DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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.
3. The following is a non-final office action in response to the preliminary amendment filed on 02/05/2025. Claims 1-26 have been canceled; new claims 27-46 have been added. Therefore, claims 27-46 are currently pending in this application.
Claim Rejections - 35 USC § 101
4. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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 27-46 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
Step 2A [Wingdings font/0xE0] Prong One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 27 and 46 as the representative claim, the following claimed limitations recite an abstract idea:
evaluating a cognitive and physiological status of a subject [by]:
[collecting] demographic data and medical history data of the subject;
presenting questions, games/exercises, and/or mental tracking exercises to the subject,
temporarily store data of the subject [collected] during the questions, games/exercises, mental tracking exercises;
preprocess the stored data; and receive:
the demographic data and the medical history data and/or on data derived therefrom;
physiological parameters/features derived therefrom; and
the data preprocessed; and
apply an algorithm on the received data to thereby compute a current and predicted cognitive and physiological status of the subject.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to mental processes, and/or certain methods of organizing human activity, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)). For instance, the claims correspond to mental processes, wherein data collected regarding a subject is evaluated; wherein the data includes (a) demographic data and medical history data, (b) physiological parameters, (c) interaction data of the subject gathered during the subject’s interaction with questions, games/exercises and/or mental tracking exercises; and accordingly, based on the evaluation—using an algorithm—of the collected data above, the subject’s current and predicted cognitive and physiological status is computed.
Similarly, the claims also correspond to managing personal behavior, wherein a subject is presented with interactive content—namely, questions, games/exercises and/or mental tracking exercises; and thereby, the subject interacts with the interactive content (e.g., responding to one or more questions, exercises, etc.).
Step 2A [Wingdings font/0xE0] Prong Two:
The claims recite additional element(s), wherein a computer-based system that incorporates existing components—such as, a user interface, physiological sensors, a cloud-based server that executes an AI algorithm, etc., is utilized to facilitate the recited functions/steps regarding: collecting data related to a subject (e.g., “a basic data module comprising a user interface for inputting and/or uploading demographic data and medical history data of the subject; a physiological sensor module comprising one or more physiological sensors”); presenting interactive content to the subject (e.g., “a cognitive monitoring module comprising a personal assistance Application (PA App), the PA App comprising a preprocessing unit configured to present questions, games/exercises, and/or mental tracking exercises to the subject”); collecting response/reaction of the subject during the subject’s interaction (e.g., “an in-memory queue configured to temporarily store even logs, video/image data and audio data of the subject, recorded during the questions, games/exercises, mental tracking exercises and to serve the stored data to the preprocessing unit for preprocessing”); analyzing the collected result and generating a relevant result representing the subject’s cognitive and physiological status (e.g., “a cloud-based server configured to receive: i. the demographic data and the medical history data and/or on data derived therefrom; ii. signals obtained from the physiological sensor module and/or parameters/features derived therefrom; and iii. the data preprocessed by the preprocessing unit; and to apply an AI algorithm on the received data to thereby compute a current and predicted cognitive and physiological status of the subject”), etc.
However, the claimed additional elements fail to integrate the abstract idea into a patent-eligible practical application since the additional elements are utilized merely as a tool to facilitate the abstract idea. Accordingly, when each of the claims is considered as a whole, the additional elements fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
Step 2B:
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions.
Accordingly, when each of the current claims is considered as a whole (e.g., see the discussion under Prong Two above regarding such consideration of the claim as a whole), none of the claims recites an element—or a combination of elements—directed to an inventive concept.
It is also worth noting—per the original disclosure—that the claimed invention is directed to a conventional and generic arrangement of the additional elements. For instance, the specification describes a computer-based system that incorporates one or more commercially available conventional computing devices (e.g., a tablet computer, a smartphone, etc.) that runs a computer application; wherein the computing device(s) also communicates—over the conventional network (e.g. the Internet)—with an online server; and accordingly, while executing one or more existing or already developed algorithms—such as, a conventional neural network (CNN), a Long-short term memory (LSTM), a Support vector machines (SVM), Open AI, Chat GPT etc., the disclosed system facilitates the process of evaluating the cognitive and physiological sate of the subject (see the specification: pages 12 -16).
