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 .
Response to Amendment
The Reply filed 09 March 2026 included claim amendments that overcome all of the 112(b) rejections but also raised new issues under 112(a) and (b).
It is noted that replacement figures have been filed 09 March 2026 but these replacement figs do not appear to improve image quality and instead appear to be resubmission of the original figures 6, 9, and 10. As such, the drawing objections have been maintained.
Response to Arguments
Applicant's arguments filed 09 March 2026 have been fully considered but they are not persuasive.
Applicant argues that Morton does not disclose the claimed invention because the claims now exclude X-ray, CT imaging and hyperspectral imaging but does not address Morton’s use of a camera and radar to gather food data as recited in the amended claims and rejected in further detail below.
In response to the embedding-based fingerprinting amendments and line of argument, see Morton in which the food products (carcasses, offal, primal, retain cuts, “meat objects” etc.) are tracked throughout the meat processing plant using a unique identifier (ID) to ensure traceability and storing such embedded data in a database for quick lookup Fig. 17, [0245]-[0249]. Furthermore, the BRI of “embedding-based fingerprinting” as per the instant specification’s broad definition of “embedding” in Fig. 16, step 1606, [0055] broadly includes “a series of vectors representing characteristic features of the food object”.
Likewise, Morton discloses an “embedding” using a series of related, numerical feature vectors representing characteristic features of the food object. For example, a carcass entering an abattoir of the meat processing plant has an embedding ID of 63, and subsequently, six primals are cut from the carcass wherein said primals have associated embeddings 63:1 through 63:6. This embedding process continues such that if primal 63:2 is processed into 15 retail cuts, said cuts have embeddings 63:2.1 to 63:2.15 which is a feature vector having three dimensions representing characteristic features (traceable identities of carcass, primal, and retail cut) of the food object (meat object). Moreover, multiple carcasses, primals and retail cuts are examples of such a labelling/embedding scheme. Still further, Morton’s disclosed embedding-based fingerprinting may also include another dimension in the feature vector embedding by associating or otherwise including the date and time stamp at which a primal was cut from a carcass or a retail cut was separated from its primal.
Such embeddings (embedding-based fingerprinting) are specifically used to provide the same traceability advantages disclosed by the instant invention. Even further, neither the claim language nor the specification broadly ties embedding-based fingerprinting to the food data or specifically uses the multi-sensor food data gathered by the cameras, depth sensors and laser to generate the embeddings.
Lastly, Morton’s number-based embedding scheme does not involve barcodes, RFID or video tracking such that Morton assigns or reconciles object IDs “without barcodes, RFID or video tracking”. Moreover, the meat tracking process of Figs. 17-19 specifically provides for using RFID tag or other ID providing element including, 1708, the animal specific ID, carcass ID, primal ID, and retail cut ID (aka embedding-based fingerprinting) to ensure traceability throughout the production chain and ease sortation of the abattoir data as per [0245]-[0247].
Applicant also argues that, in Morton, there is no knowledge of the meat data acquired prior to the butchery process as contrasted with the present application which can therefore measure yield. Such argument ignores [0205]-[0212] in which the entire carcass prior to the butchery process is subjected to a 3D scan. Further, Morton is not limited to X-ray CT imaging and also employs, inter alia, cameras and depth sensors (e.g. radar) to gather a multi-sensor data set of the meat object prior to the butchery process to facilitate yield calculations. See [0004], [0020], [0029], [0045], [0056], Fig. 7, [0110], [0155]-[0156], claim 9.
Drawings
The drawings are objected to because Figs. 6, 9, 10 are poor quality reproductions that do not have satisfactory reproduction characteristics contrary to 37 CFR 1.84(l). Every line, number, and letter must be durable, clean, black (except for color drawings), sufficiently dense and dark, and uniformly thick and well-defined. The fonts are too small for some items and the line quality particularly between the block diagrams is insufficient in Fig. 10. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18, 20 and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 has been amended to recite ”gathering a food data during a meat processing task on a meat object using a multi sensor device … , wherein the multi sensor device comprises cameras, depth sensors, laser, and does not comprise x-ray, CT or hyperspectral imaging”.
Independent Claim 6 has been similarly amended to “collecting food object data using a sensor… wherein the sensor consists of cameras, depth sensors, and twin-line lasers”.
Independent claim 11 recites a device to gather food data, wherein the device comprises at least one of the following: camera, depth sensor, IR emitter, laser and receiver, or load cell”.
The specification discloses a laser including a two light source laser but solely in terms of projecting cut guidance for the meat object and not for gathering or collecting food object data. In other words, the disclosed laser(s) merely project cut guidance light and are not used as a sensor. See [0032]-[0033], [0035], [0040], [0043]. Further as to claim 11, no laser receiver is disclosed and an IR emitter is not capable of gathering food data.
