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 .
Response to Amendment
The amendment filed 27 April 2026 has been entered. Claim(s) 1-9 and 11-19 are pending in the application. Applicant’s amendments to the claims have overcome each and every objection to the claims previously set forth in the Office Action mailed 10 February 2026. Interpretation under 35 U.S.C. 112(f) is additionally withdrawn in light of the amendments to the claims.
Claim Objections
Claims objected to because of the following informalities:
Claim 2, line 4 “information whether” should be “information regarding whether” or similar.
Claim 11, line 5 “a similarity” should be “the similarity”.
Claim 11, line 5 “a distance” should be “the distance”.
Claim 12, line 4 “information whether” should be “information regarding whether” or similar.
Claim 19, line 5 “a similarity” should be “the similarity”.
Claim 19, line 5 “a distance” should be “the distance”.
Claims 12-19 are objected to because in each claim, the preamble recites “information processing apparatus” and should be corrected to “information processing system” in accordance with claim 9, from which they depend.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US
Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline Federal Register Vol. 84, No., Jan
2019)), determination of the subject matter eligibility under the 35 U.S.C. 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong One), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then in the second part of Step 2A (Prong Two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination provide "inventive concept" that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 U.S.C. 101.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. The claim recite(s) "presenting, when having acquired a request including second body movement information serving as the time series body movement information, similar information including a similarity between the second body movement information and the first body movement information, and the response content being associated with the first body movement information, wherein the similarity is calculated based on at least one of an average value, a variance, or a distance between aligned waveform segments". This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 1 satisfies Step 1, namely the claim is directed to one of the four statutory classes, machine. Following Step 2A Prong one, any judicial exceptions are identified in the claims. In claim 1, the limitations "presenting, when having acquired a request including second body movement information serving as the time series body movement information, similar information including a similarity between the second body movement information and the first body movement information, and the response content being associated with the first body movement information wherein the similarity is calculated based on at least one of an average value, a variance, or a distance between aligned waveform segments" are abstract ideas as they are directed to a mental process as calculating an average, variance, or distance between aligned segments can be done in the human mind as a mathematical calculation. With the identification of an abstract idea, the next phase is to proceed Step 2A, Prong Two, wherewith additional elements and taken as a whole, evaluation occurs of whether the identified abstract idea is integrated into a practical application.
In Step 2A, Prong Two, the claim does not recite any additional elements or evidence that amounts to significantly more than the judicial exception. Besides the abstract idea, the claim recites the additional elements “a receiver configured to acquire, based on an output from a pressure sensor configured to detect body vibrations of a user, body movement information including information related to a sleep state of the user; a memory configured to store information in which a response content performed by a skilled worker to the user has been associated with first body movement information serving as the time series body movement information”. However, these components may be seen as the use of well-understood, routine, or conventional elements to perform a non-mental process in order to gather data for the mental process step, much like the example given in MPEP 2106.04(d)(2)(c), such that these limitations are extra-solution activity and thus do not integrate the judicial exception into a practical application. The measurement step leads to the final limitation of “processing of presenting” such that the end result of use of the system is only the generic determined indicator which may be any generic output. As this “processing of presenting” is not defined as requiring any further action, such as a form of prophylaxis or treatment or an improvement to a computer or other technology, the claim limitations constitute mere generation of data, in this case the measurement of data relating to body movement of a user who receives care assistance, such that the claim does not integrate the judicial exception into any practical application. Regarding “a processor”, the limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer, which does not render an abstract idea eligible. The steps performed by the processing unit are, as claimed, capable of being performed in the human mind similar to the examples given in MPEP 2106.04(a)(2)(III)(A)-(C), wherein it is described that “a claim to ‘collecting information, analyzing it, and displaying certain results of the collection and analysis’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” recites a mental process and that claims which merely use a computer as a tool to perform a mental process are not eligible when “there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper” such as “mental processes of parsing and comparing data” when the steps are recited at a high level of generality and a computer is used merely as a tool to perform the processes. Under the broadest reasonable interpretation, the claim elements are recited with a high level of generality (as written, each claimed step of the process may be performed by a person in an undefined manner including making a mental judgment of similarity) that there are no meaningful limitations to the abstract idea. Consequently, with the identified abstract idea not being integrated into a practical application, the next step is Step 2B, evaluating whether the additional elements provide "inventive concept" that would amount to significantly more than the abstract idea.
In Step 2B, claim 1 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 present elements amount to no more than mere indications to apply the exception. The limitation of “a receiver”, “a memory”, and “a processor” constitutes extra-solution activity to the judicial exception, which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitation constitutes adding a generic memory and processor, which Dean (US 20230165728 A1) describes as well-understood, routine, or conventional in its description of common, commercially available computing elements such as processors and memory (Paragraph 0190-0192, 0203-0207-- Such processors may comprise a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), field programmable gate arrays (FPGAs), and state machines… Examples of non-transitory computer-readable medium may include, but are not limited to, an electronic, optical, magnetic, or other storage device capable of providing a processor, such as the processor in a web server, with processor-executable instructions).
In Summary, claim 1 recites abstract idea without being integrated into a practical application, and does not provide additional elements that would amount to significantly more. As such, taken as a whole, the claim and is ineligible under the 35 U.S.C. 101.
Claims 2-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. As each of these claims depends from claim 1, which was rejected under 35 U.S.C. 101 in paragraph 6 of this action, these claims must be evaluated on whether they sufficiently add to the practical application of claim 1, or comprise significantly more than the limitations of claim 1.