In addition, the utilization of the conventional computer/network technology to facilitate the process of evaluating a subject—including: collecting data related to the subject; providing the subject with interactive content; gathering response/reaction of the subject during his/her interaction with the content; and generating, based on the analysis of the collected data, a result(s) representing the mental and/or physiological state of the subject, etc., is directed to a well-understood, routine, conventional activity in the art (e.g., see US 2017/0039045; US 2016/0262680; US 2011/0301433, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 28-45). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element(s) utilized to facilitate the abstract idea.
Accordingly, the findings above demonstrate that none of the claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
Claim Rejections - 35 USC § 112
5. 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 27-46 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 pre-AIA the applicant regards as the invention.
(a) Each of claims 27 and 46 recites the process of storing “even logs” (emphasis added); however, it is unclear whether the term “even” is intended to mean --event--. Accordingly, the claims are ambiguous at least for the reason above.
(b) Each of claims 27 and 46 also recites, “applying an AI algorithm on the received data” (emphasis added).
However, the term “AI” renders each of the current claims ambiguous since claims 27 and 46 fail to define the term “AI”, which appears to be an abbreviation. Note that given the field of the claimed system/method, the term “AI” is not necessarily a well-known term in the field.
(c) Claim 30 recites, “the event log”; however, there is insufficient antecedent basis for the above term in the claim; and accordingly, claim 30 is further ambiguous at least for the reason above.
(d) Claim 33 recites, “NLP/LLM modes”; however, each of the terms “NLP” and “LLM” appears to be an abbreviation, which the claim fails to define. Thus, claim 33 is further ambiguous since it is not necessarily clear what is encompassed per each of the above terms. It is again worth noting, given the field of the claimed system, the neither “NLP” nor “LLM” is necessarily a well-known term in the field.
(e) Claim 45 is currently depends on a canceled claim—namely, claim 24; and therefore, the scope of the claim indefinite. Nevertheless, for examination purpose, it is considered to be dependent on claim 27.
In addition, it is unclear whether the term “the subjects”, as recited in the second line of the claim, is intended to mean --the subject’s--.
(f) Regarding claim 46, it is further unclear why part of the claim, which lists the types of data elements, starts with the numerical order indicator “iv”, as opposed to “i”. It is worth nothing that claim 46 is an independent claim; and thus, it is not necessarily tied to claim 27.
Furthermore, the claim appears to unnecessarily repeat the term “and” consecutively; namely, the term “and” at the end of line 16, and the term “and” at the beginning of line 17.
Applicant is further advised to evaluate each of the current claims and make appropriate corrections if additional discrepancies are discovered.
Claim Rejections - 35 USC § 103
4. 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Note that the one or more citations (paragraphs or columns) presented in this office action regarding the teaching of a cited reference(s) are exemplary only. Accordingly, such citation(s) are not intended to limit/restrict the teaching of the reference(s) to the cited portion(s) only. Applicant is required to evaluate the entire disclosure of each reference; such as additional portions that teach or suggest the claimed limitations.
● Claims 27-31, 34, 35 and 37-46 are rejected under 35 U.S.C.103 as being unpatentable over Sadowsky 2011/0301433 in view of Abrahami 2017/0039045.
