DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Regarding claim 1, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A method comprising:. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
parsing…the raw shipper behavior data into a shipper information unit, wherein the shipper information unit comprises a subset of the shipper behavioral data; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like formatting raw data, which is either a mental process of evaluation/judgement (MPEP 2106)).
accessing…the shipper information unit; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like entering a data store, which is either a mental process of evaluation/judgement (MPEP 2106)).
extracting…a set of features from the shipper information unit, wherein each feature of the set of features represents a characteristic of at least one of the shipper or the at least one parcel; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like extracting characteristics of a package, which is either a mental process of evaluation/judgement (MPEP 2106)).
and processing…the set of features to generate an output comprising a set of probability measures wherein each probability measure of the set of probability measures represents a probability of the shipper being a member of a corresponding shipper class from a set of shipper classes. (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like guessing a class that a shipper belongs to, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
receiving, by a shipper behavioral data management tool, raw shipper behavioral data associated with a shipper scheduled to ship at least one parcel; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
…by the shipper behavioral data management tool… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…by one or more computer processors from the shipper behavioral data management tool… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…by the one or more computer processors… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…by the one or more computer processors using a shipper behavior learning model… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (V), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitations (VI-VIII), under the broadest reasonable interpretation, merely recite steps that apply generic computer components to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Similarly, limitation (IX), under the broadest reasonable interpretation, merely recite steps that apply a generic machine learning model as a tool, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 2, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 2 recites wherein extracting the set of features comprises generating at least one feature in the set of features by categorizing an element found in the shipper information unit so that the at least one feature represents a category of the characteristic of the at least one of the shipper or the at least one parcel. Under the broadest reasonable interpretation, the limitations recite categorizing characteristics which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 2 does not solve the deficiencies of claim 1.
Regarding claim 3, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 3 recites wherein extracting the set of features comprises generating at least two features in the set of features by separating an element present in the shipper information unit. Under the broadest reasonable interpretation, the limitations recite generating more than one feature by differentiating between features which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 3 does not solve the deficiencies of claim 1.
Regarding claim 4, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 4 recites wherein the shipper behavioral data management tool is configured to centrally collect and manage shipper behavior data provided by at least one of different service points, different vehicles, or different mobile computing entities. Under the broadest reasonable interpretation, the limitations recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(g)). Therefore, claim 4 does not solve the deficiencies of claim 1.
Regarding claim 5, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 5 recites wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units derived from historical shipper behavioral data with respect to the set of shipper classes. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic training using historical shipper training data, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 5 does not solve the deficiencies of claim 1.
Regarding claim 6, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 6 recites further comprising normalizing, by the shipper behavioral data management tool, the subset of the shipping behavioral data found in the shipper information unit. Under the broadest reasonable interpretation, the limitations recite scaling the data to a certain level which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 6 does not solve the deficiencies of claim 1.
Regarding claim 7, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 7 recites further comprising normalizing, by the shipper behavioral data management tool, the subset of the shipping behavioral data found in the shipper information unit. Under the broadest reasonable interpretation, the limitations recite scaling the data to a certain level which is a step of observation, evaluation, and judgement which can be performed mentally or with pen and paper. The steps of observation, evaluation, and judgement are mental processes. Therefore, claim 7 does not solve the deficiencies of claim 1.
Regarding claim 18, it is dependent upon claim 1 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 18 recites wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units derived from historical shipper behavioral data with respect to various sizes of parcels. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic training using historical shipper training data, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 18 does not solve the deficiencies of claim 1.
Regarding claim 19, the claim is similar to claim 6 and rejected under the same rationales.
Regarding claim 8, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A system comprising:… and computing hardware configured to. The claim recites a system comprising computing hardware which is interpreted as a machine. A machine is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
and parse the raw shipper behavior data into a shipper information unit, wherein the shipper information unit comprises a subset of the shipper behavioral data; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like formatting raw data, which is either a mental process of evaluation/judgement (MPEP 2106)).
access, from the shipper behavioral data management tool, the shipper information unit; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like entering a data store, which is either a mental process of evaluation/judgement (MPEP 2106)).
extract a set of features from the shipper information unit, wherein each feature of the set of features represents a characteristic of at least one of the shipper or the at least one parcel; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like extracting characteristics of a package, which is either a mental process of evaluation/judgement (MPEP 2106)).
