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 .
Status of Claims
The following is a non-final Office Action in order to update the explanation provided for why each claim is considered ineligible. Claims 1, 9, 10, 13, and 15-17 have been amended. Claims 1-17 are pending and rejected below.
Response to amendments
The amendments to claims 1, 9, 10, 13 and 15-17 are sufficient to overcome the claim objections set forth in the previous office action. These claim objections have been withdrawn. However, upon further review claim objections have been set forth below.
Response to arguments
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections of claims 1-17 have been considered but are moot because the new ground of rejections set forth below.
Applicant’s arguments with respect to the 35 U.S.C. 101 rejections have been fully considered, but they are not persuasive
Applicant argues that the amended claims, as a whole, cannot be practically performed in the human mind or be considered human activity since the combination of automatically collecting cargo logistics parameters of negatively impacted cargo by RFID chips attached to the cargo, by automatically tracking the RFID chips by tracking devices via data transmission interfaces, and further by tracking new cargo by RFID chips to predict probabilities for a possible negative impacting event during transportation cannot be carried out by a human mind.
Examiner respectfully disagrees. With regard to mental processes, per MPEP 2106.04(a)(2) III.C., claims can recite a mental process even if they are claimed as being performed on a computer. Here, based on the BRI, the claim recites limitations that can be performed in the human mind (an individual performing the claimed limitations mentally or with a pencil and paper) and that concept is merely claimed in a computer environment and/or is merely using a computer as a tool to perform the concept. The courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Here, while the claims are in a digital system environment (that includes data receipt, capture and transmission using, for example, sensors, RFID, and GPS), the receiving of information, the selection of cargo and service provider(s) and the generating / providing / aggregating of risk factors is analogous to how one (a person) would predict and generate risk measures and potential impacts in such a logistics environment. Examiner also notes that the claim is not limited to RFID; rather the tracking devices comprise RFID or telematic devices or GPS sensors installed at the cargo.
Similarly, with regards human activity, see MPEP 2106.04(a)II. where the certain methods of organizing human activity sub-groupings encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the certain methods of organizing human activity grouping. This section of the MPEP has many examples of Court decisions that recite certain methods of organizing human activities while involving computers and other machinery in the claims.
Applicant further argues that the ordered combination of steps reflects the technical improvement described in the disclosure per Desjardins.
Examiner respectfully disagrees. It is noted that in the instant remarks, applicant does not set forth portions of the specification that describe the improvement or identify the specific limitations in the claimed invention that set forth the improvement. Looking to the specification, on page 14 there is discussion about the logistics services being improved with respect to minimized risks and cargo damage, timelines and quality, (page 14); on page 22, it discusses optimizing the set of measurable cargo logistics parameters for a cargo logistics services package to minimize the probability of the occurrence of a negative impact for the selected cargo logistics parameters of the cargo logistics service package; and page 13 discusses the optimization structure comprises an optimization algorithm based on combinatorial optimization to find the combination of cargo logistics services parameters with the lowest aggregated risk measure. Other areas of the specification discuss optimization and improvement with respect to risk and cargo logistics services parameters. However, these appear to improve the recited abstract idea and not to provide an improvement to the functioning of the computer or another technology or technical field. MPEP 2106.04(d)(1). Examiner recommends that details from the disclosure and/or claims be specifically referenced so they may be considered.
Finally, Applicant argues that even if the claims do recite an abstract idea they are not directed to an abstract idea; the improvement is recited at a proper level of specificity and the office action should not evaluate the claim and its additional elements at such a high level of generality.
Examiner respectfully disagrees. Please see comments above regarding the argued improvement. Examiner relied upon MPEP 2106.05(f) in considering whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or to more than mere instructions to implement an abstract idea on a computer, as well as when evaluating the particularity or generality of the application of the recited abstract idea. The claim was considered as a whole and the rejection below provides explanation addressing each of the additional elements. Examiner considered the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. Examiner also considered whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Based on the BRI of the currently claimed invention, it is not agreed that the additional limitations integrate the recited abstract idea into a practical application or provide significantly more.