Besides the abstract idea of claim 1: claims 2-5 recite additional elements for the use of well-understood, routine, or conventional elements to perform a non-mental process in order to gather data for the mental process step; claim 6 recites additional elements of extra-solution activity in the form of mere data gathering as well as additional limitations of the abstract idea, in this case “processing of correcting the first reference point and the second reference point” which may be performed in the mind as a simple mathematical transform; claim 7 recites additional elements of extra-solution activity in the form of mere data gathering as well as additional limitations of the abstract idea, in this case “determin[ing] a degree of priority” which may be performed in the mind; claim 8 recites additional elements of extra-solution activity in the form of mere data gathering as well as additional limitations which are themselves abstract ideas, in this case “estimate an emotion change of the user…” and “perform[ing] labeling” which may be performed in the mind. The claim element of claim 1 of an information processing apparatus is recited with a high level of generality (as written, the actions of the processing unit may be carried out by a person alone or with a generic computer in any undefined manner). This limitation provides no practical application, nor does it provide meaningful limitations to the abstract idea.
Claim 9 is rejected for similar reasons to claim 1. It is additionally noted that In Step 2B, claims 9 and 10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements amount to no more than mere indications to apply the exception. The limitation of “a pressure sensor configured to detect body vibrations of a user” and “a terminal device” constitutes extra-solution activity to the judicial exception, which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitation constitutes adding a vibration generic sensor and computing device, which Meger (US 20180220897 A1) describes as well-understood, routine, or conventional in its description of common or typical sensors (Paragraph 0140-0141, 0160, 0164-0165, 0316—pressure/vibration sensor) and user interface devices (Paragraph 0138, 0247, 0332—a mobile device (such as a cellular phone, a pager, and/or a tablet computer)).
Claims 11-19 are rejected for similar reasons to claims 2-8.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, 7, 9, 11-15, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dean (US 20230165728 A1) in view of McNair (US 12488892 B1), further in view of Meger (US 20180220897 A1).
Regarding claim 1, Dean teaches an information processing apparatus (Paragraph 0055—an IoT monitoring system) comprising:
A receiver (Paragraph 0055—one or more processors; paragraph 0063—n IoT device 115 is a device that includes sensing, control and/or analytical functionality as well as a WiFi™ transceiver radio or interface, a Bluetooth™ transceiver radio or interface, a Zigbee™ transceiver radio or interface, an UWB transceiver radio or interface, a WiFi-Direct transceiver radio or interface, a BLE transceiver radio or interface, radio frequency identification (RFID) or interface, cellular radio or interface and/or any other wireless network transceiver radio or interface that allows the IoT device 115 to communicate with a LAN, wide area network (WAN), cellular network, or the like and/or with one or more other devices (e.g., sensors or other IoT devices)…IoT device 115 includes one or more processors) configured to acquire, based on an output from a pressure sensor (network of sensors 110; paragraph 0168--data is obtained from an IoT device, as discussed with respect to FIGS. 1, 2, 3A-3E, 4A-4D, 5A-5F, and 6A-6E. At step 1210, the input data is parsed to identify all sensor data collected by the IoT device from a sensor associated with a subject over a window of time; 0063-0064, 0128--pressure sensors) that can detect a body movement of a user (paragraphs 0010, 0055, 0129, 0131, 0138, 0171-0175, 0178, 0181-0182), body movement information including information related to a sleep state of the user (Paragraph 0055—receives data from various electronic devices for analysis of…movement of subjects (e.g., movement of subjects in bed to mitigate decubitus movement or movement of a subject from bed for potential fall analysis)));
a memory (Paragraph 0028-0030, 0055—a non-transitory computer readable storage medium) configured to store information in which a pattern has been associated with first body movement information serving as the time series body movement information (Paragraph 0129-0133-- for a prediction model 750 to be utilized to identify activity such as a subject flipping or rolling over in bed based on sensor or IoT device data, the input can be the sensor or IoT device data itself or features extracted from the sensor or IoT device data and the labels 757 can include energy states showing whether the activity has occurred or not in the sensor or IoT device data… In another example, medical records that include a subject's physical measurement taken by a health care provider can be used to confirm predicted health and wellbeing of the subject. In yet another example, the presence of certain healthcare works (e.g., a healthcare work with an RFID tag bracelet) can also be an indicator of certain activities. For example, a healthcare worker detected from the IoT device data can indicate that the healthcare worker has entered the room of a subject and in combination with moisture sensor data could be used as a predictor of a undergarment or absorbent pad about to be changed or checked; Paragraph 0170-0172-- a table of one or more energy levels associated with a stationary position within the environment in which the sensor is deployed (i.e., as the sensor moves closer and further away from an IoT device there is a change in energy states; however, the energy levels will obtain equilibrium while stationary)…a predetermined energy threshold associated with a motion event …); and
a processor (Paragraph 0055—an IoT device 115 is a device that includes sensing, control and/or analytical functionality…one or more processors) configured to perform processing of presenting, when having acquired a request including second body movement information serving as the time series body movement information, similar information including a similarity between the second body movement information and the first body movement information (Paragraph 0170-0172-- the first energy level and the second energy level are compared to a table of one or more energy levels associated with a stationary position within the environment in which the sensor is deployed (i.e., as the sensor moves closer and further away from an IoT device there is a change in energy states; however, the energy levels will obtain equilibrium while stationary)… determining whether a change between the first energy level and the second energy level exceeds a predetermined energy threshold associated with a motion event or whether the second energy level exceeds a predetermined energy threshold associated with a motion event…);
wherein the similarity is calculated based on at least one of an average value (Paragraph 0027--the first energy level is determined as (i) an average signal response; paragraph 0153-- the first, second, third, etc. energy states may instead be first, second, third, etc. average signal responses...; paragraph 0183-0184--In some instances, the pattern of activity predicted is one or more of: frequency of urinary incontinence events, duration of incontinent events (duration being how long the subject remained with contaminated undergarment or absorbent pad), mean patient movement in bed over a window of time...), a variance, or a distance between aligned waveform segments.
However, Dean does not explicitly disclose storing information in which a response content performed by a skilled worker to the user has been associated with first body movement information serving as the time series body movement information and presenting, when having acquired a request, the response content associated with the first body movement information.
