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 .
This action is in reply to the communications filed on November 9, 2025. The Applicant’s Amendment and Request for Reconsideration has been received and entered.
Claims 1-20 are currently pending and have been examined. Claims 1, 8, and 15 have been amended.
Response to Arguments
Applicant’s amendments necessitated the new grounds of rejection.
Regarding the rejection of claims 1-20 under 35 USC 101, Applicant’s arguments have been fully considered but they are not persuasive, for the reasons set forth infra. Additionally, the Examiner respectfully argues that “improvements to providing optimized deployment of services when a client requires two services” is not an improvement in other technology, but rather, is an improvement to business operations. An improvement is not necessarily a technological improvement merely because technology is invoked as a tool in the improvement’s implementation.
Applicant’s remaining arguments have been fully considered but they are not persuasive. Particularly, Applicant’s arguments are directed to the instantly amended claims, and are thus moot in view of the new grounds of rejection.
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 non-statutory subject matter.
Step 1. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A – Prong One. If the claims fall within one of the statutory categories, it must then be determined whether the claims recite an abstract idea, law of nature, or natural phenomenon.
Step 2A – Prong Two. If the claims recite an abstract idea, law of nature, or natural phenomenon, it must then be determined whether the claims recite additional elements that integrate the judicial exception into a practical application. If the claims do not recite additional elements that integrate the judicial exception into a practical application, then the claims are directed to a judicial exception.
Step 2B. If the claims are directed to a judicial exception, it must be evaluated whether the claims recite additional elements that amount to an inventive concept (i.e. “significantly more”) than the recited judicial exception.
In the instant case, claims 1-7 are directed to a process; claims 8-14 are directed to a manufacture; and claims 15-20 are directed to a machine.
A claim “recites” an abstract idea if there are identifiable limitations that fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106. In the instant case, claim 1, and similarly claims 8 and 15, recite the steps of: receiving a set of client requirements, each requirement in the set of client requirements having one or more constraints, wherein each requirement in the set of client requirements includes an importance ranking corresponding to the requirement and/or constraint; analyzing available service providers based on the received set of client requirements; scoring the available service providers according to the analysis; identifying one or more unstructured external data sources corresponding to available service providers; analyzing reliability of the one or more unstructured external data sources with respect to the available service providers; adjusting a scoring of the service providers based on data source reliability, comprising combining historical QoS data and unstructured QoS data for each of the service providers and each service, wherein the adjusting of the scores includes a weighting function to combine scores of both the historical QoS data and the unstructured QoS data; regression model to identify optimal service provider selections according to the adjusted scoring and a quality of service (QoS) level of a service provider, wherein the regression model comprises analyzing text corresponding to the service provider's QoS, counting word frequencies with respect to terms of interest, normalizing the word frequencies, and using the normalized frequencies as features in a classification model; providing an optimal selection of service providers based on the adjusted scoring, wherein the optimal selection is based on the adjusted scoring from an optimization model that allocates the service providers to each requirement in the set of client requirements; and updating the regression model with feedback from previous executions -- these claim limitations set forth certain methods of organizing human activity, particularly commercial interactions including advertising, marketing, and sales activities/behaviors. Additionally, these steps set forth mental processes, particularly concepts performed in the human mind, including, inter alia, the observation and evaluation of information.
Further, the limitations of the claims are not indicative of integration into a practical application. Taking the claim elements separately, the additional elements of performing the steps via one or more computer processors, one or more computer-readable storage media, program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, training a machine learning regression model, and in a federated cloud environment -- merely implement the abstract idea on a computer environment. Considered in combination, the steps of Applicant’s method add nothing that is not already present when the steps are considered separately.
The remaining claim limitations recited in dependent claims merely narrow the abstract idea and do not recite further additional elements. Thus, claims 1-20 are directed to an abstract idea.
Regarding the independent claims, the technical elements of performing the steps via one or more computer processors, one or more computer-readable storage media, and program instructions stored on the computer-readable storage media for execution by at least one of the one or more processors, and in a federated cloud environment -- merely implement the abstract idea on a computer environment. While claim 1, and similarly claims 8 and 15, recites training a machine learning regression model this limitation is recited at a high level of generality and thus does not amount to significantly more. Additionally, the dependent claims do not recite further technical elements.
