DETAILED ACTION
Status of Claims
This action is in reply to the application filed on 22 November 2024. This communication is the first action on merits. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are original / previously presented.
Claims 1-20 are currently pending and have been examined.
Priority
This application 18/956426 filed on 22 November 2024 claims priority from US provisional application 63/602203 filed on 22 November 2023.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed on 15 May 2025 has been acknowledged by the Office.
Claim Interpretation
Claims 15-20:
The computer-readable medium in claims 15-20 is being interpreted as non-transitory computer readable medium based on the Applicant Specification ¶[0084] “However, the computer-readable storage medium may not be a transitory medium”. This interpretation precludes claims 15-20 from being considered non-statutory subject matter for being directed towards a transitory propagating signal. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20:
Step 1:
Claims 1-7 recite a method; and claims 8-14 recite a system. Claims 15-20 recite a computer readable medium storing computer-executable instructions, however the Applicant specification limits a computer readable medium as non-transitory in ¶[0084] stating “However, the computer-readable storage medium may not be a transitory medium”. Since the claims recite either a process, machine, manufacture, or composition of matter, the claims satisfy Step 1 of the Subject Matter Eligibility Framework in MPEP 2106 and the 2019 Patent Examination Guidelines (PEG).
Step 2A – Prong One:
Claim(s) 1-20 recite an abstract idea. Independent claim 1 recites: receiving, from a user, a request for a recommendation for packing an item, wherein the request identifies reference dimensions for the reference item; processing, the first image and the second image to: generate a first bounding box around the reference item and a second bounding box around the item in the first image, generate a third bounding box around the reference item and a fourth bounding box around the item in the second image, and generate a prediction of a label for the item based at least in part on a texture of the item detected from at least one of the first image or the second image; comparing, based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item; processing, using a rules-based model, the prediction of the label for the item and the dimensions to generate the recommendation for packing the item, wherein the recommendation identifies a recommended package type to use in packing the item; and communicating, the recommendation to the user. Independent claim 8 recites: receiving, from a user, a request for a recommended package type for an item, wherein the request identifies reference dimensions for the reference item; receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, process[ing] the first image and the second image to: generate the first bounding box around the reference item and the second bounding box around the item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the item in the second image; comparing, based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item; processing, using a rules-based model, the dimensions to generate the recommended package type to use in packing the item; and communicating the recommended package type to the user. Independent claim 15 recites: receiving, from a user, a request for a recommended package type for a first item and a second item; receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, a fifth bounding box, a sixth bounding box, seventh bounding box, and an eighth bounding box, process[ing] the first image, the second image, the third image, and the fourth image to: generate the first bounding box around the reference item and the second bounding box around the first item in the first image, generate the third bounding box around the reference item and the fourth bounding box around the first item in the second image, generate the fifth bounding box around the reference item and the sixth bounding box around the second item in the third image, and generate the seventh bounding box around the reference item and the eighth bounding box around the second item in the fourth image; comparing, based at least in part on reference dimensions for the reference item, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate first dimensions of the first item; comparing, based at least in part on the reference dimensions for the reference item, the fifth bounding box to the sixth bounding box and the seventh bounding box to the eighth bounding box to generate second dimensions of the second item; processing the first dimensions and the second dimensions to generate the recommended package type to use in packing the first item and the second item together; and communicating the recommended package type to the user. The claims as a whole recite certain methods of organizing human activities.
First, the limitations of (claim 1) receiving, from a user a request for a recommendation for packing an item, wherein the request identifies reference dimensions for the reference item; processing, the first image and the second image to: generate a first bounding box around the reference item and a second bounding box around the item in the first image, generate a third bounding box around the reference item and a fourth bounding box around the item in the second image, and generate a prediction of a label for the item based at least in part on a texture of the item detected from at least one of the first image or the second image; comparing, based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item; processing, using a rules-based model, the prediction of the label for the item and the dimensions to generate the recommendation for packing the item, wherein the recommendation identifies a recommended package type to use in packing the item; and communicating, the recommendation to the user; (claim 8) receiving, from a user a request for a recommended package type for an item, wherein the request identifies reference dimensions for the reference item; receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, process[ing] the first image and the second image to: generate the first bounding box around the reference item and the second bounding box around the item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the item in the second image; comparing, based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item; processing, using a rules-based model, the dimensions to generate the recommended package type to use in packing the item; and communicating the recommended package type to the user; (claim 15) receiving, from a user a request for a recommended package type for a first item and a second item; receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, a fifth bounding box, a sixth bounding box, seventh bounding box, and an eighth bounding box, process[ing] the first image, the second image, the third image, and the fourth image to: generate the first bounding box around the reference item and the second bounding box around the first item in the first image, generate the third bounding box around the reference item and the fourth bounding box around the first item in the second image, generate the fifth bounding box around the reference item and the sixth bounding box around the second item in the third image, and generate the seventh bounding box around the reference item and the eighth bounding box around the second item in the fourth image; comparing, based at least in part on reference dimensions for the reference item, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate first dimensions of the first item; comparing, based at least in part on the reference dimensions for the reference item, the fifth bounding box to the sixth bounding box and the seventh bounding box to the eighth bounding box to generate second dimensions of the second item; processing the first dimensions and the second dimensions to generate the recommended package type to use in packing the first item and the second item together; and communicating the recommended package type to the user are certain methods of organizing human activities. For instance, these limitations represent the sub-groupings of commercial interactions, managing personal behavior or relationships or interactions between people, and following rules or instructions. For example, commercial interactions includes receiving a user request for a recommended package type…, processing images to generate bounding boxes around items…, receiving identification of bounding boxes…, detecting texture of the item from an image…, generate a prediction of a label for the item…, comparing reference dimensions and bounding boxes to generate item dimensions…, processing prediction of label / dimensions to generate recommended package type…, communicating recommended package type to the user; managing personal behavior or relationships or interactions between people includes receiving a user request for a recommended package type…, processing images to generate bounding boxes around items…, receiving identification of bounding boxes…, detecting texture of the item from an image…, generate a prediction of a label for the item…, comparing reference dimensions and bounding boxes to generate item dimensions…, processing prediction of label / dimensions to generate recommended package type…, communicating recommended package type to the user; and following rules or instructions includes receiving a user request for a recommended package type…, processing images to generate bounding boxes around items…, detecting texture of the item from an image…, generate a prediction of a label for the item…, comparing reference dimensions and bounding boxes to generate item dimensions…, processing prediction of label / dimensions to generate recommended package type…, communicating recommended package type to the user.
The presence of generic computer components such as computing hardware, a machine learning model, user computing device, non-transitory computer readable medium / computer readable medium, processing device does not preclude the steps from reciting certain methods of organizing human activities, since the number of people involved in the activities is not dispositive as to whether a claim limitation falls within this grouping and instead it is based on whether an activity itself falls within one of the sub-groupings. If a claim limitation, under its broadest reasonable interpretation, covers certain methods of organizing human activity (e.g. commercial (or legal) interactions, managing personal behavior or relationships or interactions between people, following rules or instructions) regardless of the recitation of generic computer components or other machinery in its ordinary capacity, then it falls within the ‘Certain Methods of Organizing Human Activity’ grouping of abstract ideas.
Accordingly, the claims recite an abstract idea. Analysis proceeds to Step 2A Prong Two.
