DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to the amendment filed 1/22/26. Claims 1-13 are pending. Claims 12-13 are new.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/22/26 has been entered.
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.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites: “said care including at least one ingestible item being made available on a serving support upon which is also placed at least one indicator comprising: one or more human- readable signs corresponding to the consumer; and a machine- readable visible sign upon which is encoded a consumer code allowing for the consumer to be identified within the private database….” It is unclear to the examiner how the elements listed in the “said care” clause are related to one another, and which components are integral to claim 1, a process claim.
As drafted, it is unclear how the “the serving support (upon which an ingestible item is placed)” is related to the steps performed in claim 1. The preamble contains multiple implied steps which are not expressly or actively recited. (i.e. placing an ingestible item on a serving support, said serving support including one or more human readable signs corresponding to a/the consumer; identifying the consumer based on a machine- readable visible sign containing a consumer code) It should be noted that the preamble of a claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02. For claim elements/limitations which are integral to the steps performed, the claim limitation should be included in the body of the claim to receive patentable weight.
Also, it is unclear how the listed “machine- readable visible sign upon which is encoded a consumer code allowing for the consumer to be identified within the private database” is intended to further define the claim language. In particular, it is unclear whether the machine visible sign is an element which is part of the “serving support,” whether it further defines the “ingestible item;” or whether it further defines the “care” in the “said care” clause (i.e. an item that is provided to a consumer in additional to the ingestible item).
Claim 8 similarly recites: “said care including at least one ingestible item being made available on an identifiable serving support upon which is also placed at least one indicator comprising: one or more human-readable signs corresponding to the consumer; and a machine-readable visible sign upon which is encoded a consumer code allowing for the consumer to be identified within the private database; the computer vision system being communicably connected to the private database, the private database being accessible by authorised personnel…”
Again, the preamble contains multiple implied steps/functions which are not expressly or actively recited. (i.e. providing an ingestible item on a serving support; identifying the consumer based on a machine- readable visible sign containing a consumer code; authorized personal accessing the private database ) It should be noted that the preamble of a claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02. For claim elements/limitations which are integral to the steps performed, the claim limitation should be included in the body of the claim to receive patentable weight.
Also, it is unclear how the listed “machine- readable visible sign upon which is encoded a consumer code allowing for the consumer to be identified within the private database” is intended to further define the claim language. In particular, it is unclear whether the machine visible sign is an element which is part of the “serving support,” whether it further defines the “ingestible item;” or whether it further defines the “care” in the “said care” clause (i.e. an item that is provided to a consumer in additional to the ingestible item).
Additionally, the claim includes the phrase “characterised in that…” It is unclear how the claim elements included after this transitional phrase relate to the rest of the claim. More specifically, it is unclear which aspect/element of the system is further defined by the “characterised in that” clause. (e.g. the training database, the processing unit, the inspection and analysis unit for inspecting contents; or the computer vision system as a whole)
Claims 2-7 and 12-13 inherit the deficiencies of claim 1 through dependency, and are therefore also rejected.
Claims 9-11 inherit the deficiencies of claim 8 through dependency, and are therefore also rejected.
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 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.
Claim(s) 1-3, 5, 7-8, and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pugsley, (US 2021/0205180 A1) in view of Hanina et al. (US 20110231202 A1), and in further view of Chudy et al., (US 20130218326 A1).