McNair, in the same field of endeavor of a system and method for monitoring a patient and providing decision support for a caregiver (Abstract), discloses an information processing apparatus (operating environment 100) comprising:
A receiver which acquires and transmits information (computer system 120 comprising one or more processors; Col. 8, line 24-36; Col. 38, line 21-24-- interaction-activity including queries, selections, recommendations, preferences, use-behavior, and patient information associated with the interaction is received and processed)
a memory (Col. 3, line 50-67—computer storage media; storage (or data store) 121) configured to store information in which a response content performed by a skilled worker to the user has been associated with information serving as the time series information (Fig. 4D—steps 4310-4330—presenting a first clinical user interface for a first patient having a condition and associating a clinical decision support event with the condition; Col. 38, line 12-31—data-mining including identifying and mapping new knowledge…includes machine learning from user-caregiver interaction…); and
a processor (computer system 120 comprising one or more processors; Col. 8, line 24-36) configured to perform processing of presenting, when having acquired a request including information serving as the time series information, similar information including a similarity between the second information and the first information, and the response content associated with the first information (Fig. 4D—steps 4360-4380—determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient and presenting the clinical recommendation for the second patient in the second user interface; Col. 37, line 53-Col. 38, line 5—identify patients having patient records with a target condition (s) and a set of concepts (including attributes) similar to a target patient, a caregiver for the target patient might perform a query to retrieve (1) the orders related to the condition that were issued for the other patients).
McNair additionally teaches wherein the similarity is calculated based on at least one of an average value, a variance, or a distance between aligned waveform segments (Col. 17, line 31-35, Col. 18, line 5-57—Tanimoto distance matching; Col. 52, line 53-Col. 53, line 44-- a distance for matching the vector against one or more of the set of reference epoch vectors is suitably small, and by the offset of vector elements' values such that the distance is larger than a threshold denoting a close or satisfactory match…).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Dean, which includes finding a similarity between a first body movement and a second body movement to identify a type of activity being performed by a user such as inactivity, turning over, or bed exiting, to store information in which a response content performed by a skilled worker to the user has been associated with first body movement information serving as the time series body movement information and present the response content associated with the first body movement information as suggested by McNair in order to predictably improve the ability of the system to support caregiver decision making by not only supporting the identification of an activity but also the identification of a best or most common response to the identified activity such as the need to move a user to avoid decubitus ulcers or not (see Dean, paragraph 0022-0023, 0055, 0174-- Predicted models could be employed with historic movement data in those with and without decubiti to predict safe, normal ranges for subject movement in bed and predictive overall health or wellness of the subject (e.g., at risk for decubitus). Data analytics for use of an additional RFID sensor worn by the clinical staff, which would be read by the in-room IoT devices, could be used to document clinical staff assisting with a fall event or movement of a subject. This could be used to provide clinical staff tracking and confirmation for assist with a fall event or movement to avoid decubitus.).
It would additionally have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Dean, including determining a similarity, to additionally include the similarity determination based on distance between aligned waveform segments as disclosed by McNair in order to predictably improve the accuracy of the system by enabling the determination of similarity by different means, which would enable any response content decision to be confirmed by a back-up determination of a response content.
However, the combination fails to explicitly disclose a pressure sensor configured to detect body vibrations of a user.
Meger, in the same field of endeavor of a system for monitoring a sleeping patient to assist a provider in treating the patient (Paragraph 0033), teaches that body movement information including information related to a sleep state of a user may be received from a pressure sensor configured to detect body vibrations of a user (Paragraph 0140--Motion sensor 30 may comprise a ceramic piezoelectric sensor, vibration sensor, pressure sensor, or strain sensor, for example, a strain gauge, configured to be installed under a resting surface 37, and to sense motion of patient 12. The motion of patient 12 sensed by sensor 30, during sleep, for example, may include regular breathing movement, heartbeat-related movement, and other, unrelated body movements, as discussed below, or combinations thereof).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean to utilize a pressure sensor configured to detect body vibrations of a user as described by Meger as a matter of simple substitution of known elements in the art, wherein Dean already discloses the use of a pressure sensor for measuring body movements of a user and Meger notes that a vibration sensor may perform the same measurements.
Regarding claim 2, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 1. Dean additionally teaches wherein the user includes a user who receives care at home (Paragraph 0064-- may be used in various environments or venues, such as a hospital, a nursing home, an establishment, a personal care home, a subject's house, or any place that can support the management platform 100 to enable communication with IoT devices 115).
Dean additionally teaches that an identified pattern may correspond to whether a caregiver should visit the home of the user (Paragraph 0172-0175-- one or more of: (1) absence of activity, (2) rolling over in bed activity, (3) getting out of bed activity, (4) fallen on the floor activity, and (5) entering the bathroom activity (e.g., trained activity identification models 770 used in the activity identification stage 720 described with respect to FIG. 7) are predictable… In those subjects falling below a certain threshold of movement, clinical staff could intervene to facilitate rolling or repositioning to minimize pressure sores. Predicted models could be employed with historic movement data in those with and without decubiti to predict safe, normal ranges for subject movement in bed and predictive overall health or wellness of the subject (e.g., at risk for decubitus). Data analytics for use of an additional RFID sensor worn by the clinical staff, which would be read by the in-room IoT devices, could be used to document clinical staff assisting with a fall event or movement of a subject. This could be used to provide clinical staff tracking and confirmation for assist with a fall event or movement to avoid decubitus).
As the combination of Dean, McNair, and Meger teaches storing and presenting the response content associated with the first body movement information as shown above, the teaching of Dean of an identified pattern corresponding to whether a caregiver should visit the home of the user may be used to further modify the apparatus to present, as part of the response content, a necessity or unnecessity of any response by a caregiver in order to predictably improve the ability of the apparatus to support a caregiver by minimizing unnecessary visits to a patient.