Claim objection
Claims 1-17 are objected to because of the following informalities:
Claim 1 recites “providing the at least one generated risk factor to the allocation structure of the processor, each of the risk factors at least corresponds”. “Each of the risk factors” should instead be --each of the at least one generated risk factor--. Claim 16 also contains this language. Appropriate correction is required.
Also in claim 1, claim 1 recites “generating at least one risk factor […] based on the captured sets of the measured cargo logistic parameters,” which should instead be –logistics--. Claim 16 also contains this language. Appropriate correction is required.
Claims 2-15 and 17 depend from claims 1 and 16 and are also objected to based on their dependency.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In the generating limitation of claim 1, claim 1 recites “with the measured parameter values of the impact strength and impact types and/or measured quantified damages”. First, there is no antecedent basis for “the measured parameter values”. The claim previously recited “the cargo logistics parameters measuring the impact strength or impact type” and thus the term “the measured parameter values” is interpreted as the cargo logistics parameters. Second, the claim previously recited that these parameters measured the impact strength or impact type, whereas in the generating step it states it is the impact strength and impact type. This limitation is unclear because previously the claim only measured the impact strength or impact type. Clarification is required. Claim 16 includes a substantially similar limitation and is rejected on the same basis.
Additionally, claim 1 recites “automatically aggregating an aggregated risk measure for the specified cargo by an aggregating structure of the processor based on the at least one risk factors allocated to the cargo logistics parameters of the set of cargo logistics parameters of the specified cargo.” Previously the claim generated “at least one risk factor indicating a measured impact probability for the specified cargo or the specified cargo logistics services.” Thus, it is unclear how an aggregated risk measure could be based on the at least one risk factors (previously determined in the generating step) in the instance when it is only for the specified cargo (if only risk factors for the specified cargo logistics services were generated). Clarification is requested. For examination purposes, this is interpreted as automatically aggregating an aggregated risk measure for the specified cargo or the specified cargo logistics services. See page 7, lines 19-20 of the specification. Claim 16 includes a substantially similar limitation and is rejected on the same basis.
Claims 2-15 and 17 depend from claims 1 and 16, inheriting these deficiencies and thus rejected for the same reasons set forth above.
With respect to claim 10, claim 10 recites “wherein the allocation structure assigns a risk factor to a measurable cargo logistics parameter by […]” and “receiving a measurable cargo logistics services parameter.” Claim 1 previously recited “the measured cargo logistics parameters” and “generating at least one risk factor […] based on the captured sets of the measured cargo logistic parameters” the measured cargo logistics parameters”. Claim 1 also recited “providing the at least one generated risk factor to the allocation structure of the processor […] the allocation structure assigning the at least one generated risk factor to the one or more logistics services of the specified cargo.” It is unclear whether “a measurable cargo logistics parameter” in claim 10 is the same measurable cargo logistics parameter of claim 1 and whether “a risk factor” of claim 10 is the same at least one generated risk of claim 1. This is because in claim 1, the at least one risk factor is generated using the measurable cargo logistics parameter. The instant specification does not appear to have separate risk factors that are assigned to the measurable cargo logistics parameter and generated using the measurable cargo logistics parameters in the manner claimed. See for example page 7, lines 1-25; page 21, line 33 through page 22, line 12; page 23, lines 3-24. Thus, the scope of the claim is unclear. Clarification is requested.
