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 § 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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1/5 recites “extract an asset that is usable by a consignor from use status data…extract an asset owner satisfying the read score threshold from the extracted use status data”. The bolded limitations lacks proper antecedent basis. The earlier limitation does not extract use status data; it extracts an asset from use status data. Consequently, it is unclear whether “the extracted use status data refers to the earlier mentioned use status data or a different set of use status data. For examination purposes, the limitation will be interpreted to mean “the use status data”.
Claims 3/7 recite “a score close to a certain extent”. This limitation is a relative term of degree that does not provide an objective boundary for determining which asset owners are included in the comparison group. Neither the claims nor the specification provide a standard to ascertain the subjective limitation. Therefore, the limitation is indefinite. For examination purposes, the limitation will be interpreted “score is within a predetermined similarity range of the score of the asset owner being estimated”.
Claims 4/8 recites “an asset owner that is required in the operation plan”. This limitation renders the claim indefinite because it does not identify the owner-related variable used to calculate the reference value. It is unclear whether the limitation refers to: the identity or score of a particular required asset owner, a required number of asset owners; a required quantity of assets, the number of owners capable of supplying a required quantity of assets; or another requirement associated with an asset owner. The specification does not resolve the uncertainty. Paragraph 62 describes determining a reference value based on the minimum quantity of assets required to perform the operation plan, whereas paragraphs 66 and 83 describe determining or confirming the number of asset owners satisfying a score threshold. These disclosures present materially different possible inputs to the claimed reference value calculation.
For examination purposes, the limitation will be interpreted to mean “the number of asset owners needed to secure the minimum quantity of assets required to perform the operation plan”
Claims 2 and 5-6 are also rejected under 112b for failing to cure the deficiency above.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1/5 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim(s) 1/5 is/are directed towards a computer system (i.e. machine), a method (i.e. a process), respectively. Thus, each of the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Claim(s) 1/5 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “read, for an asset owner of the extracted usable asset, an evaluation index of a concern that is important to the consignor when making an operation plan for the asset, from evaluation data for evaluating the asset owner and the asset owned by the asset owner, read a score threshold for the evaluation index, which is determined according to a weight of the evaluation index, from policy data for determining an evaluation policy of an asset owner for each consignor, extract an asset owner satisfying the read score threshold from the extracted use status data, and output the operation plan for the asset to be used by the consignor based on asset data indicating a state of an asset owned by the extracted asset owner”.
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “creating an operation plan for a consignor” which is a method of organizing a human activity and mental process. That is, the method allows for fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); concepts performed in the human mind.
This judicial exception is not integrated into a practical application. In particular, the claim only recites “computer including a processor and memory” (claim 1/5). Each of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional element(s), alone or in combination, do(es) not integrate the abstract idea into a practical application because it/they do(es) not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s), alone or in combination, is/are nothing more than mere instructions to apply the exception on a general computer.
Dependent claim(s) 2-4 and 6-8 is/are also directed to an abstract idea without significantly more because it/they further narrow(s) the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4-6, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fujita (JP 2010231258) in view of Krieg (US 2018/0165642).
As per claim 1/5, Fujita discloses an information management system for supporting construction of an operation plan related to delivery of a cargo by using a computer including a processor and a memory,
wherein the processor is configured to extract an asset that is usable by a consignor from use status data indicating use status of assets schedule such as which vehicle is used for each delivery destination and which route is used for delivery (step 32).”,
output the operation plan for the asset to be used by the consignor based on asset data indicating a state of an asset
However, Fujita does not expressly teach that the candidate EVs are owned by different asset owners or that the asset owners are evaluated and filtered using consignor specific weighted performance criteria and thresholds.
