DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 2-21 are pending for examination.
Non-Statutory Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-14 of U.S. Patent No. 12,136,480 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims are obvious modification of the patented claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Regarding claims 4 and 21, recite the limitation “wherein the plurality of behavioral data is non-biologic” with emphasis underlined. The limitation renders the claims indefinite because the claims include elements not actually disclosed (those encompassed by "non-biologic"), thereby rendering the scope of the claim unascertainable.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 2-5 and 7-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Utley (WO 2016/164484 A1).
Regarding claim 2, Utley teaches a method for modifying a behavior of a patient (Abstract, System and method for predicating smoking behavior to assist in smoking cessation), the method comprising:
collecting patient data from the patient from a patient device associated with the patient, wherein the patient data comprises one or more parameters measured in real-time (Fig. 1, para [0058], “When wearing the wearable device for a suitable period of time, e.g., five days, a number of parameters may be measured real-time or near real time.” and para [0071], “Device 102 includes a processor, a memory, and a communications link for sending and receiving data from device 104 and/or server 106. Device 102 includes one or more sensors to measure the patient's smoking behavior based on measuring one or more of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse velocity, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters.”. The watch 102 collects one or more user data in real time);
transmitting the patient data to a server from the patient device, wherein transmitting the patient data is automatically entered (Fig. 14, step 1402, para [0075], “Device 104 or server 106 (subsequent to receiving the data) may compile the data, analyze the data for trends, and correlate the data either real time or after a specified period of time is complete. Server 106 includes a processor, a memory, and a communications link for sending and receiving data from device 102 and/or device 104.”. The watch 102 automatically transmits the collected user data to the server 106);
updating a patient database with the patient data in a cloud server (Fig. 14, step 1404 and para [0119], “At step 1404, the processor updates a patient database that is stored locally or at a remote location, such as a healthcare database in server 106, with the received patient data.”);
analyzing the patient data to determine an event associated with the behavior via a processor, wherein the processor determines the event by compiling parameters of the patient data before the event (Fig. 12, Fig. 14, steps 1406-1410, para [0119], “At step 1406, the processor analyzes the current and prior measurements for the patient parameters and determines whether a smoking event is expected. For example, the SpCO trend may be at a local minimum which indicates the user may be reaching for a cigarette to raise their SpCO level. The processor may apply a gradient descent algorithm to determine the local minimum. At step 1408, the processor determines whether the SpCO trend indicates an expected smoking event. If the processor determines a smoking event is not expected, at step 1410, the processor determines if the time and/or location are indicative of an expected smoking event. For example, the processor may determine that the patient typically smokes when they wake up in the morning around 7 a.m. In another example, the processor may determine that the patient typically smokes soon after they arrive at work. In yet another example, the processor may determine that the patient typically smokes in the evening whenever they visit a particular restaurant or bar.”. The server determines whether the user will smoke based on the received user data.); and
preparing a reporting interface based on the patient data analyzed by the processor (Fig. 12, Fig. 14, step 1412, para [0132], “at step 1412, the processor initiates a prevention protocol for the patient to prevent the smoking event. Information regarding the prevention protocol may be stored in memory of device 102, 104, or 202, or server 106 or 204, or a combination thereof. The information for the prevention protocol may include instructions for one or more intervention options to initiate when the patient is about to smoke. For example, the processor may initiate an alarm in the patient's mobile phone and display an app screen similar to FIG. 12.”. The user’s device displays a message to prevent the user to smoke).
Regarding claim 3, Utley teaches the method of claim 2, wherein the patient database includes a plurality of user-specific input data specific to the patient, where the plurality of user-specific input data includes a subset of individual-user biological input data and at least one of a subset of individual-user psychographic information and a subset of individual-user personal information, where at least a portion of the plurality of user-specific input data is previously collected (para [0071], “Device 102 includes one or more sensors to measure the patient's smoking behavior based on measuring one or more of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse velocity, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters.” and para [0131], “at step 1410, the processor determines if the time and/or location are indicative of an expected smoking event. For example, the processor may determine that the patient typically smokes when they wake up in the morning around 7 a.m. In another example, the processor may determine that the patient typically smokes soon after they arrive at work. In yet another example, the processor may determine that the patient typically smokes in the evening whenever they visit a particular restaurant or bar.” Heart rate is biological data, stressor and/or daily smoke in the morning are psychographic data, and life event and/or user location are personal data.).
Regarding claim 4, Utley teaches the method of claim 2, wherein the patient database further includes a behavior summary of the patient, where the behavior summary comprises an association of patient biological input data from the patient with at least one of a plurality of behavioral data supplied by the patient, wherein the plurality of behavioral data is non-biologic (Fig. 13, the server provides a summary of the user’s progress based on the collected biological data and the user has stopped smoking for 15 days. The user has stopped smoking for 15 days is considered as the user’s behavioral data and time is non-biologic.).
Regarding claim 5, Utley teaches the method of claim 3, wherein the subset of individual-user personal information in the patient database includes information from a group consisting of background, traits, demographics, and previous notes about the patient (para [0058], “For example, some of the patient entered data may include information regarding phone calls, athletics, work, sport, stress, sex, drinking, smoking, and other suitable patient entered data.”).