Regarding claim 27, Sadowsky teaches the following claimed limitations: a system for evaluating a cognitive and physiological status of a subject, the system comprising: a basic data module comprising a user interface for inputting and/or uploading demographic data of the subject ([0026] lines 1-9; [0034] lines 11-20; [0036]; [0054]: e.g., a computer-based system/method for analyzing the mental state of a subject; wherein the system comprises various input and output devices and/or user interfaces; such as, touchscreen, for inputting data of the subject. Note that at least the subject’s demographic data is inputted or uploaded since the system considers the subject’s demographic data to determine content pertinent to the subject); a physiological sensor module comprising one or more physiological sensors, a cognitive monitoring module comprising a personal assistance Application (PA App), the PA App comprising a preprocessing unit configured to present questions, games/exercises, and/or mental tracking exercises to the subject ([0040]; [0054]: e.g., the system already implements sensors for capturing the subject’s physiological data; such as, skin temperature, heartrate, etc.; and furthermore, the system presents, via the subject’s laptop or cellphone, one or more interactive content items, including computer games. Accordingly, besides implementing physiological sensors to capture the subject’s physiological data, the system also implements at least one computer application, which is executed via the subject’s device, in order to present the subject with interactive computer games. Thus, the system incorporates a personal assistance application with preprocessing unit to present games or exercises), and an in-memory queue configured to temporarily store [event] logs and video/image data recorded during the questions, games/exercises, mental tracking exercises and to serve the stored data to the preprocessing unit for preprocessing ([0040]; [0053]: e.g., as already pointed out above, the system presents the subject with an interactive content—such as, a computer game, which require the subject to respond; and the system captures, via one or more sensors, the user’s physiological data, including facial expressions captures via a video camera; and the collected data above is stored into a disk/memory of the computer system. It is understood that “data which was captured may be correlated to an experience by the individual. The experience may be . . . a computer game, a video game”; and this indicates part of the preprocessing performed via the preprocessing unit); and a cloud-based server configured to receive: i. the demographic data of the subject and/or on data derived therefrom; ii. signals obtained from the physiological sensor module and/or parameters/features derived therefrom; and iii. the data preprocessed by the preprocessing unit; and to apply an AI algorithm on the received data to thereby compute a current and predicted cognitive and physiological status of the subject ([0041] to [0044]; [0056]; [0062] to [0065]: e.g., the system also sends the data captured locally—namely, the data captured via the subject’s computer—to a web service for “further mental state analysis”, which indicates that the preprocessed data is transmitted to a server for further analysis using “larger group of analytical tools” available on the server. In this regard, the system also implements a machine-learning algorithm—with or without human intervention—for determining various mental states of the subject, wherein “[a] series of mental states may be presented with the likelihood of each state for a given point in time. Likewise a series of probabilities for each mental state may be presented over the timeline”, etc. Thus, such presentation the likelihood or probability of each mental state over a time line indicates the process of computing a current and predicted cognitive and physiological status of the subject. Note that the machine-learning algorithm is essentially an artificial-intelligence algorithm; and furthermore, the mental state already encompasses both cognitive state and physiological state, see [0025]. Note also that an emotional state/response always involves a physiological sate/response).
Sadowsky does not expressly teach that the data of the subject above further includes medical history and audio of the subject recorded during games/exercises.
However, Abrahami teaches a system/method that analyzes a user’s emotional and/or mental state based on the analysis of various data, including the user’s voice data—e.g., the user’s voice recorded as part of a response—analyzed via a voice recognition software that implements blind source separation (BSS) feature to clearly recognize the user’s speech ([0290] to [0294]; [0298] to [0302]); and furthermore, the system considers the subject’s demographic data and medical history data as part of the user’s mental analysis ([0427]; [0457]; [0460]), etc.
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Sadowsky in view of Abrahami; for example, by incorporating additional data related to the subject—such as the subject’s medical history; and the system’s algorithm is further upgraded by incorporating one or more well-known/existing algorithms—such as, a voice recognition software that implements a blind source separation feature, including an NLP analyzer, etc., and accordingly, besides providing the subject with additional flexibility to easily interact—via voice/speech—with the system (e.g., allowing the subject to easily respond to a game scenario and/or questions, while excluding irrelevant voices or ambient sounds, etc.), the system further utilizes the subject’s medical history and demographic data, along with the subject’s voice/speech recorded during the subject’s interaction, to more accurately evaluate the subject’s mental state during the preprocessing and/or final analysis; so that the system would be able to generate a result(s) that more accurately signifies the subject’s mental state.
