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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-16 in the reply filed on 6/1/2026 is acknowledged.
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.
Claims 1-4, 7, 10-11, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zohar et al (US 2018/0,137,462) and further in view of Kumar et al (US 11,468,681).
For claim 1, Zohar et al teach a merchandise security system comprising:
a plurality of articles of merchandise on display in a retail store (e.g. paragraph 292: “inventory item” relates to any object which a user stocks in a home, retail, or commercial setting, including purchasable products, retail products,…); and
at least one camera (e.g. paragraph 313) configured to capture images of one or more of the articles of merchandise for obtaining information regarding the articles of merchandise (e.g. paragraph 313: “Each camera 104…may be used for collecting visual information related to inventory items…”, also see paragraphs 452-459), making predictions based on such information, and providing notifications based on such information (e.g. under paragraph 460, 1: “…The processor may then correlate the information received from the camera signal and the calendar signal, predict that the milk will have expired by Jun. 16, 2017, and add ‘milk’ to the grocery list…”).
Zohar et al teach machine learning module (e.g. 242 and 284, the processor is adapted to machine-learning techniques and the processor, which can be generator computer 140 in figure 1 and the camera can be a separate unit 104 as shown in figure 1). Zohar et al do not further specify a machine learning camera. Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
Claim 10 is rejected for the same reasons as discussed in claim 1 above.
For claim 2, Zohar et al teach a plurality of cameras, each camera associated with a respective article of merchandise (e.g. paragraph 313: “…Each camera 104 is adapted to capture images of the storage area and of inventory items therein…”). Zohar et al do not further specify a machine learning camera. Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
For claim 3, Zohar et al teach at least one controller configured to communicate with the at least one camera in order to obtain the information and/or receive the notifications (e.g. figure 1, processor 140 and camera 104). Zohar et al do not further specify a machine learning camera. Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
For claim 4, Zohar et al teach the at least camera is configured to wirelessly communicate with a remote device for providing the information obtained from the machine learning camera (e.g. paragraph 33: “wireless communication network”). Zohar et al do not further specify a machine learning camera. Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
For claim 7, Zohar et al teach the at least one camera is configured to obtain the information comprising at least one of logos and text of the article of merchandise, a flashing light source of the articles of merchandise, screen display details of the articles of merchandise, movement of the articles of merchandise, types of articles of merchandise, display positions of the articles of merchandise, or locations of the articles of merchandise (e.g. paragraph 368: “…Motion of inventory items…may also be detected by one or more cameras…). Zohar et al do not further specify a machine learning camera. Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
For claim 11, Zohar et al teach the providing notifications step is performed by the at least one camera. (e.g. under paragraph 460, 1: “…The processor may then correlate the information received from the camera signal and the calendar signal, predict that the milk will have expired by Jun. 16, 2017, and add ‘milk’ to the grocery list…”).
Zohar et al teach machine learning module (e.g. 242 and 284, the processor is adapted to machine-learning techniques and the processor, which can be generator computer 140 in figure 1 and the camera can be a separate unit 104 as shown in figure 1). Zohar et al do not further specify a machine learning camera. Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
For claim 14, Zohar et al teach populating the at least one machine learning camera with information (e.g. paragraph 368: “…Motion of inventory items…detected by one or more cameras…”).
For claim 15, Zohar et al teach populating comprises populating with information comprising at least photographs of the articles of merchandise, geometries of the articles of merchandise, applications operating on the articles of merchandise, logos and text on the articles of merchandise, light signatures or patterns emitted by the articles of merchandise, sounds originating from the articles of merchandise, details regarding surroundings of the articles of merchandise, or motions (e.g. paragraph 368: “…Motion of inventory items…detected by one or more cameras…”) or behaviors that are indicative of an honest consumer or theft.
Claims 8-9 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Zohar et al (US 2018/0,137,462) and Kumar et al (US 11,468,681), as applied to claims 1-4, 7, 11-12, 15-16 and 22 above, and further in view of Cato et al (e.g. 2009/0121017).
For claim 8, Zohar et al further disclose at least one machine learning camera is configured to make make predictions comprising at least one of shopping behavior, vandalism, or theft.
Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
Zohar et al and Kumar et al do not further disclose make predictions comprising at least one of shopping behavior, vandalism, or theft.
Cato et al teach make predictions comprising at least one of shopping behavior, vandalism, or theft. (e.g. paragraph 38: “For example, the method of monitoring inventory may detect abnormal or suspicious changes in inventory that suggest theft. Accordingly, when large quantities of a single inventory item are taken at once, such as ten packages of razor blades or cold medicine, then the system or method may issue an alert for investigation or intervention”). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cato et al into the teaching of Zohar et al and Kumar et al to improve the system and method to monitor the actual inventory so that theft, waste and breakage are taken into account (e.g. paragraph 7, Cato et al).
For claim 12, Zohar et al and Kumar et al do not further disclose making predictions based on shopping behavior or theft behavior. Cato et al teach making predictions based on shopping behavior or theft behavior. (e.g. paragraph 38: “For example, the method of monitoring inventory may detect abnormal or suspicious changes in inventory that suggest theft. Accordingly, when large quantities of a single inventory item are taken at once, such as ten packages of razor blades or cold medicine, then the system or method may issue an alert for investigation or intervention”). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cato et al into the teaching of Zohar et al and Kumar et al to improve the system and method to monitor the actual inventory so that theft, waste and breakage are taken into account (e.g. paragraph 7, Cato et al).