Regarding claim 7, Utley teaches the method of claim 2, wherein transmitting the patient data is based on a prompt displayed to the patient, wherein the prompt is drawn from a database of behavioral information from a plurality of users (Fig. 11, para [0088], “The patient entered data may be received in response to a prompt to the patient on, e.g., a mobile device such as device 104, or entered without prompting on the patient's volition. For example, some of the patient entered data may include information regarding phone calls, athletics, work, sport, stress, sex, drinking, smoking, and other suitable patient entered data.”. Fig. 11, shows the mobile device displays a prompt to collect patient data. The prompt is a template used by the server to collect behavior information from a plurality of users.).
Regarding claim 8, Utley teaches the method of claim 7, wherein the prompt is configured to be responded to by the patient (Fig. 11 and para [0077], the patient enters the data via the display).
Regarding claim 9, Utley teaches the method of claim 8, wherein the parameters comprise one or more of movement data, location data, and time of day data (Fig. 14, steps 1406-1410, para [0119], “at step 1410, the processor determines if the time and/or location are indicative of an expected smoking event. For example, the processor may determine that the patient typically smokes when they wake up in the morning around 7 a.m. In another example, the processor may determine that the patient typically smokes soon after they arrive at work. In yet another example, the processor may determine that the patient typically smokes in the evening whenever they visit a particular restaurant or bar.”. The server determines whether the user will smoke based movement, location and/or time of day.).
Regarding claim 10, Utley teaches the method of claim 7, wherein the reporting interface allows a coach-counselor to electronically access the database of behavioral information (para [0086], “Server 106 (e.g., a healthcare database server) may receive such data from one or both of devices 102 and 104. In some embodiments, the data may be stored on a combination of one or more of devices 102, 104, and 106. The data may be reported to various stakeholders, such as the patient, patient's doctor, peer groups, family, counselors, employer, and other suitable”.).
Regarding claim 11, Utley teaches the method of claim 7, wherein the behavioral information from the plurality of users includes information from a group consisting of background, traits, demographics, and previous notes about each of the plurality of users (Fig. 11, para [0058], “For example, some of the patient entered data may include information regarding phone calls, athletics, work, sport, stress, sex, drinking, smoking, and other suitable patient entered data.” and para [0119], “at step 1410, the processor determines if the time and/or location are indicative of an expected smoking event. For example, the processor may determine that the patient typically smokes when they wake up in the morning around 7 a.m. In another example, the processor may determine that the patient typically smokes soon after they arrive at work. In yet another example, the processor may determine that the patient typically smokes in the evening whenever they visit a particular restaurant or bar.”).
Regarding claim 12, Utley teaches the method of claim 2, further comprising updating the patient database with the event determined by the processor (Fig. 14, the system detects an expected smoking event at step S1406-1410 and updates the result of the prevention at step 1416 and 1418).
Regarding claim 13, Utley teaches the method of claim 2, wherein the processor is configured to determine a smoking event based on trends of carbon monoxide, exhaled carbon monoxide, or carboxyhemoglobin levels and time (Fig. 14, and para [0119], “At step 1408, the processor determines whether the SpCO trend indicates an expected smoking event.”. and para [0009], “The systems and methods non-invasively can detect and quantify smoking behavior for a patient based on measuring one or more of the patient's biometric data such as CO level or exhaled CO level. However other biometric data can also be used. Such data includes carboxyhemoglobin (SpCO),”).
Regarding claim 14, Utley teaches the method of claim 13, wherein the processor is configured to initiate a prevention protocol upon detection of the smoking event (Fig. 14, step 1412).
Regarding claim 15, Utley teaches the method of claim 14, wherein the prevention protocol comprises an alarm on a mobile device (Fig. 12, shows the expected smoking alarm).
Regarding claim 16, Utley teaches the method of claim 2, wherein the patient device comprises a photoplethysmography sensor (Para [0071], “For example, device 102 may include PPG-based sensors for measuring CO, eCO, SpCO and SpO2,”. PPG sensor.).
Regarding claim 17, Utley teaches the method of claim 2, wherein the behavior comprises oral placement of substances (para [0157], “While exemplary embodiments of the systems and methods described above focus on smoking behaviors, examples of which include but are not limited to smoking of tobacco via cigarettes, pipes, cigars, and water pipes, and smoking of illegal products such as marijuana, cocaine, heroin, and alcohol related behaviors, it will be immediately apparent to those skilled in the art that the teachings of the present invention are equally applicable to any number of other undesired behaviors. Such other examples include: oral placement of certain substances, with specific examples including but not limited to placing chewing tobacco and snuff in the oral cavity,”).
Regarding claim 18, Utley teaches the method of claim 2, wherein the behavior comprises transdermal absorption of substances (para [0157], “While exemplary embodiments of the systems and methods described above focus on smoking behaviors, examples of which include but are not limited to smoking of tobacco via cigarettes, pipes, cigars, and water pipes, and smoking of illegal products such as marijuana, cocaine, heroin, and alcohol related behaviors, it will be immediately apparent to those skilled in the art that the teachings of the present invention are equally applicable to any number of other undesired behaviors. Such other examples include: … transdermal absorption of certain substances,”).