Regarding claim 28, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Abrahami further teaches, a preprocessing unit configured for synchronous, real-time processing of the recorded audio by applying a voice recognition algorithm thereon, the voice recognition algorithm configured to recognize the voice of the subject and to cancel out environmental noise and other speakers' audio and to identify a beginning and an end of a speech of the subject ([0290]; [0294; [0298] to [0302]: e.g., the system already implements a voice recognition software in order to evaluate the subject’s speech during a session or dialog with the system; wherein the voice recognition software already includes a blind source separation—a BBS—feature; and thereby, it isolates the subject’s voice/speech from the voice/speech of other individuals and background noise. It is also worth noting that the implementation above enables the system in “recognizing what the user said”—i.e., the system identifies a beginning and an end of a speech of the subject).
Also see the motivation discussed above regarding claim 27 since the same motivation applies to claim 28. This is because the modified system already incorporates a voice recognition software with a BSS feature; and furthermore, the modified system already achieves the features that claim 28 is requiring.
Regarding claim 29, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 28.
The limitation, “the preprocessing unit is configured to time a next exercise, question or game based on the identified beginning and end of the subject's speech”, is also already addressed per the discussion presented with respect to claim 28. This is again because the modified system utilizes its voice recognition software to analyze the voice of the user “as part of voice UI responses or script responses” and/or “recognizing what the user said”, etc.
Thus, such analysis of the voice of the user “as part of voice UI responses or script responses”, etc., already indicates that the system times at least the next question based on the identified beginning and end of the subject's speech. Of course, here also the same modification discussed per claim 27 above applies to claim 29 since the modification already encompasses the features of claim 29.
Regarding claim 30, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Sadowsky further teaches, the preprocessing unit is configured for synchronous, real-time processing of the event log, and to time a next exercise, question or game, based thereon ([0040]: e.g., as part of evaluating the subject’s mental state, the subject interacts with computer/video games; and accordingly, it is understood that computer/video games normally time the presentation of each subsequent/next game scenario based on the subject’s actions/inputs during the game in real-time. Thus, the preprocessing unit is configured for synchronous, real-time processing of the event log, and to time a next exercise or game. based thereon).
Regarding claim 31, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Abrahami further teaches, the queue is configured to serve the recorded audio for asynchronous processing by the preprocessing unit, wherein the preprocessing comprises extracting voice features from the audio, and transmitting the voice features to the server ([0114]; [0188]; [0212]: e.g., the subject’s device incorporates client coordinator, which receives and records information on local database from all available sensors to create baseline values for the subject. In this regard, all available sensors already include the microphone that captures the subject’s voice/speech during the subject’s interaction with the with the system. Thus, the creation of baseline sensor values specific for the user indicates the extraction of voice features from the subject’s voice/speech. In addition, the subject’s device, via its client coordinator, sends the preprocessed data above to the server, so that the data would be further analyzed using global analyzer).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the invention of Sadowsky in view of Abrahami; for example, by incorporating a server-based global analyzing scheme, which implements additional features for properly evaluating sensor data gathered via one or more of the sensors, including the microphone that records the user’s voice/speech; and accordingly, the subject’s device transmits to the server the raw or preprocessed data of the subject, which includes the subject’s raw or preprocessed voice/speech; so that the server generates a more refined result(s) that represents the subject’s mental state; and the server further transmits the result(s) back to the subject’s device—such as, displaying the refined result(s) to the subject, etc., so that the subject would have a more accurate assessment regarding his/her mental state.
Regarding claim 34, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Sadowsky further teaches, the queue is configured to serve the stored image/video data for asynchronous preprocessing by the preprocessing unit, wherein the preprocessing comprises applying emotion extraction and key point extraction algorithms on the video/image data and transmitting the extracted emotions and key points to the server for further processing ([0026]; [0027]; [0039] to [0043]; [0046] to [0048]: e.g., when the subject is interacting with a computer game/questions, the subject’s device captures the user’s response/reaction; and accordingly, besides correlating the data with the specific subject, the subject’s device also performs facial analysis and facial recognition in order to identify attributes—such as, facial expressions, head gestures, etc., which signify the emotional state of the subject. It is worth nothing that facial expressions—such as a smiling face—signify emotion of the subject. Thus, the above indicates the asynchronous preprocessing that applies emotion extraction and key point extraction algorithms on the captured video/image. The system further sends the above data to a web service or server, which has a larger group of analytical tools, for further analysis).