and process…the set of features to generate an output comprising a probability measure, wherein the probability measure of represents a probability of a timeliness of the at least one parcel arriving at a sort. (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like guessing a timeliness of a package, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
receive raw shipper behavioral data associated with a shipper scheduled to ship at least one parcel; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
…by the shipper behavioral data management tool configured to:… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
… and computing hardware configured to:… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…using a shipper behavior learning model… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (V), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitations (VI and VII), under the broadest reasonable interpretation, merely recite steps that apply generic computer components to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Similarly, limitation (VIII), under the broadest reasonable interpretation, merely recite steps that apply a generic machine learning model as a tool, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claims 9-11, the claims are similar to claims 2-4 and rejected under the same rationales.
Regarding claim 12, it is dependent upon claim 8 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 12 recites wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units derived from historical shipper behavioral data with respect to timeliness of parcels arriving at sorts. Under the broadest reasonable interpretation, the limitations merely recite steps that apply generic training using historical shipper training data, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 12 does not solve the deficiencies of claim 8.
Regarding claim 13, the claim is similar to claim 6 and rejected under the same rationales.
Regarding claim 14, in step 1 of the 101 analysis set forth in MPEP 2106, the claim recites A method comprising:. The claim recites a method. A method is one of the four statutory categories of invention.
In Step 2A, Prong 1 of the 101 analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under broadest reasonable interpretation, covers a mental process or mathematical concept but for the recitation of generic computer components:
parsing…the raw shipper behavior data into a shipper information unit, wherein the shipper information unit comprises a subset of the shipper behavioral data; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like formatting raw data, which is either a mental process of evaluation/judgement (MPEP 2106)).
accessing…the shipper information unit; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like entering a data store, which is either a mental process of evaluation/judgement (MPEP 2106)).
extracting…a set of features from the shipper information unit, wherein each feature of the set of features represents a characteristic of at least one of the shipper or the at least one parcel; (i.e., the broadest reasonable interpretation includes a step of evaluation and judgement and could be performed mentally or with pen and paper like extracting characteristics of a package, which is either a mental process of evaluation/judgement (MPEP 2106)).
and processing…the set of features to generate a prediction output corresponding to a size of the at least one parcel. (i.e., the broadest reasonable interpretation includes a step of observation, evaluation, and judgement and could be performed mentally or with pen and paper like guessing a size of a package, which is either a mental process of observation/evaluation/judgement (MPEP 2106)).
If the claim limitations, under their broadest reasonable interpretation, covers activities classified under Mental processes: concepts performed in the human mind (including observation, evaluation, judgement, or opinion) (see MPEP 2106.04(a)(2), subsection (III)) or Mathematical concepts: mathematical relationships, mathematical formulas or equations, or mathematical calculations (see MPEP 2106.04(a)(2), subsection (I)). Accordingly, the claim recites an abstract idea.
In Step 2A, Prong 2 of the 101 analysis, set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
receiving, by a shipper behavioral data management tool, raw shipper behavioral data associated with a shipper scheduled to ship at least one parcel; (i.e., the broadest reasonable interpretation of receiving a data instance is mere data gathering, which is an insignificant extra solution activity (MPEP 2106.05(g))).
…by the shipper behavioral data management tool… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…by one or more computer processors from the shipper behavioral data management tool… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…by the one or more computer processors… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
…by the one or more computer processors using a shipper behavior learning model… (i.e., the generic computer components recited in this limitation merely add the words “apply it”, or an equivalent, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f))).
Since the claim does not contain any other additional elements, that amount to integration into a practical application, the claim is directed to an abstract idea.
In Step 2B of the 101 analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (V), under the broadest reasonable interpretation, recite steps of mere data gathering, which has been recognized by the courts as being well-understood, routine, and conventional functions. Specifically, the courts have recognized computer functions directed to mere data gathering as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018)).
Examiner uses Berkheimer: Option 2, a citation to one or more of the court decisions discussed in MPEP 2106.05(d)(II) as noting well-understood, routine, and conventional nature of the additional elements:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Further, limitations (VI-VIII), under the broadest reasonable interpretation, merely recite steps that apply generic computer components to perform judicial exceptions, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Similarly, limitation (IX), under the broadest reasonable interpretation, merely recite steps that apply a generic machine learning model as a tool, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Considering additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claims 15-17, the claims are similar to claims 2-4 and rejected under the same rationales.