Furthermore, there is not disclosure of claim 6’s amended “sensor consists of cameras, depth sensors, and twin-line lasers”. As also noted below in the 112(b) rejection, it is not even clear how many cameras, depth sensors and twin line lasers are within sensor consisting of these elements. Moreover, where in the specification is there a sensor consisting of only these elements and nothing more? Also, there is no support for “twin-line lasers” particularly used for collecting food object data; [0035] merely states that the process is guided by two light source or laser”. The adjective “two” only applies to the light source and not the laser. Also, even if there are two lasers, is this a “twin-line laser”?
It is noted that part of the problematic language are negative limitations due to the use of the phrases “does not comprise” in claim 1 and “without barcodes, RFID or video tracking” of claims 1 and 6. Guidance on negative limitations may be found in MPEP 2173.05(i) stating in relevant part that:
The current view of the courts is that there is nothing inherently ambiguous or uncertain about a negative limitation. So long as the boundaries of the patent protection sought are set forth definitely, albeit negatively, the claim complies with the requirements of 35 USC 112, second paragraph … Any negative limitation or exclusionary proviso must have basis in the original disclosure….Any claim containing a negative limitation which does not have basis in the original disclosure should be rejected under 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement
Furthermore and contrary to MPEP 2163(II)(A) and 21603.04(I)(B), no disclosure has been identified by Applicant in their 09 March 2026 Reply pointing out where the amended claim language added to claims 1, 6, 11 or where the new claim 21 is supported by the application as filed. Moreover, the Examiner could not find any disclosure as filed supporting the concept of gathering or collecting food data with a laser or IR emitter. Nor is there disclosure supporting the “without barcodes, RFID or video tracking” of claims 1 and 6” or the downstream use of such food data for an embedding-based fingerprint. Still further, claim 6 embedding of food object data is not disclosed as including pictorial representations of the food object; to the contrary the pictorial representations of the food object appear to be ancillary data that is merely stored as per [0037] and not used to create the embedding such that claim 6’s embedding step, as amended, is not supported by the specification as filed.
New claim 21 includes the following steps that has not been adequately disclosed, nor has Applicant pointed to where these features are supported in the recent filing as follows: “identify and link the food object to a unique type using image characteristics of the actual food object”. The term “linked” is only used once, [0037] in the entire specification and this paragraph links food specification data 704 with the device used for capturing the data and not the food object to a “unique type”.
In conclusion, the disclosure does not reasonably convey that the inventors had possession of the subject matter expressed by the claim 1, 6, 11, or 21 claim amendments/new claim at the time of the filing of the application.
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 6-10 and 21 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 6’s embedding step is indefinite for several reasons. First, it is unclear if the term “the data” in the second line of this step is meant to refer to the “food object data” in the first line. Second, is the “pictorial representation of the food object” derived from the cameras of the sensor? Lastly, the amendment is clearly incomplete by stating “the food object that was processing in various” leaving the reader to wonder to what “various” may refer.
Claim 6 now recites “the sensor consists of cameras, depth sensors, and twin-line lasers”. The term “consists of” is closed claim language that limits the claim to only the listed elements. But it is unclear what the plural form of the sensor component lists includes. In other words, “cameras” includes at least two cameras and perhaps three or more cameras. Likewise for the depth sensors and twin-line lasers—are there two, three or more of the twin-line lasers? Such open-ended plural language conflicts sharply with the “consists of” closed claim language defining the sensor and thus leaves the claim scope uncertain and unclear.
Claim 6 also now recites “the filtering comprises an embedding-based fingerprinting that assigns and reconciles object IDs solely by learned-embedding similarity thresholds, without barcodes, RFID or video tracking”. The highlighted phrase is not understood. First of all, what is meant by “solely” and to what actions does “solely” apply? For example, does “solely” apply to the fingerprinting or to the assigning or to the reconciling or to all of these elements? What is the role played by the similarity thresholds? Are these similarity thresholds applied for assigning the object IDs? Or are they used for the reconciling? Also, what is being reconciled as reconciliation involves two entities while the claim only indefinitely refers to plural object IDs but not what potentially different meat objects to which the object IDs refer?
Claim 21 states “identify and link the food object to a unique type using image characteristics of the actual food object {It is not clear to what “unique type” refers. Does unique type refer to a unique type of food object or perhaps the unique type of sensor used to gather the food object data?