Regarding claim 35, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 34.
Sadowsky further teaches, the further processing comprises applying image analysis algorithms on the extracted emotions and key points ([0027]; [0039] to [0043]; [0046] to [0048]: e.g., it is evident that the server—during its “further” analysis—is also performing image analysis using one or more algorithms since the data sent to the server already includes the captured image/video of the subject; and accordingly, using “larger group of analytical tools”, which are essentially algorithms, the server performs “further” analysis to determine the mental state of the user).
Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27. Sadowsky further teaches:
Regarding claim 37, the questions, games/exercises, and/or mental tracking exercises are dynamic and/or personalized and wherein the preprocessing unit is configured to adjust a next question, game and/or exercise based on the preprocessed data ([0047]; [0049]: e.g., based on the analysis of the subject’s mental state, the system performs one or more tasks—such as, modifying a question, and/or changing the direction of an electronic game, etc. Thus, this indicates that the questions, games/exercises, and/or mental tracking exercises are already dynamic and/or personalized; and furthermore, the preprocessing unit is already configured to adjust a next question, game and/or exercise based on the preprocessed data);
Regarding claim 38, wherein the server further configured to provide exercises/stimulations configured to prevent and/or ameliorate cognitive decline, based on the computed current and predicted cognitive and physiological status of the subject ([0047]; [0049]; [0065]: e.g., similar to the case above, the system generates, based on the analysis results signifying the subject’s mental state, one or more recommended actions—such as, changing the direction of an electronic game, changing a medical consultation presentation, editing a confusing section of a tutorial, etc. It is worth nothing that the mental state analysis already encompasses both cognitive and physiological state of the subject; see [0025]. Thus, the teaching above indicates that the server is already configured to provide, based on computed current and predicted cognitive and physiological status of the subject, exercises/stimulations configured to prevent and/or ameliorate cognitive decline);
Regarding claim 39, wherein the server is further configured to determine a probable cause of a decline in the cognitive and/or physiological status of the subject, based on an integrated analysis of data obtained from the physiological sensor module and data obtained from the cognitive monitoring module ([0047]; [0049]; [0065]; [0071]: e.g., the system is “recommending a course of action based on the mental state of the individual”, wherein the system presents, “a series of probabilities for each mental state” over the timeline “for which facial and physiological data was analyzed”; and wherein “an action may be recommended based on the mental state 1042 which was detected”, etc. Accordingly, the server is already configured to determine a probable cause of a decline in the cognitive and/or physiological status of the subject, based on an integrated analysis of data obtained from the physiological sensor module and data obtained from the cognitive monitoring module);
Regarding claim 40, the PA App communicates with the subject via a touch screen, via speaker/microphone, via a dedicated user interface or any combination thereof ([0054]: e.g., the subject’s device already implements various interaction units, including a touch screen, that allow the subject to interact with the application);
Regarding claim 41, wherein the server is further configured to rank the cognitive and physiological status of the subject with respect to a plurality of evaluated subjects ([0072]: e.g., the system generates a graph, which depicts the subject’s mental state analysis along with an aggregated result from a group of people. Thus, the server is already configured to rank the cognitive and physiological status of the subject with respect to a plurality of evaluated subjects).
Regarding claim 42, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Regarding the limitation, “the server is further configured to apply a classification algorithm on the demographic data and the medical history data prior to the applying of the AI algorithm, thereby classifying the subject into a relevant patient group, representing the risk of the subject of developing delirium, and wherein the data derived from the demographic data and the medical history data comprises the risk of the subject to develop delirium”, Abrahami teaches that the system evaluates, as part of analyzing the user’s mental state, various data related to the user, including the user’s medical history and demographic data; and thereby, the system “detect[s] new behavioral or medical issues” related to the user—such as: one or more types of stress, phobia, restless leg syndrome, etc. (see [0427] to [0431]); and furthermore, as part of evaluating the user’s conditions, the system also correlates the user with other users in the same/similar situation comparing parameters of the user (e.g., state parameters, profile parameters) against corresponding parameters of other users ([0457]; [0459]; [0460]: note that such process of correlating the user with other users already indicate the implementation of a classification algorithm).