When considering the elements and combinations of elements, the claim(s) as a whole, do not amount to significantly more than the abstract idea itself. This is because the claims do not amount to an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment; the claims merely amounts to the application or instructions to apply the abstract idea on a computer; or the claims amounts to nothing more than requiring a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry.
The analysis above applies to all statutory categories of invention. Accordingly, claims 1-20 are rejected as ineligible for patenting under 35 USC 101 based upon the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-3, 6-10, 13-16, 19, and 20 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable over Psota (US PGP 2015/0073929) in view of Mazhar (US PGP 2012/0311012).
As per claim 1, Psota teaches [a] computer implemented method for selecting service providers, the computer implemented method comprising:
receiving a set of client requirement, each requirement in the set of client requirements having one or more constraints, wherein each requirement in the set of client requirements includes an importance ranking corresponding to the requirement and/or constraint; (Psota: [0059] (providing a search facility for enabling a search for an entity, wherein the search facility allows searching based on geographic region, industry specialization, entities participating in the transactions, and likelihood of interest in a transaction with the searcher. In the aspect, the search facility is adapted to be used by a buyer searching for a supplier); [0160] (A user may enter the search criteria through a programming interface (e.g. an API), and the like. The similar company suggestions may include a ranking that is based on the level of similarity between the search criteria and the searched results. For example, a user may search for suppliers that supply a specific product and operate in a specific location.); Fig. 32; [0291] (FIG. 32 illustrates an exemplary user interface 3200 that may be presented to a user when the user selects the supplier category 3002 of the user interface 3000. As shown, the user may select one or more country locations so as to retrieve information from a database associated with these locations. Another option 3210 may allow the user to select only those profiles of the suppliers that may be active in the last predetermined number of days. The user interface 3200 may further include additional options 3212 for the user to selectively view the profiles as per the selected options as listed in the user interface 3200.); Fig. 1; [0133]-[0134](The supplier rating list 100 in FIG. 1 includes a keyword 102 around which the list is based. Although one keyword is shown in FIG. 1, a keyword phrase, group of keywords, logical combination of keywords, and the like may be used as a basis for the list 100. In an interactive embodiment of the list of FIG. 1 selecting the keyword 102 (e.g. knitting) may allow a user to make changes to the keyword 102 to present a revised list 100.); Fig. 2; [0142] (In embodiments, the entity score may be based on one more factors including country context, business legitimacy information, public recognition, amount of experience, caliber of customers of the supplier, customer loyalty for the supplier, degree of specialization of the supplier, and feedback from previous customers or some other factors. Further, each factor or group of factors may include a list of parameters. A user interface may be configured to allow a user to select some or all the parameters from this group to generate an entity rating.); [0287]; [0144] (In embodiments, a supplier rating facility or buyer rating facility may take ratings along each of key dimensions, weight the ratings to account for the fact that some dimensions are more important than others, calculate an overall rating 110, and the like.); [0232] (An overall rating 110 and any sub-rating may be weighted, normalized, and curve fitted to ensure the rating is providing a consistent reliable measure of a supplier, buyer, and the like. Additionally, the weighting may be customer specified to enable a customer to identify portions of the ratings that are most important.))
analyzing available service providers based on the received set of client requirements; (Psota: [0160] (A user may enter the search criteria through a programming interface (e.g. an API), and the like. The similar company suggestions may include a ranking that is based on the level of similarity between the search criteria and the searched results. For example, a user may search for suppliers that supply a specific product and operate in a specific location. The similarity engine may generate one or more suggestions for companies that are similar and may rank the suggestions based on similarity to a search criteria. Similarity suggestions may be based on user behaviors and interactions with the system. The criteria used for making the suggestions may be weighted based on the user history of searches, rating reviews, and the like.); Fig. 32; [0291]); Fig. 1; [0133]-[0134] (The supplier rating list 100 in FIG. 1 includes a keyword 102 around which the list is based. Although one keyword is shown in FIG. 1, a keyword phrase, group of keywords, logical combination of keywords, and the like may be used as a basis for the list 100. In an interactive embodiment of the list of FIG. 1 selecting the keyword 102 (e.g. knitting) may allow a user to make changes to the keyword 102 to present a revised list 100. The list 100 may include any number of suppliers that satisfy the keyword 102 criteria; in the example of FIG. 1 the list includes 10 suppliers. The list 100 may include entries 108 for each supplier that satisfies the keyword 102 criteria. Preferences as indicated above may impact what information is presented in an entry 108 and the embodiment of FIG. 1 is only an example of one set of information to be presented.); Fig. 2; [0142]; [0287])