Regarding claim 20, it is dependent upon claim 14 and fails to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. For example, claim 20 recites wherein the shipper behavior learning model comprises at least one of a random forest based machine learning model or a gradient boosting based machine learning model. Under the broadest reasonable interpretation, the limitations merely recite steps that apply a generic random forest or generic gradient boosted model as a tool, which represents merely adding the words “apply it”, or an equivalent, which are not indicative of an inventive concept (MPEP 2106.05(f)). Therefore, claim 20 does not solve the deficiencies of claim 14.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-9, 11-15, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bateman, US Pre-Grant Publication 2017/0154347A1 (“Bateman”) in view of Clem, et al., US Pre-Grant Publication 2018/0365634A1 (“Clem”).
Regarding claim 1, Bateman discloses:
A method comprising: receiving, by a shipper behavioral data management tool, raw shipper behavioral data associated with a shipper scheduled to ship at least one parcel; (Bateman, ⁋11, “As shown in FIGS. 1 and 2, an embodiment of a method for predicting transit time for a parcel [scheduled to ship at least one parcel;] associated with a user includes: retrieving historical delivery data (e.g., past delivery data) from a plurality of shipping carriers S110 [receiving…raw shipper behavioral data associated with a shipper]”, and Bateman, ⁋12, “The method 100 and/or system 200 [by a shipper behavioral data management tool,] function to use parcel data (e.g., tracking data collected in S140) in combination with historic delivery data across multiple carriers”).
parsing, by the shipper behavioral data management tool, the raw shipper behavior data into a shipper information unit, (Bateman, ⁋18, “Block S110 recites: retrieving historical delivery data from a plurality of shipping carriers. Block S110 functions to collect past data informative of deliveries administered by shipping carriers [parsing, by the shipper behavioral data management tool, the raw shipper behavior data into a shipper information unit,]”).
wherein the shipper information unit comprises a subset of the shipper behavioral data; (Bateman, ⁋19, “Regarding Block S110, retrieving delivery data preferably includes retrieving cross-carrier delivery data (e.g., historical delivery records originating and/or associated with a plurality of shipping carriers, such as both USPS and UPS), but can alternatively include retrieving delivery data from a single carrier and/or other suitable entity [wherein the shipper information unit comprises a subset of the shipper behavioral data;].”).
accessing,…from the shipper behavioral data management tool, the shipper information unit; (Bateman, ⁋18, “Block S110 recites: retrieving historical delivery data from a plurality of shipping carriers [accessing,…from the shipper behavioral data management tool, the shipper information unit;].”).
extracting…a set of features from the shipper information unit, wherein each feature of the set of features represents a characteristic of at least one of the shipper or the at least one parcel; (Bateman, ⁋29, “Block S120 functions to normalize (e.g., standardize, filter, convert, otherwise process, etc.) the delivery data into a form suitable for use in generating a delivery prediction model (e.g., in Block S130). Block S120 preferably includes generating cross-carrier delivery features (e.g., features generated from delivery data spanning across a plurality of shipping carriers), but can include generating any suitable delivery features including any one or more of: address features, delivery route features, contextual features (e.g., weather features, demographic features, etc.) [extracting…a set of features from the shipper information unit, wherein each feature of the set of features represents a characteristic of at least one of the shipper or the at least one parcel;].”).
and processing,…using a shipper behavior learning model, the set of features to generate an output comprising a set of probability measures (Bateman, ⁋48, “Block S160 functions to determine a delivery estimate for one or more parcels [the set of features to generate an output comprising a set of probability measures] scheduled to be delivered or in the process of being delivered. Delivery estimates can include any one or more of: temporal parameters (e.g., delivery day, time, time window, amount of time until delivery, etc.), delivery-related data (e.g., confidence levels, type of parcels being delivered, probable parcel pick-up times, tracking status changes, mode of transportation, location of delivery vehicle, delivery vehicle analytics such as driver behavior, etc.) and/or any other suitable data. Determining a delivery estimate preferably is based on executing one or more delivery prediction models [and processing,…using a shipper behavior learning model,]”).
wherein each probability measure of the set of probability measures represents a probability of the shipper being a member of a corresponding shipper class from a set of shipper classes. (Bateman, ⁋48, “Delivery estimates can include any one or more of: temporal parameters (e.g., delivery day, time, time window, amount of time until delivery, etc.), delivery-related data (e.g., confidence levels, type of parcels being delivered, probable parcel pick-up times, tracking status changes, mode of transportation, location of delivery vehicle, delivery vehicle analytics such as driver behavior, etc.) and/or any other suitable data [wherein each probability measure of the set of probability measures represents a probability of the shipper being a member of a corresponding shipper class from a set of shipper classes.].”).