Although Abrahami does not expressly describe that the system classifies the subject into a relevant patient group—namely, one representing the risk of the subject of developing delirium, the above is merely indicating a possible outcome that may be determined based on the analysis of the subject’s data (e.g., based on the analysis of the data, it may be determined that the subject may—or may not—have the risk to develop delirium). In this regard, Abrahami already identifies one or more exemplary risks; such as, one or more types of stress, phobia, restless leg syndrome, etc. (see discussion above).
Accordingly, given the teaching above, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the invention of Sadowsky in view of Abrahami; for example, by correlating or grouping the subject, using the subject’s profile that already comprises the subject’s medical history and demographic data, with other individuals of the same/similar conditions; and accordingly, besides utilizing the data captured regarding the user to determine of or more conditions of the subject, the system further considers the correlation above to supplement or verify the results that the system is generating regarding the mental state of the subject; so that the system automatically, and/or the healthcare provider (if any) monitoring the system’s result, would be able to easily verify the consistency of the results being generated.
Regarding claim 43, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Sadowsky further teaches, the one or more physiological sensors comprises a skin temperature sensor, a heart rate sensor, blood pressure sensor, an accelerometer, an oximeter, an ECG sensor, a respiration sensor, a sleep-tracker and/or any combination thereof and wherein the one or more parameters/features derived from the one or more physiological sensors comprises skin temperature, blood pressure changes during changes in posture, heart rate variability (HRV), HRV during rest, saturation, respiration rate, hours of sleep, depth of sleep and any combination thereof ([0026]; [0031]; [00585]; [0058]: e.g., the system already incorporates various sensors to detect various physiological conditions of the subject—such as, a sensor for detecting/measuring the subject’s heartrate and/or heartrate variability; and further, the system provides/displays the results of the measurements. Note that the claim does not necessarily require all listed sensors; rather, just one of the sensors the corresponding data derived from that sensor).
Regarding claim 44, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Sadowsky further teaches, the processing unit is further configured to output a suggested diagnosis of conditions, wherein the conditions comprise physiological conditions, cognitive/emotional conditions, and lifestyle conditions ([0025]; [0047] to [0049]; [0062]: e.g., the system already generates/displays, based on the analysis of the subject’s captured data, results signifying one or more mental sates—such as, a timeline of an experience and the metal sate encountered during that experience; a mental state and the probability of the subject being in that mental state etc., and the results being generated also include parameters signifying the physiological state of the subject. It is again worth noting that mental state encompasses various conditions; see [0025]. Thus, the processing unit is already configured to output a suggested diagnosis of conditions, wherein the conditions comprise physiological conditions, cognitive/emotional conditions, and lifestyle conditions. Note that the subject’s state of being happy or sad signifies the subject’s lifestyle).
It is worth noting—given the scope of Sadowsky’s teaching—that Sadowsky’s system already determines, based on the analysis the subject’s captured data (e.g., captured video images, captured physiological data, etc.), a condition (if any) that is associated with delirium. This is because delirium merely to one of the different mental states that the subject may—or may not—have depending on the symptoms that the subject is showing.
Thus, if the subject is showing symptoms consistent with delirium, Sadowsky does diagnose conditions associated with delirium since the Sadowsky’s system generates “textual or numeric output indicating one or more mental states. The output may be a graph with a timeline of an experience and the mental states encountered during that experience . . . a result may be rendered which shows a mental state and the probability of the individual being in that mental state” (see [0048], emphasis added).
Regarding claim 45, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Sadowsky further teaches, the processing unit is further configured to produce a report indicative of the subjects cognitive and physiological status and to output a potential therapeutic recommendation ([0048; [0049]: e.g., the system generates textual and/or graphical results that indicate one or more mental states of the subject; and thus, the above corresponds to the report that indicates the subject’s cognitive and physiological status. The system further generates, based on the results of the subject’s mental state, one or more recommended actions—such as, changing a medical consultation presentation, changing direction of an electronic game, etc. This corresponds to the process of outputting a potential therapeutic recommendation).