Regarding claim 3, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 1. Dean further teaches wherein the receiver is further configured to: acquire, when an input operation to determine the response content has been performed in a first terminal device for the skilled worker, information in which the response content has been associated with the first body movement information (Paragraph 0131-0133-- for a prediction model 750 to be utilized to identify activity such as a subject flipping or rolling over in bed based on sensor or IoT device data, the input can be the sensor or IoT device data itself or features extracted from the sensor or IoT device data and the labels 757 can include energy states showing whether the activity has occurred or not in the sensor or IoT device data). The teaching of Dean of an identified pattern corresponding to a necessity or unnecessity of a visit to a residence of the user and these identified patterns being stored as training data to be compared to future data may be considered an association of a response content and the first body movement information.
McNair additionally teaches wherein the receiver is configured to: acquire, when an input operation to determine the response content has been performed in a first terminal device for the skilled worker, information in which the first information and the response content have been associated with each other in which the response content has been associated with the first body movement information (Col. 38, line 12-48-- At a step 4320, receiving a command to initiate a clinical decision support event associated with the first patient. At a step 4330, associating the clinical decision support event with the condition.; Fig. 4D).
Regarding claim 4, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 3. However, Dean does not explicitly disclose determine a first reference point based on execution timing of the input operation in the first terminal device, and cause the memory to store the body movement information in a period determined based on the first reference point, as the first body movement information.
McNair teaches wherein the processor is configured to: determine a first reference point based on execution timing of the input operation in the first terminal device (Col. 52, line 53-60-- In one embodiment, the epoch A is not a transient or momentary state; rather, it persists for a finite period that is commensurate with timeframes that are customary for ordinary, decision-making in health care services.; Col. 53, line 4-15-- Epochs may be as short as several seconds or minutes in length in the case of critical care and perioperative care or may be as long as several years in the case of slowly-evolving chronic diseases. In an embodiment, care decision epochs are defined by parameters 2120 (discussed in connection to FIG. 1C), and may indicate the specific sensor information (such as clinical conditions (including sequences or patterns of clinical conditions) and clinical variables (including patient demographic variables, treatment history and caregiver/health care entity/insurance information) used to determine the decision epoch.), and causes the storing unit to store the body movement information in a period to be determined based on the first reference point, as the first body movement information (Col. 38, line 12-48).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean with the reference point teaching of McNair to predictably improve the accuracy of any determinations made by the apparatus by ensuring that data is monitored over time rather than only monitoring for sensor signal magnitude generally, as patterns in sensor data over particular amounts of time may more accurately reflect patterns of inactivity or movement of the user.
Regarding claim 5, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 4. However, Dean does not explicitly disclose wherein the processor is configured to: determine, when the processor has acquired the request based on a second input operation in a second terminal device for an unskilled worker, a second reference point based on execution timing of the second input operation, and determine the body movement information in a period determined based on the second reference point, as the second body movement information.
McNair teaches wherein the processor is configured to:
determine, when the processor has acquired the request based on a second input operation in a second terminal device for an unskilled worker, a second reference point based on execution timing of the second input operation ((Col. 52, line 53-60-- In one embodiment, the epoch A is not a transient or momentary state; rather, it persists for a finite period that is commensurate with timeframes that are customary for ordinary, decision-making in health care services.; Col. 53, line 4-15-- Epochs may be as short as several seconds or minutes in length in the case of critical care and perioperative care or may be as long as several years in the case of slowly-evolving chronic diseases. In an embodiment, care decision epochs are defined by parameters 2120 (discussed in connection to FIG. 1C), and may indicate the specific sensor information (such as clinical conditions (including sequences or patterns of clinical conditions) and clinical variables (including patient demographic variables, treatment history and caregiver/health care entity/insurance information) used to determine the decision epoch.)), and determine the body movement information in a period determined based on the second reference point, as the second body movement information (Col. 37, line 53-Col. 38, line 5).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean with the reference point teaching of McNair to predictably improve the accuracy of any determinations made by the apparatus by ensuring that data is monitored over time rather than only monitoring for sensor signal magnitude generally, as patterns in sensor data over particular amounts of time may more accurately reflect patterns of inactivity or movement of the user.
Regarding claim 7, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 1. Dean further teaches an additional embodiment wherein the processor is configured to:
perform processing of obtaining first similarity information based on the first body movement information and the second body movement information in a period having a first length (Paragraph 0178-- For example, a first energy level obtained by the first antenna at a first period of time may be compared to a second energy level obtained by the second antenna at a second period of time…), and
processing of obtaining second similarity information based on the first body movement information and the second body movement information in a period having a second length different from the first length, and determine a degree of priority of the first similarity information and the second similarity information in accordance with the response content associated with the first body movement information (Paragraph 0178-0181-- a determination is made as to whether the sensor data includes additional energy levels (e.g., same or different energy level from the first and second energy levels, but identified as a separate recording of an energy level as compared to the recording for the first and second energy levels) at a different period of time for processing that was collected by the first and/or second antenna within a predefined period of time after the first period of time and the second period of time… the position of the subject determined at different time periods (e.g., the first period of time and the second period of time versus the third period of time and the fourth period of time) over the window of time in accordance with steps 1260 and 1265 are compared to one another to determine whether the subject's position is static or dynamic over the window of time. When the subject's position changes, for example from lying on their back to lying on their side it is determinable that the subject's position is dynamic over the window of time; whereas when the subject's position does not change, for example the subject remained lying on their back it is determinable that the subject's position is static over the window of time…; paragraph 0175-- The position of a subject in bed is of importance to the nursing home staff. If a subject is left in an unchanged position, then they are at high risk for skin breakdown and the development of decubitus ulcers. The nursing home staff may initiate a timed subject re-positioning schedule to overcome this. At the same time, subjects may spontaneously reposition themselves, obviating the need for this re-positioning assistance. Without positional monitoring, the nursing home staff have no current system to confirm this positional movement of subjects).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean and McNair to additionally include the teaching of Dean to monitor similarity over two different periods of time to determine a degree of priority in order to predicably improve the ability of the apparatus to support a caregiver by minimizing unnecessary visits to a patient by determining whether a patient needs imminent repositioning or not.