Step 2A – Prong Two:
This judicial exception is not integrated into a practical application. First, claims 1, 8, and 15 as a whole merely describes how to generally ‘apply’ the concept of certain methods of organizing human activities in a computer environment. The claimed computer components (i.e. computing hardware, a machine learning model (computing system, see Applicant Specification ¶[0052]), user computing device, non-transitory computer readable medium / computer readable medium, processing device) are recited at a high-level of generality and are merely invoked as tools to perform an existing manual process. Simply implementing the abstract idea on a generic / general purpose computer is not a practical application of the abstract idea. See MPEP 2106.04(d) and 2016.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Next, the additional element of receiving images and its steps of receiving, by computing hardware and from a user computing device… a first image of a top view of the item and a reference item, and a second image of a front view of the item and the reference item; receiving, from a user computing device… a first image of a top view of the first item and a reference item, a second image of a front view of the first item and the reference item, a third image of a top view of the second item and the reference item, and a fourth image of a front view of the second item and the reference item are recited at a high level of generality (i.e. as a general means of gathering data for subsequent generating / comparing), and amounts to mere data gathering, which is a form of insignificant extra-solution activity and not a practical application. See MPEP 2106.04(d) and 2106.05(g). Furthermore, the computing hardware and computing device (generic computers / general computer components) are only being used as a tool in the receiving, which is also not indicative of integration into a practical application. See MPEP 2106.04(d) and 2106.05(f). Note that there are no particular technical steps regarding receiving more than using computers as a tool to perform an otherwise manual process (e.g. obtaining data). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Next, the additional element of machine learning in the limitations (e.g. processing, by the computing hardware, the first image and the second image using at least one machine learning model to: generate a first bounding box around the reference item and a second bounding box around the item in the first image…; wherein at least one machine learning model processes the first image, the second image, the third image, and the fourth image to: generate the first bounding box…) does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. AI, machine learning), and as such does not provide integration into a practical application. See MPEP 2106.04(d) and 2106.05(h). Note there are no technical steps regarding how the machine learning model is performing the tasks. Hence, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Also, while identified above as an organizing human activity in Step 2A Prong One, note that the step of receiving a request (e.g. receiving, by computing hardware and from a user computing device, a request for a recommendation for packing an item; receiving, from a user computing device, a request for a recommended package type for a first item and a second item) is/are recited at a high level of generality (i.e. as a general means of gathering data for subsequent generating / comparing), and also amounts to mere data gathering, which is a form of insignificant extra-solution activity and not a practical application. See MPEP 2106.04(d) and 2106.05(g). Furthermore, the computing hardware, user computing device (generic computers / general computer components) are only being used as a tool in the receiving, which is also not indicative of integration into a practical application. See MPEP 2106.04(d) and 2106.05(f). Note that there are no particular technical steps regarding receiving more than using computers as a tool to perform an otherwise manual process (i.e. obtaining a request). Accordingly, this element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Also, while identified above as an organizing human activity in Step 2A Prong One, note that the step of communicating the recommendation (e.g. communicating, by the computing hardware, the recommendation to the user computing device; communicating the recommended package type to the user computing device) is/are recited at a high level of generality (i.e. as a general means of outputting data results of the generated recommendation), and also amounts to mere transmitting data / outputting data, which is a form of insignificant extra-solution activity and not a practical application. See MPEP 2106.04(d) and 2106.05(g). Furthermore, the computing hardware, user computing device (generic computers / general computer components) are only being used as a tool in the communicating, which is also not indicative of integration into a practical application. See MPEP 2106.04(d) and 2106.05(f). Note that there are no particular technical steps regarding receiving more than using computers as a tool to perform an otherwise manual process (i.e. sharing information). Accordingly, this element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The combination of these additional elements is no more than mere instructions to apply the exception using generic computers / general computer components (computing hardware, a machine learning model (computing system, see Applicant Specification ¶[0052]), user computing device, non-transitory computer readable medium / computer readable medium, processing device), generally applying the judicial exception with a technology / field of use (machine learning); and adding high-level extra-solution and/or post-solution activities (data gathering, transmitting data). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Hence, the claim is directed to an abstract idea. Analysis proceeds to Step 2B.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above in Step 2A Prong Two with respect to integration of the abstract idea into a practical application, the additional element of using computing hardware, a machine learning model (computing system, see Applicant Specification ¶[0052]), user computing device, non-transitory computer readable medium / computer readable medium, processing device to perform receiving a user request for a recommended package type…, processing images to generate bounding boxes around items…, receiving identification of bounding boxes…, detecting texture of the item from an image…, generate a prediction of a label for the item…, comparing reference dimensions and bounding boxes to generate item dimensions…, processing prediction of label / dimensions to generate recommended package type…, communicating recommended package type to the user amounts to no more than mere instructions to ‘apply’ the exception using generic computers. The same analysis applies here in Step 2B, i.e. mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f). Hence, these features do not provide an inventive concept / significantly more.
As discussed above in Step 2A Prong Two with respect to integration of the abstract idea into a practical application, the additional elements regarding the receiving images are recited at a high level of generality (i.e. as a general means of gathering data for subsequent generating / comparing), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The same analysis applies here in Step 2B, i.e. adding insignificant extra-solution activity to the judicial exception does not provide integration into a practical application in Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(g). The use of the computers (i.e. computing hardware, user computing device) in these steps merely represents using generic / general purpose computers as a tool, and is not indicative of an inventive concept. See MPEP 2106.05(f). Furthermore, these receiving images steps are also claimed at a high level of generality, and/or as insignificant extra-solution activities (e.g. data gathering) representing computer functions that the courts have recognized as well-understood, routine, and conventional functions that do not present an inventive concept. See MPEP 2106.05(d)(II) in particular receiving or transmitting data over a network (Symantec), using a telephone for image transmission (TLI Communications), sending messages over a network (OIP Techs), a computer receives and sends information over a network (buySAFE). Hence, these features do not provide an inventive concept / significantly more.
As discussed above in Step 2A Prong Two with respect to integration of the abstract idea into a practical application, the additional element regarding machine learning does no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. AI, machine learning). The same analysis applies here in Step 2B, i.e. generally linking the use of the judicial exception to a particular technological environment or field of use does not provide integration into a practical application in Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(h). The recitation of machine learning in the claims amounts to results-based language without any technical steps to show how machine learning or machine learning technology achieves the claimed results (e.g. generating bounding boxes, generating labels). Furthermore, see the Applicant’s specification ¶[0072-76] describing the additional element of machine learning and example machine learning models at a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe technical particulars to satisfy 35 USC 112(a). Hence, these features do not provide an inventive concept / significantly more.
Also, as discussed above in Step 2A Prong Two with respect to integration of the abstract idea into a practical application, the Step 2A Prong One organizing human activity elements regarding receiving a request are recited at a high level of generality (i.e. as a general means of gathering data for subsequent generating / comparing), and also amounts to the extra-solution activity of data gathering, which is not a practical application or an inventive concept. See MPEP 2106.05(g). The use of the computers (i.e. computer hardware, user computing device) in these steps merely represents using generic / general purpose computers as a tool, and is not indicative of an inventive concept. See MPEP 2106.05(f). Furthermore, these receiving steps are also claimed at a high level of generality, and/or as insignificant extra-solution activities (e.g. data gathering) representing computer functions that the courts have recognized as well-understood, routine, and conventional functions that do not present an inventive concept. See MPEP 2106.05(d)(II) in particular receiving or transmitting data over a network (Symantec), sending messages over a network (OIP Techs), a computer receives and sends information over a network (buySAFE). Hence, these features do not provide an inventive concept / significantly more.
Also, as discussed above in Step 2A Prong Two with respect to integration of the abstract idea into a practical application, the Step 2A Prong One organizing human activity elements regarding communicating the recommendation are recited at a high level of generality (i.e. as a general means of outputting data results of the generated recommendation), and also amounts to the extra-solution activity of outputting / transmitting data, which is not a practical application or an inventive concept. See MPEP 2106.05(g). The use of the computers (i.e. computer hardware, user computing device) in these steps merely represents using generic / general purpose computers as a tool, and is not indicative of an inventive concept. See MPEP 2106.05(f). Furthermore, these communicating steps are also claimed at a high level of generality, and/or as insignificant extra-solution activities (e.g. data gathering) representing computer functions that the courts have recognized as well-understood, routine, and conventional functions that do not present an inventive concept. See MPEP 2106.05(d)(II) in particular receiving or transmitting data over a network (Symantec), sending messages over a network (OIP Techs), a computer receives and sends information over a network (buySAFE). Hence, these features do not provide an inventive concept / significantly more.