scoring the available service providers according to the analysis; (Psota: [0160] (A user may enter the search criteria through a programming interface (e.g. an API), and the like. The similar company suggestions may include a ranking that is based on the level of similarity between the search criteria and the searched results. For example, a user may search for suppliers that supply a specific product and operate in a specific location. The similarity engine may generate one or more suggestions for companies that are similar and may rank the suggestions based on similarity to a search criteria. Similarity suggestions may be based on user behaviors and interactions with the system. The criteria used for making the suggestions may be weighted based on the user history of searches, rating reviews, and the like.); Fig. 32; [0291]); Fig. 1; [0133]-[0134] ( The supplier rating list 100 in FIG. 1 includes a keyword 102 around which the list is based. Although one keyword is shown in FIG. 1, a keyword phrase, group of keywords, logical combination of keywords, and the like may be used as a basis for the list 100. In an interactive embodiment of the list of FIG. 1 selecting the keyword 102 (e.g. knitting) may allow a user to make changes to the keyword 102 to present a revised list 100. The list 100 may include any number of suppliers that satisfy the keyword 102 criteria; in the example of FIG. 1 the list includes 10 suppliers. The list 100 may include entries 108 for each supplier that satisfies the keyword 102 criteria. An entry 108 may include an overall rating 110 also known as the “Panjiva rating”, the supplier name 112, selected bibliographic data 114, and the like. Preferences as indicated above may impact what information is presented in an entry 108 and the embodiment of FIG. 1 is only an example of one set of information to be presented. Each supplier may be given an overall rating 110 that may be based on a 100 point scale so that an overall rating 110 may be between one and one-hundred as shown in FIG. 1.); Fig. 2; [0140] (In embodiments, rating a supplier, buyer, or other entity may result in a score that is at least partially based on predefined criteria, such as a user provided criteria.); Fig. 2; [0142] (In embodiments, the entity score may be based on one more factors including country context, business legitimacy information, public recognition, amount of experience, caliber of customers of the supplier, customer loyalty for the supplier, degree of specialization of the supplier, and feedback from previous customers or some other factors. Further, each factor or group of factors may include a list of parameters. A user interface may be configured to allow a user to select some or all the parameters from this group to generate an entity rating.); [0138]-[0146])
identifying one or more unstructured external data sources corresponding to available service providers; (Psota: [0160] (The similarity engine may generate one or more suggestions for companies that are similar and may rank the suggestions based on similarity to a search criteria. Similarity suggestions may be based on user behaviors and interactions with the system. The criteria used for making the suggestions may be weighted based on the user history of searches, rating reviews, and the like.); Fig. 32; [0291]); Fig. 1; [0133]-[0134]; Fig. 2; [0135] (This scorecard 200 may assess the supplier's relative strength along a variety of dimensions. Leveraging a wide variety of data sources, the supplier rating facility may rate suppliers along key dimensions 204 also known as “Panjiva Analysis” ratings, in a plurality of categories such as business basics, international track record, certifications, and the like. The rating platform may also allow buyers to rate suppliers along several dimensions. The scorecard 200 may include the buyer ratings 208. In embodiments, the ratings platform may become a place where buyers go to hold suppliers accountable.); Fig. 2; [0138]-[0142] (In embodiments, the entity score may be based on one more factors including country context, business legitimacy information, public recognition, amount of experience, caliber of customers of the supplier, customer loyalty for the supplier, degree of specialization of the supplier, and feedback from previous customers or some other factors. Further, each factor or group of factors may include a list of parameters.); [0151])
analyzing reliability of the one or more unstructured external data sources with respect to the available service providers; (Psota: [0138]-[0142] (In embodiments buyers may rate suppliers with whom they have done business. After a buyer rates a supplier, the supplier rating facility may verify that that the two have actually done business together, such as by identifying a corresponding customs records that shows an actual import transaction in which the buyer imported goods from the supplier, from a bill of lading, from a bank-issued receipt, and the like. Thus, methods and systems disclosed herein include methods and systems for deterring fraudulent ratings by verifying the existence of the transaction purportedly rated by the buyer. This may prevent false ratings that are either too positive (such as by an affiliate or cohort of the supplier) or too negative (such as by a competing supplier posing as a buyer); [0440] (In embodiments, the bid/offer/request rating facility may use algorithms in order to generate ratings. In embodiments, several different algorithms may be used in order to accommodate market behavior within different industries. For example, past transactional history may be a more accurate predictor of future transactional reliability for producers of toy cars, whereas governmental regulation and changing weather patterns may be the most important consideration for wheat farmers. Thus, bids and requests for toy cars may employ algorithms that completely ignore data about weather whereas requests for wheat may trigger algorithms that weigh weather or other force majeure concerns more heavily.); [0490])