Regarding claim 9, Dean teaches an information processing system (Paragraph 0055—an IoT monitoring system) comprising:
a pressure sensor (network of sensors 110; paragraph 0168--data is obtained from an IoT device, as discussed with respect to FIGS. 1, 2, 3A-3E, 4A-4D, 5A-5F, and 6A-6E. At step 1210, the input data is parsed to identify all sensor data collected by the IoT device from a sensor associated with a subject over a window of time; 0063-0064, 0128--pressure sensors) configured to detect body movement of a user (paragraphs 0010, 0055, 0129, 0131, 0138, 0171-0175, 0178, 0181-0182);
a server system (Paragraph 0055-0056, 0067—one or more processors…a server) configured to acquire, based on an output from the pressure sensor (network of sensors 110; paragraph 0168--data is obtained from an IoT device, as discussed with respect to FIGS. 1, 2, 3A-3E, 4A-4D, 5A-5F, and 6A-6E. At step 1210, the input data is parsed to identify all sensor data collected by the IoT device from a sensor associated with a subject over a window of time) and
stores information in which a pattern has been associated with first body movement information serving as the time series body movement information (Paragraph 0129-0133-- for a prediction model 750 to be utilized to identify activity such as a subject flipping or rolling over in bed based on sensor or IoT device data, the input can be the sensor or IoT device data itself or features extracted from the sensor or IoT device data and the labels 757 can include energy states showing whether the activity has occurred or not in the sensor or IoT device data… In another example, medical records that include a subject's physical measurement taken by a health care provider can be used to confirm predicted health and wellbeing of the subject. In yet another example, the presence of certain healthcare works (e.g., a healthcare work with an RFID tag bracelet) can also be an indicator of certain activities. For example, a healthcare worker detected from the IoT device data can indicate that the healthcare worker has entered the room of a subject and in combination with moisture sensor data could be used as a predictor of a undergarment or absorbent pad about to be changed or checked; Paragraph 0170-0172-- a table of one or more energy levels associated with a stationary position within the environment in which the sensor is deployed (i.e., as the sensor moves closer and further away from an IoT device there is a change in energy states; however, the energy levels will obtain equilibrium while stationary)…a predetermined energy threshold associated with a motion event …); and
a terminal device (client devices 105) configured to transmit a request including second body movement information serving as the time series body movement information (Paragraph 0060, 0069-0071-- during monitoring, the IoT devices 115 capture data (e.g., sensor data from one or more subjects) and transmit the data to one or more client devices 105 and/or the remote servers 140. The one or more client devices 105 process and output the data to one or more displays, such as a display at the client device 105 or another location, such as client device 105), wherein the server system is configured to:
perform processing of presenting, when having acquired the request from the terminal device, similar information including a similarity between the second body movement information and the first body movement information (Paragraph 0170-0172-- the first energy level and the second energy level are compared to a table of one or more energy levels associated with a stationary position within the environment in which the sensor is deployed (i.e., as the sensor moves closer and further away from an IoT device there is a change in energy states; however, the energy levels will obtain equilibrium while stationary)… determining whether a change between the first energy level and the second energy level exceeds a predetermined energy threshold associated with a motion event or whether the second energy level exceeds a predetermined energy threshold associated with a motion event…)
wherein the similarity is calculated based on at least one of an average value (Paragraph 0027--the first energy level is determined as (i) an average signal response; paragraph 0153-- the first, second, third, etc. energy states may instead be first, second, third, etc. average signal responses...; paragraph 0183-0184--In some instances, the pattern of activity predicted is one or more of: frequency of urinary incontinence events, duration of incontinent events (duration being how long the subject remained with contaminated undergarment or absorbent pad), mean patient movement in bed over a window of time...), a variance, or a distance between aligned waveform segments.
However, Dean does not explicitly disclose storing information in which a response content performed by a skilled worker to the user has been associated with first body movement information serving as the time series body movement information and presenting, when having acquired a request from the terminal device, the response content associated with the first body movement information.
McNair, in the same field of endeavor of a system and method for monitoring a patient and providing decision support for a caregiver (Abstract), discloses an information processing apparatus (operating environment 100) comprising:
A server system which acquires and transmits information (computer system 120 comprising one or more processors; Col. 8, line 24-36; Col. 38, line 21-24-- interaction-activity including queries, selections, recommendations, preferences, use-behavior, and patient information associated with the interaction is received and processed) and
Stores information in which a response content performed by a skilled worker to the user has been associated with information serving as the time series information (Fig. 4D—steps 4310-4330—presenting a first clinical user interface for a first patient having a condition and associating a clinical decision support event with the condition; Col. 38, line 12-31—data-mining including identifying and mapping new knowledge…includes machine learning from user-caregiver interaction…); and
a terminal device (Interface 142; col. 8, line 4-18-- Embodiments of provider/clinician interface 142 may take the form of a user interface and application, which may be embodied as a software application operating on one or more mobile computing devices, tablets, smart-phones, front-end terminals in communication with one or more servers, back-end computing systems, laptops or other computing devices.;) configured to transmit a request including second body movement information serving as the time series body movement information to the server system (Col. 37, line 53-67-- a caregiver can initiate a query by selecting (such as right-clicking or holding-down on a touch surface) an item, such as a clinical element, presented on a graphical user interface, such as provider/clinician interface 142 of FIG. 1A; Fig. 4D—steps 4360-4380), wherein the server system is configured to:
perform processing of presenting, when having acquired the request from the terminal device, similar information including a similarity between the second information and the first information, and the response content associated with the first information (Fig. 4D—steps 4360-4380—determining a clinical recommendation for the second patient based on the clinical decision support event associated with the first patient and the change in condition of the first patient and presenting the clinical recommendation for the second patient in the second user interface; Col. 37, line 53-Col. 38, line 5—identify patients having patient records with a target condition (s) and a set of concepts (including attributes) similar to a target patient, a caregiver for the target patient might perform a query to retrieve (1) the orders related to the condition that were issued for the other patients).