Claims 1 and 8 Pugsley discloses a computer implemented process for monitoring care provided to a consumer in a care facility or hospitality environment according to a pharmacological or nutritional regimen for the consumer as recorded in a private database, (par. 55-database storing medication information; par. 64-65-storing patient information including medications and history) said care including at least one ingestible item being made available on a serving support (Fig. 4(4011), Fig. 8 (8005) upon which is also placed at least one indicator comprising: one or more human- readable signs corresponding to the consumer; and a machine- readable visible sign upon which is encoded a consumer code allowing for the consumer to be identified within the private database;
the process comprising:
capturing, using an image capture device of a monitoring system, one or more first images of at least the ingestible items and the indicator;(par. 94-95: The camera can visually record the dispensing of each pill (or plurality of pills), or items dispensed from the device,; par. 101-102: The image detector may be configured to be able to capture a visual graphical element, such as a bar code (e.g., one-dimensional, two-dimensional), text, a picture, a sequence thereof, or any other forms of graphical authentication indicia; par. 121-Some embodiments may include a camera 1007 to capture images or video of the user, and the device 1001 may be configured to provide live video interaction between a user and a third party, such as a medical professional; par. 132- Device 4001 may include an external camera 4007 to take images or video of the user, such as to support video conferencing or to confirm or record the identity of the user.)
analysing the captured first image using a machine learning model to identify one or more of the ingestible items and to identify the human-readable sign on the indicator; and (par. 141-Sensor 8013, which may be a camera or other device capable of identifying a pill in a tray, has a clear line of site into tray slot 1441. The sensor 8013 may scan the contents of slot 1441, and using one or more algorithms determine whether the contents of slot 1441 may be as expected (e.g., whether a pill was dispensed from the cartridge, and whether the dispensed pill may be the expected medication)…this pill verification may be automated, such that sensor 8013 may operate pursuant to one or more algorithms. In some embodiments, pill verification may be supplemented by an image capture for subsequent analysis by a medical professional or other third party. In some embodiments, the pill verification may also be performed live, such that a medical professional or other third party may receive the image or video footage from the patient-side device and confirm in or near real-time that the contents of slot 1441 may be correct; par. 148-use of machine learning algorithms to implement features of the invention)
Claim 1 further recites:
storing, in a training database, an anonymised version of the captured first image, said anonymised version of the captured first image being generated by electronically obfuscating all or part of the human-readable sign on the indicator identified in the captured first image.
Pugsley discloses capturing images and storing captured images in a database (par. 93-95; par. 121-Some embodiments may include a camera 1007 to capture images or video of the user, and the device 1001 may be configured to provide live video interaction between a user and a third party, such as a medical professional; par. 132- Device 4001 may include an external camera 4007 to take images or video of the user, such as to support video conferencing or to confirm or record the identity of the user) and further discloses anonymizing data collected by the system. (par. 115- the patient-side device may collect and anonymize data for mass analysis; par. 125- server(s) 3009 may retain data generated by patient-side devices 3001, and/or store data generated by and/or provided to medical professionals 3003 and pharmacies 3005….server(s) 3009 may perform data anonymizing and analyzing services.)
Pugsley does not expressly disclose storing, in a training database, an anonymised version of the captured first image, said anonymised version of the captured first image being generated by electronically obfuscating all or part of the human-readable sign on the indicator identified in the captured first image. Hanina discloses a system for capturing and storing anonymized versions of captured image data, wherein the anonymized version is generated by electronically obfuscating all or part of the identifying/ sensitive information in the captured image. (par. 34: a preferred method for such confirmation may include capturing a video sequence of the patient actually administering the medication by apparatus 100… but may also provide for the ability to obscure the face or other identifying feature of a user to allow for the storage and use of such images while protecting the identity of the patient…par. 41: a user has a still or video image captured of their face by apparatus 100, and facial recognition techniques are employed to confirm the identity of the user in analysis module 135... any unique individual identifiers may be obscured, as noted above, when the images are to be used as a more general report regarding adherence, rather than an individual patient response.)
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the system and method of Pugsley with the teaching of Hanina to store , in a (training) database an anonymised version of a captured first image, said anonymised version of the captured first image being generated by electronically obfuscating all or part of the human-readable sign on the indicator identified in the captured first image. One would have been motivated to include this feature to effectively provide general reports regarding adherence, while also guarding individual user privacy.