Claim 10 also recites “selecting at least one risk factor.” Similar to the discussion above, it is unclear if this is the same or a different at least one risk factor as that which is recited in claim 1 (“generating at least one risk factor”). Clarification is requested.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to Step 2A Prong One, the claims recite an abstract idea. Claims 1 and 16 recite limitations about predicting and measuring risk measures for an occurrence of an impact event on cargo, including:
measuring cargo logistics parameters of cargo with cargo logistics services, the cargo being negatively impacted by the impact event by physical loss or damage to a cargo and cargo logistics service and recording the measured cargo logistics parameters as data parameters, the cargo logistics parameters measuring the impact strength or impact type of the negative impact on the cargo or on the cargo logistics services, and/or a quantified damage at the cargo or on the cargo logistics services resulting from the negative impact,
selecting a specified cargo and/or one or more logistics services for the specified cargo, the logistics services of the specified cargo being defined by a received set of cargo logistics parameters,
capturing sets of the measured cargo logistics parameters as logistics input of the cargo logistics services and transmitting the sets of the measured cargo logistics parameters to an allocation structure, the measured cargo logistics parameters at least comprises cargo parameter values capturing cargo characteristics including cargo weight and/or cargo size and/or cargo fragility and/or cargo value, and logistics parameter values capturing logistics characteristics including cargo packaging design and/or departure location characteristics and/or destination location characteristics and/or transportation channel characteristics and/or transportation means,
wherein the cargo and logistics parameters are at least partially detected and transmitted and measure parameter values for geo location of the cargo, the orientation of the cargo or the velocity of the cargo and transmitting the parameter values,
generating at least one risk factor indicating a measured impact probability for the specified cargo or the specified cargo logistics services by a risk modelling structure based on the captured sets of the measured cargo logistic parameters with the measured parameter values of the impact strength and impact types and/or measured quantified damages,
providing the at least one generated risk factor to the allocation structure, each of the risk factors at least corresponds to a measured impact strength or impact type of a negative impact on the cargo or the cargo logistics services, or a quantified damage at the cargo or on the cargo logistics services resulting from the impact, the allocation structure assigning the at least one generated risk factor to the one or more logistics services of the specified cargo, and
automatically aggregating an aggregated risk measure for the specified cargo by an aggregating structure based on the at least one risk factors allocated to the cargo logistics parameters of the set of cargo logistics parameters of the specified cargo, and
providing the aggregated risk measure for the specified cargo as output to predict an occurrence of a measurable negative impact on cargo and/or the cargo logistics services.
These recited limitations reasonably fall within the abstract idea grouping of certain methods of organizing human activity. They specifically relate to fundamental economic practices or principles as well as commercial interactions of marketing or sales activities or behaviors in that that claims involve the commercial practices of service providers in supply chain systems / logistics, as well as risk management and modeling to assess and predict risk factors and the impact of risks on the cargo.
Further, the gathering of cargo related parameters and information, the selection of cargo and/or logistics service(s), and the generating / providing / aggregating of at least one risk factor indicating a measured impact probability and impact value for the specified cargo or the specified cargo logistics services is also reasonably categorized as mental processes. Per MPEP 2106.04(a)(2) III.C., claims can recite a mental process even if they are claimed as being performed in a computing environment and / or is merely using a computer as a tool to perform the concept.. While in a digital system environment (that includes data receipt, capture and transmission using, for example, sensors, RFID, and GPS), the receiving of information, the selection of cargo and service provider(s) and the generating / providing / aggregating of risk factors is analogous to how a person would predict and generate risk measures and potential impacts in such a logistics environment.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claims 1 and 16 include the following additional elements: a processor, at least one data interface associated with accessing a database, recording data to a database, capturing input signals from the database via a data interface and transmitting to a structure of a processor, data partially detected and transmitted by tracking devices comprising Radio Frequency Identification (RFID) chips or telematic devices or Global Position System (GPS) sensors installed at the cargo, the tracking devices measuring values and transmitting the parameter values via a data transmission network to the database, that certain aspects being performed by the processor, and providing an output signal by a signal generator.
When considered in view of the claim as a whole, Examiner submits that the additional elements do not integrate the abstract idea into a practical application because these elements are claimed at a high level of generality in a manner that merely uses the computer elements and other machinery as a tool its ordinary capacity (e.g., to receive, store, or transmit data) for economic or other tasks. The processor, data interface associated with accessing a database, recording data to a database, capturing input signals from the database via a data interface and transmitting to a structure of a processor are claimed in a generic manner and amount to mere instructions to apply the abstract idea on a computer. Further, data partially detected and transmitted by tracking devices comprising RFID chips or telematic devices or GPS sensors installed at the cargo, the tracking devices measuring values and transmitting the values via a data transmission network to the database, and providing an output signal by a signal generator again is claimed at a high level of generality and uses these computing components and/or machinery in a its ordinary capacity to capture, receive, store, and transmit data necessary for the recited abstract process. See MPEP 2106.05(f).