The claims do not improve another technology or technical field. Instead the claims represent a generic implementation of organizing human activities ‘applied’ by generic / general purpose computers, generally ‘applied’ to a field of use, and using general computer components in extra-solution capacities such as data gathering and transmitting data. The claims do not provide meaningful limitations beyond generally linking the user of an abstract idea to a particular technological environment. At best, the claims are more directed towards solving a business / economic / entrepreneurial problem (i.e. how to package an item/items), that is tangentially associated with a technology element (e.g. computers, machine learning), rather than solving a technology based problem. See MPEP 2106.05(a). The claims do not improve the functioning of a computer itself. The claims do not improve the functioning of machine learning itself. The claims are more directed towards improving a business / economic / entrepreneurial process rather than improving a computer outside of a business use, i.e. using computers a tool. The claims do not apply the judicial exception with or by use of a particular machine. The claims do not effect a transformation or reduction to a particular article to a different state or thing. The claims do not add a specific limitation other than what is well understood, routine, and conventional in a way that confines the claim to a particular useful application.
Viewing the claim limitations as an ordered combination does not add anything further than looking at each of the claim limitations individually, both with respect to the independent claims 1, 8, 15, and further considering the addition of dependent claims 2-7, 9-14, and 16-20. Note that the combination of limitations and claim elements add nothing that is not already present when the steps are considered separately, simply reciting implementation as performed by using generic computers / general computer components, see Alice (2014), and does not provide a non-conventional and non-generic arrangement of various computer components to achieve a technical improvement, see BASCOM Global Internet v. AT&T Mobility LLC (2016). Hence, the ordered combination of elements does not provide significantly more. With respect to the dependent claims:
Dependent claims 2 and 12: First, the limitation wherein the dimensions comprise a width, a height, and a depth of the item, merely narrow the previously recited abstract idea limitations. Second, the limitations comparing the first bounding box to the second bounding box involves deriving the width and the height of the item, and comparing the third bounding box to the second bounding box involves deriving the depth of the item are further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claims 3 and 13: The limitation wherein the rules-based model applies a set of rules based at least in part on package dimensions for each package type of a plurality of package types to the dimensions of the item to identify the recommended package type to use in packing the item is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 4: The limitation wherein the prediction of the label identifies the item as fragile or non-fragile merely narrows the previously recited abstract idea limitations. For the reasons described above with respect to the independent claims, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Dependent claim 5: The limitation wherein the prediction of the label identifies the item as fragile, and the recommendation identifies a recommended filler material to use in packing the item in the recommended package type merely narrows the previously recited abstract idea limitations. For the reasons described above with respect to the independent claims, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Dependent claim 6: The limitation wherein the rules-based model applies a set of rules based at least in part on using different types of filler material with respect to the recommended package type and the dimensions to identify the recommended filler material from the different types of filler material is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 7: First, the limitation wherein the request comprises a description of the item, the recommendation comprises an estimated cost for shipping the item, merely narrows the previously recited abstract idea limitations. Second, the limitations generating, by the computing hardware and based at least in part on the description and the dimensions, an estimated weight of the item; and generating, by the computing hardware and based at least in part on the estimated weight, the estimated cost are further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. The recitation of computing hardware is a computer component recited at a high level of generality and amounts to ‘applying’ the abstract idea on a generic computer. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 9: The limitations of receiving identification of a prediction of a label for the item, wherein the at least one machine learning model processes at least one of the first image or the second image to generate the prediction of the label for the item; responsive to the prediction of the label identifying the item as fragile, processing, using the rules-based model, the dimensions and the recommended package type to generate a recommended filler material to use in packing the item in the recommended package type; and communicating the recommended filler material to the user computing device are further directed to certain methods of organizing human activity (commercial interactions, managing personal behavior or relationships or interactions between people, following rules or instructions) as described in the independent claim. The recitation of the machine learning model (i.e. a computing system, see Applicant Specification ¶[0052]) and computing device are computer components recited at a high level of generality and amounts to ‘applying’ the abstract idea on a generic / general purpose computer and only a general linkage of the judicial exception to a technology / field of use (i.e. AI, machine learning). Furthermore, see the Applicant’s specification ¶[0072-76] describing the additional element of machine learning and example machine learning models, and ¶[0046] detailing use of a machine learning model to generate predictions of a label of an item (e.g. fragile) at a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe technical particulars to satisfy 35 USC 112(a). Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 10: The limitation wherein the rules-based model applies a set of rules based at least in part on using different types of filler material with respect to the recommended package type and the dimensions to identify the recommended filler material from the different types of filler material is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 11: The limitations wherein the operations further comprise receiving identification of a prediction of a label for the item, wherein the at least one machine learning model processes at least one of the first image or the second image to generate the prediction of the label for the item, and generating the recommended package type is further based at least in part on the prediction of the label for the item are further directed to certain methods of organizing human activity (commercial interactions, managing personal behavior or relationships or interactions between people, following rules or instructions) as described in the independent claim. The recitation of the machine learning model (i.e. a computing system, see Applicant Specification ¶[0052]) and computing device are computer components recited at a high level of generality and amounts to ‘applying’ the abstract idea on a generic / general purpose computer and only a general linkage of the judicial exception to a technology / field of use (i.e. AI, machine learning). Furthermore, see the Applicant’s specification ¶[0072-76] describing the additional element of machine learning and example machine learning models, and ¶[0046] detailing use of a machine learning model to generate predictions of a label of an item (e.g. fragile) at a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe technical particulars to satisfy 35 USC 112(a). Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 14: First, the limitation wherein the request comprises a description of the item, the recommendation comprises an estimated cost for shipping the item, merely narrows the previously recited abstract idea limitations. Second, the limitations generating, based at least in part on the description and the dimensions, an estimated weight of the item; generating, based at least in part on the estimated weight, an estimated cost for shipping the item; and communicating the estimated cost to the user computing device are further directed to certain methods of organizing human activity (managing personal behavior or relationships or interactions between people, following rules or instructions) as described in the independent claim. The recitation of the user computing device is a computer component recited at a high level of generality and amounts to ‘applying’ the abstract idea on a generic computer. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 16: The limitations of receiving identification of a first prediction of a first label for the first item and a second prediction of a second label for the second item, wherein the at least one machine learning model processes at least one of the first image or the second image to generate the first prediction and processes at least one of the third image or the fourth image to generate the second prediction; responsive to at least one of the first prediction identifying the first item as fragile or the second prediction identifying the second item as fragile, processing the first dimensions, the second dimensions, and the recommended package type to generate a recommended filler material to use in packing the first item and the second item in the recommended package type; and communicating the recommended filler material to the user computing device are further directed to certain methods of organizing human activity (commercial interactions, managing personal behavior or relationships or interactions between people, following rules or instructions) as described in the independent claim. The recitation of the machine learning model (i.e. a computing system, see Applicant Specification ¶[0052]) and computing device are computer components recited at a high level of generality and amounts to ‘applying’ the abstract idea on a generic / general purpose computer and only a general linkage of the judicial exception to a technology / field of use (i.e. AI, machine learning). Furthermore, see the Applicant’s specification ¶[0072-76] describing the additional element of machine learning and example machine learning models, and ¶[0046] detailing use of a machine learning model to generate predictions of a label of an item (e.g. fragile) at a high level that indicates this additional element is sufficiently well-known that the specification does not need to describe technical particulars to satisfy 35 USC 112(a). Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 17: The limitation wherein processing the first dimensions, the second dimensions, and the recommended package type to generate the recommended filler material involves processing the first dimensions, the second dimensions, and the recommended package type via a rules-based model that applies a set of rules based at least in part on using different types of filler material with respect to the first dimensions, the second dimensions, and the recommended package type to identify the recommended filler material from the different types of filler material is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 18: First, the limitation wherein the first dimensions comprise a first width, a first height, and a first depth of the first item, the second dimensions comprise a second width, a second height, and a second depth of the second item, merely narrow the previously recited abstract idea limitations. Second, the limitations comparing the first bounding box to the second bounding box involves deriving the first width and the first height of the first item, comparing the third bounding box to the second bounding box involves deriving the first depth of the first item, comparing the fifth bounding box to the sixth bounding box involves deriving the second width and the second height of the second item, and comparing the seventh bounding box to the eighth bounding box involves deriving the second depth of the second item are further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 19: The limitation wherein processing the first dimensions and the second dimensions to generate the recommended package type involves processing the first dimensions and the second dimensions via a rules-based model that applies a set of rules based at least in part on package dimensions for each package type of a plurality of package types to the first dimensions and the second dimensions to identify the recommended package type to use in packing the first item and the second item together is further directed to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as described in the independent claim. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Dependent claim 20: First, the limitation wherein the request comprises a first description of the first item and a second description of the second item, the recommendation comprises an estimated cost for shipping the item, merely narrows the previously recited abstract idea limitations. Second, the limitations generating, based at least in part on the first description and the first dimensions, a first estimated weight of the first item; generating, based at least in part on the second description and the second dimensions, a second estimated weight of the second item; generating, based at least in part on the first estimated weight and the second estimated weight, an estimated cost for shipping the first item and the second item together; and communicating the estimated cost to the user computing device are further directed to certain methods of organizing human activity (managing personal behavior or relationships or interactions between people, following rules or instructions) as described in the independent claim. The recitation of the user computing device is a computer component recited at a high level of generality and amounts to ‘applying’ the abstract idea on a generic computer. Similar to the independent claims, this recitation does not meaningfully integrate the abstract idea in a practical application, and is not significantly more than the abstract idea.