But, Krieg discloses use status data indicating use status of assets of one or more asset owners ([0003] The shipment of goods between shippers and recipients is typically carried out by carriers that own assets (trucks, cars, cargo ships, airplanes, rail cars) and that are contracted by manufacturers and retailers of those goods…. [0021] Embodiments described herein provide a shipping management system that integrates with a variety of carrier systems, analyzes carrier tracking data and other data and allows selection of carriers on an individual shipment basis based on dynamic factors. The shipping management system can facilitate shipping across shippers for an account holder and enforce carrier selection policies for the account holder…. [0026] Shipping management system 120 can interact with carrier systems 150 to execute shipments, collect tracking information, or take other actions. According to one embodiment, shipping management system 120 may provide an interface 132 through which a user can define a shipment. The shipment information may include an account holder if different than the shipper (e.g., a drop shipper can indicate (or it may be implicit) that the shipment is on behalf of a particular retailer), recipient, from address (shipper), to address (consignee), weight, product class and dimensions and other information. [0027] In one embodiment, shipping management system applies rules to a combination of: [0028] transportation signals, such as cost for a specific item (specific weight, origin, destination), current conditions; [0029] historical signals such as carrier performance, both overall and area-specific; [0030] business signals such as desired carrier mix or spend threshold. Business signals may include recipient value signals such as lifetime value or a specific purchase value. [0031] In support of carrier selection, transportation signal data is collected as part of creating shipments. A transportation signal typically includes parameters necessary to order shipping, including information such as the shipment origin and destination, items (dimensions, weight and quantity). Other transportation signal data may include shipment preferences entered by the user.”)
read, for an asset owner of the extracted usable asset, an evaluation index of a concern that is important to the consignor when making an operation plan for the asset, from evaluation data for evaluating the asset owner and the asset owned by the asset owner ([0027] In one embodiment, shipping management system applies rules to a combination of: [0028] transportation signals, such as cost for a specific item (specific weight, origin, destination), current conditions; [0029] historical signals such as carrier performance, both overall and area-specific; [0030] business signals such as desired carrier mix or spend threshold. Business signals may include recipient value signals such as lifetime value or a specific purchase value. [0047] Selection module 136 may process historical signal data to generate one or more metrics for a carrier/mode. Examples include, but are not limited to, average transit time (ATT) from an origin to destination, on time percentage (OTP), delivery before exception percentage (DBE) (deliveries that were delivered without an exception prior to achieving a “delivered” status), average length of haul (LOH), average cost per pound per mile (cost/lb/mi), number of in transit shipments. DBE may account for all types of exceptions or only certain types of exceptions and there may be different DBEs for different types of exceptions (e.g., damage exception vs. delay exception)...
read a score threshold for the evaluation index, which is determined according to a weight of the evaluation index, from policy data for determining an evaluation policy of an asset owner for each consignor ([0051]… The factors may be weighted where the weights are tunable based user (e.g., account holder) preferences. If the shipper indicates that speed and on time delivery are more important for a shipment, the scoring algorithm may weight ATT and OTP more heavily than cost/lb/mi. On the other hand, if cost is a higher priority, a scoring algorithm that weights cost more heavily may be selected…, [0054] While in the previous examples, selection module 136 was described in terms of using a weighted selection scoring algorithm to score carriers, selection module 136 may use other rules. In one embodiment, minimum criteria for one or more performance metrics may be set by default or by the shipper and the selection module 136 can select from the carriers that meet those criteria based on selection rules. For example, the shipper may specify a minimum OTP of 85%. When the shipper initiates a new shipment, shipping management system 120 can, for example, select the lowest cost carrier from the carriers that have an OTP of 85% or greater. Using the data of FIG. 2, the shipping management system 120 could select the lowest cost carrier between Carrier 3 and Carrier 5 because these are the only carriers with an OTP>85%....”determined according to a weight” does not require a mathematical calculation of the threshold from the numerical weight. It requires the weight to be taken into consideration in the scoring and threshold evaluation policy. Krieg teaches a shipper specific policy that assigns weights according to the shipper’s priorities and applies shipper specified minimum criteria to the resulting carrier evaluation.)