While Bateman teaches a system that estimates package delivery times based on shipper behavior, Bateman does not explicitly teach:
by one or more computer processors
Clem teaches by one or more computer processors (Clem, ⁋49, “The computing devices may include one or more electronic storages (e.g., prediction model database(s) 132, shipping information database(s) 134, or other electric storages), one or more physical processors programmed with one or more computer program instructions, and/or other components [by one or more computer processors].”).
Bateman and Clem are both in the same field of endeavor (i.e. shipping efficiency). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bateman and Clem to teach the above limitation(s). The motivation for doing so is that a computer and its components are required in order to run a machine learning process.
Regarding claim 2, Bateman in view of Clem teaches the method of claim 1. Bateman further teaches wherein extracting the set of features comprises generating at least one feature in the set of features by categorizing an element found in the shipper information unit so that the at least one feature represents a category of the characteristic of the at least one of the shipper or the at least one parcel. (Bateman, ⁋45, “the parcel data into a form suitable for input into a delivery prediction model to generate a delivery estimate (e.g., in Block S160). Parcel features preferably include conditioned parcel data (e.g., standardized parcel weight; carrier-provided delivery estimate converted into a standardized form used by a shipping services platform across a plurality of shipping carriers, etc.), but can additionally or alternatively include raw parcel data, parcel feature types analogous to types of delivery features (e.g., in Block S120) [generating at least one feature in the set of features by categorizing an element found in the shipper information unit], and/or any other suitable data. Parcel features preferably share at least a portion of feature types with the delivery features (e.g., generated in Block S120) [so that the at least one feature represents a category of the characteristic of the at least one of the shipper or the at least one parcel.]”).
Regarding claim 4, Bateman in view of Clem teaches the method of claim 1. Bateman further teaches wherein the shipper behavioral data management tool is configured to centrally collect and manage shipper behavior data provided by at least one of different service points, different vehicles, or different mobile computing entities. (Bateman, ⁋19, “Regarding Block S110, retrieving delivery data preferably includes retrieving cross-carrier delivery data (e.g., historical delivery records originating and/or associated with a plurality of shipping carriers, such as both USPS and UPS) [wherein the shipper behavioral data management tool is configured to centrally collect and manage shipper behavior data provided by at least one of different service points, different vehicles, or different mobile computing entities.]”).
Regarding claim 5, Bateman in view of Clem teaches the method of claim 1. Bateman further teaches wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units derived from historical shipper behavioral data with respect to the set of shipper classes. (Bateman, ⁋36, “In another variation, Block S130 includes training a delivery prediction machine learning model [wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units]. In a specific example, a training sample can correspond to a single delivery by a shipping carrier. The training sample can include delivery features (e.g., vectorized; generated in Block S120, etc.) and a corresponding label (e.g., actual delivery time for the delivery, which can be directly obtained from the raw delivery data and/or through processing of the raw delivery data such as calculating the amount of minutes between timestamps for an “Out for Delivery” status change and a “Delivered” status change, etc.) [derived from historical shipper behavioral data with respect to the set of shipper classes.].”).
Regarding claim 6, Bateman in view of Clem teaches the method of claim 1. Bateman further teaches further comprising normalizing, by the shipper behavioral data management tool, the subset of the shipping behavioral data found in the shipper information unit. (Bateman, ⁋29, “Block S120 functions to normalize (e.g., standardize, filter, convert, otherwise process, etc.) the delivery data into a form suitable for use in generating a delivery prediction model (e.g., in Block S130) [further comprising normalizing, by the shipper behavioral data management tool, the subset of the shipping behavioral data found in the shipper information unit.].”).
Regarding claim 7, Bateman in view of Clem teaches the method of claim 1. Bateman further teaches wherein the set of shipper classes comprises a timely shipper class, an early shipper class, and a late shipper class. (Bateman, ⁋48, “Delivery estimates can include any one or more of: temporal parameters (e.g., delivery day, time, time window, amount of time until delivery, etc.), delivery-related data (e.g., confidence levels, type of parcels being delivered, probable parcel pick-up times, tracking status changes [wherein the set of shipper classes]”, and Bateman, ⁋19, “status change data (e.g., timestamps associated with a status change, types of status changes including pre-transit, in transit, out for delivery, available for pickup, delivered, returned to sender, failure, cancelled, error, etc.) [comprises a timely shipper class, an early shipper class, and a late shipper class.]”).