Therefore claims 1, 8, 15, and the dependent claims 2-7, 9-14, 16-20 and all limitations taken both individually and as an ordered combination, do not integrate the judicial exception into a practical application, nor do they include additional elements that are sufficient to amount to significantly more than the judicial exception. Accordingly, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US patent application publication 2020/0057988 A1 to Streebin et al. in view of “An Embedded Real-Time Object Detection and Measurement of it Size” (2018) to Othman et al.
Claim 1:
Streebin, as shown, teaches the following:
A method comprising:
receiving, by computing hardware and from a user computing device, a request for a recommendation for packing an item (Streebin ¶[0042-43], ¶[0076-77] details querying shipping carriers for service levels provided that can satisfy shipping the desired object(s), and the service level includes determining the shipping containers and materials for packing), a first image of a top view of the item and a reference item (Streebin ¶[0025] details the user providing multiple images including a top view of the item and the image includes one or more objects of known size), and a second image of a front view of the item and the reference item (Streebin ¶[0025] details the user providing multiple images including a front view / side view of the item and the image includes one or more objects of known size), wherein the request identifies reference dimensions for the reference item (Streebin Fig 3, ¶[0025], ¶[0030], ¶[0032] details one or more objects of known size and/or scale (i.e. reference item) used with the ODM (user device) and are included in the requested images of the objects);
processing, by the computing hardware, the first image and the second image using at least one machine learning model (Streebin ¶[0025], ¶[0032], ¶[0061], ¶[0066], ¶[0088] details using machine learning on the images to classify the object and further determine service level with object characteristics and labels, and processing image data into rectangular prism bounding boxes) to:
With respect to the following:
generate a first bounding box around the reference item and a second bounding box around the item in the first image,
generate a third bounding box around the reference item and a fourth bounding box around the item in the second image, and
Streebin, as shown in ¶[0022], ¶[0025], ¶[0029] details taking images of varying views (e.g. one front view, top view, and side view) of the object (e.g. first image, second image) and using those to generate a rectangular prism bounding box around the object, noting that a rectangular prism is defined by having a top face box and side face box and front face box (i.e. generating a second bounding box around the item in the first image, generating a fourth bounding box around the item in the second image), and also include taking images of one or more objects of known size in the image that are used to determine the dimensions of the object (i.e. reference item in the first image, reference item in the second image), but does not explicitly state generating a first bounding box around the reference item in the first image, or generating a third bounding box around the reference item in the second image. However, Othman teaches these remaining features, with a computer vision (i.e. machine learning) system that takes an image of both a reference object (with the reference object always as the left-most object in an image) and other objects to generate bounding boxes around each the reference object and the other objects to predict the two dimensional measurements of the objects; and provides examples including a first image taken from a side view of the reference object and the objects and the resulting bounding boxes (i.e. generate a first bounding box around the reference item and a second bounding box around the item in the first image) and a second image from a top view of the reference object and the objects and the resulting bounding boxes (i.e. generate a third bounding box around the reference item and a fourth bounding box around the item in the second image) (Othman Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 Results Experimental ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to generate a first bounding box around the reference item and a second bounding box around the item in the first image, and generate a third bounding box around the reference item and a fourth bounding box around the item in the second image as taught by Othman with the teachings of Streebin, with the motivation of “real-time object detection and dimensioning of objects” (Othman Abstract) In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include generating a first bounding box around the reference item and a second bounding box around the item in the first image, and generating a third bounding box around the reference item and a fourth bounding box around the item as taught by Othman in the system of Streebin, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Streebin (in view of Othman) also teaches the following:
generate a prediction of a label for the item based at least in part on a texture of the item detected from at least one of the first image or the second image (Streebin Fig 8, ¶[0024], ¶[0061], ¶[0088] details capturing texture information, and using machine learning to analyze the images and determine its properties, classifying the object type based on its dimensions and image dataset and its deterministic properties (e.g. wine glass, fragile));
With respect to the following:
comparing, by the computing hardware and based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item;
Streebin, as shown in ¶[0025], ¶[0030], ¶[0032] details the shipping automation system using the reference dimensions of the object of known size to assist in generating a rectangular prism bounding box that can enclose an object and generate dimensions of length, width, and height of the item; but does not explicitly state comparing by the computing hardware and based in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item. However, Othman teaches these features, using the known dimensions of the reference object to calculate the size of the other objects with calibration, using the bounding box generated around both the reference object and the desired object(s) to predict the dimensions of the desired object(s), and the process is performed for both a top view (i.e. first bounding box to second bounding box) and a front / side view (i.e. third bounding box to the fourth bounding box) (Othman Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…” and Results Experimental section ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing, by the computing hardware and based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item as taught by Othman with the teachings of Streebin (in view of Othman), with the motivation of a “powerful real time object measurement method [] proposed for industrial systems” (Othman pg. 4 col 1 Conclusion section ¶ beginning “In this study, an powerful real time object measurement…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing, by the computing hardware and based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item as taught by Othman in the system of Streebin (in view of Othman), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Streebin (in view of Othman) also teaches the following:
processing, by the computing hardware using a rules-based model, the prediction of the label for the item and the dimensions to generate the recommendation for packing the item, wherein the recommendation identifies a recommended package type to use in packing the item (Streebin ¶[0052], ¶[0076-78] details determining the shipping materials (e.g. containers, object padding) and configurations available for the service level based on the object characteristics, dimensions, service-level related information, and presenting ranked shipping material configuration options); and
communicating, by the computing hardware, the recommendation to the user computing device (Streebin ¶[0078] details presenting the shipping material configuration options to the user with quotes).
Claim 2:
Streebin in view of Othman, as shown above, teach the limitations of claim 1. Streebin also teaches the following:
wherein the dimensions comprise a width, a height, and a depth of the item (Streebin ¶[0022], ¶[0032] details three object dimensions include a length (i.e. depth), width, and height),
Othman (of Streebin in view of Othman) also teaches the following:
comparing the first bounding box to the second bounding box involves deriving the width and the height of the item (Othman pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…”, and pg. 3 col 2 Figure 8 and Table 1 including ¶s beginning “In the first experiment we measured size of objects…” detail using the bounding box around the reference object (first bounding box) to calibrate and bounding boxes around the other objects (second bounding box) from a side view image to derive a height and width of each object), and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the first bounding box to the second bounding box involves deriving the width and the height of the item as taught by Othman with the teachings of Streebin (in view of Othman), with the motivation of a “powerful real time object measurement method [] proposed for industrial systems” (Othman pg. 4 col 1 Conclusion section ¶ beginning “In this study, an powerful real time object measurement…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the first bounding box to the second bounding box involves deriving the width and the height of the item as taught by Othman in the system of Streebin (in view of Othman), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Streebin (in view of Othman, applying the second bounding box is a top view bounding box of the object and the third bounding box is a bounding box of the reference object of known size, per Othman above) also teaches the following:
comparing the third bounding box to the second bounding box involves deriving the depth of the item (Streebin ¶[0022], ¶[0032] details using multiple images including a top view and a reference object of known size (third bounding box, per Othman) to derive object length (i.e. depth), width, and height of the object as a rectangular prism around the object, noting that a rectangular prism includes a top face box (i.e. second bounding box) defined by length (depth) and width).