extract an asset owner satisfying the read score threshold from the extracted use status data ([0054]… For example, the shipper may specify a minimum OTP of 85%. When the shipper initiates a new shipment, shipping management system 120 can, for example, select the lowest cost carrier from the carriers that have an OTP of 85% or greater. Using the data of FIG. 2, the shipping management system 120 could select the lowest cost carrier between Carrier 3 and Carrier 5 because these are the only carriers with an OTP>85%.... [0064] In one embodiment, the carriers are presented in rank order based on selection module 136 scoring the carriers on historical shipping data, recipient value data or other data as discussed above. The carriers displayed may be the top “n” carriers based on the scoring algorithm, the carriers achieving a designated minimum score or carriers selected based on other criteria. The carrier(s) presented to a drop shipper may be presented based on a scoring algorithm selected by the retailer/manufacturer etc. for which the drop shipper is shipping…abstract “The shipping management system can send a signal to the computer system of the chosen carrier to execute the new shipment.”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Krieg, in order provides the user with a choice of carriers to ship the new shipment based on selection rules (please see Krieg abstract).
As per claim 2/6, Fujita does not disclose but Krieg discloses wherein the processor calculates a score for the evaluation index based on the evaluation index read from the evaluation data and the weight of the evaluation index ([0051] Selection module 136 may score carriers/modes based on one or more factors from historical data, current transportation signal data and business signal data…[0051]… The factors may be weighted where the weights are tunable based user (e.g., account holder) preferences. If the shipper indicates that speed and on time delivery are more important for a shipment, the scoring algorithm may weight ATT and OTP more heavily than cost/lb/mi. On the other hand, if cost is a higher priority, a scoring algorithm that weights cost more heavily may be selected.)(please see claim 1 rejection for combination rationale).
As per claim 4/8, Fujita discloses an operation plan for the asset ([0036] [Planning of vehicle operation schedule] On the other hand, the customer / sales information server 4 collectively sends information to be delivered to the vehicle management server 2 on the next day in response to an order received from the customer (step 31)… [0037] The vehicle management server 2 receives the delivery information, and the vehicle operation management unit 21 devises a vehicle operation schedule such as which vehicle is used for each delivery destination and which route is used for delivery (step 32). [0038] [Determination of vehicle operation / charge schedule] In order to determine the charge and operation schedule of each electric vehicle, the planned charge schedule and vehicle operation schedule are integrated and the schedule is rearranged (step 41). [0042] In this way, the charging / operation schedule is determined for each electric vehicle (step 42). The determined vehicle operation schedule is transmitted to each electric vehicle (step 43). The charging schedule is transmitted to the charging station 3 (step 44)… these passages teach an operation plan for delivery assets because Fujita generates a vehicle operation schedule identifying the EV used for each delivery destination and the delivery route and integrates that schedule with the EV charging schedule). However, Fujita does not disclose but Krieg discloses calculating a reference value for the score threshold based on the calculated score for the evaluation index and an asset owner that is required 136 may score carriers/modes based on one or more factors from historical data, current transportation signal data and business signal data. According to one embodiment, for example, when a user enters shipment information, selection module 136 may apply a scoring algorithm to score carriers/modes based on one or more factors from the historical data, such as ATT for the origin/destination postal codes, OTP, DBE, cost/lb/mi and other factors. The factors may be weighted where the weights are tunable based user (e.g., account holder) preferences. If the shipper indicates that speed and on time delivery are more important for a shipment, the scoring algorithm may weight ATT and OTP more heavily than cost/lb/mi. On the other hand, if cost is a higher priority, a scoring algorithm that weights cost more heavily may be selected” this passage teach calculated scores for evaluation indices. ATT, OTP, DBE, and cost are carrier evaluation indices, and Krieg applies shipper selected weights to those indices when calculating carrier scores. Krieg further teaches the operational inputs from which the claimed reference threshold would be calculated “[0053] The carriers presented to the shipper for selection can be based on the scoring. For example, when a shipper wishes to select a carrier for a particular shipment, the shipper may be shown the carriers in rank-order based on the scoring. In some embodiments, the shipper's selection of carrier is limited to the top “n” carrier(s) based on the scoring…. [0054] While in the previous examples, selection module 136 was described in terms of using a weighted selection scoring algorithm to score carriers, selection module 136 may use other rules. In one embodiment, minimum criteria for one or more performance metrics may be set by default or by the shipper and the selection module 136 can select from the carriers that meet those criteria based on selection rules. For example, the shipper may specify a minimum OTP of 85%. When the shipper initiates a new shipment, shipping management system 120 can, for example, select the lowest cost carrier from the carriers that have an OTP of 85% or greater. Using the data of FIG. 2, the shipping management system 120 could select the lowest cost carrier between Carrier 3 and Carrier 5 because these are the only carriers with an OTP>85%.)(please see claim 1 rejection for combination rationale).