Claim 1 further recites: a serving support upon which is also placed at least one indicator comprising: one or more human- readable signs corresponding to the consumer; and a machine- readable visible sign upon which is encoded a consumer. code allowing for the consumer to be identified within the private database.
Pugsley discloses a system including an ingestible on a serving support, (Fig. 4(4011), Fig. 8 (8005),and further discloses a system including machine and human readable codes (par. 54-55; par. 137) but does not disclose that the serving support incudes at least one indicator comprising: one or more human- readable signs corresponding to the consumer; and a machine- readable visible sign upon which is encoded a consumer.
Chudy discloses a serving support (e.g. prescription tray) including at least one indicator comprising: one or more human- readable signs corresponding to the consumer; and a machine- readable visible sign upon which is encoded a consumer. (Abstract; par. 17; par. 38-39; par. 50-rfid tag and labels identifying a patient placed with tray.) At the time of the effective filing date, it would have been obvious to one of ordinary skill in the to further modify the method and system of Pugsley and Hanina in combination with the teaching of Chudy to include machine and human readable indicia on the serving support tray containing the ingestible. One would have been motivated to include these features to minimize error risks by incorporating redundant identification mechanisms to verify data related to the ingestible and consumer. .
Claim 2 Pugsley discloses a system computer implemented process according to claim 1, further comprising: further analysing the captured first image to identify the machine-readable visible sign on the indicator; extracting the consumer code from the machine-readable visible sign; identifying the consumer in the private database by matching the extracted consumer code to the consumer; accessing a menu database to obtain a pharmacological or nutritional content of the identified ingestible items; comparing, using a processor, the pharmacological or nutritional content of the identified ingestible items with the pharmacological or nutritional regimen of the identified consumer; and providing a warning if the result of the comparison is negative. (par. 67, par. 99-cross referencing patient information and warning of problems, including contraindications; par. 62-data validation and alerts to ensure proper medication is provided; par. 73: monitoring the patient's compliance with a reference medication treatment schedule, obtaining the patient health status from the database, obtaining a predicted medication treatment schedule of the patient through one or more algorithms, comparing the reference medication treatment schedule with the predicted medication treatment schedule, and updating the new medication treatment schedule based on the comparison.)
Claim 3 Pugsley teaches the computer implemented process according to claim 1, further comprising updating a record corresponding to the identified consumer in the private database to record the pharmacological or nutritional content of the identified ingestible items. (par. 35-37; par. 73)
Claim 5 Pugsley does not disclose, but Hanina teaches the computer implemented process according to claim 1, wherein said electronic obfuscation involves a process using blurring techniques, encryption techniques or image replacement techniques. (par. 34: a preferred method for such confirmation may include capturing a video sequence of the patient actually administering the medication by apparatus 100… but may also provide for the ability to obscure the face or other identifying feature of a user to allow for the storage and use of such images while protecting the identity of the patient…par. 41: a user has a still or video image captured of their face by apparatus 100, and facial recognition techniques are employed to confirm the identity of the user in analysis module 135... any unique individual identifiers may be obscured, as noted above, when the images are to be used as a more general report regarding adherence, rather than an individual patient response.) At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the system and method of Pugsley with the teaching of Hanina to store , in a (training) database an anonymised version of a captured first image, said anonymised version of the captured first image being generated by electronically obfuscating all or part of the human-readable sign on the indicator identified in the captured first image. One would have been motivated to include this feature to effectively provide general reports regarding adherence, while also guarding individual user privacy.
Claim 7 Pugsley teaches the computer implemented process according to claim 1, further including: updating the machine learning model using one or more first images and/or further images from the training database by a user to whom access to the private database is electronically excluded. (par. 148-use of machine learning algorithms; Machine learning algorithms may be used to train data sets to better dispensing pills, measuring patient's health status, identifying pills dispensed, communicating with and receiving instructions from medical professionals, and the like.)
Claim 8 The limitations of claim 8 are addressed by the rejection of claim 1.