adjusting a scoring of the service providers based on the data source reliability, comprising combining historical QoS data and unstructured QoS data for each of the service providers and each service, wherein the adjusting of the scores includes a weighting function to combine scores of both the historical QoS data and the unstructured QoS data; (Psota: [0138]-[0142] (In embodiments buyers may rate suppliers with whom they have done business. After a buyer rates a supplier, the supplier rating facility may verify that that the two have actually done business together, such as by identifying a corresponding customs records that shows an actual import transaction in which the buyer imported goods from the supplier, from a bill of lading, from a bank-issued receipt, and the like. Thus, methods and systems disclosed herein include methods and systems for deterring fraudulent ratings by verifying the existence of the transaction purportedly rated by the buyer. This may prevent false ratings that are either too positive (such as by an affiliate or cohort of the supplier) or too negative (such as by a competing supplier posing as a buyer). After verification, the buyer's rating may become part of the supplier's scorecard.); [0440] (In embodiments, the bid/offer/request rating facility may use algorithms in order to generate ratings. In embodiments, several different algorithms may be used in order to accommodate market behavior within different industries. For example, past transactional history may be a more accurate predictor of future transactional reliability for producers of toy cars, whereas governmental regulation and changing weather patterns may be the most important consideration for wheat farmers. Thus, bids and requests for toy cars may employ algorithms that completely ignore data about weather whereas requests for wheat may trigger algorithms that weigh weather or other force majeure concerns more heavily.); [0490])
training a machine learning regression model to identify optimal service provider selections according to the adjusted scoring and a quality of service (QoS) level of a service provider . . . , wherein the machine learning regression model comprises analyzing text corresponding to the service provider's QoS, counting word frequencies with respect to terms of interest, normalizing the word frequencies, and using the normalized frequencies as features in a classification model; (Psota: [0138]-[0153] (In embodiments buyers may rate suppliers with whom they have done business. After a buyer rates a supplier, the supplier rating facility may verify that that the two have actually done business together, such as by identifying a corresponding customs records that shows an actual import transaction in which the buyer imported goods from the supplier, from a bill of lading, from a bank-issued receipt, and the like. Thus, methods and systems disclosed herein include methods and systems for deterring fraudulent ratings by verifying the existence of the transaction purportedly rated by the buyer. This may prevent false ratings that are either too positive (such as by an affiliate or cohort of the supplier) or too negative (such as by a competing supplier posing as a buyer). After verification, the buyer's rating may become part of the supplier's scorecard. In addition, ratings may be customized to individual buyer preferences, such as by having buyer's rate suppliers with whom they have done business. ratings may then be tuned to best match this empirical view of a buyer's preferences. Such an approach may use a machine learning technique such as a support vector machine. Over time, trends in ratings may then be captured and displayed to the buyer. Such trends may enable a graph-theory analysis (e.g., minimum cut, maximum flow, cliques, and the like) on buyer-supplier networks to determine the relationships between groups of buyers and suppliers, which may lead to additional value-added services such as improving production allocation for buyers.); [0414] (The inquiry process algorithms may also comprise a free text component, which may use methods such as a term frequency-inverse document frequency (TF-IDF) ranking function in order to generate a potential match. The significance of certain terms determined by such TF-IDF analyses may be used to determine various ratings as well. For instance, a supplier who has fulfilled orders in the past with large amounts of significant words may be a supplier that is more flexible, and thus may receive a higher reputation rating. Term significance may also be used in conjunction with other factors in order to demonstrate the weight that should be afforded to such factor when determining a rating. For instance, a supplier who receives a negative review that contains a large amount of significant terms may be a more accurate review than a supplier who receives a review with less significant terms. The inquiry process algorithms may also use word significance to weigh certain words within an inquiry in order to determine the likelihood of a successful match between a buyer and a supplier. For instance, industry specific terms are likely more significant than non-industry terms. Analyzing an inquiry for significant terms may reveal such industry specific terms and highlight those words of the inquiry as the most descriptive. The inquiry process algorithms may then conduct a text search of suppliers within the marketplace system to determine matches. Such matches may be determined by identifying data sources that contain the greatest amount of matching terms with the highest frequency, or any combination thereof.))