McNair additionally teaches wherein the similarity is calculated based on at least one of an average value, a variance, or a distance between aligned waveform segments (Col. 17, line 31-35, Col. 18, line 5-57—Tanimoto distance matching; Col. 52, line 53-Col. 53, line 44-- a distance for matching the vector against one or more of the set of reference epoch vectors is suitably small, and by the offset of vector elements' values such that the distance is larger than a threshold denoting a close or satisfactory match…).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Dean, which includes finding a similarity between a first body movement and a second body movement to identify a type of activity being performed by a user such as inactivity, turning over, or bed exiting, to store information in which a response content performed by a skilled worker to the user has been associated with first body movement information serving as the time series body movement information and present the response content associated with the first body movement information as suggested by McNair in order to predictably improve the ability of the system to support caregiver decision making by not only supporting the identification of an activity but also the identification of a best or most common response to the identified activity such as the need to move a user to avoid decubitus ulcers or not (see Dean, paragraph 0022-0023, 0055, 0174-- Predicted models could be employed with historic movement data in those with and without decubiti to predict safe, normal ranges for subject movement in bed and predictive overall health or wellness of the subject (e.g., at risk for decubitus). Data analytics for use of an additional RFID sensor worn by the clinical staff, which would be read by the in-room IoT devices, could be used to document clinical staff assisting with a fall event or movement of a subject. This could be used to provide clinical staff tracking and confirmation for assist with a fall event or movement to avoid decubitus.).
It would additionally have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Dean, including determining a similarity, to additionally include the similarity determination based on distance between aligned waveform segments as disclosed by McNair in order to predictably improve the accuracy of the system by enabling the determination of similarity by different means, which would enable any response content decision to be confirmed by a back-up determination of a response content.
However, the combination fails to explicitly disclose a pressure sensor configured to detect body vibrations of a user.
Meger, in the same field of endeavor of a system for monitoring a sleeping patient to assist a provider in treating the patient (Paragraph 0033), teaches that body movement information including information related to a sleep state of a user may be received from a pressure sensor configured to detect body vibrations of a user (Paragraph 0140--Motion sensor 30 may comprise a ceramic piezoelectric sensor, vibration sensor, pressure sensor, or strain sensor, for example, a strain gauge, configured to be installed under a resting surface 37, and to sense motion of patient 12. The motion of patient 12 sensed by sensor 30, during sleep, for example, may include regular breathing movement, heartbeat-related movement, and other, unrelated body movements, as discussed below, or combinations thereof).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean to utilize a pressure sensor configured to detect body vibrations of a user as described by Meger as a matter of simple substitution of known elements in the art, wherein Dean already discloses the use of a pressure sensor for measuring body movements of a user and Meger notes that a vibration sensor may perform the same measurements.
Regarding claim 11, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 5. McNair additionally teaches wherein the processor is further configured to: align a time axis of the first body movement information and a time axis of the second body movement information such that the first reference point and the second reference point coincide with each other, and perform processing of obtaining a similarity based on a distance between the first body movement information and the second body movement information that have been aligned (Col. 17, line 31-35, Col. 18, line 5-57—Tanimoto distance matching; Col. 52, line 53-Col. 53, line 44-- epoch A is not a transient or momentary state; rather, it persists for a finite period that is commensurate with timeframes that are customary for ordinary, decision-making in health care services. Epochs are delimited in time by the onset of sensor predicate vector elements' values such that a distance for matching the vector against one or more of the set of reference epoch vectors is suitably small, and by the offset of vector elements' values such that the distance is larger than a threshold denoting a close or satisfactory match… a CareDecision epoch reference database predicate vector that the patient's information matched, and in an embodiment contains a Tanimoto or Jaccard or other distance metric denoting the degree of similarity or quality of matching of the patient's sensor vector to the reference vector associated with the nominated epoch.).
It would additionally have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Dean, including determining a similarity, to additionally include the similarity determination based on distance between aligned waveform segments as disclosed by McNair in order to predictably improve the accuracy of the system by enabling the determination of similarity by different means, which would enable any response content decision to be confirmed by a back-up determination of a response content.
Regarding claim 12, the combination of Dean, McNair, and Meger teaches the information processing system according to claim 9. Dean additionally teaches wherein the user includes a user who receives care at home (Paragraph 0064-- may be used in various environments or venues, such as a hospital, a nursing home, an establishment, a personal care home, a subject's house, or any place that can support the management platform 100 to enable communication with IoT devices 115).
Dean additionally teaches that an identified pattern may correspond to whether a caregiver should visit the home of the user (Paragraph 0172-0175-- one or more of: (1) absence of activity, (2) rolling over in bed activity, (3) getting out of bed activity, (4) fallen on the floor activity, and (5) entering the bathroom activity (e.g., trained activity identification models 770 used in the activity identification stage 720 described with respect to FIG. 7) are predictable… In those subjects falling below a certain threshold of movement, clinical staff could intervene to facilitate rolling or repositioning to minimize pressure sores. Predicted models could be employed with historic movement data in those with and without decubiti to predict safe, normal ranges for subject movement in bed and predictive overall health or wellness of the subject (e.g., at risk for decubitus). Data analytics for use of an additional RFID sensor worn by the clinical staff, which would be read by the in-room IoT devices, could be used to document clinical staff assisting with a fall event or movement of a subject. This could be used to provide clinical staff tracking and confirmation for assist with a fall event or movement to avoid decubitus).