As for the capturing input signals via a data interface, transmitting data to a structure of a processor, data transmitted by tracking devices comprising RFID chips or telematic devices or GPS sensors installed at the cargo, transmitting the parameter values via a data transmission network, and providing an output signal by a signal generator, these aspects are also viewed as insignificant extra-solution activity that amounts to mere data gathering, selecting of a particular data source or type of data to be manipulated, and outputting. See MPEP 2106.05(g).
Thus, claims 1 and 16 do not include additional elements that would integrate the recited abstract idea into a practical application.
With respect to Step 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. As discussed above, when considering the claim as a whole and the additional elements alone and in combination, Examiner submits that the additional elements do not amount to significantly more than the abstract idea because, as discussed above, these additional elements are recited at a high level of generality and uses these computing components and/or machinery in a its ordinary capacity.
As for the capturing input signals via a data interface, transmitting data to a structure of a processor, data transmitted by tracking devices comprising RFID chips or telematic devices or GPS sensors installed at the cargo, transmitting the parameter values via a data transmission network, and providing an output signal by a signal generator, these aspects were also viewed as insignificant extra-solution activity that amounts to mere data gathering, selecting of a particular data source or type of data to be manipulated, and outputting. See MPEP 2106.05(g).
These additional elements have been re-evaluated and it has been determined they are claimed in a generic manner, amount to mere data gathering and output, and define only well-understood, routine, conventional activity. See MPEP 2106.05(d). The Courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in at a high level of generality: Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Receiving or transmitting data over a network, 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 also Cornell (US 10,854,055), Figures 1, 5A-E, 6; Col 5, lines 49-59; Col 16, line 44 – Col 17, line 2; Col 19, lines 13-51; and Col 23, lines 33-51.
Thus, claims 1 and 16 do not include additional elements that provide significantly more.
As for the dependent claims, claim 3-8, 11-12, 14 do not include further additional elements beyond those identified for claims 1 and 16 above, and thus serve to further narrow the recited abstract idea.
The following dependent claims contain further additional elements – claim 2 (extended output signal to database by the signal generator via a data interface); 9, 10, 13 (risk factor database, processor, input signal); 15 (artificial intelligence structure comprising a machine learning model); and 17 (persistent data storage).
With regard to claim 15, the artificial intelligence structure comprising a machine learning model generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). In additional, the AI structure comprising an ML model is claimed at a high level of generality and recites an idea of a solution with the details of how that solution is accomplished. See MPEP 2106.05(f). When considered as a whole and in combination with the features of claims 1 and 12, this is still viewed to be at a high level of generality that does not integrate the recited abstract idea into a practical application or provide significantly more.
With regards to claims 2, 9, 10, 13, and 17, the extended output signal to database by the signal generator via a data interface, the risk factor database, processor, input signal, and persistent data storage are claimed at a high level of generality in a manner that merely uses the computer elements and other machinery as a tool its ordinary capacity (e.g., to receive, store, or transmit data) for economic or other tasks. When considered as a whole and in combination with the features of claims 1 and 16, this is still viewed to be at a high level of generality that does not integrate the recited abstract idea into a practical application or provide significantly more.
Accordingly, claims 1-17 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-9, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kalinski (US 2022/0374797).
As per claim 1, Kalinski discloses a method for a digital system for predicting and measuring risk measures for an occurrence of an impact event impacting physical loss or damage on cargo or cargo logistics services, the impact event having an impact with a measurable impact strength related to an impact type on the cargo or the cargo logistics services, the impact strength being quantified by measuring an impact severity or intensity and an impact duration and/or an impact frequency, the method comprising:
measuring cargo logistics parameters of cargo with cargo logistics services, the cargo being negatively impacted by the impact event by physical loss or damage to a cargo and cargo logistics service and recording the measured cargo logistics parameters as data parameters to a cargo logistics services database, the cargo logistics parameters measuring the impact strength or impact type of the negative impact on the cargo or on the cargo logistics services, and/or a quantified damage at the cargo or on the cargo logistics services resulting from the negative impact (See at least figures 4C (element 418) and 4F, paragraphs [0078], [0081], [0150]-[0151], [0154], [0202], [0225], where data is collected, stored and/or transmitted about impact events and types, such as theft, loss history, and damage to the cargo. Historical information is stored and information is collection using devices and/or sensors);
selecting a specified cargo and/or one or more logistics services for the specified cargo, the logistics services of the specified cargo being defined by a received set of cargo logistics parameters (See paragraphs [0058]-[0059], [0094], [0135], [0152], where there is an identified cargo shipment and details of that shipment),
capturing sets of the measured cargo logistics parameters as logistics input signals from the cargo logistics services database via a data interface and transmitting the sets of the measured cargo logistics parameters to an allocation structure of a processor, the set of the measured cargo logistics parameters from the cargo logistics services database at least comprises cargo parameter values capturing cargo characteristics including cargo weight and/or cargo size and/or cargo fragility and/or cargo value, and logistics parameter values capturing logistics characteristics including cargo packaging design and/or departure location characteristics and/or destination location characteristics and/or transportation channel characteristics and/or transportation means (See paragraphs [0116], [0141], [0152]-[0153], and [0202] disclosing captured parameters and details of the cargo shipment, the details and information including monetary value, shipment origination and destination location, weight value type. The system software interfaces with internal and external databases, and transmits information. See also paragraph [0086] and [0148]).