Claim 8 additionally recites and Pugsley teaches the computer vision system being communicably connected to the private database, the private database being accessible by authorised personnel, (Par. 30-prescription management access and security protocols for professionals and patients) and computer vision system is configured to receive instructions from a user interface, when executed, to provide a user with access to at least t-the processing unit and the training database and to deny the user access to the private database. (par. 3-security features for the system; par. 30; par. 105)
Claim 10 Pugsley teaches the computer vision system according to claim 8, wherein the image capture device is a camera, an infrared camera, a video camera or a scanner. (par. 37, par. 101)
Claim 11 Pugsley teaches the computer vision system according to claim 8, wherein said one or more sensors comprise a weight sensor. (par. 69)
Claim 12 Pugsley teaches the computer implemented process according to claim 1, wherein the private database is accessible by authorised personnel. (Par. 30-prescription management access and security protocols for professionals and patients; par. 3-security features for the system; par. 105)
Claim 13 Pugsley teaches the computer implemented process according to claim 12, wherein the monitoring system is configured to provide a user with access at least to the training base and to deny the user access to the private database (Par. 30-prescription management access and security protocols for professionals and patients; par. 3-security features for the system; par. 105)
Claim(s) 4, 6, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pugsley, (US 2021/0205180 A1) in view of Hanina et al. (US 20110231202 A1), and in further view of Chudy et al., (US 20130218326 A1), in further view of Kumar et al ( US 20190279281 A1)
Claim 4 Pugsley, Hanina and Chudy in combination teach the computer implemented process according to claim 1, wherein the serving support comprises a machine-readable code to allow for the serving support to be identified using a suitable code reader, as explained in the rejection of claim 1. Pugsley, Hanina and Chudy in in combination further disclose “storing, in the training database, an anonymised version of the captured further image, said anonymised version of the captured further image being obtained by electronically obfuscating all or part of the human- readable sign on the indicator should the indicator have been identified in the captured further image”. as explained in the rejection of claim 1.
Pugsley and Hanina do not expressly disclose, but Chudy teaches a method further comprising:
upon delivery of said care to the consumer: automatically identifying the serving support by reading a machine-readable code of the serving support; (par. 41-43)
correlating the identified serving support with the previously extracted consumer code; and (par. 50-52)
upon collection of said serving support after the consumer has finished: automatically identifying the serving support by re- reading the machine-readable code of the serving support;(Fig. 9, par. 19-21; par. 42-44)
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the to further modify the method and system of Pugsley and Hanina in combination with the teaching of Chudy to include the recited features . One would have been motivated to include these features to minimize error risks by incorporating redundant identification mechanisms to verify data related to the ingestible and consumer.
Pugsley, Hanina and Chudy do not expressly disclose but Kumar teaches a method of capturing quantitative consumption images for a user/consumer (par. 78: quantitative consumption service 260 can apply computer vision/object recognition and machine learning processes (e.g., classification algorithms) to image data associated with the user to determine user consumption. For example, the user may take a photo of every snack and meal to send to network 210 or network 210 can collect image or video data from various user data sources (e.g., mobile device cameras, cameras on smart home devices, IP cameras, photos shared on a social network feed, etc.). Quantitative consumption service 260 may identify food items in the image data and correlate those food items with their associated nutritional data to determine dietary amounts (e.g., calories, nutrients, vitamins, minerals, etc.) consumed by the user; par. 81) and updating the patient’s profile based upon the information captured. (par. 86)
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method/system of Pugsley, Hanina, and Chudy in combination with the teachings Kumar to include a quantitative assessment of how much of an ingestible a user has actually consumed compared to a prescribed regimen and the steps of capturing, using an image capture device, one or more further images at least of any remnants of the ingestible items on the serving support; analysing the captured further image using the machine learning model to identify one or more of the remnants of the ingestible items and to identify the indicator if present; and further updating the record corresponding to the correlated consumer in the private database to record the remnants of the ingestible items. One would have been motivated to include these features to monitor the users and ensure the users are keeping up with their health regimens.(Kumar- par. 3)
Claim 6 Pugsley, Hanina and Chudy do not expressly disclose but Kumar teaches a computer implemented process, further comprising: comparing, using a processing unit, the first image and the further image to estimate a consumption amount of the ingestible items by the identified consumer. (par. 108- user may be associated with an unstructured data stream comprising images of food items consumed by the user within a predetermined time period (e.g., the user may take photos of one or more of his/her meals and explicitly share it with the network for the network to identify the food items, determine their nutritional values, and analyze how the nutritional values affect the user's dietary goals and conditions; the user shares photos of a meal over social media and the network scrapes the photos to perform similar analysis on the user's behalf; the network conducts forensic analysis from image data captured by the user's registered IoT devices, mobile devices, and wearable devices and publicly available image data captured by other devices and sensors; etc.).