Claims 7-10 are indefinite due to their dependency upon claim 6.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5, and 11-13, 15, 20, and 21 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Morton (US 20210041378 A1)
Claim 1
In regards to claim 1, Morton discloses a method {Figs. 14-17 and cites below}, comprising:
gathering a food data during a meat processing task on a meat object using a multi sensor device continuously or discreetly, at least once during the processing task, at a user specified time or algorithmically specified time, wherein the multi sensor device comprises cameras, depth sensors, laser, and does not comprise X-ray CT or hyperspectral imaging
{Fig. 1A, 16A (copied below) including cameras 1630 and radar range finders 360 (depth/range sensors) that gathers a wide variety of food data at a user/algorithmically specified time on an inspection workstation 1612 during the inspection processing step of the meat processing task as per [0217]-[0222], [0236]-[0242] wherein the data from the sensing elements is passed in real-time to the automated cutting system such as meat cutting workstation 1650, Fig. 16B for cutting a beef carcass, as well as to human operators for AR guided meat cutting. Further as to depth sensors, see radar range/depth sensor [0029], [0032], [0087], [0242], while [0144]-[0148] discloses that various 3D surface sensing technologies may be used including point cloud optical and radar imaging sensors that acquire depth/range information. As to laser, see laser projector 1656, [0225]-[0226] while noting that the instant specification only employs laser for projecting light onto the meat for cut guidance and not for gathering food data. See also the related 112(a) rejection}
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generating a unique identifier for each meat object according to an associated specification data, wherein an identification number is created for each food object that is processed in a database, wherein the associated specification data is linked to a specific device used for capturing the data, wherein the unique identifier is an embedding-based fingerprinting that assigns or reconciles object IDs solely by learned-embedding similarity thresholds, without barcodes, RFID, or video tracking
{see Morton in which the food products (carcasses, offal, primal, retain cuts, “meat objects” etc.) are tracked throughout the meat processing plant using a unique identifier (ID) to ensure traceability and storing such embedded data in a database for quick lookup Fig. 17, [0245]-[0249]. Furthermore, the BRI of “embedding-based fingerprinting” as per the instant specification’s broad definition of “embedding” in Fig. 16, step 1606, [0055] broadly includes “a series of vectors representing characteristic features of the food object”.
Likewise, Morton discloses an “embedding” using a series of related, numerical feature vectors representing characteristic features of the food object. For example, a carcass entering an abattoir of the meat processing plant has an embedding ID of 63, and subsequently, six primals are cut from the carcass wherein said primals have associated embeddings 63:1 through 63:6. This embedding process continues such that if primal 63:2 is processed into 15 retail cuts, said cuts have embeddings 63:2.1 to 63:2.15 which is a feature vector having three dimensions representing characteristic features (traceable identities of carcass, primal, and retail cut) of the food object (meat object). Moreover, multiple carcasses, primals and retail cuts are examples of such a labelling/embedding scheme. Still further, Morton’s disclosed embedding-based fingerprinting may also include another dimension in the feature vector embedding by associating or otherwise including the date and time stamp at which a primal was cut from a carcass or a retail cut was separated from its primal.
Such embeddings (embedding-based fingerprinting ) are specifically used to provide the same traceability advantages disclosed by the instant invention. Even further, neither the claim language nor the specification broadly ties embedding-based fingerprinting to the food data or specifically uses the multi-sensor food data gathered by the cameras, depth sensors and laser to generate the embeddings.
Lastly, Morton’s number-based embedding scheme does not involve barcodes, RFID or video tracking such that Morton assigns or reconciles object IDs “without barcodes, RFID or video tracking”. Moreover, the meat tracking process of Figs. 17-19 specifically provides for using RFID tag or other ID providing element including, 1708, the animal specific ID, carcass ID, primal ID, and retail cut ID (aka embedding-based fingerprinting) to ensure traceability throughout the production chain and ease sortation of the abattoir data as per [0245]-[0247]};
providing individual feature performance for every meat object as processed data; calculating a yield
{see [0234], Fig. 16B illustrating a meat cutting workstation 1652 including a computer 1654 performing a food processing task (cutting carcass into primal beef cuts) and producing individual feature performance data including, inter alia, many of the processes of Fig. 16A such as meat grading algorithms 1618, carcass valuation algorithms 1620, production planning algorithms 1622, animal health algorithms 1624, and/or product QC and validation algorithms 1626. See also [0216] deep learning (machine learning) for quality control using computer vision for quality control and validation that individually and collectively produce processed data including individual feature performance for every meat object. See also production planning algorithms 1622, animal health algorithms 1624 and/or product QC validation algorithms 1626 as per [0218], [0222]-[0224], [0230], [0233]-[0235] including workstations 1612 which operates as a plant management dashboard providing an operator (user) of a meat plant with updates and “individual performance data” of products and processes within the plant.