wherein the cargo and logistics parameters are at least partially detected and transmitted by tracking devices comprising Radio Frequency Identification (RFID) chips or telematic devices or Global Position System (GPS) sensors installed at the cargo, the tracking devices measuring parameter values for geo location of the cargo, the orientation of the cargo or the velocity of the cargo and transmitting the parameter values via a data transmission network to the cargo logistics services database (See at least [0078]-[0079], [0081], [0102], discussing telematic devices, which can include GPS or RFID, that capture and transmit data about the cargo and logistics. See at least [0102], [0106]-[0108] which discuss at least location tracking functionality, global positioning, and velocity),
generating at least one risk factor indicating a measured impact probability for the specified cargo or the specified cargo logistics services by a risk modelling structure of the processor based on the captured sets of the measured cargo logistic parameters of the logistic input signals with the measured parameter values of the impact strength and impact types and/or measured quantified damages (See paragraphs [0116], [0148], [0171], [0224]-[0225] and [0255] which discuss determining risk factors and using risk models / risk modeling engines to generate risk probabilities and values. See also paragraph [0086] and [0272]),
providing the at least one generated risk factor to the allocation structure of the processor, each of the risk factors at least corresponds to a measured impact strength or impact type of a negative impact on the cargo or the cargo logistics services, or a quantified damage at the cargo or on the cargo logistics services resulting from the impact, the allocation structure assigning the at least one generated risk factor to the one or more logistics services of the specified cargo (See paragraphs [0148], [0171], [0224]-[0225] and [0255] which discuss the risk factors and values being provided to a processor and associated with the shipment and the plurality of service providers. See also paragraph [0272]),), and
automatically aggregating an aggregated risk measure for the specified cargo by an aggregating structure of the processor based on the at least one risk factors allocated to the cargo logistics parameters of the set of cargo logistics parameters of the specified cargo (See paragraphs [0206]-[0207], [0271]-[0272], discussing total aggregate risk distribution models and where the risk engine may determine total risk for a particular shipment based on an aggregation of individual risks), and
providing the aggregated risk measure for the specified cargo as output signal by a signal generator to predict an occurrence of a measurable negative impact on the cargo and/or the cargo logistics services (See paragraphs [0086], [0205]-[0207], [0219], [0271]-[0273] which disclose output signal generation and transmission, and output layers to output and provide the model risk probabilities. See also paragraph [0092], [0099]-[0101], [0103]).
As per claim 3, Kalinski discloses wherein at least one risk factor is based on measurable risk parameters indicating a damage in form of delay, damaging and/or loss of cargo, and/or in form of damaging of goods caused by the cargo logistics services (See paragraphs [0081], [0150], [0154], [0225], [0272] which discuss damage or loss of the cargo, including loss and theft and depreciation in value).