providing an optimal selection of service providers based on the adjusted scoring, wherein the optimal selection is based on the adjusted scoring from an optimization model that allocates the service providers to each requirement in the set of client requirements; and (Psota: [0138]-[0153] (In addition, ratings may be customized to individual buyer preferences, such as by having buyer's rate suppliers with whom they have done business. ratings may then be tuned to best match this empirical view of a buyer's preferences. Such an approach may use a machine learning technique such as a support vector machine. Over time, trends in ratings may then be captured and displayed to the buyer. Such trends may enable a graph-theory analysis (e.g., minimum cut, maximum flow, cliques, and the like) on buyer-supplier networks to determine the relationships between groups of buyers and suppliers, which may lead to additional value-added services such as improving production allocation for buyers.); Fig. 1; [0133]-[0134] (The ratings may be presented in various forms including a listing of supplier ratings as shown in FIG. 1. The list 100 may include any number of suppliers that satisfy the keyword 102 criteria; in the example of FIG. 1 the list includes 10 suppliers. Aspect of the list 100, such as a limit on the number of supplies in the list 100 may be controlled by preferences (e.g. user, platform, supplier, and the like). The list 100 may include entries 108 for each supplier that satisfies the keyword 102 criteria. An entry 108 may include an overall rating 110 also known as the “Panjiva rating”, the supplier name 112, selected bibliographic data 114, and the like. Preferences as indicated above may impact what information is presented in an entry 108 and the embodiment of FIG. 1 is only an example of one set of information to be presented. Each supplier may be given an overall rating 110 that may be based on a 100 point scale so that an overall rating 110 may be between one and one-hundred as shown in FIG. 1.); [0265]-[0266] (In accordance with an embodiment of the present invention, the search results obtained from the above described searches for the entities 2708 may also be ranked. In an embodiment, the ranking may be based on a supplier rating. In an embodiment, the supplier rating may be based on the context of a party, the business legitimacy of a party, . . . customer loyalty, . . . , feedback from customers, feedback from buyers, feedback on product quality, feedback on customer service, feedback on timeliness of delivery, feedback on language skills, feedback on sample making ability, . . . , and some other types of factors and parameters. Search ranking may be done using a ranking or relevance algorithm that functions more than merely matching buyers to suppliers who have had large quantities of similar shipments in the past. In embodiments, the ranking algorithm may include logarithmically weighting the sum of a number of different factors that may be relevant to a supplier's past and potential future performance, as well as matching a buyer's needs.); Fig. 2); [0294]-[0295] (In embodiments, an interface may be provided for rating a supplier, such as on dimensions including an overall rating, product quality, customer service, timeliness, English language capability, sample-making ability, respect for intellectual property, and the like. Buyer ratings may be averaged or otherwise normalized and reported as part of a supplier's overall rating 110. In embodiments, transactional data may be used to ensure that a transaction occurred (to keep ratings unpolluted). If a buyer rating is good, this can give a significant boost to an overall rating 110. In embodiments, buyers could specify which dimensions are most important to them, and the overall rating 110 could be customized and weighted according to the buyer's preferences.)
