress and configured to measure body pressure of a user, touching the frame of the bed or the mattress, by using a plurality of body pressure sensors (¶[0046], “IoT component can manage smart bed 103, which includes the capability of interfacing to various sensors throughout the bed (e.g., position, pressure sensor, etc.).” (emphasis added) “[T]hroughout the bed” viewed with the broadest reasonable interpretation includes sensors that are both in and on the bed.)
a sample body pressure distribution data generation module configured to learn and generate the corresponding user’s sample body pressure distribution data based on receiving provision of the corresponding user’s actual body pressure measurement data measured by the body pressure sensor module (¶[0014-18,0057-62], “Digital twin computing technology leverages IoT, artificial intelligence (i.e., leveraging machine/deep learning) and software analytics to create living digital simulation models that update and change as their physical counterparts change. A digital twin continuously learns and updates itself to represent its near real-time status.” and “a simulation of digital twin of the patient and the environment IoT data.” As such, the digital requires using continuous data corresponding to the distribution data of the sensor, e.g., body pressure measurement, to simulate patient movement, posture, etc., and cannot be performed without such data because it is not a static model),
a posture discrimination module configured to analyze the corresponding user’s actual body pressure distribution data on the basis of receiving the provision of the corresponding user’s actual body pressure measurement data measured by the body pressure sensor module, and discriminate the corresponding user’s lying postures on the basis of learned and analyzed time-series body pressure distribution data in a two-dimensional format after learning and predicting the time-series body pressure distribution data in the two-dimensional format the basis of the corresponding user’s analyzed actual body pressure distribution data and the corresponding user’s sample body pressure distribution data generated by the sample body pressure distribution data generation module (¶[0022,0051,0058,0062], “generate a position profiling based on time duration,” “patient component 111, through patient component 121, retrieves data associated with a patient,” “AI component 124, generates various patient bed/lying positions based on the digital twin copy of the patient. The bed positions may include the following, non-compliant position, degree of non-compliances, pain modeling in one/many portions of patient, one/more bed bedsores/pressure ulcers, the time period of lying in one position and stress”, “data related to patient_A, can include the position of the patient (patient is lying on his back in the middle of the bed,” and “simulates patient movement (on digital twin server 104) based on the medical requirements/parameters.” The excerpts above teach determining the user’s posture and other parameters based on the user’s actual time-series data, generated sample data (digital twin), simulated/predicted patient movement).
Ghosh fails to teach using a Generative Adversarial Network (GAN) preset to learn and generate the user’s sample body pressure distribution based on the user’s actual body pressure measurement data measured by the body pressure sensor module; and using an ensemble artificial intelligence deep learning technique combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) neural network model, which have characteristics different from each other to discriminate the user’s lying posture based on analyzing the distribution of data of the user corresponding to the user’s sample data and actual data.
Amer teaches techniques using artificial intelligence to generate user/actor data structures, based on inputs such as posture, specifying the attributes associated with the user, e.g., posture (abstract, ¶[0014,0035], fig. 2A-C). The artificial intelligence module uses a GAN based on the data distribution of the input to generate and use training data that looks real and assign/discard generated data that looks fake (¶0048-49], the GAN users a generator to generate the data and a discriminator to determine the “real” and “fake” data). The GAN can comprise a combined CNN and LSTM as the discriminator and generator (¶[0051]).
As a result it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Ghosh, to use a Generative Adversarial Network (GAN) preset to learn and generate the user’s sample body pressure distribution based on the user’s actual body pressure measurement data measured by the body pressure sensor module; and using an ensemble artificial intelligence deep learning technique combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) neural network model, which have characteristics different from each other to discriminate the user’s lying posture based on analyzing the distribution of data of the user corresponding to the user’s sample data and actual data, as taught by Amer, because Ghosh requires determining posture of a user, but fails to provide details, and Amer teaches identification of user attributes can be accomplished by using a GAN, CNN, and LSTM along with the user data, sample/generated data, and learned data.
Regarding claims 2 and 10, Amer teaches wherein learning of the LSTM, loss is calculated by equation 1,
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, where, p denotes prediction result, t denotes actual data value, i denotes data number, and j denotes class (It is noted, based on the specification of the present invention, ¶[0143], the loss function is a cross-entropy loss function, but fails to teach what the variables are correlated to. ¶[0067] of Amer teaches, the LSTM model is trained with a cross entropy loss function and has been interpreted to teach the limitation due to missing information of the claimed equation).