Regarding claim 46, Sadowsky teaches the following claimed limitations: a computer implemented method for evaluating a cognitive and physiological status of a subject, the method comprising: obtaining via input and/or uploading demographic data of the subject ([0026] lines 1-9; [0034] lines 11-20; [0036]; [0054]: e.g., a computer-based system/method for analyzing the mental state of a subject; wherein the system comprises various input and output devices and/or user interfaces; such as, touchscreen, for inputting data of the subject. Note that at least the subject’s demographic data is inputted or uploaded since the system considers the subject’s demographic data to determine content pertinent to the subject); receiving a plurality of signals from a physiological sensor module comprising one or more physiological sensors, receiving data from a cognitive monitoring module comprising a personal assistance Application (PA App), the PA App configured to present questions, games/exercises, and/or mental tracking exercises to the subject ([0040]; [0054]: e.g., the system already implements sensors for capturing the subject’s physiological data; such as, skin temperature, heartrate, etc.; and furthermore, the system presents, via the subject’s laptop or cellphone, one or more interactive content items, including computer games. Accordingly, besides implementing physiological sensors to capture the subject’s physiological data, the system also implements at least one computer application, which is executed via the subject’s device, in order to present the subject with interactive computer games. Thus, the system incorporates a personal assistance application with preprocessing unit to present games or exercises), to store in an in-memory queue [event] logs and video/image data of the subject recorded during the questions, games/exercises, mental tracking exercises and to serve the stored data for preprocessing ([0040]; [0053]: e.g., as already pointed out above, the system presents the subject with an interactive content—such as, a computer game, which require the subject to respond; and the system captures, via one or more sensors, the user’s physiological data, including facial expressions captures via a video camera; and the collected data above is stored into a disk/memory of the computer system. It is understood that “data which was captured may be correlated to an experience by the individual. The experience may be . . . a computer game, a video game”; and this indicates part of the preprocessing performed via the preprocessing unit); transmitting to a cloud-based server: iv. the demographic data and/or on data derived therefrom; v. signals obtained from the physiological sensor module and/or parameters/features derived therefrom; and vi. the data preprocessed by the preprocessing unit; and at the cloud-based server, applying an AI algorithm on the received data to thereby compute a current and predicted cognitive and physiological status of the subject ([0041] to [0044]; [0056]; [0062] to [0065]: e.g., the system also sends the data captured locally—namely, the data captured via the subject’s computer—to a web service for “further mental state analysis”, which indicates that the preprocessed data is transmitted to a server for further analysis using “larger group of analytical tools” available on the server. In this regard, the system also implements a machine-learning algorithm—with or without human intervention—for determining various mental states of the subject, wherein “[a] series of mental states may be presented with the likelihood of each state for a given point in time. Likewise a series of probabilities for each mental state may be presented over the timeline”, etc. Thus, such presentation the likelihood or probability of each mental state over a time line indicates the process of computing a current and predicted cognitive and physiological status of the subject. Note that the machine-learning algorithm is essentially an artificial-intelligence algorithm; and furthermore, the mental state already encompasses both cognitive state and physiological state, see [0025]. Note also that an emotional state/response always involves a physiological sate/response).
Sadowsky does not expressly teach that the data of the subject above further includes medical history and audio of the subject recorded during games/exercises.
However, Abrahami teaches a system/method that analyzes a user’s emotional and/or mental state based on the analysis of various data, including the user’s voice data—e.g., the user’s voice recorded as part of a response—analyzed via a voice recognition software that implements blind source separation (BSS) feature to clearly recognize the user’s speech ([0290] to [0294]; [0298] to [0302]); and furthermore, the system considers the subject’s demographic data and medical history data as part of the user’s mental analysis ([0427]; [0457]; [0460]), etc.