As the combination of Dean, McNair, and Meger teaches storing and presenting the response content associated with the first body movement information as shown above, the teaching of Dean of an identified pattern corresponding to whether a caregiver should visit the home of the user may be used to further modify the apparatus to present, as part of the response content, a necessity or unnecessity of any response by a caregiver in order to predictably improve the ability of the apparatus to support a caregiver by minimizing unnecessary visits to a patient.
Regarding claim 13, the combination of Dean, McNair, and Meger teaches the information processing system according to claim 9. Dean further teaches wherein the receiver is further configured to: acquire, when an input operation to determine the response content has been performed in a first terminal device for the skilled worker, information in which the response content has been associated with the first body movement information (Paragraph 0131-0133-- for a prediction model 750 to be utilized to identify activity such as a subject flipping or rolling over in bed based on sensor or IoT device data, the input can be the sensor or IoT device data itself or features extracted from the sensor or IoT device data and the labels 757 can include energy states showing whether the activity has occurred or not in the sensor or IoT device data). The teaching of Dean of an identified pattern corresponding to a necessity or unnecessity of a visit to a residence of the user and these identified patterns being stored as training data to be compared to future data may be considered an association of a response content and the first body movement information.
McNair additionally teaches wherein the receiver is configured to: acquire, when an input operation to determine the response content has been performed in a first terminal device for the skilled worker, information in which the first information and the response content have been associated with each other in which the response content has been associated with the first body movement information (Col. 38, line 12-48-- At a step 4320, receiving a command to initiate a clinical decision support event associated with the first patient. At a step 4330, associating the clinical decision support event with the condition.; Fig. 4D).
Regarding claim 14, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 9. However, Dean does not explicitly disclose determine a first reference point based on execution timing of the input operation in the first terminal device, and cause the memory to store the body movement information in a period determined based on the first reference point, as the first body movement information.
McNair teaches wherein the processor is configured to: determine a first reference point based on execution timing of the input operation in the first terminal device (Col. 52, line 53-60-- In one embodiment, the epoch A is not a transient or momentary state; rather, it persists for a finite period that is commensurate with timeframes that are customary for ordinary, decision-making in health care services.; Col. 53, line 4-15-- Epochs may be as short as several seconds or minutes in length in the case of critical care and perioperative care or may be as long as several years in the case of slowly-evolving chronic diseases. In an embodiment, care decision epochs are defined by parameters 2120 (discussed in connection to FIG. 1C), and may indicate the specific sensor information (such as clinical conditions (including sequences or patterns of clinical conditions) and clinical variables (including patient demographic variables, treatment history and caregiver/health care entity/insurance information) used to determine the decision epoch.), and causes the storing unit to store the body movement information in a period to be determined based on the first reference point, as the first body movement information (Col. 38, line 12-48).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean with the reference point teaching of McNair to predictably improve the accuracy of any determinations made by the apparatus by ensuring that data is monitored over time rather than only monitoring for sensor signal magnitude generally, as patterns in sensor data over particular amounts of time may more accurately reflect patterns of inactivity or movement of the user.
Regarding claim 15, the combination of Dean, McNair, and Meger teaches the information processing system according to claim 14. However, Dean does not explicitly disclose wherein the processor is configured to: determine, when the processor has acquired the request based on a second input operation in a second terminal device for an unskilled worker, a second reference point based on execution timing of the second input operation, and determine the body movement information in a period determined based on the second reference point, as the second body movement information.
McNair teaches wherein the processor is configured to:
determine, when the processor has acquired the request based on a second input operation in a second terminal device for an unskilled worker, a second reference point based on execution timing of the second input operation ((Col. 52, line 53-60-- In one embodiment, the epoch A is not a transient or momentary state; rather, it persists for a finite period that is commensurate with timeframes that are customary for ordinary, decision-making in health care services.; Col. 53, line 4-15-- Epochs may be as short as several seconds or minutes in length in the case of critical care and perioperative care or may be as long as several years in the case of slowly-evolving chronic diseases. In an embodiment, care decision epochs are defined by parameters 2120 (discussed in connection to FIG. 1C), and may indicate the specific sensor information (such as clinical conditions (including sequences or patterns of clinical conditions) and clinical variables (including patient demographic variables, treatment history and caregiver/health care entity/insurance information) used to determine the decision epoch.)), and determine the body movement information in a period determined based on the second reference point, as the second body movement information (Col. 37, line 53-Col. 38, line 5).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean with the reference point teaching of McNair to predictably improve the accuracy of any determinations made by the apparatus by ensuring that data is monitored over time rather than only monitoring for sensor signal magnitude generally, as patterns in sensor data over particular amounts of time may more accurately reflect patterns of inactivity or movement of the user.
Regarding claim 17, the combination of Dean, McNair, and Meger teaches the information processing system according to claim 9. Dean further teaches an additional embodiment wherein the processor is configured to:
perform processing of obtaining first similarity information based on the first body movement information and the second body movement information in a period having a first length (Paragraph 0178-- For example, a first energy level obtained by the first antenna at a first period of time may be compared to a second energy level obtained by the second antenna at a second period of time…), and
processing of obtaining second similarity information based on the first body movement information and the second body movement information in a period having a second length different from the first length, and determine a degree of priority of the first similarity information and the second similarity information in accordance with the response content associated with the first body movement information (Paragraph 0178-0181-- a determination is made as to whether the sensor data includes additional energy levels (e.g., same or different energy level from the first and second energy levels, but identified as a separate recording of an energy level as compared to the recording for the first and second energy levels) at a different period of time for processing that was collected by the first and/or second antenna within a predefined period of time after the first period of time and the second period of time… the position of the subject determined at different time periods (e.g., the first period of time and the second period of time versus the third period of time and the fourth period of time) over the window of time in accordance with steps 1260 and 1265 are compared to one another to determine whether the subject's position is static or dynamic over the window of time. When the subject's position changes, for example from lying on their back to lying on their side it is determinable that the subject's position is dynamic over the window of time; whereas when the subject's position does not change, for example the subject remained lying on their back it is determinable that the subject's position is static over the window of time…; paragraph 0175-- The position of a subject in bed is of importance to the nursing home staff. If a subject is left in an unchanged position, then they are at high risk for skin breakdown and the development of decubitus ulcers. The nursing home staff may initiate a timed subject re-positioning schedule to overcome this. At the same time, subjects may spontaneously reposition themselves, obviating the need for this re-positioning assistance. Without positional monitoring, the nursing home staff have no current system to confirm this positional movement of subjects).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the apparatus of Dean and McNair to additionally include the teaching of Dean to monitor similarity over two different periods of time to determine a degree of priority in order to predicably improve the ability of the apparatus to support a caregiver by minimizing unnecessary visits to a patient by determining whether a patient needs imminent repositioning or not.