Regarding claims 3 and 11, Ghosh teaches wherein the corresponding user’s lying postures discriminated through the posture discrimination module comprises a supine posture (¶[0059], “can include the position of the patient (patient is lying on his back in the middle of the bed),”(emphasis added) ).
Regarding claims 5 and 13, Ghosh teaches a storage module configured to convert, in a database for each user, store, and manage user information data of at least the corresponding user’s lying posture information data discriminated by the posture discrimination module (¶[0041], “storage device capable of storing data and configuration files that can be accessed and utilized by server” and “Database 116 may store information associated with, but is not limited to, knowledge corpus, patient medical history, IoT devices, smart bed profile and setting, modeling of patient, patient activities and home automation routines”).
Regarding claims 6 and 14, Ghosh teaches a communication module configured to transmit, to an external terminal or a server in a wired or wireless method, the user information data of at least the corresponding user’s lying posture information data discriminated by the posture discrimination module (fig. 2 and ¶[0038], “Server 110 and/or digital twin server 104 can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data”).
Regarding claims 8 and 16, Ghosh teaches wherein the external terminal or the server converts, into a database (DB) for each user, stores, and manages the user information data of at least the corresponding user’s lying posture information data, the user information data being transmitted from the communication module through a pre-installed specific application service (¶[0041], “database 116 resides on server 110 . . . database 116 may reside elsewhere within patient mobility environment 100, provided that patient component 111 has access to database 116” and “invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration” (emphasis added) indicating that computer program products (pre-installed specific application service) can be integrated in the various devices, computers, etc., as described including the computer(s) that communicates with the server/database. It is noted, the specification of the present invention does not specify what the specific application service is.).
Claims 4, 7, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh in view of Amer, as applied to claims 1, 6, 9, and 14, and further in view of Nourani et al. (US 20130090571), hereinafter Nourani.
Regarding claims 4 and 12, Ghosh-Amer fail to teach the body pressure sensor module comprises the plurality of body pressure sensors configured in a three-dimensional structure having time (t) and a plurality of rows and columns.
Nourani teaches a method and system of monitoring and preventing pressure ulcers via pressure sensor matrix distributed on or embedded in a bed (abstract and ¶[0032,0071]).
It would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Ghosh-Amer, such that the body pressure sensor module comprises the plurality of body pressure sensors configured in a three-dimensional structure and a plurality of rows and columns, as taught by Nourani, to aid in monitoring and preventing pressure ulcers.
Regarding claims 7 and 15, Ghosh-Amber teaches wherein the external terminal or the server (110 of Ghosh) allows the user information data to be displayed on a display screen so as to enable a manager to check the user information data of the corresponding user visually (¶[0079] of Ghosh, “Display 409 provides a mechanism to display data to a user”) the user information data being transmitted from the communication module through a pre-installed specific application service (¶[0041], application service of Ghosh), but fails to teach displaying the user information data of at least the corresponding user’s actual body pressure distribution data, or the corresponding user’s lying posture information data.
Nourani teaches that the display can output the body pressure data with the corresponding lying posture information data (fig. 10 and [0081]).
As such, it would have been obvious to one of ordinary skill in the art at the time the invention was effectively filed to have modified the device of Ghosh-Amber, such that the user information data of at least the corresponding user’s actual body pressure distribution data, or the corresponding user’s lying posture information data, is displayed, as taught by Nourani, because Ghosh requires display user information, but fails to provide details, and Nourani teaches that user information can be the corresponding user’s actual body pressure distribution data, or the corresponding user’s lying posture information data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. teaches an interaction interface may also provide the feedback information for the user, for example, an alarm about an incorrect sitting posture using GAN, LSTN, or CNN. US 20200117519 A1
Holicki et al. teaches using a GAN to determine a relative posture of an image of the second training data set relative to an image generated from the training map. US 20230350418 A1
Poodeh et al. teaches a bedding system uses a convolutional neural network (CNN)-based machine vision to makes adjustments for comfort and/or support.
Ahmadian et al. teaches an unsupervised generative adversarial network to produce healthy plantar pressure image for patients who have hallux valgus disease. Unsupervised Generative Adversarial Network for Plantar Pressure Image-to-Image Translation 2021
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARTIN NATHAN ORTEGA whose telephone number is (571)270-7801. The examiner can normally be reached M-F 7:10 am - 5:00 pm.
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/MARTIN NATHAN ORTEGA/Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791