Claim 3:
Streebin in view of Othman, as shown above, teach the limitations of claim 1. Streebin also teaches the following:
wherein the rules-based model applies a set of rules based at least in part on package dimensions for each package type of a plurality of package types to the dimensions of the item to identify the recommended package type to use in packing the item (Streebin ¶[0060], ¶[0076-77] details determining shipping materials based on object characteristics suitable based on the object dimensions, and grouping objects together based on dimensions).
Claim 4:
Streebin in view of Othman, as shown above, teach the limitations of claim 1. Streebin also teaches the following:
wherein the prediction of the label identifies the item as fragile or non-fragile (Streebin Fig 8, ¶[0054], ¶[0060] details identifying the object as glass / fragile materials).
Claim 5:
Streebin in view of Othman, as shown above, teach the limitations of claim 1. Streebin also teaches the following:
wherein the prediction of the label identifies the item as fragile, and the recommendation identifies a recommended filler material to use in packing the item in the recommended package type (Streebin Fig 1, ¶[0064], ¶[0076-77] details classifying an object as fragile, recommending object padding based on the object characteristics / object type, and additional handling for particular object types (e.g. fragile materials)).
Claim 6:
Streebin in view of Othman, as shown above, teach the limitations of claim 5. Streebin also teaches the following:
wherein the rules-based model applies a set of rules based at least in part on using different types of filler material with respect to the recommended package type and the dimensions to identify the recommended filler material from the different types of filler material (Streebin ¶[0076-78] details recommending different types of object padding (e.g. bubble wrap, packing peanuts) relevant to the packaging and/or shipping of an object based on the object characteristics (dimensions, object type), and ranking available shipping material options).
Claim 7:
Streebin in view of Othman, as shown above, teach the limitations of claim 1. Streebin also teaches the following:
wherein the request comprises a description of the item (Streebin ¶[0054] details the user device providing object type datasets, e.g. model number or construction materials),
the recommendation comprises an estimated cost for shipping the item (Streebin ¶[0062], ¶[0064], ¶[0072], ¶[0078] details determining rate quotes for the service levels and shipping material options), and the method further comprises:
generating, by the computing hardware and based at least in part on the description and the dimensions, an estimated weight of the item (Streebin ¶[0030], ¶[0054] details estimating the weight based on the dimensions dataset, images, and user inputted data, e.g. object material); and
generating, by the computing hardware and based at least in part on the estimated weight, the estimated cost (Streebin ¶[0018], ¶[0030] details using dimensional and weight measurements (from network connected ODMs and OWMs) to calculate viable service levels and corresponding rate quotes across a plurality of shipping carriers, and the weight can be an estimated weight (from ODMs) omitting the OWM).
Claim 8:
Streebin, as shown, teaches the following:
A system comprising:
a non-transitory computer-readable medium storing instructions (Streebin ¶[0087] details a computer-readable medium storing instructions); and
a processing device communicatively coupled to the non-transitory computer-readable medium, wherein the processing device is configured to execute the instructions and thereby perform operations (Streebin ¶[0087] details a processor that executes the instructions stored on the computer-readable medium) comprising:
receiving, from a user computing device, a request for a recommended package type for an item (Streebin ¶[0042-43], ¶[0076-77] details querying shipping carriers for service levels provided that can satisfy shipping the desired object(s), and the service level includes determining the shipping containers and materials for packing), a first image of a top view of the item and a reference item (Streebin ¶[0025] details the user providing multiple images including a top view of the item and the image includes one or more objects of known size), and a second image of a front view of the item and the reference item (Streebin ¶[0025] details the user providing multiple images including a front view / side view of the item and the image includes one or more objects of known size), wherein the request identifies reference dimensions for the reference item (Streebin Fig 3, ¶[0025], ¶[0030], ¶[0032] details one or more objects of known size and/or scale (i.e. reference item) used with the ODM (user device) and are included in the requested images of the objects);
With respect to the following:
receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box,
wherein at least one machine learning model processes the first image and the second image to:
generate the first bounding box around the reference item and the second bounding box around the item in the first image, and
generate the third bounding box around the reference item and the fourth bounding box around the item in the second image;
Streebin, as shown in ¶[0022], ¶[0025], ¶[0029] details taking images of varying views (e.g. one front view, top view, and side view) of the object (e.g. first image, second image) and using those to generate a rectangular prism bounding box around the object, noting that a rectangular prism is defined by having a top face box and side face box and front face box (i.e. generating a second bounding box around the item in the first image, generating a fourth bounding box around the item in the second image), and also include taking images of one or more objects of known size in the image that are used to determine the dimensions of the object (i.e. reference item in the first image, reference item in the second image), but does not explicitly state receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, wherein at least one machine learning model processes the first image and the second image to: generate the first bounding box around the reference item in the first image, and generate the third bounding box around the reference item around the item in the second image. However, Othman teaches these remaining features, with a computer vision (i.e. machine learning) system that takes an image of both a reference object (with the reference object always as the left-most object in an image) and other objects to generate bounding boxes around each the reference object and the other objects to predict the two dimensional measurements of the objects; and provides examples including a first image taken from a side view of the reference object and the objects and the resulting bounding boxes (i.e. generate a first bounding box around the reference item and a second bounding box around the item in the first image) and a second image from a top view of the reference object and the objects and the resulting bounding boxes (i.e. generate a third bounding box around the reference item and a fourth bounding box around the item in the second image) (Othman Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 Results Experimental ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, wherein at least one machine learning model processes the first image and the second image to: generate the first bounding box around the reference item and the second bounding box around the item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the item in the second image as taught by Othman with the teachings of Streebin, with the motivation of “real-time object detection and dimensioning of objects” (Othman Abstract) In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, wherein at least one machine learning model processes the first image and the second image to: generate the first bounding box around the reference item and the second bounding box around the item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the item in the second image as taught by Othman in the system of Streebin, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
With respect to the following:
comparing, based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item;
Streebin, as shown in ¶[0025], ¶[0030], ¶[0032] details the shipping automation system using the reference dimensions of the object of known size to assist in generating a rectangular prism bounding box that can enclose an object and generate dimensions of length, width, and height of the item; but does not explicitly state comparing based in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item. However, Othman teaches these features, using the known dimensions of the reference object to calculate the size of the other objects with calibration, using the bounding box generated around both the reference object and the desired object(s) to predict the dimensions of the desired object(s), and the process is performed for both a top view (i.e. first bounding box to second bounding box) and a front / side view (i.e. third bounding box to the fourth bounding box) (Othman Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…” and Results Experimental section ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item as taught by Othman with the teachings of Streebin (in view of Othman), with the motivation of a “powerful real time object measurement method [] proposed for industrial systems” (Othman pg. 4 col 1 Conclusion section ¶ beginning “In this study, an powerful real time object measurement…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the item as taught by Othman in the system of Streebin (in view of Othman), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Streebin (in view of Othman) also teaches the following:
processing, using a rules-based model, the dimensions to generate the recommended package type to use in packing the item (Streebin ¶[0052], ¶[0076-78] details determining the shipping materials (e.g. containers) and configurations available for the service level based on the object characteristics, dimensions, service-level related information, and presenting ranked shipping material configuration options); and
communicating the recommended package type to the user computing device (Streebin ¶[0076], ¶[0078] details presenting the shipping material (e.g. container) configuration options to the user with quotes).
Claim 9:
Streebin in view of Othman, as shown above, teach the limitations of claim 8. Streebin also teaches the following:
wherein the operations further comprise:
receiving identification of a prediction of a label for the item, wherein the at least one machine learning model processes at least one of the first image or the second image to generate the prediction of the label for the item (Streebin Fig 8, ¶[0024], ¶[0061], ¶[0088] details capturing texture information, and using machine learning to analyze the images and determine its properties, classifying the object type based on its dimensions and image dataset and its deterministic properties (e.g. wine glass, fragile));
responsive to the prediction of the label identifying the item as fragile, processing, using the rules-based model, the dimensions and the recommended package type to generate a recommended filler material to use in packing the item in the recommended package type (Streebin ¶[0052], ¶[0076-78] details determining the shipping materials (e.g. containers, object padding) and configurations available for the service level based on the object characteristics, dimensions, service-level related information, and presenting ranked shipping material configuration options); and
communicating the recommended filler material to the user computing device (Streebin ¶[0076], ¶[0078] details presenting the shipping material configuration options to the user with quotes, filler material may include bubble wrap or peanuts).