Regarding claim 19, the combination of Dean, McNair, and Meger teaches the information processing apparatus according to claim 15. McNair additionally teaches wherein the processor is further configured to: align a time axis of the first body movement information and a time axis of the second body movement information such that the first reference point and the second reference point coincide with each other, and perform processing of obtaining a similarity based on a distance between the first body movement information and the second body movement information that have been aligned (Col. 17, line 31-35, Col. 18, line 5-57—Tanimoto distance matching; Col. 52, line 53-Col. 53, line 44-- epoch A is not a transient or momentary state; rather, it persists for a finite period that is commensurate with timeframes that are customary for ordinary, decision-making in health care services. Epochs are delimited in time by the onset of sensor predicate vector elements' values such that a distance for matching the vector against one or more of the set of reference epoch vectors is suitably small, and by the offset of vector elements' values such that the distance is larger than a threshold denoting a close or satisfactory match… a CareDecision epoch reference database predicate vector that the patient's information matched, and in an embodiment contains a Tanimoto or Jaccard or other distance metric denoting the degree of similarity or quality of matching of the patient's sensor vector to the reference vector associated with the nominated epoch.).
It would additionally have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Dean, including determining a similarity, to additionally include the similarity determination based on distance between aligned waveform segments as disclosed by McNair in order to predictably improve the accuracy of the system by enabling the determination of similarity by different means, which would enable any response content decision to be confirmed by a back-up determination of a response content.
Response to Arguments
Applicant's arguments filed 27 April 2026 regarding the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive.
While applicant argues that the pressure sensor as claimed is not generic but is specially configured for detection of vibrations related to a sleep state of a user, such a sensor is additionally well-understood, routine, or conventional as noted in Meger, cited above. It is additionally noted that this sensor is not positively recited as part of the apparatus of claim 1. It is further noted that “related to a sleep state of the user”, under the broadest reasonable interpretation of this current claim language, includes movements by a user in bed including changes in position, rolling over, and general presence or absence of motion which would each necessarily would relate to a sleep state as increased movement would correspond to waking or restlessness while an absence of motion may correspond to deep sleep.
The performance of waveform analysis additionally does not integrate the judicial exception into significantly more, as the waveform analysis as claimed may be performed in the human mind with the aid of pen and paper such as by performing mathematical transforms or comparisons between two printed waveform segments.
Applicant's arguments filed 27 April 2026 regarding the rejection of the claims under 35 U.S.C. 103 have been fully considered but they are not persuasive.
The applicant argues that Dean and McNair fail to teach “time series body movement information” or “body movement information including information related to a sleep state of the user”. As noted in the arguments above, however, the broadest reasonable interpretation of this current claim language, includes movements by a user in bed including changes in position, rolling over, and general presence or absence of motion which would each necessarily would relate to a sleep state as increased movement may correspond to being awake or restlessness while an absence of motion may correspond to deep sleep such that Dean may be seen to disclose this limitation. Furthermore, while McNair lacks particular teaching or suggestion of analyzing similarity between time-series sleep data, it does disclose analyzing a similarity between epochs from a first period and a second period (in the case of McNair, where one of these may be a patient epoch occurring in a given time period and the other being a reference epoch occurring in some other time period prior to the patient epoch) to enable determining clinical decisions. As a result, it may be seen that the combination of the references, where Dean discloses determining a similarity between time-series body movement data as claimed and McNair teaches determining a response content based on a similarity between time-series data from sensors, in order to produce a system which determines a similarity between time-series body movement data in order to determine a response content would be obvious to one having ordinary skill in the art at the time of filing as described in detail above in this action.
Conclusion
Claims 6, 8, 16, and 18 are not currently rejected under 35 U.S.C. 102/103.
Regarding claims 6 and 16, the most pertinent prior art of the record Dean (cited above) generally teaches to acquire, from an environment detection device disposed in a surrounding of the user, environment information indicating an environment in the surrounding (Paragraph 0062, 0094-0095) but is silent as to perform processing of correcting the first reference point and the second reference point based on a changing amount of the environment information per unit time. McNair is similarly silent as to perform processing of correcting the first reference point and the second reference point based on a changing amount of the environment information per unit time.
Regarding claims 8 and 18, the most pertinent prior art of the record Dean (cited above) generally discloses obtaining image information (Paragraph 0075) but is silent regarding acquiring emotion information or estimating an emotion change of the user. McNair (cited above) generally discloses estimating a change of the user resulting from performing a response indicated by the response content (Fig. 4D; Col. 37, line 53-67 and Col. 38, line 32-48-- At a step 4340, determining a change in the condition of first patient), but is silent as to acquiring emotion information or estimating an emotion change of a user resulting from performing a response.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNA ROBERTS whose telephone number is (571)272-7912. The examiner can normally be reached M-F 8:30-4:30 EST.
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/ANNA ROBERTS/ Examiner, Art Unit 3791
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