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method/system of Pugsley, Hanina, and Chudy in combination with the teachings Kumar to include a quantitative assessment of how much of an ingestible a user has actually consumed compared to a prescribed regimen. One would have been motivated to include these features to monitor the users and ensure the users are keeping up with their health regimens.(Kumar- par. 3)
Claim 9 Pugsley, Hanina and Chudy do not expressly disclose but Kumar teaches a computer implemented process, further comprising: comparing, using a processing unit, the first image and the further image to estimate a consumption amount of the ingestible items by the identified consumer. (par. 78: quantitative consumption service 260 can apply computer vision/object recognition and machine learning processes (e.g., classification algorithms) to image data associated with the user to determine user consumption. For example, the user may take a photo of every snack and meal to send to network 210 or network 210 can collect image or video data from various user data sources (e.g., mobile device cameras, cameras on smart home devices, IP cameras, photos shared on a social network feed, etc.). Quantitative consumption service 260 may identify food items in the image data and correlate those food items with their associated nutritional data to determine dietary amounts (e.g., calories, nutrients, vitamins, minerals, etc.) consumed by the user; par. 81; par. 108)
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the method/system of Pugsley, Hanina, and Chudy in combination with the teachings Kumar to include a quantitative assessment of how much of an ingestible a user has actually consumed compared to a prescribed regimen. One would have been motivated to include these features to monitor the users and ensure the users are keeping up with their health regimens.(Kumar- par. 3)
Response to Arguments
Applicant's arguments filed 1/22/26 have been fully considered.
(A) The applicant argues that amendments to claim 8, overcome the claim rejections of claims 8, and 10-11 under 35 USC 112(b).
In response, the amended language is noted, and the 35 USC 112(b) rejections and the claim interpretations under 112(f) have been withdrawn.
(B) On page 13 of the response, the Applicant argues the Examiner has misinterpreted the language of claim 1, and that the machine visible sign is not on the ingestible item, but on the serving support.
In response, regarding claim 1, the limitations argued by the Applicant are in the preamble of the claim. Applicant’s arguments rely on language solely recited in preamble recitations in claim(s) 1. When reading the preamble in the context of the entire claim, the recitation of the details of the serving support is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
(C) Additional limitations argued by the applicant regarding the prior art are moot in view of the new grounds of rejection provided.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Carlson et al ( US 20210240853 A1)- discloses methods and an apparatus for centralized de-identification of protected data associated with subjects
Balakrishnan (US 2008/0118150 A1 )- discloses a system and method for obscuring text in a document.
.Keller et al (WO 2020035282 A1)-discloses a system in a patient-care environment for optimizing the process of meal preparation for patients having particular dietary requirements and dietary preferences.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachel L Porter whose telephone number is (571)272-6775. The examiner can normally be reached M-F, 10-6:30.
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, Shahid Merchant can be reached on 571-270-1360. 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.
/Rachel L. Porter/Primary Examiner, Art Unit 3684