Further as to yield see [0205]-[0212] in which the entire carcass prior to the butchery process is subjected to a 3D scan. Further, Morton is not limited to X-ray CT imaging and also employs, inter alia, cameras and depth sensors (e.g. radar) to gather a multi-sensor data set of the meat object prior to the butchery process to facilitate yield calculations. See also [0004], [0020], [0029], [0045], [0056], Fig. 7, [0110], [0155]-[0156], claim 9};
analyzing the processed data using a system to distinguish between a non-food data and the food data
{see Fig. 16A carcass evaluation algorithm 1620 identifies defects (non-food) such as metal contamination (e.g. fence wire, syringe needles), tumors, cysts, worms, etc. and also distinguishes between and identifies 3D spatial locations of bone structures (non-food), muscles and fat (food) as per [0227]-[00229};
generating a final data after analysis of the processed data by the system for a user
{see production planning algorithms 1622, animal health algorithms 1624 and/or product QC validation algorithms 1626 as per [0218], [0222]-[0224], [0230], [0233]-[0235] including workstations 1612 which operates as a plant management dashboard providing an operator (user) of a meat plant with updates and “final data” of products and processes within the plant}; and
producing a guided process using a guidance system to produce a protocol for a new food object processing task, wherein the guided process confirms completion of specified features against target specifications using dimensional guidance and displaying relevant results and next step to a user
{See the mapping for claim 2 below. See also the production planning algorithms 1622, animal health algorithms 1624 and product QC validation algorithms 1626 as per [0218], [0222]-[0224], [0230], [0233]-[0235] which also disclose “a guided process” and next step of a user as broadly claimed. Further as to completion against target specifications see [0224]-[0227], [0231]-[0234] and Fig. 16C including step 1670 in which production specification data including desired shape, weight and dimensions of primal and retail cuts is used to provide cut guidance protocol.}.
Claim 2
In regards to claim 2, Morton discloses wherein the guidance system implements a cut guidance protocol for a beef primal meat to comply with user requirement
{Figs. 16a including carcass production planning algorithms 1622, Fig. 16c including step 1670 receive specifications (user requirements) for desired shape, weight and dimensions of primal beef buts required from a given carcass. The guidance protocol may include, Fig. 16C step 1672 generating images illustrating a manner in which carcass should be cut, steps 1674-1676 generating and projecting an augmented reality (AR) overlay for the trimming/cutting process. The guidance may also be transmitted to an operator (steps 1680-1682) to guide an operator to cut the carcass as shown in the projected images and also receive further guidance in the form of haptic feedback to cut in the required manner to comply with user requirements as per [0226]-[0232]}
Claim 3
In regards to claim 3, Morton discloses wherein the specific algorithm uses an image data and a depth data of the food object gathered from the food data to provide a cut guidance protocol for performing the food processing task of trimming the food object {see the mapping for claim 2 while noting that the food data includes 3D X-Ray computed tomography images and/or radar range/depth sensor [0029], [0032], [0087], [0242], while [0144]-[0148] discloses that various 3D surface sensing technologies may be used including point cloud optical and radar imaging sensors that acquire depth/range information that comprise image data and depth data of the carcass to provide a cut guidance protocol as claimed.}.
Claim 5
In regards to claim 5, Morton discloses wherein the specific algorithm uses an image data and a depth data of the food object to produce the food data and a production data is included to provide a cut guidance protocol for performing the food processing task of trimming the food object
{see the mapping for claims 1-3 while noting that the food data includes 3D X-Ray computed tomography images and/or radar range/depth images that comprise image data and depth data of the carcass to provide a cut guidance protocol as claimed. See also Fig. 16C step 1670 in which production specification data including desired shape, weight and dimensions of primal and retail cuts is used to provide cut guidance protocol.}.
Claims 11-13, 15, and 18
The rejection of method claims 1-3, and 5, and 10 above applies mutatis mutandis to the corresponding limitations of system claims 11-13, 15, and 18 respectively while noting that the rejection above cites to both device and method disclosures. Independent claim 11 additionally recites that the food processing task is performed “automatically or manually” using a processing station both of which limitations are fully met by Morton and Allman as per the mapping of claim 2 which also addresses the guidance system (Fig. 16B including AR projector 1656, active viewer 1660 and/or haptic feedback 1658 as discussed above). Further as to the process of claim 11 see Fig. 16A, 16B and computer 1654.
Claim 20
In regards to claim 20, Morton/Allman discloses wherein the cut guidance protocol can be used by displaying results in augmented reality form, overlay a trimming process on the food object at the processing station, and human machine interface output {see mapping for claim 2}.