For claim 13, Zohar et al and Kumar et al do not further disclose providing notifications comprises generating an alarm in response to a security event. Cato et al teach providing notifications comprises generating an alarm in response to a security event. (e.g. paragraph 38: “For example, the method of monitoring inventory may detect abnormal or suspicious changes in inventory that suggest theft. Accordingly, when large quantities of a single inventory item are taken at once, such as ten packages of razor blades or cold medicine, then the system or method may issue an alert for investigation or intervention”). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cato et al into the teaching of Zohar et al and Kumar et al to improve the system and method to monitor the actual inventory so that theft, waste and breakage are taken into account (e.g. paragraph 7, Cato et al).
For claim 9, Zohar et al and Kumar et al do not further disclose at least one machine learning camera is configured to provide notifications comprising at least one of an audible or visual alarm in response to one of the articles of merchandise disappearing from view of the at least one machine learning camera, a notification to assist a customer, or a notification regarding planogram non-compliance.
Kumar et al teach a machine learning camera (e.g. column 2, lines 29-33: “…the present disclosure are directed to imaging devices (e.g. digital cameras) that are configured to capture imaging data and to processing the imaging data using one or more machine learning systems or techniques operating on the imaging devices…). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the machine learning camera of Kumar et al into the machine system of Zohar et al to monitor the arrivals or departures of goods or the performance of services (e.g. column 1, lines 5-20, Kumar et al) to view large numbers of people, objects or machines of varying sizes or shapes (e.g. column 1, lines 40-45, Kumar et al) to improve convenience for user.
Zohar et al and Kumar et al do not further disclose provide notifications comprising at least one of an audible or visual alarm in response to one of the articles of merchandise disappearing from view of the at least one machine learning camera, a notification to assist a customer, or a notification regarding planogram non-compliance.
Cato et al teach provide notifications comprising at least one of an audible or visual alarm in response to one of the articles of merchandise disappearing from view of the at least one machine learning camera, a notification to assist a customer, or a notification regarding planogram non-compliance. (e.g. paragraph 38: “For example, the method of monitoring inventory may detect abnormal or suspicious changes in inventory that suggest theft. Accordingly, when large quantities of a single inventory item are taken at once, such as ten packages of razor blades or cold medicine, then the system or method may issue an alert for investigation or intervention”). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Cato et al into the teaching of Zohar et al and Kumar et al to improve the system and method to monitor the actual inventory so that theft, waste and breakage are taken into account (e.g. paragraph 7, Cato et al).
Claims 5-6, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zohar et al (US 2018/0,137,462) and Kumar et al (US 11,468,681), as applied to claims 1-4, 7, 11-12, 15-16 and 22 above, and further in view of Fawcett et al (US 2010/0118144).
For claim 5, Zohar et al and Kumar et al do not further disclose a plurality of security devices each configured to be attached to a respective one of the articles of merchandise, wherein the at least one machine learning camera is configured to capture images of the security devices for detecting information regarding the security devices making predictions based on such information, and providing notifications based on such information. Fawcett et al teach a plurality of security devices each configured to be attached to a respective one of the articles of merchandise, wherein the at least one machine learning camera is configured to capture images of the security devices for detecting information regarding the security devices making predictions based on such information, and providing notifications based on such information (e.g. abstract, paragraphs 6, 17, figures 1-3). It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Fawcett et al into the teaching of Zohar et al and Kumar et al to allow purchaser to closely examine and operate a protected merchandise and to deter theft and prevent unauthorized or accidental removal of the article of merchandise (e.g. paragraph 16, Fawcett et al) to improve security and convenience for user.
Claim 16 is rejected for the same reasons as discussed in claim 5 above.
Claim 6 is rejected for the same reasons as discussed in claim 5 above with the same motivation applied, wherein Fawcett et al also disclose he plurality of security devices comprises alarming circuitry for generating an audible and/or a visual signal in response to removal of the article of merchandise from the security device (e.g. paragraph 17).
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 1-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 and 14-20 of U.S. Patent No. 12,170,007. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claims broader in every aspect than the patent claim and is therefore an obvious variant thereof.
Claim 1 of the instant application corresponds to claim 1 of the Patent.
Claim 2 of the instant application corresponds to claim 2 of the Patent.
Claim 3 of the instant application corresponds to claim 1 of the Patent.
Claims 4-9 of the instant application corresponds to claims 3-8 of the Patent, respectively.
Claims 10-16 of the instant application corresponds to claims 14-20 of the Patent, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Grimaud (US 2013/00729) teach, see figure 1, abstract: real world layout has inventory of products distributed across the three dimensions. The computer-based method further includes comparing a 3D model of a planned layout of the environment to the received image data. The 3D model represents the planned layout across the three dimensions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAQUAN ZHAO whose telephone number is (571)270-1119. The examiner can normally be reached M-Thur: 7:00 am-5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Thai Tran can be reached on 571-272-7382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Email: daquan.zhao1@uspto.gov.
Phone: (571)270-1119
/DAQUAN ZHAO/Primary Examiner, Art Unit 2484
Prosecution Insights