As per claim 4, Kalinski discloses wherein at least one risk factor is based on measurable risk parameter values capturing a time of delay, a quantified damaging extend and/or loss of the cargo, and/or a quantified damaging extend of goods caused by the cargo logistics services (See paragraphs [0150], [0232], [0244] which discloses loss in terms of shortfall, real time shipment data indicting delay in start or delivery time, as well as damage and depreciate in value)
As per claim 5, Kalinski discloses wherein a risk factor for an associated cargo logistics parameter is established by measuring the physical impact strength or impact type of impact events on the cargo and/or the cargo logistics services affected by the impact event, measuring the impact strength on the cargo or the cargo logistics services, measuring a damage of the cargo and/or the cargo logistics services caused by the impact event, and/or quantifying a probability of impact occurrence (See paragraphs [0116], [0148], [0171], [0224]-[0225] and [0255] which discuss risk factors and impact type, damage, and probability of occurrence. See also paragraph [0272]).
As per claim 6, Kalinski discloses wherein cargo logistics services include cargo handling, packaging, transportation, tracking, delivery and/or insuring, and/or logistics quoting, booking, scheduling, alerting, controlling and/or processing cargo formalities (See paragraphs [0056], [0064], [0125], [0257], [0270] which discusses at least transportation and insurance services. See also paragraphs [0056], [0106]-[0107], [0197] which discuss tracking).
As per claim 7, Kalinski discloses wherein the aggregated risk measure is determined based on historical measures of risk parameter values for one or more cargo logistics parameters defining the cargo logistic services, wherein the output signal is indicative of the aggregated risk measure for the cargo logistic services in respect to the set of measured values for the cargo logistics parameters (See [0271]-[0272], wherein historical data is used in the aggregated risk measure. See also paragraphs [0206]-[0207]).
As per claim 8, Kalinski discloses wherein the aggregated risk measure is generated by the aggregating structure and allocated to the set of measurable cargo logistics parameters, the aggregated risk measure providing an aggregated impact probability measure value for the cargo logistics services to be involved in one or more impact events having a negative impact with a measurable impact strength and/or impact type (See paragraphs [0206]-[0207], [0271]-[0272], discussing total aggregate risk distribution models and aggregate risk probability).
As per claim 9, Kalinski discloses wherein the processor and/or a risk factor database include a damage or risk modelling structure generating a risk factor for a measurable cargo logistics parameter based on a measured impact strength and/or impact type, and/or a measured quantified damage (See paragraphs [0056], [0078], [0081], [0116], [0150]-[0151], [0154], [0202], [0225], [0272], where data is collected, stored and/or transmitted about items such as theft, loss history, and damage to the cargo, and risk modeling methods are used).
Claim 16 recites substantially similar limitations to claim 1 and is therefore rejected using the same art and rationale set forth above.
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Kalinski (US 2022/0374797) in view of Lesesky (US 2003/0222770).
As per claim 2, Kalinski discloses wherein the aggregated risk measure is assigned to the set of measurable cargo logistics parameters by the allocation structure and provided as an output signal to the cargo logistics services database by the signal generator via a data interface (See paragraphs [0086], [0205]-[0207], [0219], [0271]-[0273] which disclose output signal generation and transmission, and output layers to output and provide the model risk probabilities. See also paragraphs [0092], [0099]-[0101], [0103]).
While Kalinski does disclose signal amplifiers, processors, modulators and the like (See paragraph [0099]), Kalinski does not expressly disclose an extended output signal. Lesesky discloses a signal booster and signal generator that extends the range of an output or transmission (See at least paragraphs [0026], [0028], [0029], [0031], where a signal generator and a signal booster is associated with a vehicle transporting cargo)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include extended output signals and the signal booster of Lesesky in the system of Kalinski because the signal booster can advantageously boost transmission signals from the transceiver to increase the transmission range or increase the strength of the signal. See paragraph [0029] of Lesesky.
11. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kalinski (US 2022/0374797) in view of Billeter (WO 2019234130).
As per claim 11, Kalinski discloses loss of the cargo by the cargo logistics services is generated based on the risk factors associated to the measured cargo logistics services parameter by the aggregating structure and further discloses loss as a risk factor used in the aggregated risk measure for the specified cargo (See paragraphs [0158], [0206]-[0207], [0271]-[0272] for aggregated risk. See at least paragraphs [0002], [0148], [0150]-[0151], [0155], for loss as a risk factor).
However, Kalinski does not explicitly disclose an aggregated loss risk measure indicating a loss.