Claim 21
In regards to claim 21, Morton/Allman discloses a method, comprising:
gathering food data during a food processing task on a food object using a muti-sensor device, gathering the food data continuously or discreetly and at least once during the processing task at a user specified time or an algorithmically specified time, wherein the food data can include position and orientation data;
{Fig. 1A, 16A (copied below) including cameras 1630 and radar range finders 360 (depth/range sensors) that gathers a wide variety of food data at a user/algorithmically specified time on an inspection workstation 1612 during the inspection processing step of the meat processing task as per [0217]-[0222], [0236]-[0242] wherein the data from the sensing elements is passed in real-time to the automated cutting system such as meat cutting workstation 1650, Fig. 16B for cutting a beef carcass, as well as to human operators for AR guided meat cutting. Further as to depth sensors, see radar range/depth sensor [0029], [0032], [0087], [0242], while [0144]-[0148] discloses that various 3D surface sensing technologies may be used including point cloud optical and radar imaging sensors that acquire depth/range information. Note that “can include” is optional claim language in which the food can but does not necessarily include position and orientation data. Further, Morton tracks the location (position) as per [0068], [0073]-[0075] and orientation as per [0236]}};
confirm if the food processing task has been completed to achieve a prescribed specifications to target metrics and the degree of variance between actual specifications and target achievement of the food item utilizing user input, gathered food data, or both user input and gather food data and at least one food processing algorithm
{see [0234], Fig. 16B illustrating a meat cutting workstation 1652 including a computer 1654 confirming a food processing task completion (cutting carcass into primal beef cuts) and producing individual feature performance data including, inter alia, many of the processes of Fig. 16A such as meat grading algorithms 1618, carcass valuation algorithms 1620, production planning algorithms 1622, animal health algorithms 1624, and/or product QC and validation algorithms 1626. See also [0216] deep learning (machine learning) for quality control using computer vision for quality control and validation that individually and collectively produce processed data including individual feature performance for every meat object. See also production planning algorithms 1622, animal health algorithms 1624 and/or product QC validation algorithms 1626 as per [0218], [0222]-[0224], [0230], [0233]-[0235] including workstations 1612 which operates as a plant management dashboard providing an operator (user) of a meat plant with updates and “individual performance data” of products and processes within the plant.
Further as to yield see [0205]-[0212] in which the entire carcass prior to the butchery process is subjected to a 3D scan. Further, Morton is not limited to X-ray CT imaging and also employs, inter alia, cameras and depth sensors (e.g. radar) to gather a multi-sensor data set of the meat object prior to the butchery process to facilitate yield calculations. See also [0004], [0020], [0029], [0045], [0056], Fig. 7, [0110], [0155]-[0156], claim 9},
wherein the food processing algorithm includes food object data and non-food object data
{see Fig. 16A carcass evaluation algorithm 1620 identifies defects (non-food) such as metal contamination (e.g. fence wire, syringe needles), tumors, cysts, worms, etc. and also distinguishes between and identifies 3D spatial locations of bone structures (non-food), muscles and fat (food) as per [0227]-[00229};
identify and link the food object to a unique type using image characteristics of the actual food object and generating an embedding utilizing user input, gathered food data, or both user input and gather food data and at least one food processing algorithm to, wherein the embedding includes a series of vectors representing characteristics features of the food object
{see Morton in which the food products (carcasses, offal, primal, retain cuts, “meat objects” etc.) are tracked throughout the meat processing plant using a unique identifier (ID) to ensure traceability and storing such embedded data in a database for quick lookup Fig. 17, [0245]-[0249]. Furthermore, the BRI of “embedding-based fingerprinting” as per the instant specification’s broad definition of “embedding” in Fig. 16, step 1606, [0055] broadly includes “a series of vectors representing characteristic features of the food object”.
Likewise, Morton discloses an “embedding” using a series of related, numerical feature vectors representing characteristic features of the food object. For example, a carcass entering an abattoir of the meat processing plant has an embedding ID of 63, and subsequently, six primals are cut from the carcass wherein said primals have associated embeddings 63:1 through 63:6. This embedding process continues such that if primal 63:2 is processed into 15 retail cuts, said cuts have embeddings 63:2.1 to 63:2.15 which is a feature vector having three dimensions representing characteristic features (traceable identities of carcass, primal, and retail cut) of the food object (meat object). Moreover, multiple carcasses, primals and retail cuts are examples of such a labelling/embedding scheme. Still further, Morton’s disclosed embedding-based fingerprinting may also include another dimension in the feature vector embedding by associating or otherwise including the date and time stamp at which a primal was cut from a carcass or a retail cut was separated from its primal.