Claim 10:
Streebin in view of Othman, as shown above, teach the limitations of claim 9. Streebin also teaches the following:
wherein the rules-based model applies a set of rules based at least in part on using different types of filler material with respect to the recommended package type and the dimensions to identify the recommended filler material from the different types of filler material (Streebin ¶[0076-78] details recommending different types of object padding (e.g. bubble wrap, packing peanuts) relevant to the packaging and/or shipping of an object based on the object characteristics (dimensions, object type), and ranking available shipping material options).
Claim 11:
Streebin in view of Othman, as shown above, teach the limitations of claim 8. Streebin also teaches the following:
wherein the operations further comprise receiving identification of a prediction of a label for the item (Streebin ¶[0024] details capturing information about the product, e.g. texture information),
wherein the at least one machine learning model processes at least one of the first image or the second image to generate the prediction of the label for the item (Streebin Fig 8, ¶[0024], ¶[0061], ¶[0088] details capturing texture information, and using machine learning to analyze the images and determine its properties, classifying the object type based on its dimensions and image dataset and its deterministic properties (e.g. wine glass, fragile)), and
generating the recommended package type is further based at least in part on the prediction of the label for the item (Streebin ¶[0052], ¶[0075-78] details recommending service levels, and determining the shipping materials (e.g. containers, object padding) and configurations available for the service level based on the object characteristics).
Claim 12:
Streebin in view of Othman, as shown above, teach the limitations of claim 8. Streebin also teaches the following:
wherein the dimensions comprise a width, a height, and a depth of the item (Streebin ¶[0022], ¶[0032] details three object dimensions include a length (i.e. depth), width, and height),
Othman (of Streebin in view of Othman) also teaches the following:
comparing the first bounding box to the second bounding box involves deriving the width and the height of the item (Othman pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…”, and pg. 3 col 2 Figure 8 and Table 1 including ¶s beginning “In the first experiment we measured size of objects…” detail using the bounding box around the reference object (first bounding box) to calibrate and bounding boxes around the other objects (second bounding box) from a side view image to derive a height and width of each object), and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the first bounding box to the second bounding box involves deriving the width and the height of the item as taught by Othman with the teachings of Streebin (in view of Othman), with the motivation of a “powerful real time object measurement method [] proposed for industrial systems” (Othman pg. 4 col 1 Conclusion section ¶ beginning “In this study, an powerful real time object measurement…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the first bounding box to the second bounding box involves deriving the width and the height of the item as taught by Othman in the system of Streebin (in view of Othman), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Streebin (in view of Othman, applying the second bounding box is a top view bounding box of the object and the third bounding box is a bounding box of the reference object of known size, per Othman above) also teaches the following:
comparing the third bounding box to the second bounding box involves deriving the depth of the item (Streebin ¶[0022], ¶[0032] details using multiple images including a top view and a reference object of known size (third bounding box, per Othman) to derive length (i.e. depth), width, and height of the object as a rectangular prism around the object, noting that a rectangular prism includes a top face box (i.e. second bounding box) defined by length (depth) and width).
Claim 13:
Streebin in view of Othman, as shown above, teach the limitations of claim 8. Streebin also teaches the following:
wherein the rules-based model applies a set of rules based at least in part on package dimensions for each package type of a plurality of package types to the dimensions of the item to identify the recommended package type to use in packing the item (Streebin ¶[0060], ¶[0076-77] details determining shipping materials based on object characteristics suitable based on the object dimensions, and grouping objects together based on dimensions).
Claim 14:
Streebin in view of Othman, as shown above, teach the limitations of claim 8. Streebin also teaches the following:
wherein the request comprises a description of the item (Streebin ¶[0054] details the user device providing object type datasets, e.g. model number or construction materials), and the operations further comprise:
generating, based at least in part on the description and the dimensions, an estimated weight of the item (Streebin ¶[0030], ¶[0054] details estimating the weight based on the dimensions dataset, images, and user inputted data, e.g. object material);
generating, based at least in part on the estimated weight, an estimated cost for shipping the item (Streebin ¶[0018], ¶[0030], ¶[0033] details using dimensional and weight measurements (from network connected ODMs and OWMs) to calculate viable service levels and corresponding rate quotes across a plurality of shipping carriers, and the weight can be an estimated weight (from ODMs) omitting the OWM); and
communicating the estimated cost to the user computing device (Streebin ¶[0033], ¶[0073], ¶[0075] details identifying and calculating the rate quote, and ranking the service levels provided based on rate quotes from lowest rate quote to highest quote, informing the user of identified service levels and service level related information including rate quotes presented).
Claim 15:
Streebin, as shown, teaches the following:
A computer-readable medium storing computer-executable instructions that, when executed by computing hardware, configure the computing hardware to perform operations (Streebin ¶[0087-88] details a computer-readable medium storing instructions that are executed by a processor) comprising:
receiving, from a user computing device, a request for a recommended package type for a first item and a second item (Streebin ¶[0042-43], ¶[0056], ¶[0069], ¶[0076-77] details querying shipping carriers for service levels provided that can satisfy shipping the desired object(s), and the service level includes determining the shipping containers and materials for packing, and receiving aggregate object data for multiple objects, i.e. first and second items), a first image of a top view of the first item and a reference item (Streebin ¶[0025] details the user providing multiple images including a top view of the item and the image includes one or more objects of known size), a second image of a front view of the first item and the reference item (Streebin ¶[0025] details the user providing multiple images including a front view / side view of the item and the image includes one or more objects of known size), a third image of a top view of the second item and the reference item (Streebin ¶[0025], ¶[0056], ¶[0069] details the user providing multiple images including a top view of the item and the image includes one or more objects of known size, and receiving aggregate object data for multiple objects, i.e. a second item), and a fourth image of a front view of the second item and the reference item (Streebin ¶[0025], ¶[0056], ¶[0069] details the user providing multiple images including a front view / side view of the item and the image includes one or more objects of known size, and receiving aggregate object data for multiple objects, i.e. a second item);
With respect to the following:
receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, a fifth bounding box, a sixth bounding box, seventh bounding box, and an eighth bounding box,
wherein at least one machine learning model processes the first image, the second image, the third image, and the fourth image to:
generate the first bounding box around the reference item and the second bounding box around the first item in the first image,
generate the third bounding box around the reference item and the fourth bounding box around the first item in the second image,
generate the fifth bounding box around the reference item and the sixth bounding box around the second item in the third image, and
generate the seventh bounding box around the reference item and the eighth bounding box around the second item in the fourth image;
Streebin, as shown in ¶[0022], ¶[0025], ¶[0029], ¶[0056], ¶[0069] details taking images of varying views (e.g. one front view, top view, and side view) of the object (e.g. first image, second image) and using those to generate a rectangular prism bounding box around the object, noting that a rectangular prism is defined by having a top face box and side face box and front face box (i.e. generating a second bounding box around the item in the first image, generating a fourth bounding box around the item in the second image), and also include taking images of one or more objects of known size in the image that are used to determine the dimensions of the object (i.e. reference item in the first image, reference item in the second image); and receiving aggregate object data for multiple objects, (i.e. a second item, third image, fourth image, generating a sixth bounding box around the second item in the third image, generating a eight bounding box around the item in the fourth image), but does not explicitly state receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, a fifth bounding box, a sixth bounding box, seventh bounding box, and an eighth bounding box, wherein at least one machine learning model processes the first image, the second image, the third image, and the fourth image to: generate the first bounding box around the reference item in the first image, generate the third bounding box around the reference item around the item in the second image, generate the fifth bounding box around the reference item in the third image, and generate the seventh bounding box around the reference item in the fourth image (breaking this limitation into (1) receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box…, wherein at least one machine learning model processes the first image and the second image… to: generate the first bounding box around the reference item and the second bounding box around the first item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the first item in the second image; and (2) receiving identification of… a fifth bounding box, a sixth bounding box, seventh bounding box, and an eighth bounding box, wherein at least one machine learning model processes… the third image and the fourth image to: …generate the fifth bounding box around the reference item and the sixth bounding box around the second item in the third image, and generate the seventh bounding box around the reference item and the eighth bounding box around the second item in the fourth image).