updating the machine learning regression model with feedback from previous executions. (Psota: [0034]; [0138]-[0153] (In embodiments buyers may rate suppliers with whom they have done business. After a buyer rates a supplier, the supplier rating facility may verify that that the two have actually done business together, such as by identifying a corresponding customs records that shows an actual import transaction in which the buyer imported goods from the supplier, from a bill of lading, from a bank-issued receipt, and the like. Thus, methods and systems disclosed herein include methods and systems for deterring fraudulent ratings by verifying the existence of the transaction purportedly rated by the buyer. This may prevent false ratings that are either too positive (such as by an affiliate or cohort of the supplier) or too negative (such as by a competing supplier posing as a buyer). After verification, the buyer's rating may become part of the supplier's scorecard. In addition, ratings may be customized to individual buyer preferences, such as by having buyer's rate suppliers with whom they have done business. ratings may then be tuned to best match this empirical view of a buyer's preferences. Such an approach may use a machine learning technique such as a support vector machine. Over time, trends in ratings may then be captured and displayed to the buyer. Such trends may enable a graph-theory analysis (e.g., minimum cut, maximum flow, cliques, and the like) on buyer-supplier networks to determine the relationships between groups of buyers and suppliers, which may lead to additional value-added services such as improving production allocation for buyers.); [0414] (The inquiry process algorithms may also comprise a free text component, which may use methods such as a term frequency-inverse document frequency (TF-IDF) ranking function in order to generate a potential match. The significance of certain terms determined by such TF-IDF analyses may be used to determine various ratings as well. For instance, a supplier who has fulfilled orders in the past with large amounts of significant words may be a supplier that is more flexible, and thus may receive a higher reputation rating. Term significance may also be used in conjunction with other factors in order to demonstrate the weight that should be afforded to such factor when determining a rating. For instance, a supplier who receives a negative review that contains a large amount of significant terms may be a more accurate review than a supplier who receives a review with less significant terms. The inquiry process algorithms may also use word significance to weigh certain words within an inquiry in order to determine the likelihood of a successful match between a buyer and a supplier. For instance, industry specific terms are likely more significant than non-industry terms. Analyzing an inquiry for significant terms may reveal such industry specific terms and highlight those words of the inquiry as the most descriptive. The inquiry process algorithms may then conduct a text search of suppliers within the marketplace system to determine matches. Such matches may be determined by identifying data sources that contain the greatest amount of matching terms with the highest frequency, or any combination thereof.))
Psota does not explicitly disclose the following known technique which is taught by Mazhar:
training a machine learning regression model . . . in a federated cloud environment, wherein the machine learning regression model . . . (Mazhar: [0039] (The M cloud configurations may be provided by M unique cloud providers (e.g., each individual cloud configuration may be provided by a different entity). Alternately, the M cloud configurations may be provided by less than M unique cloud providers, with one or more cloud provider supplying more than one of the M cloud configurations. Each individual cloud configurations may be a “private” cloud (e.g., the operator of may be a related entity to the cloud provider, and access to the cloud provider may not be generally accessible to outside entities) or a “public” cloud (e.g., the cloud provider may be generally accessible to some outside entities). Each cloud provider may be remote from the other cloud providers, and from system 10.); [0054] (Predictive forecasting of an optimal cloud environment may be performed using techniques such as, for example, neural networks, time-series algorithms, and regression analysis to predict).
This known technique is applicable to the method of Psota as they both share characteristics and capabilities, namely, they are directed to computing environments for training models.
One of ordinary skill in the art at the time of filing would have recognized that applying the known technique of Mazhar would have yielded predictable results and resulted in an improved method. It would have been recognized that applying the technique of Mazhar to the teachings of Psota would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such federated cloud environment features into similar methods. Further, applying the training a machine learning regression model in a federated cloud environment to the teachings of Psota would have been recognized by those of ordinary skill in the art as resulting in an improved method that would facilitate the use of resources by providing users (which may be remote to the cloud provider) with access to their resources. (Mazhar: Para [0002]-[0004]).
As per claim 2, Psota/Mazhar teach wherein analyzing available service providers includes determining capabilities of each available service provider with respect to the received set of client requirements. (Psota: [0287] (The search feature may be focused on customers and capabilities. For example, the search feature may assist a user in finding someone who manufactures the product that the user is interested in.); [0346]-[0347] (In embodiments, a search interface may allow for a search based on supplier capability, such as based on information retrieved from transactional data, such as customs records. In embodiments, a data analytics platform may be provided for analyzing supplier capabilities, such as based at least in part on transactional data about supplier activities, such as transactional data from customs records.); [0409] (The inquiries posted by the buyers may generate and get converted into leads for the suppliers associated with the marketplace system. This may be automated within the marketplace system by proactively reaching out to the suppliers who may be capable of completing the inquiry based on associating information in the inquiry and in the various supplier profiles.); [0424] (Though Supplier A may have a higher reliability and performance rating than Supplier B, (e.g. as represented in the supplier Overall rating (e.g. Panjiva rating)) in this specific instance, Supplier B may be in a better position to fulfill this specific request simply because the required time from request to order delivery highly favors Supplier B.))