In a risk quantifying system that considers multi-risk exposure and assessment, Billeter discloses an aggregated loss risk measure indicating a loss such as with regards to cargo (See page 38, lines 29-32, page 85, line 31 through page 86, line 2 and also lines 5-11, and page 87, lines 5-15, which discusses an aggregator and event losses aggregated by applying risk-transfer. Page 62, line 26 through page 63, line 5 discloses the application of the risk aggregation method to cargo and the supply chain environment).
Both Kalinski and Billeter discuss assessing aggregate risk with respect to cargo and insurance. Kalinski discloses an aggregate risk measure aggregated from risk factors including loss. It would have been obvious to one of ordinary skill in the art before the effective filing date to include the aggregated loss risk measure for loss in the system of Kalinski in order to better distinguish the different loss events with respect to the risks and impacts and the degree of loss outcome. See page 86, lines 5-11.
12. Claims 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kalinski (US 2022/0374797) in view of Shah et al. (US 2009/0248488).
As per claim 12, Kalinski teaches dynamically optimize the model cargo shipments, processed using the system / processor, optimizing the set of measurable cargo logistics parameters for a cargo logistics service, and that steps can be taken to lower risk (See paragraphs [0112], [0189], [0197]-[0198], that dynamically optimize the model cargo shipments and optimizes the parameters by removing low quality data. They also describe that steps that can be taken to lower risk). However, Kalinski does not explicitly disclose and optimization structure that optimizes the set of supply chain parameters to minimize the probability of the occurrence of an impact event on the logistic service indicated by an aggregated risk measure.
Shah et al. teaches an optimization structure of the processor optimizes the set of supply chain parameters to minimize the probability of the occurrence of an impact event on the logistic service indicated by an aggregated risk measure (See [0010] where the risk index provides an aggregated picture of risk exposure. See [0008], [0023]-[0025], [0050], [0089], [0100], and [0115], which a processor adapted to receive, process, and output data related to events in a supply chain, further discusses event and impact types, parameters of the risks, and applying mitigation to the events to solve an optimization problem to modify events and parameters).
Both Shah et al. and Kalinski disclose risks in a supply chain environment. It would have been obvious to one of ordinary skill in the art before the effective filing date to include the explicit risk mitigation of Shah et al. that optimizes the set of supply chain parameters to minimize the probability of the occurrence of an impact event in order to more effectively determine risk probabilities associated with moving cargo in the supply chain and determine actions to reduce risks. Se paragraph [0001] of Kalinski.
As per claim 15, Kalinski teaches wherein the aggregation structure and/or the optimization structure may be combined with an artificial intelligence structure comprising a machine learning algorithm (See figure 4D, paragraphs [0056]-[0057], [0120], [0206], and [0272] where a machine learning algorithm is utilized in the PSCI platform system).
13. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Kalinski (US 2022/0374797) in view of White (US 2021/0325906).
As per claim 17, Kalinski discloses wherein the cargo logistics services database and/or a risk factor database comprise data storage storing data representing the measurable cargo logistics services parameters and/or representing the risk factors (See at least figures 1, 2, and 3A, paragraph [0056]). However, Kalinski does not expressly disclose that the database comprises a persistent data storage.
In a system that considers risk factors and stores factors in a database for risk modeling and prediction, White discloses persistent data storage (See paragraphs [0137]).
It would have been obvious to include persistent data storage of White in the system databases of Kalinski databases 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kalinski (WO 2022236059) discusses risk assessment of losses occurring to cargo including risk probability values and factors that may impact the cargo shipment.
Clark et al. (US 2013/0027556) discusses cargo theft and loss and tracking systems that using devices such as GPS and RFID to track location information.
Jones (US 2019/0114714) discusses risk information with a cargo transport and generating risk management recommendations.
Tracy et al. (US 2011/0137685) teaches cargo with tracking equipment (such as GPS and RFID tagging), and considering risk factors like damage, theft and loss.
Boerger (US 2021/0082220) discloses managing and monitoring cargo, RFID sensors including impact sensors, and considering risks.
Tam (US 2022/0261483) teaches potential risks and an operational database of current and past incidents, applied to operational specific parameters around vessel type, cargo carried, and vessel route to identify interventions required to reduce risk, where aggregated risk profiles can be used.
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/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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