Regarding claim 8, the claim is similar to claim 1 and rejected under the same rationales. Bateman further teaches the additional limitation and process, using a shipper behavior learning model, the set of features to generate an output comprising a probability measure, wherein the probability measure of represents a probability of a timeliness of the at least one parcel arriving at a sort. (Bateman, ⁋48, “Block S160 functions to determine a delivery estimate for one or more parcels [the set of features to generate an output comprising a probability measure,] scheduled to be delivered or in the process of being delivered. Delivery estimates can include any one or more of: temporal parameters (e.g., delivery day, time, time window, amount of time until delivery, etc.), delivery-related data (e.g., confidence levels, type of parcels being delivered, probable parcel pick-up times, tracking status changes, mode of transportation, location of delivery vehicle, delivery vehicle analytics such as driver behavior, etc.) and/or any other suitable data [wherein the probability measure of represents a probability of a timeliness of the at least one parcel arriving at a sort.]. Determining a delivery estimate preferably is based on executing one or more delivery prediction models [and process, using a shipper behavior learning model,]”).
Clem further teaches the additional limitation of and computing hardware configured to: (Clem, ⁋49, “The computing devices may include one or more electronic storages (e.g., prediction model database(s) 132, shipping information database(s) 134, or other electric storages), one or more physical processors programmed with one or more computer program instructions, and/or other components [and computing hardware configured to:].”).
Bateman and Clem are both in the same field of endeavor (i.e. shipping efficiency). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bateman and Clem to teach the above limitation(s). The motivation for doing so is that a computer and its components are required in order to run a machine learning process.
Regarding claim 9, the claim is similar to claim 2 and rejected under the same rationales.
Regarding claim 11, the claim is similar to claim 4 and rejected under the same rationales.
Regarding claim 12, Bateman in view of Clem teaches the system of claim 8. Bateman further teaches wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units derived from historical shipper behavioral data with respect to timeliness of parcels arriving at sorts. (Bateman, ⁋36, “In another variation, Block S130 includes training a delivery prediction machine learning model [wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units]. In a specific example, a training sample can correspond to a single delivery by a shipping carrier. The training sample can include delivery features (e.g., vectorized; generated in Block S120, etc.) and a corresponding label (e.g., actual delivery time for the delivery, which can be directly obtained from the raw delivery data and/or through processing of the raw delivery data such as calculating the amount of minutes between timestamps for an “Out for Delivery” status change and a “Delivered” status change, etc.) [derived from historical shipper behavioral data with respect to timeliness of parcels arriving at sorts.].”).
Regarding claim 13, the claim is similar to claim 6 and rejected under the same rationales.
Regarding claim 14, the claim is similar to claim 1 and rejected under the same rationales. Bateman further teaches the additional limitation and processing,…using a shipper behavior learning model, the set of features to generate a prediction output corresponding to a size of the at least one parcel. (Bateman, ⁋48, “Block S160 functions to determine a delivery estimate for one or more parcels [the set of features to generate a prediction output] scheduled to be delivered or in the process of being delivered. Delivery estimates can include any one or more of: temporal parameters (e.g., delivery day, time, time window, amount of time until delivery, etc.), delivery-related data (e.g., confidence levels, type of parcels being delivered, probable parcel pick-up times, tracking status changes, mode of transportation, location of delivery vehicle, delivery vehicle analytics such as driver behavior, etc.) and/or any other suitable data. Determining a delivery estimate preferably is based on executing one or more delivery prediction models [and processing,…using a shipper behavior learning model,]”, and Bateman, ⁋30, “Regarding Block S120, determining delivery features is preferably based on processing delivery data according to one or more computer-implemented rules (e.g., a feature engineering rule, a user preference rule, etc.), but delivery features can be determined based on any suitable information… additionally or alternatively include applying computer-implemented rules to process delivery data on a parcel-specific basis (e.g., generating features for packages above certain dimensions in a different manner than for packages below certain dimensions, where delivery features useful for tracking letter size packages may not be useful for tracking oversized and/or heavy packages, etc.) [corresponding to a size of the at least one parcel.]”).