Claim(s) 3 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fujita (JP 2010231258) in view of Krieg (US 2018/0165642), as disclosed in the rejection of claim 2/6, in further view of Richter (US 2014/0067470) and Davar (US 2016/0132811).
As per claim 3/7, Fujita does not disclose but Krieg teaches a plurality of asset owners as shown in claim 1 because Krieg’s carrier is the asset owning transportation service provider to which shipment orders are assigned. Fujita in view of Krieg does not disclose but Richter discloses accumulate score history data in which the calculated score for the evaluation index is associated with the number of orders of the asset in a predetermined period ([0072]… FIG. 2 is an illustration of the operation of data agglomeration module 104 according to embodiments of the invention. The Data Agglomeration Module 104 associated with the DW 102 retrieves snapshots of raw sales records/transactions 112 and transcribes the numerical and quantitative data of the records with an attached timestamp at regular intervals. In addition, the records 112 may be accumulated per sales representative and per that representative's management hierarchy. During this process a number of calculations can be made, including but not limited to, standard ‘sales metrics’ like the percentage of a representative's (or group of representatives') sales quota they have attained as of a given date. A data cache and metric score calculated by the Data Agglomeration can be stored back on the DW 102…. For Each Sales-Period (blocks 1606 1612) Extract `Revenue Goals` for given Sales-Period (block 1606) Extract previous learned Sales-Period models for given Sales-Strategy, if any. (block 1068) Query Sales Transaction Data for Sales-Period {Ongoing, Closed, Lost, Deferred} (block 1610) Extract Seasonality Model for given Sales-Period (block 1612) For Each Sales Transaction (blocks 1620-1628) Use smoothing method to fill in missing data (DMA Module, block 1620) Exclude erroneous or biasing data points (DMA Module, block 1622) Calculate Revenue Goal Attainment (DMA Module, block 1624) Classify Quality Score according to Quality Heuristic (Pre-HE, block 1628) Accumulate data and Scores to the sales-person-subject of the transaction (block 1630)”…Richter expressly accumulates transaction data and corresponding scores for each evaluated subject during identifies sales periods. Each sales transaction corresponds to an order or sale. Consequently, the number of accumulated sales transactions during a sales period teaches the “number of orders…in a predetermined period”…Richter uses sales representatives as the evaluated subjects rather than carrier assets owners. Krieg supplies that above.)
among the accumulated score history data, (FIGS. 4 and 12) is applied to the raw sales data to align it with records from the `data cache` using data transforms. Supplementary calculations are also made to the metric scores. An example output data stream from the module would be a large list of quantitative data associated with sales-people, sales-periods, products sold and performance metrics for each of these units…Richter supplies periodic score and transaction histories from which an increase in the number of accumulated transactions between periods can be determined. ).
estimate an improved number of orders of the asset time associated with performance levels, sales groups and products. This engine also is able to learn new data models with various algorithms by examining previous data in a posteriori plus feedback fashion.” Richter discloses estimating the future performance of an evaluated subject from historical scores and sales transactions. When the forecasted commercial performance variable is the number of accumulated sales transactions, the forecast is an estimated number of orders.).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Richter in the teaching of Fujita in view of Krieg, in order to scoring sales representative performance and forecasting future sales representative performance (Richter abstract).