Such embeddings (embedding-based fingerprinting ) are specifically used to provide the same traceability advantages disclosed by the instant invention. Even further, neither the claim language nor the specification broadly ties embedding-based fingerprinting to the food data or specifically uses the multi-sensor food data gathered by the cameras, depth sensors and laser to generate the embeddings.
Lastly, Morton’s number-based embedding scheme does not involve barcodes, RFID or video tracking such that Morton assigns or reconciles object IDs “without barcodes, RFID or video tracking”. Moreover, the meat tracking process of Figs. 17-19 specifically provides for using RFID tag or other ID providing element including, 1708, the animal specific ID, carcass ID, primal ID, and retail cut ID (aka embedding-based fingerprinting) to ensure traceability throughout the production chain and ease sortation of the abattoir data as per [0245]-[0247]};
providing a user dimensional guidance to utilizing user input, gathered food data, or both user input and gather food data user input to ensure certain key butchery features are met, wherein the dimensional guidance confirms completion of the actual specifications against the target specifications using dimensional guidance and displaying relevant results and a next step to a user
{See the mapping for claim 2 above. See also the production planning algorithms 1622, animal health algorithms 1624 and product QC validation algorithms 1626 as per [0218], [0222]-[0224], [0230], [0233]-[0235] which also disclose “user dimensional guidance” and next step of a user as broadly claimed. Further as to completion against target specifications see [0224]-[0227], [0231]-[0234] and Fig. 16C including step 1670 in which production specification data including desired shape, weight and dimensions of primal and retail cuts is used to provide cut guidance protocol.}.
Claims 1-5, 11-15, 20, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Allman (US 2024/0402098 A1).
Initially, it is recognized that Allman’s earliest effective filing date (EEFD) of 5 June 2023 is between the instant application’s provisional filing date of 20 September 2022 and the non-provisional filing date of 18 September 2023. Nevertheless, the instant provisional application (serial number 63/408,355) does not provide adequate written description support for at least claims 4 and 14. As such, at least claims 4 and 14 only enjoy priority to the non-provisional application which is after Allman’s (EEFD).
Allman is also a related patent application filed by the same Applicant, Rapidscan Holdings, Inc., as Morton and includes the same corresponding disclosure as Morton that is mapped above with Allman being an improvement patent application that includes, inter alia, more extensive machine learning disclosure. As such, all of the above citations and explanations regarding Morton including matching Figs. 1-19 and their corresponding descriptions thereof are hereby incorporate by reference as to Allman regarding claims 1-3, 5, and 11-13, 15, 20, and 21).
Claims 4 and 14
In regards to claims 4 and 14, Allman discloses wherein the specific algorithm uses an image data, a depth data of the food object from the food data and a machine learning algorithm is applied to identify an unidentified food object to provide a cut guidance protocol for performing the food processing task of trimming the food object.
{see the mapping for claim 2 while noting that the food data includes 3D X-Ray computed tomography images that comprise image data and depth data of the carcass which is used by an algorithm that identifies muscles, fat and bone structure as well as red offal and green offal to provide the cut guidance protocol as further discussed in ;
Morton [0226]-[0232] and identically in Allman [0287]-[0392].
Further as to machine learning algorithm see Fig. 32 Discriminator 3202 that uses image data and depth data of the food object and a machine learning algorithm to identify unidentified food objects (identify an unidentified food object including anomalous pixels such as unhealthy meat, defects) as discussed in the Deep Learning Network Classification and related sections in [0362]-[0379], [0317], [0384]-[0390].
See also [0226]-[0232] of Morton and [0287]-[0292] of Allman in which the identified 3D spatial location of defects, bone, muscles and health defects within carcasses and primals are used to drive automated cutting equipment and to direct human operators. See also the mapping of claim 2 above.}
Claim 6-7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over (Morton or Allman) and Gonzalez {DIBET GONZALEZ: "Automated Vision System for Cutting Fixed-weight or Fixed-length Frozen Fish Portions ", PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS, SCITEPRESS - SCIENCE AND TECHNOLOGY PUBLICATIONS, 1 January 2019 (2019-01-01) - 21 February 2019 (2019-02-21), pages 707 - 714, XP093157081, ISBN: 978-989-7583-51-3, DOI: 10.5220/0007482407070714}.