Regarding claim 15, the claim is similar to claim 2 and rejected under the same rationales.
Regarding claim 17, the claim is similar to claim 4 and rejected under the same rationales.
Regarding claim 19, the claim is similar to claim 6 and rejected under the same rationales.
Regarding claim 20, Bateman in view of Clem teaches the method of claim 14. Bateman further teaches wherein the shipper behavior learning model comprises at least one of a random forest based machine learning model or a gradient boosting based machine learning model. (Bateman, ⁋36, “Additionally or alternatively, Block S130 and/or other portions of the method 100 can implement any one or more of:…a decision tree learning method (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, chi-squared automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.) [wherein the shipper behavior learning model comprises at least one of a random forest based machine learning model or a gradient boosting based machine learning model.]”).
Claims 3, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bateman, US Pre-Grant Publication 2017/0154347A1 (“Bateman”) in view of Clem, et al., US Pre-Grant Publication 2018/0365634A1 (“Clem”) and further in view of Garcia, et al., Non-Patent Literature “Big data preprocessing: methods and prospects” (“Garcia”).
Regarding claim 3, Bateman in view of Clem teaches the method of claim 1. However, the combination does not explicitly teach wherein extracting the set of features comprises generating at least two features in the set of features by separating an element present in the shipper information unit.
Garcia teaches wherein extracting the set of features comprises generating at least two features in the set of features by separating an element present in the shipper information unit. (Garcia, pg. 8, “Discretization is gaining more and more consideration in the scientific community [36] and it is one of the most used data preprocessing techniques. It transforms quantitative data into qualitative data by dividing the numerical features into a limited number of non-overlapped intervals. Using the boundaries generated, each numerical value is mapped to each interval, thus becoming discrete [wherein extracting the set of features comprises generating at least two features in the set of features by separating an element present in the shipper information unit.].”).
Bateman, in view of Clem, and Garcia are both in the same field of endeavor (i.e. machine learning). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bateman, in view of Clem, and Garcia to teach the above limitation(s). The motivation for doing so is that discretization improves learning speeds by simplifying data (cf. Garcia, pg. 8, “Discretization also produce added benefits. The first is data simplification and reduction, helping to produce a faster and more accurate learning. The second is readability, as discrete attributes are usually easier to understand, use and explain [36].”).
Regarding claim 10, the claim is similar to claim 3 and rejected under the same rationales.
Regarding claim 16, the claim is similar to claim 3 and rejected under the same rationales.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Bateman, US Pre-Grant Publication 2017/0154347A1 (“Bateman”) in view of Clem, et al., US Pre-Grant Publication 2018/0365634A1 (“Clem”) and further in view of Bielefeld, et al., US Pre-Grant Publication 2007/0016538A1 (“Bielefeld”).
Regarding claim 18, Bateman in view of Clem teaches the method of claim 1. Bateman further teaches wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units…(Bateman, ⁋36, “In another variation, Block S130 includes training a delivery prediction machine learning model [wherein the shipper behavior learning model has been trained to recognize patterns within a set of shipper information units…].”).
However, the combination does not explicitly teach derived from historical shipper behavioral data with respect to various sizes of parcels.
Bielefeld teaches derived from historical shipper behavioral data with respect to various sizes of parcels. (Bielefeld, ⁋9, “In one embodiment of the present invention, a system for estimating the size of a select parcel having a weight and associated with one of a plurality of size categories is provided. This system includes one or more databases storing data associated with a plurality of parcels, said data including weight data, size data, and size category data for each of said plurality of parcels and a size estimating module [derived from historical shipper behavioral data with respect to various sizes of parcels.].”).
Bateman, in view of Clem, and Bielefeld are both in the same field of endeavor (i.e. shipping efficiency). It would have been obvious for a person having ordinary skill in the art before the effective filing date of the claimed invention to combine Bateman, in view of Clem, and Bielefeld to teach the above limitation(s). The motivation for doing so is that knowing various packages sizes improves downstream scheduling decisions (cf. Bielefeld, ⁋5, “determining future equipment needs such as, for example, the number of trucks needed to transport an expected volume of parcels from point to point. A parameter presently used to determine these needs is the number of items per container for a particular route, which is often referred to as the container density.”).
Conclusion
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/N.S.W./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148