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Sadowsky in view of Abrahami; for example, by incorporating additional data related to the subject—such as the subject’s medical history; and the system’s algorithm is further upgraded by incorporating one or more well-known/existing algorithms—such as, a voice recognition software that implements a blind source separation feature, including an NLP analyzer, etc., and accordingly, besides providing the subject with additional flexibility to easily interact—via voice/speech—with the system (e.g., allowing the subject to easily respond to a game scenario and/or questions, while excluding irrelevant voices or ambient sounds, etc.), the system further utilizes the subject’s medical history and demographic data, along with the subject’s voice/speech recorded during the subject’s interaction, to more accurately evaluate the subject’s mental state during the preprocessing and/or final analysis; so that the system would be able to generate a result(s) that more accurately signifies the subject’s mental state.
● Claims 32 and 33 are rejected under 35 U.S.C.103 as being unpatentable over Sadowsky 2011/0301433 in view of Abrahami 2017/0039045 and Al-khowaiter 2012/0124227.
Regarding claim 32, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per clam 31.
Sadowsky, as discussed per claim 31, does not teach that the preprocessing unit is further configured to distort the recorded audio and subsequently transmit the distorted audio to the server for further processing, thereby ensuring the anonymity of the subject.
However, Al-khowaiter teaches a system/method that allows a user to securely transmit—over the Internet—a recorded audio message to a server ([0024]); and the system also allows the user to distort the audio message via audio distortion software in order to maintain the anonymity of the user ([0043] lines 21-33; [0081]).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the invention of Sadowsky in view of Al-khowaiter; for example, by enabling the subject to install an audio distortion software, so that the subject would be able to record, distort and send one or more audible message to the server; so that the server would evaluate—using its NLP analyzer—the content of the subject’s audio message without the need to reveal the identify the subject; and thus, while maintaining the anonymity of the subject, the server would make a more pertinent and unbiased assessment regarding the subject’s condition.
Regarding claim 33, Sadowsky in view of Abrahami and Al-khowaiter teaches the claimed limitations as discussed above per claim 32.
The limitation, “extracting response content from the distorted audio, using one or more transcription algorithms and/or NLP/LLM models thereon”, is already addressed per the modification discussed with respect to claim 32. In particular, the modified system already incorporates—based on the feature incorporated from Abrahami—an NLP analyzer, which includes a natural language processing; and thereby the system recognizes or extracts—from the voice message—words/phrases that the subject is saying (Note also that the same motivation discussed per claim 32 and/or claim 27 applies to claim 33 since it is referring to the feature that the modified system already incorporates).
● Claim 36 is rejected under 35 U.S.C.103 as being unpatentable over Sadowsky 2011/0301433 in view of Abrahami 2017/0039045 and Baldwin 2014/0129627.
Regarding claim 36, Sadowsky in view of Abrahami teaches the claimed limitations as discussed above per claim 27.
Although Sadowsky already teaches the process of uploading preprocessed data after completion of preprocessing of the stored data ([0026]; [0027]; [0039]; also [0053]), Sadowsky does not teach deleting the stored data. upon completion of preprocessing.
However, Baldwin teaches a computer-based system/method that allows a user to send—via the user’s device—data, including one or more recorded images, to a server ([0030]; [0035]; [0042]); and the system further provides the user with privacy settings, which the user configures; and thereby, one or more of the images are automatically deleted once the images are sent to the server; and thus, besides maintaining the user’s privacy, the implementation allows the user to conserve storage space on his/her device ([0054]; [0064]).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the invention of Sadowsky in view of Baldwin; for example, by upgrading the system’s algorithm; so that the subject’s device provides the subject with one or more privacy settings, which allow the subject to set one or more data elements (e.g., one or more images/videos, etc.) that need to be automatically deleted from the local storage once the client device transmits the required data to the server; so that, besides maintaining the subject’s privacy (e.g., minimizing or eliminating any chance that an unauthorized user may have to access the stored data while borrowing the subject’s device, etc.), the implementation above also helps the subject to conserve storage space since the subject’s device, which may be in the form of a laptop or a cellphone ([0054]), may have a limited storage capacity.
Conclusion
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