Claim 6
In regards to claim 6, Morton/Allman both discloses a method, comprising:
collecting a food object data using a sensor continuously or discreetly at least one of a user specified time or algorithmically specified time on a processing station of a food object, wherein the sensor consists of cameras, depth sensors, and twin-line lasers
{cameras 1630 and radar range finders 360 (depth/range sensors) gather f food data at a user/algorithmically specified time on an inspection workstation 1612 during the inspection processing step of the meat processing task as per [0217]-[0222], [0236]-[0242] wherein the data from the sensing elements is passed in real-time to the automated cutting system such as meat cutting workstation 1650, Fig. 16B for cutting a beef carcass, as well as to human operators for AR guided meat cutting.
Further as to depth sensors, see radar range/depth sensor [0029], [0032], [0087], [0242], while [0144]-[0148] discloses that various 3D surface sensing technologies may be used including point cloud optical and radar imaging sensors that acquire depth/range information. As to laser, see laser projector 1656, [0225]-[0226] while noting that the instant specification only employs laser for projecting light onto the meat for cut guidance and not for gathering food data.
See also the 112(a) rejection of collecting food object data using a twin-line laser. See also the 112(b) rejection of the “sensor”};
embedding the food object data with a unique identifier, wherein the unique identifier is linked to a specific device used for capturing the data; wherein the data comprises a pictorial representation of the food object that was processed in various; and storing the unique identifier in a database as an embedded data specific for the food object before transforming the food object;
{see Morton in which the food products (carcasses, offal, primal, retain cuts, “meat objects” etc.) are tracked throughout the meat processing plant using a unique identifier (ID) to ensure traceability and storing such embedded data in a database for quick lookup Fig. 17, [0245]-[0249]. Furthermore, the BRI of “embedding” as per the instant specification’s broad definition of “embedding” in Fig. 16, step 1606, [0055] broadly includes “a series of vectors representing characteristic features of the food object”.
Likewise, Morton discloses an “embedding” using a series of related, numerical feature vectors representing characteristic features of the food object. For example, a carcass entering an abattoir of the meat processing plant has an embedding ID of 63, and subsequently, six primals are cut from the carcass wherein said primals have associated embeddings 63:1 through 63:6. This embedding process continues such that if primal 63:2 is processed into 15 retail cuts, said cuts have embeddings 63:2.1 to 63:2.15 which is a feature vector having three dimensions representing characteristic features (traceable identities of carcass, primal, and retail cut) of the food object (meat object). Moreover, multiple carcasses, primals and retail cuts are examples of such a labelling/embedding scheme. Still further, Morton’s disclosed embedding-based fingerprinting may also include another dimension in the feature vector embedding by associating or otherwise including the date and time stamp at which a primal was cut from a carcass or a retail cut was separated from its primal.
Such embeddings are specifically used to provide the same traceability advantages disclosed by the instant invention. Even further, neither the claim language nor the specification broadly ties embedding to the food data or specifically uses the multi-sensor food data gathered by the cameras, depth sensors and laser to generate the embeddings.
Further as to “the data comprises a pictorial representation of the food object that was processed in various”, see the 112(b) rejection above. Morton also employs cameras (e.g. cameras 1630) to capture a pictorial representation of the food object as cited above before the meat is cut (see response to arguments section above) and also in Fig. 16A, [0218], [0224]. [0236]-[0242]};
filtering the food object data gathered using a food object
{see the BRI of embedding and embedding-based fingerprinting above in claims 1 and 6. See also the 112(b) rejection. Further as to reconcile object IDS, Moton’s embeddings/object IDs are used to provide traceability of each meat object which is a process that assigs and reconciles the object IDs as cited above and in Figs. 17-18, [0243]-[0249].
Moreover, Morton’s number-based embedding scheme does not involve barcodes, RFID or video tracking such that Morton assigns or reconciles object IDs “without barcodes, RFID or video tracking”. Moreover, the meat tracking process of Figs. 17-19 specifically provides for using RFID tag or other ID providing element including, 1708, the animal specific ID, carcass ID, primal ID, and retail cut ID (aka embedding-based fingerprinting) to ensure traceability throughout the production chain and ease sortation of the abattoir data as per [0245]-[0247]};
performing a food processing task for a food object using the food data generated by the device using a specific software algorithm, a computer vision and machine learning algorithm residing in a processor to produce a processed data
{See Fig. 16B copied below illustrating a meat cutting workstation 1652 including a computer 1654 performing a food processing task (cutting carcass into primal beef cuts) using a specific software algorithm, a computer vision and machine learning algorithm. The broadly-worded software algorithm, computer vision and machine learning algorithm to produce processed data include, inter alia, many of the processes of Fig. 16A including meat grading algorithms 1618, carcass valuation algorithms 1620, production planning algorithms 1622, animal health algorithms 1624, and/or product QC and validation algorithms 1626. See also [0216] deep learning (machine learning) for quality control using computer vision for quality control and validation that individually and collectively produce processed data. See also the guidance protocol mapping below and in claim 2};
generating a final data after analysis of the processed data by the system for a user
{see production planning algorithms 1622, animal health algorithms 1624 and/or product QC validation algorithms 1626 as per [0218], [0222]-[0224], [0230], [0233]-[0235] including workstations 1612 which operates as a plant management dashboard providing an operator (user) of a meat plant with updates and “final data” of products and processes within the plant}; and
producing a guided process from the final data to produce a protocol for a new food object processing task, wherein the guided process confirms completion and refrains from generating cut paths
{See the mapping for claim 2 below. See also the production planning algorithms 1622, animal health algorithms 1624 and product QC validation algorithms 1626 as per [0218], [0222]-[0224], [0230], [0233]-[0235] which also disclose “a guided process confirms completion and refrains from generating cut paths” as broadly claimed.}.