However, Fujita in view of Krieg and Richter still do not disclose but Davar discloses calculate a transition amount of a score based on a transition history of the score for the evaluation index and among asset owners having a score close to a certain extent of the score for the evaluation index of an asset owner to be estimated ([0074] Attributes may be recorded as a time-series or as static values, preferably with a time stamp. [0075] …A similarity metric may be calculated between every organization in the database. This is computationally expensive and so this calculation is preferably processed offline and stored as a data object in the database. Preferably the processor only creates similarity edges for similarity scores that are greater than a threshold score, so as to reduce the need to store data for minimally similar organizations.” Davar determines whether an organization is sufficiently similar to a target organization using a numerical similarity score and a threshold. “close to a certain extent” is taught by this thresholded similarity determination. “[0088] The system may use algorithms to capture the concept of organizational goodness, advancement or strength in order to make a calculations and relative comparisons. The system may compute a strength score for an organizations from a) their attribute values, b) their connections in the database c) their products/service quality or popularity, d) their consumption of products/services and e) their presence in social media (Tweets, likes, shared links). This score may be stored with the organization data objects, optionally recording the score as it changes over time. The strength score may be a global score but preferably is at least partially dependent on the organization's industry, as not all industries measure strength in the same sense….[0091] In some embodiments, the system comprises a model agent to create a model of how attribute importance varies across industries, size and maturity levels, such that the weight used to measure strength could vary based on the industry, size and maturity level of the organization concerned. There are many ways to build such a model. For example the model may a) use historical data in a regression to fit a regression model based on historical data to predict directions and rates of advancement in attributes, b) employ heuristic rules from industry experts or c) be a look-up-table (LUT). The model will determine the magnitude of advancement for an organization and direction of advancement for each attribute, as some attributes have negative associations (debt, accidents, recalls, delays). More details are provided below of illustrations of such models.”)
An asset owner who has improvement…among asset owners having a score close to a certain extent of the score..of an asset owner to be estimated ([0134] In some embodiments, continuous historical attribute data of peers are compared to the historical attribute data of the target organization. There may be a plurality of attributes to compare, wherein the selection of attributes and weighting of attributes towards a peer score depends on attributes that are most important to the peers' industry or the product or service being searched (see tables of FIGS. 3 and 5)….[0135]… One of the outcomes of the time-series analysis is preferably metrics regarding the degree of similarity between two organization and degree that one is more advanced than the other. This enables the system to determine which peers' attribute values were/are most similar to the target and also improved in one or more of the attributes, preferably in one of the success factors. Such metrics may require modification of some of the above techniques or interpretation of the parameters.” Claims 12-14. Davar does not expressly state that the improved attribute must be the number of orders. Richter supplies that variable by accumulating sales transactions by subject and period. Applying Davar’s peer transition analysis to Richter transaction count attribute results in identifying, among similarly scored carrier asset owners, a carrier asset owner whose number of orders improved)
Based on the calculated transition amount of the score, the score for the evaluation index of the asset owner to be estimated ([0091]… For example the model may a) use historical data in a regression to fit a regression model based on historical data to predict directions and rates of advancement in attributes, b) employ heuristic rules from industry experts or c) be a look-up-table (LUT). The model will determine the magnitude of advancement for an organization and direction of advancement for each attribute, as some attributes have negative associations (debt, accidents, recalls, delays). More details are provided below of illustrations of such models….[0101] then for a given target organization, its present (and possibly past) attribute values are fed into the model to project an expected improvement in the attributes. In FIG. 2C, the target organization's present attributes are low revenue and medium web traffic and the model indicates that it should experience moderate revenue growth and high web traffic growth. Optionally, the time parameter may be eliminated by expressing one attribute in terms of another, e.g. how revenue increases as a function of employees or web traffic as shown in FIG. 2C. [0138]… The model agent compares the historical data for the Target 1 to the peers' data to determine that Company A is most similar in form to Target 1, although having higher revenue. The system scores Company A as highly similar and an influential organization to Target 1. [0139] The current revenue of Target 2 is less than the others, which could support the supposition that Companies A-C are all influential peers. Using time-series analysis, the system determines that historically Company C's data curve is most similar to Target 2's data curve (both being exponential), whereby Company C can be seen as having consistently higher revenue or leading Target 2 by 220 days. Thus Company C will have the highest influence score with respect to Target 2.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Davar in the teaching of Fujita in view of Krieg and Richter, in order to determine which product, service or organization are recorded in the database as connected to an influential organization (Davar abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR ZEROUAL whose telephone number is (571)272-7255. The examiner can normally be reached Flex schedule.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached at (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
Prosecution Insights