As per claim 3, Psota/Mazhar teach further comprising determining available capacities of required resources corresponding to the set of client requirements with respect to each available service provider. (Psota: [0424] (Though Supplier A may have a higher reliability and performance rating than Supplier B, (e.g. as represented in the supplier Overall rating (e.g. Panjiva rating)) in this specific instance, Supplier B may be in a better position to fulfill this specific request simply because the required time from request to order delivery highly favors Supplier B.); [0485] (Macro-economic data may also be used with private, public, and semi-private data to help identify relationships between these data sources that may help identify potential impact on multiple suppliers that operate in a region. If macro-economic data support private shipper data that indicates a decrease in activity for a region, then one can predict that suppliers, shippers, logistics providers, carriers, and the like that operate in that region may be similarly affected negatively. This information may be helpful in determining opportunities for buyers and suppliers, such as lower prices, availability of shipping resources, production capability availability, and the like. These and many other types of region-based assessments may be done based on aggregated private data.); [0020]; [0138] (In embodiments, information utilized in the formation of the ratings scorecard 200 may be from shipment history, such as frequency, quantity, and the like; shipment capacity estimation, which may be based on shipment data as opposed to information provided by the supplier.); [0172] (n an example, the buyer 420 may require 40 tons of silk within 4 days. The recommendation facility 602 may recommend buying 40 tons of silk from supplier 430 based on its manufacturing capacity of 50 tons per day and ability to provide the required silk within the stipulated time.); [0175]; [0261]; [0328]; [0355]; [0413])
As per claim 6, Psota/Mazhar teach further comprising utilizing the optimal selection of service providers to execute one or more functions. (Psota: Fig. 1; [0133]-[0134] (The ratings may be presented in various forms including a listing of supplier ratings as shown in FIG. 1. The list 100 may include any number of suppliers that satisfy the keyword 102 criteria; in the example of FIG. 1 the list includes 10 suppliers. Aspect of the list 100, such as a limit on the number of supplies in the list 100 may be controlled by preferences (e.g. user, platform, supplier, and the like). The list 100 may include entries 108 for each supplier that satisfies the keyword 102 criteria. An entry 108 may include an overall rating 110 also known as the “Panjiva rating”, the supplier name 112, selected bibliographic data 114, and the like. Preferences as indicated above may impact what information is presented in an entry 108 and the embodiment of FIG. 1 is only an example of one set of information to be presented.); [0265]-[0266]); Fig. 2)
As per claim 7, Psota/Mazhar teach further comprising enabling an opt-in analytics mechanism configured to determine whether different providers can complement each other to provide increased efficiency. (Psota: [0400] (In addition to providing data services, the marketplace system may provide a prospecting facility. The prospecting facility may utilize shipper information to predict delays and disruptions in future transactions. As an example, and not a limitation, a delayed shipment of parts to a manufacturer may result in that manufacturer being unable to ship its product in a timely fashion. The prospecting facility may recognize the delayed parts shipment and alert the supplier's customers of the upcoming delay. As another example, the prospecting facility may alert the manufacturer of the impending delay and present options for purchasing parts from other parts suppliers who are capable of delivering replacement parts within a needed timeframe.))
As per claims 8-10, 13, and 14, these claims are substantially similar to claims 1-3, 6, and 7, respectively, and are therefore rejected in the same manner as these claims, as set forth above.
As per claims 15, 16, 19, and 20, these claims are substantially similar to claims 1, 3, 6, and 7, respectively, and are therefore rejected in the same manner as these claims, as set forth above
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 4, 5, 11, 12, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Psota/Mazhar in view of Da Costa (WO 2018217115 A1).
As per claim 4, Psota/Mazhar teach the invention of claim 1 as set forth above. Additionally, Psota/Mazhar teach
further comprising building an . . . model to allocate service providers according to the adjusted scoring. . (Psota: [0138]-[0153] (In addition, ratings may be customized to individual buyer preferences, such as by having buyer's rate suppliers with whom they have done business. ratings may then be tuned to best match this empirical view of a buyer's preferences. Such an approach may use a machine learning technique such as a support vector machine. Over time, trends in ratings may then be captured and displayed to the buyer. Such trends may enable a graph-theory analysis (e.g., minimum cut, maximum flow, cliques, and the like) on buyer-supplier networks to determine the relationships between groups of buyers and suppliers, which may lead to additional value-added services such as improving production allocation for buyers.); Fig. 1; [0133]-[0134]; [0265]-[0266] (In accordance with an embodiment of the present invention, the search results obtained from the above described searches for the entities 2708 may also be ranked. In an embodiment, the ranking may be based on a supplier rating. In an embodiment, the supplier rating may be based on the context of a party, the business legitimacy of a party, . . . customer loyalty, . . . , feedback from customers, feedback from buyers, feedback on product quality, feedback on customer service, feedback on timeliness of delivery, feedback on language skills, feedback on sample making ability, . . . , and some other types of factors and parameters. Search ranking may be done using a ranking or relevance algorithm that functions more than merely matching buyers to suppliers who have had large quantities of similar shipments in the past. In embodiments, the ranking algorithm may include logarithmically weighting the sum of a number of different factors that may be relevant to a supplier's past and potential future performance, as well as matching a buyer's needs.); [0500]; [0138]; [0172])
Psota/Mazhar, however, does not explicitly disclose that model is an optimization model. Still, one of ordinary skill in the art would have recognized such features to be obvious, as they were well established at the time of invention.