Gonzalez is a highly analogous method and system in the same field of a guidance system for a food processing task protocol and solves the same problem of optimizing the cutting protocol based on machine vision. See abstract, section 3.3 fish segmentation and 3.4 fish cut optimization strategies.
Gonzalez also teaches filtering of the food object data gathered using a food object and non-food object to produce a filtered food object data {see section 3.1 Data Acquisition and Preprocessing performing highly conventional pre-processing that removes/filters information that does not belong to the food (e.g. foreign objects and the conveyor belt background pixels to produce a filtered food object data that includes only the fish.
It 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 to have modified Morton/Allman which already discloses wherein the specific algorithm uses an image data, a depth data of the food object from the food data and algorithm is applied to identify an unidentified food object to provide a cut guidance protocol for performing the food processing task of trimming the food object such that Morton’s/Allman’s process includes a pre-processing step of filtering of the food object data gathered using a food object and non-food object to produce a filtered food object data because such filtering removes pixels that are non-food object pixels such that the downstream processing can work more efficiently by processing fewer pixels, because removing non-food pixels also increases the accuracy of the meat cutting guidance determinations, because there is a reasonable expectation of success in that such pre-processing is routinely and conventionally performed to provide a focused region of interest suitable for downstream processing, and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 7
The rejection of method claim 3 above applies mutatis mutandis to the corresponding limitations of system claim 7.
Claim 10
In regards to claim 10, Morton/Allman discloses wherein the food processing tasks includes trimming, cutting, slicing, dicing, pealing, deboning, freezing, packing, compressing, moving, or any combinations thereof {see above mappings for claim 1 and 2}.
Claims 8, 9, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Allman and Gonzalez.
Further with respect to base antecedent claim 6 Allman teaches
embedding the food object data with a unique identifier and store in a database as an embedded data specific for the food object before transforming the food object
{see the mapping of claims 1 and 6 above regarding embedding. The food products (carcasses, offal, primal, retain cuts, etc.) are tracked throughout the meat processing plant using various technologies such as barcodes and video tracking as per [0234], [0240]-[0244], Fig. 17, [0245]-[0249] including providing each such food product a unique identifier (ID) to ensure traceability and storing such embedded data in a database for quick lookup. As to before transforming the food object see the response to arguments section above regarding gathering food object data of the carcass before meat processing};
Claims 8 and 9
Allman also discloses comparing an old embedded data to a newly generated embedded data to identify the food object (claim 8) and (claim 9) wherein if the newly generated embedded data is similar to the old embedded data, the unique identifier for the old and the newly generated embedded data are set to the same value in a database
{see K-means clustering [0367]-[0376] in which similar pixel vector (embeddings) will have the same label (unique identifier set to be the same in database) if they are similar using a distance metric}.
Claims 16-18
The rejection of method claims 8-10 above applies mutatis mutandis to the corresponding limitations of system claims 16-18 while noting that the rejection above cites to both device and method disclosures.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
SHUAI MU: "Robotic 3D Vision-Guided System for Half-Sheep Cutting Robot", MATHEMATICAL PROBLEMS IN ENGINEERING, GORDON AND BREACH PUBLISHERS , BASEL, CH, vol. 2020, 5 October 2020 (2020-10-05), CH , pages 1 - 11, XP093157092, ISSN: 1024-123X, DOI: 10.1155/2020/1520686 discloses automated meat cutting including semantic segmentation and calculation of cutting curves. See Fig. 1 copied below.
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US 12193450 B1 discloses a meat cutting guidance system that employs identification of structures using templates (embeddings). See Fig. 1 A copied below.
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Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL ROBERT CAMMARATA/Primary Examiner, Art Unit 2667
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