Regarding (1) receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box…, wherein at least one machine learning model processes the first image and the second image… to: generate the first bounding box around the reference item and the second bounding box around the first item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the first item in the second image…; Othman teaches these features, with a computer vision (i.e. machine learning) system that takes an image of both a reference object (with the reference object always as the left-most object in an image) and other objects to generate bounding boxes around each the reference object and the other objects to predict the two dimensional measurements of the objects; and provides examples including a first image taken from a side view of the reference object and the objects and the resulting bounding boxes (i.e. generate a first bounding box around the reference item and a second bounding box around the item in the first image) and a second image from a top view of the reference object and the objects and the resulting bounding boxes (i.e. generate a third bounding box around the reference item and a fourth bounding box around the item in the second image) (Othman Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 Results Experimental ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, wherein at least one machine learning model processes the first image and the second image to: generate the first bounding box around the reference item and the second bounding box around the first item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the first item in the second image as taught by Othman with the teachings of Streebin, with the motivation of “real-time object detection and dimensioning of objects” (Othman Abstract). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include receiving identification of a first bounding box, a second bounding box, a third bounding box, a fourth bounding box, wherein at least one machine learning model processes the first image and the second image to: generate the first bounding box around the reference item and the second bounding box around the first item in the first image, and generate the third bounding box around the reference item and the fourth bounding box around the first item in the second image as taught by Othman in the system of Streebin, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Regarding (2) receiving identification of… a fifth bounding box, a sixth bounding box, seventh bounding box, and an eighth bounding box, wherein at least one machine learning model processes… the third image and the fourth image to: …generate the fifth bounding box around the reference item and the sixth bounding box around the second item in the third image, and generate the seventh bounding box around the reference item and the eighth bounding box around the second item in the fourth image, these are an obvious modification of Othman, noting that this remaining modification merely constitutes a duplication of parts. Even though the Othman reference does not explicitly state a third / fourth image, and fifth / sixth / seventh / eighth bounding boxes, this is no different than a mere duplication in which an expected results are produced (e.g. a duplication of the repeating processes with the first / second image, and first / second / third / fourth bounding boxes). See In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960) finding that prior art disclosing a 'plus sign' configuration, teaches a ‘plurality of ribs projecting outwardly in a web pattern’ configuration, because it is a mere duplication that has no patentable significance unless a new and unexpected result is produced. This is analogous to that of Othman which presents bounding boxes around both a reference item and objects from images (see Othman Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 Results Experimental ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2), repeating this process with additional images and objects would not produce a new and unexpected result; therefore it would be an obvious modification to duplicate the functionality of receiving identification of first / second / third / fourth bounding boxes, and generating identification of first / second / third / fourth bounding boxes (as taught by Othman), to produce the claimed functionality of receiving identification of fifth / sixth / seventh / eighth bounding boxes, and generating identification of fifth / sixth / seventh / eighth bounding boxes in the system of Streebin (in view of Othman) with the motivation that “The proposed technique works very fast” (Othman pg. 5 col 1 Conclusion section ¶ beginning “The proposed technique works very fast and five frames can be processed…”). See MPEP 2144.04.VI(B).
With respect to the following:
comparing, based at least in part on reference dimensions for the reference item, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate first dimensions of the first item;
Streebin, as shown in ¶[0025], ¶[0030], ¶[0032] details the shipping automation system using the reference dimensions of the object of known size to assist in generating a rectangular prism bounding box that can enclose an object and generate dimensions of length, width, and height of the item; but does not explicitly state comparing based in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the first item. However, Othman teaches these features, using the known dimensions of a reference object to calculate the size of the other objects with calibration, using the bounding box generated around both a reference object and the desired object(s) to predict the dimensions of the desired object(s), and the process is performed for both a top view (i.e. first bounding box to second bounding box) and a side / front view (i.e. third bounding box to the fourth bounding box) (Othman Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…” and Results Experimental section ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the first item as taught by Othman with the teachings of Streebin (in view of Othman), with the motivation of a “powerful real time object measurement method [] proposed for industrial systems” (Othman pg. 4 col 1 Conclusion section ¶ beginning “In this study, an powerful real time object measurement…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing based at least in part on the reference dimensions, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate dimensions of the first item as taught by Othman in the system of Streebin (in view of Othman), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
With respect to the following:
comparing, based at least in part on the reference dimensions for the reference item, the fifth bounding box to the sixth bounding box and the seventh bounding box to the eighth bounding box to generate second dimensions of the second item;
Othman, as shown above in the Abstract, pg. 1 col 1 Introduction ¶s beginning “To calculate the size of each object…”, pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…” and Results Experimental section ¶s beginning “For the experiment the camera has been effectively…” including Figures 7-9 and Tables 1-2 details ‘comparing, based at least in part on reference dimensions for the reference item, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate first dimensions of the first item’. While Othman does not explicitly state comparing, based at least in part on the reference dimensions for the reference item, the fifth bounding box to the sixth bounding box and the seventh bounding box to the eighth bounding box to generate second dimensions of the second item, this is an obvious modification of Othman, noting that this modification merely constitutes a duplication of parts. Even though the Othman reference does not explicitly state comparing fifth / sixth / seventh / eighth bounding boxes to generate dimensions for a second item, this is no different than a mere duplication in which expected results are produced (e.g. repeating a comparing processes of first / second / third / fourth bounding boxes and another item to generate dimensions). See In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960) finding that prior art disclosing a 'plus sign' configuration, teaches a ‘plurality of ribs projecting outwardly in a web pattern’ configuration, because it is a mere duplication that has no patentable significance unless a new and unexpected result is produced. This is analogous to that of Othman which compares bounding boxes to generate dimensions of an item, repeating this process with additional objects and bounding boxes would not produce a new and unexpected result; therefore it would be an obvious modification to duplicate the functionality of comparing, based at least in part on reference dimensions for the reference item, the first bounding box to the second bounding box and the third bounding box to the fourth bounding box to generate first dimensions of the first item (as taught by Othman), to produce the claimed functionality of comparing, based at least in part on the reference dimensions for the reference item, the fifth bounding box to the sixth bounding box and the seventh bounding box to the eighth bounding box to generate second dimensions of the second item in the system of Streebin (in view of Othman) with the motivation that “The proposed technique works very fast” (Othman pg. 5 col 1 Conclusion section ¶ beginning “The proposed technique works very fast and five frames can be processed…”). See MPEP 2144.04.VI(B).
Streebin (in view of Othman) also teaches the following:
processing the first dimensions and the second dimensions to generate the recommended package type to use in packing the first item and the second item together (Streebin ¶[0069], ¶[0076-78], ¶[0083] details sending a plurality of objects together in a batch, combining dimensions of the plurality of objects to determine the service level for the shipment batch, identified shipping materials can be used in determining service levels including containers and object padding, and determining shipping materials based on dimension characteristics of the service-related information including type of material, dimensions, weight; and any number of shipping materials can be determined for one or more objects); and
communicating the recommended package type to the user computing device (Streebin ¶[0078] details presenting the shipping material configurations for the one or more objects and shipments with quotes to the user ranked based on cost).