For example, Da Costa teaches further comprising building an optimization model to allocate service providers according to . . . scoring. (Da Costa: Page 12 (Obtaining the optimal solution for each of the scenarios or (sub) proposal queries (104), results automatically from the application of a linear programming algorithm that uses linear sections approximations of integer programming functions with a numerable infinity of. variables (binary and integer) and functional constraints to determine the optimal combination of multiple product quantities (102) to be awarded to multiple product suppliers (102), so as to minimize the total cost of product procurement (102)); Page 21 (The computer-implemented invention described in the present invention comprises the use of a mixed integer linear programming algorithm with continuous and integer variables subject to a numerable number of constraints, which in most cases has a very high number of possible solutions (with 10 products and 5 suppliers, there are at least 9,765,625 possible solutions), to determine very quickly the optimal combination of multiple product quantities to be awarded to multiple suppliers so as to minimize the total cost of purchase.); Pages 23-24 (The Purchasing Center may adopt an optimal procedure for selecting the best supply bids according to suppliers' qualitative evaluation criteria or scores (909) in addition to price and any discount premiums.))
This known technique is applicable to the method of Psota/Mazhar as they both share characteristics and capabilities, namely, they are directed to determining service providers.
One of ordinary skill in the art at the time of filing would have recognized that applying the known technique of Da Costa would have yielded predictable results and resulted in an improved method. It would have been recognized that applying the technique of Da Costa to the teachings of Psota/Mazhar would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such optimization model features into similar methods. Further, applying the optimization model to the model of Psota/Mazhar would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow obtaining the optimal solution (Da Costa: Page 12).
As per claim 5, Psota/Mazhar/Da Costa teach the invention of claim 4 as set forth above. Additionally, Psota/Mazhar/Da Costa teach wherein the optimization model is an integer programming assignment model. (Da Costa: Page 12 (Obtaining the optimal solution for each of the scenarios or (sub) proposal queries (104), results automatically from the application of a linear programming algorithm that uses linear sections approximations of integer programming functions with a numerable infinity of. variables (binary and integer) and functional constraints to determine the optimal combination of multiple product quantities (102) to be awarded to multiple product suppliers (102), so as to minimize the total cost of product procurement (102)); Page 21 (The computer-implemented invention described in the present invention comprises the use of a mixed integer linear programming algorithm with continuous and integer variables subject to a numerable number of constraints, which in most cases has a very high number of possible solutions (with 10 products and 5 suppliers, there are at least 9,765,625 possible solutions), to determine very quickly the optimal combination of multiple product quantities to be awarded to multiple suppliers so as to minimize the total cost of purchase.)
The motivation for applying the known techniques of Da Costa to the teachings of Psota/Mazhar is the same as that set forth above, in the rejection of Claim 4.
As per claims 11 and 12, these claims are substantially similar to claims 4 and 5, respectively, and are therefore rejected in the same manner as these claims, as set forth above.
As per claims 17 and 18, these claims are substantially similar to claims 4 and 5, respectively, and are therefore rejected in the same manner as these claims, as set forth above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chu (US PGP 2005/0065811) – review and/or ratings from two sources assigned to different rating tiers.
Cohen (US PGP 2006/0106774) -- determine a confidence level or value or other assessment of the user information being corroborated
Dotterer (US PGP 2016/0162969) -- scoring of a service provider can include assessing a service provider's reliability as a reviewer
Lopez (US PGP 2018/0240181) -- the availability of the deliverer may also be considered
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JENNIFER V LEE/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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