Regarding claim 19, Utley teaches the method of claim 2, wherein the behavior comprises nasal ingestion of substances (para [0157], “While exemplary embodiments of the systems and methods described above focus on smoking behaviors, examples of which include but are not limited to smoking of tobacco via cigarettes, pipes, cigars, and water pipes, and smoking of illegal products such as marijuana, cocaine, heroin, and alcohol related behaviors, it will be immediately apparent to those skilled in the art that the teachings of the present invention are equally applicable to any number of other undesired behaviors. Such other examples include: oral placement of certain substances, with specific examples including but not limited to placing chewing tobacco and snuff in the oral cavity, transdermal absorption of certain substances, with specific examples including but not limited to application … nasal sniffing of drugs”).
Regarding claim 20, Utley teaches a method of providing customized content to an individual-user participating in a behavioral-modification program (Abstract, System and method for predicating smoking behavior to assist in smoking cessation), the method comprising:
providing a database of information comprised of a plurality of user-specific data specific to the individual-user (Fig. 1, para [0071], “Device 102 includes a processor, a memory, and a communications link for sending and receiving data from device 104 and/or server 106. Device 102 includes one or more sensors to measure the patient's smoking behavior based on measuring one or more of the patient's CO, eCO, SpCO, SpO2, heart rate, respiratory rate, blood pressure, body temperature, sweating, heart rate variability, electrical rhythm, pulse velocity, galvanic skin response, pupil size, geographic location, environment, ambient temperature, stressors, life events, and other suitable parameters.” and para [0075], “Device 104 or server 106 (subsequent to receiving the data) may compile the data, analyze the data for trends, and correlate the data either real time or after a specified period of time is complete. Server 106 includes a processor, a memory, and a communications link for sending and receiving data from device 102 and/or device 104.”. The server 106 stores a plurality of user data.);
electronically monitoring an activity of the individual-user (Fig. 14, step 1406-1410, para [0119], “For example, the processor may determine that the patient typically smokes when they wake up in the morning around 7 a.m. In another example, the processor may determine that the patient typically smokes soon after they arrive at work. In yet another example, the processor may determine that the patient typically smokes in the evening whenever they visit a particular restaurant or bar.”. The server monitors the user’s activity of arriving at work or visiting a particular restaurant);
updating a patient database with the plurality of user-specific data in a cloud server (Fig. 14, step 1404, the server receives a plurality of data, such as SpCO, from the user to update its database);
using the activity and the database to customize a program-related content comprising an electronic media content from a database of generic information applicable to the behavioral-modification program (Fig. 14, step 1412, the server initiates a prevention based on the user’s activity and SpCO data. Fig. 12, shows the prevention protocol comprises of contents 1202-1208 for the user to select to prevent the smoking behavior.);
electronically transmitting the program-related content to the individual-user as an electronic message (Fig. 12. The server transmits the prevention protocol to the mobile device.); an
monitoring an electronic interaction of the individual-user with a program-related content (para [0111], “FIG. 12 shows an illustrative embodiment of an app screen 1200 implementing such a prevention protocol. For example, if a patient tends to become tachycardic twenty minutes before every cigarette, the processor may detect tachycardia and prompt the patient to administer nicotine via option 1202. The patient may vary the nicotine dose via option 1204. In some embodiments, the nicotine is administered automatically. The amount may be determined based on the patient's current SpCO level or another suitable parameter. The patient may receive a call from a peer group via option 1206, a doctor via 1208, or another suitable stakeholder. The caller may provide the patient encouragement to abstain from smoking and suggest seeking out other activities to divert the patient's attention.”. The mobiles phone monitors the content selected by the user.).
Regarding claim 21, Utley teaches a method of claim 20, wherein the database of information further includes a behavior summary of the individual-user, where the behavior summary comprises an association of an individual-user biological input data with at least one of a plurality of behavioral data supplied by the individual-user where the plurality of behavioral data is non- biologic (Fig. 13, the server provides a summary of the user’s progress based on the collected biological data and the user has stopped smoking for 15 days. The user has stopped smoking for 15 days is considered as the user’s behavioral data and time is non-biologic.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Utley (WO 2016/164484 A1) in view of Cobb (Pub. No.: US 2007/0168501 A1).
Regarding claim 6, Utley teaches the method of claim 3, wherein Utley’s method includes a user input interface to receive user data to assist the user to quit smoking and but fails to expressly teach wherein the subset of individual-user psychographic information in the patient database includes milestones and targets.
However, in the same field of behavior modification, Cobb teaches a method that includes a user input interface to receive the user’s target of quitting smoke within the next 6 months. See Fig. 13 item 112 and Para [0064].
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Utley’s user input interface, such as Fig. 11, to include a target time for quitting smoking to improve performance.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHEN Y WU whose telephone number is (571)272-5711. The examiner can normally be reached Monday-Friday, 10AM-6PM, EST.
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/ZHEN Y WU/Primary Examiner, Art Unit 2685