Claim 16:
Streebin in view of Othman, as shown above, teach the limitations of claim 15. Streebin also teaches the following:
wherein the operations further comprise:
receiving identification of a first prediction of a first label for the first item and a second prediction of a second label for the second item (Streebin ¶[0024], ¶[0056], ¶[0069] details capturing information about the first / second item, e.g. texture information; and receiving aggregate object data for multiple objects),
wherein the at least one machine learning model processes at least one of the first image or the second image to generate the first prediction and processes at least one of the third image or the fourth image to generate the second prediction (Streebin Fig 8, ¶[0024], ¶[0061], ¶[0088], ¶[0056], ¶[0069] details capturing texture information for an object, capturing top view and front / side view images of an object, and receiving aggregate object data for multiple objects, (i.e. a first / second item and first / second / third / fourth image), and using machine learning to analyze the images and determine the object properties, classifying the object type based on its dimensions and image dataset and its deterministic properties (e.g. wine glass, fragile));
responsive to at least one of the first prediction identifying the first item as fragile or the second prediction identifying the second item as fragile, processing the first dimensions, the second dimensions, and the recommended package type to generate a recommended filler material to use in packing the first item and the second item in the recommended package type (Streebin Fig 1, ¶[0064], ¶[0069], ¶[0076-77], ¶[0083] details sending a plurality of objects together in a batch, classifying an object as fragile, recommending object padding based on an object characteristics / object type, and additional handling for particular object types (e.g. fragile materials), and combining dimensions of the plurality of objects to determine the service level for the shipment batch, and any number of shipping materials can be determined for one or more objects); and
communicating the recommended filler material to the user computing device (Streebin ¶[0078] details presenting the shipping material configuration options to the user with quotes).
Claim 17:
Streebin in view of Othman, as shown above, teach the limitations of claim 16. Streebin also teaches the following:
wherein processing the first dimensions, the second dimensions, and the recommended package type to generate the recommended filler material involves processing the first dimensions, the second dimensions, and the recommended package type via a rules-based model that applies a set of rules based at least in part on using different types of filler material with respect to the first dimensions, the second dimensions, and the recommended package type to identify the recommended filler material from the different types of filler material (Streebin ¶[0056], ¶[0069], ¶[0076-78], ¶[0083] details receiving aggregate object data for multiple objects, sending the plurality of objects together in a batch and using the combined dimensions, recommending different types of object padding (e.g. bubble wrap, packing peanuts) relevant to the packaging and/or shipping an object based on the object characteristics (dimensions, object type), and ranking available shipping material options for the one or more objects).
Claim 18:
Streebin in view of Othman, as shown above, teach the limitations of claim 16. Streebin also teaches the following:
wherein the first dimensions comprise a first width, a first height, and a first depth of the first item (Streebin ¶[0022], ¶[0032] details three object dimensions include a length (i.e. depth), width, and height),
the second dimensions comprise a second width, a second height, and a second depth of the second item (Streebin ¶[0022], ¶[0032], ¶[0056], ¶[0069] details three object dimensions include a length (i.e. depth), width, and height; and receiving aggregate object data for multiple objects),
Othman (of Streebin in view of Othman) also teaches the following:
comparing the first bounding box to the second bounding box involves deriving the width and the height of the first item (Othman pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…”, and pg. 3 col 2 Figure 8 and Table 1 including ¶s beginning “In the first experiment we measured size of objects…” detail using the bounding box around the reference object (first bounding box) to calibrate and bounding boxes around the other objects (second bounding box) from a side view image to derive a height and width of each object), and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the first bounding box to the second bounding box involves deriving the width and the height of the first item as taught by Othman with the teachings of Streebin (in view of Othman), with the motivation of a “powerful real time object measurement method [] proposed for industrial systems” (Othman pg. 4 col 1 Conclusion section ¶ beginning “In this study, an powerful real time object measurement…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the first bounding box to the second bounding box involves deriving the width and the height of the first item as taught by Othman in the system of Streebin (in view of Othman), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Streebin (in view of Othman, applying the second bounding box is a top view bounding box of the object and the third bounding box is a bounding box of the reference object of known size, per Othman above) also teaches the following:
comparing the third bounding box to the second bounding box involves deriving the depth of the item (Streebin ¶[0022], ¶[0032] details using multiple images including a top view and a reference object of known size (third bounding box, per Othman) to derive length (i.e. depth), width, and height of the object as a rectangular prism around the object, noting that a rectangular prism includes a top face box (i.e. second bounding box) defined by length (depth) and width).
Othman (of Streebin in view of Othman, applying that the fifth / sixth / seventh / eight bounding box and second item are an obvious duplication of the first / second / third / fourth bounding box and first item of Othman, per the rejection of claim 15 above) also teaches the following:
comparing the fifth bounding box to the sixth bounding box involves deriving the second width and the second height of the second item (Othman pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…”, and pg. 3 col 2 Figure 8 and Table 1 including ¶s beginning “In the first experiment we measured size of objects…” detail using the bounding box around the reference object (fifth bounding box) to calibrate and bounding boxes around the other objects (sixth bounding box) from a side view image to derive a height and width of each object), and
comparing the seventh bounding box to the eighth bounding box involves deriving the second depth of the second item (Othman pg. 3 col 1 ¶ beginning “…We arrange contours from left to right…”, and pg. 3 col 2 Figure 9 and Table 2 including ¶s beginning “In the second experiment we set the camera…” detail using the bounding box around the reference object (seventh bounding box) to calibrate and bounding boxes around the other objects (eighth bounding box) from a top view image to derive a height and width (i.e. depth) of each object).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the fifth bounding box to the sixth bounding box involves deriving the second width and the second height of the second item; and comparing the seventh bounding box to the eighth bounding box involves deriving the second depth of the second item as taught by Othman with the teachings of Streebin (in view of Othman), with the motivation of a “powerful real time object measurement method [] proposed for industrial systems” (Othman pg. 4 col 1 Conclusion section ¶ beginning “In this study, an powerful real time object measurement…”). In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include comparing the fifth bounding box to the sixth bounding box involves deriving the second width and the second height of the second item; and comparing the seventh bounding box to the eighth bounding box involves deriving the second depth of the second item as taught by Othman in the system of Streebin (in view of Othman), since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. See MPEP 2141 citing KSR International Co. v. Teleflex Inc., 82 USPQ2d 1385 (2007).
Claim 19:
Streebin in view of Othman, as shown above, teach the limitations of claim 16. Streebin also teaches the following:
wherein processing the first dimensions and the second dimensions to generate the recommended package type involves processing the first dimensions and the second dimensions via a rules-based model that applies a set of rules based at least in part on package dimensions for each package type of a plurality of package types to the first dimensions and the second dimensions to identify the recommended package type to use in packing the first item and the second item together (Streebin ¶[0060], ¶[0069], ¶[0076-77] details grouping objects together in a batch for the shipment and using combined dimensions of the first object and the additional object, determining shipping materials based on object characteristics suitable based on the object dimensions, and determining the shipping materials for the one or more objects to present to the user in ranked configurations).
Claim 20:
Streebin in view of Othman, as shown above, teach the limitations of claim 15. Streebin also teaches the following:
wherein the request comprises a first description of the first item and a second description of the second item (Streebin ¶[0054], ¶[0056], ¶[0069] details the user device providing object type datasets for an object, e.g. model number or construction materials, and receiving aggregate object data for multiple objects, i.e. first / second items), and the operations further comprise:
generating, based at least in part on the first description and the first dimensions, a first estimated weight of the first item (Streebin ¶[0023], ¶[0030], ¶[0054] details estimating the weight of an object based on the dimensions dataset, images, and user inputted data, e.g. object material);
generating, based at least in part on the second description and the second dimensions, a second estimated weight of the second item (Streebin ¶[0023], ¶[0030], ¶[0054], ¶[0056], ¶[0069] details receiving aggregate object data for multiple objects, i.e. second item dimensions and user input data; and estimating the weight of an object based on the dimensions dataset, images, and user inputted data, e.g. object material);
generating, based at least in part on the first estimated weight and the second estimated weight, an estimated cost for shipping the first item and the second item together (Streebin ¶[0018], ¶[0030], ¶[0069] details receiving aggregate object data for multiple objects, and estimating the weight of an object; batching a plurality of objects into a shipment and combining the weight to determine a service level, and the dimensional weight measurements corresponds to a rate quote); and
communicating the estimated cost to the user computing device (Streebin ¶[0033], ¶[0073], ¶[0075] details identifying and calculating the rate quote, and ranking the service levels provided based on rate quotes from lowest rate quote to highest quote, informing the user of identified service levels and service level related information including rate quotes presented).
Additional Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US patent application publication 2022/0374834 A1 to Lundeen et al. details a package sorting platform sensory system.
US patent publication 6,721,762 B1 to Levine et al. details a method and system for packing a plurality of articles in a container.
Conclusion
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BRIAN TALLMAN
Examiner
Art Unit 3628
/BRIAN A TALLMAN/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628