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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 7-8 are objected to because of the following informalities: "any one of claims 1" in each claim should be amended to --claim 1--. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: acquisition unit in claims 1, 3, 6, and 8; storing unit in claims 1 and 4; processing unit in claims 1 and 4-8; detection device in claims 1 and 9; and terminal device in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Acquisition unit is interpreted according to paragraph 0050 of the instant specification: “the acquisition unit may correspond to the communicator 130 in FIG. 2… The acquisition unit is not limited to that to correspond to the communicator 130, but may correspond to the processing unit 110 that performs computing processing of body movement information based on the pressure value”.
Storing unit is interpreted according to paragraph 0024 of the instant specification: “The storing unit 120 is a work area of the processing unit 110, and stores various kinds of information. The storing unit 120 can be implemented by various kinds of memories, and the memory may be a semiconductor memory such as SRAM, DRAM, a read only memory (ROM), and a flash memory, may be a register, may be a magnetic storage device, and may be an optical storage device”.
Processing unit is interpreted according to paragraph 0022-0023 of the instant specification: “The processing unit 110 in the embodiment is implemented by hardware described below. The hardware can include at least one of a circuit for processing digital signals and a circuit for processing analog signals. For example, the hardware may be implemented by one or a plurality of circuit devices mounted to a circuit substrate and/or one or a plurality of circuit elements. One or a plurality of circuit devices are, for example, an integrated circuit (IC) and a field-programmable gate array (FPGA). One or a plurality of circuit elements are, for example, a resistance and a capacitor. Moreover, the processing unit 110 may be implemented by processors described below. The server system 100 in the embodiment includes a memory that stores information, and a processor that operates based on the information stored in the memory. The information is, for example, a program and various kinds of data. The memory may be the storing unit 120, or may be another memory. The processor includes hardware. As the processors, various kinds of processors including a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), and the like can be used. The memory may be a semiconductor memory such as a static random access memory (SRAM), a dynamic random access memory (DRAM), and a flash memory, may be a register, may be a magnetic storage device such as a hard disk device (HDD: hard disk drive), and may be an optical storage device such as an optical disc device. For example, the memory stores an instruction readable by a computer, and the processor executes the instruction, thereby implementing a function of the processing unit 110 as processing”.
Detection device is interpreted according to paragraphs 0033-0036 of the instant specification: “The detection device 430 is a sheet-shaped or plate-shaped device that is provided between the sections of the bed 610 and a mattress 620, for example, as illustrated in FIG. 4. The detection device 430 includes a pressure sensor (for example, pneumatic sensor) that outputs a pressure value, and is a device that detects a body vibration (body movement, vibration) of a user via the mattress 620 when the user has gone to bed… For example, the detection device 430 may calculate a respiratory rate and a heart rate from a peak frequency by analyzing the periodicity of the body movement.”.
Terminal device is interpreted according to paragraph 0015 of the instant specification: “The terminal device 200 in FIG. 1 is a device that performs communication with the server system 100, and is a terminal that is used by a care giver, for example. The terminal device 200 is, for example, a mobile terminal device such as a smartphone or a tablet terminal. Note that, the terminal device 200 may be another device including a personal computer (PC), a headset, a wearable device such as augmented reality (AR) glasses and mixed reality (MR) glasses, and the like. A plurality of care givers are assumed in the embodiment, and the terminal device 200 may include a plurality of devices that are used by the different care givers.”.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 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 associated with the first body movement information" which necessitates determining a similarity. 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 associated with the first body movement information" are abstract ideas as they are directed to a mental process. 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 “an acquisition unit configured to acquire, based on an output from a detection device that can detect a body movement of a user who receives care assistance, body movement information including information related to a sleep state of the user; a storing unit 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 processing unit”, 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 “an acquisition unit”, “a storing unit”, and “a processing unit” 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 11 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.
Claims 9 and 10 are 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 detection device” 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 generic sensor and computing device, which Dean (US 20230165728 A1) describes as well-understood, routine, or conventional in its description of common or typical sensors (Paragraph 0056, 0062-- Conventional incontinence monitoring systems typically have a sensor…a sensor 110 is a device, module, machine, or subsystem whose purpose is to detect events (e.g., physical properties) or changes in its environment and send the information to other electronic devices (e.g., an IoT device). The sensors 110 may include any number and type of sensors for sensing any number and type of event or change in the environment, and are configured to communicate data regarding the event or change in the environment to one or more IoT devices 115) and client devices (Paragraph 0060-- the client devices 105 may include a stand-alone interface (e.g., a cellular telephone, a smartphone, a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, a wearable device such as a smart watch, a wall panel, a keypad, or the like), an interface that is built into an appliance or other device (e.g., a television, a refrigerator, a security system, a game console, a browser, or the like), a speech or gesture interface (e.g., a Kinect™ sensor, a Wiimote™, or the like), an IoT device interface (e.g., an Internet enabled appliance such as a medical device, a control interface, or other suitable interface), or the like).
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, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dean (US 20230165728 A1) in view of McNair (US 12488892 B1).
Regarding claim 1, Dean teaches an information processing apparatus (Paragraph 0055—an IoT monitoring system) comprising:
an acquisition unit (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 detection device (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) that can detect a body movement of a user who receives care assistance, 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 storing unit (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 processing unit (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…).
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:
an acquisition unit 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 storing unit (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 processing unit (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).
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.).
Regarding claim 2, the combination of Dean and McNair teaches the information processing apparatus according to claim 1. Dean additionally teaches wherein the user includes a user who receives at-home care (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 a necessity or unnecessity of a visit to a residence 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 and McNair 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 a necessity or unnecessity of a visit to a residence 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 and McNair teaches the information processing apparatus according to claim 1. Dean further teaches wherein the acquisition unit is configured to: acquire, when an input operation to determine the response content has been performed in a first terminal device that the skilled worker uses, information in which the first body movement information and the response content have been associated with each other (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 the first body movement information and a response content.
McNair additionally teaches wherein the acquisition unit is configured to: acquire, when an input operation to determine the response content has been performed in a first terminal device that the skilled worker uses, information in which the first information and the response content have been associated with each other (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 and McNair 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 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.
McNair teaches wherein the processing unit 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 and McNair teaches the information processing apparatus according to claim 4. However, Dean does not explicitly disclose wherein the processing unit is configured to: determine, when the processing unit has acquired the request based on a second input operation in a second terminal device that an unskilled worker uses, a second reference point based on execution timing of the second input operation, and determines the body movement information in a period determined based on the second reference point, as the second body movement information.
McNair teaches wherein the processing unit is configured to:
determine, when the processing unit has acquired the request based on a second input operation in a second terminal device that an unskilled worker uses, 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 determines 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 and McNair teaches the information processing apparatus according to claim 1. Dean further teaches an additional embodiment wherein the processing unit 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 detection device configured to detect a body movement of a user who receives care assistance (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; 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 server system (Paragraph 0055-0056, 0067—one or more processors…a server) configured to acquire, based on an output from the detection device (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…).
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).
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.).
Regarding claim 10, Dean teaches an information processing method (Paragraph 0055—an IoT monitoring system and in particular to techniques (e.g., systems, methods…))) comprising:
acquiring, based on an output from a detection device (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) that can detect a body movement of a user who receives care assistance, 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)));
storing 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
performing 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…).
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:
an acquisition unit 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 storing unit (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 processing unit (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).
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the method 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 method 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.).
Conclusion
Claims 6 and 8 are not currently rejected under 35 U.S.C. 102/103.
Regarding claim 6, the most pertinent prior art of the record Dean (cited above) general teaches 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 claim 8, 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.
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