DETAILED ACTION
The following is a Non-Final, First Office Action on the Merits in response to communications filed July 9, 2025. Claims 1–14 are currently pending.
Claim Objections
Claims 1, 4, and 8 are objected to because of the following informalities:
Claim 1 recites an element to “retrieve historical ridesharing records”, and claim 8 recites an element for “retrieving historical rideshare allocation records”. However, claims 1 and 8 subsequently recite “the records” and “the retrieved historical rideshare allocation records” in the elements to “retrieve” and “generate”, respectively.
Examiner recommends amending claim 1 to recite an element to “retrieve historical rideshare historical rideshare allocation records comprising” and “generate training data based on the
Examiner similarly recommends amending claim 8 to recite “retrieving historical rideshare allocation records, the historical rideshare allocation records comprising” and “generating training data based on the
Claims 1 and 8 further recite an element to “generate training date” and subsequently recite “the generated training data” in the element to “train”. However, dependent claims 2 and 9 subsequently recite “training data”. As a result, Examiner recommends amending claims 1 and 8 to recite an element to “train … based on the training data” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claim 4 recites “the historical rideshare allocation records”. However, claim 1, from which claim 4 depends, previously introduces “historical ridesharing allocation records”. Examiner recommends amending claim 1 as indicated above in order to address the inadvertent typographical error.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 2, 5, 9, and 11–12 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.
Claims 2 and 9 recite “wherein generation of training data comprises”. However, as noted above, claims 1 and 8 previously recite an element to “generate training data”. As a result, the scope of claims 2 and 9 are indefinite because it is unclear whether Applicant intends for the recitations of claims 2 and 9 to reference the previous recitations or intends to introduce second, different “training data”.
For purposes of examination, claims 2 and 9 are interpreted as reciting “wherein generation of the training data comprises”.
In view of the above, claims 2 and 9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5 and 12 recite “the candidate allocation times” in line 2. There is insufficient antecedent basis for this limitation in the claims.
For purposes of examination, the claims are interpreted as reciting “one or more
In view of the above, claims 5 and 12 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 11 recites “the predetermined list of potential allocation times” in lines 1–2. There is insufficient antecedent basis for this limitation in the claim.
For purposes of examination, claim 11 is interpreted as reciting the “method of claim [[8]]10”.
In view of the above, claim 11 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
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 1–14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–14 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements to “retrieve historical ridesharing allocation records, the records comprising actual allocation time values and historical request attributes”; “generate training data based on the retrieved historical rideshare allocation records, wherein each record in the training data comprises an allocation status label and a candidate allocation time, the allocation status label indicating a status of allocation with respect to the candidate allocation time”; “predict an allocation probability for each candidate allocation time based on the generated training data”; “receive a request for a ride, the request comprising request attributes”; and “process the request attributes to estimate an allocation time for the request.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing activity related to commercial business relations and/or managing personal behavior or relationships or interactions between people because the elements describe a process for estimating an allocation request for a ridesharing request. Further, the elements recite mental processes because the elements embody observations or evaluations that can be practically performed in the human mind or by a human using pen and paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 8 include substantially similar limitations to those included with respect to claim 1. As a result, claim 8 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–7 and 9–14 further describe the process for estimating an allocation request for a ridesharing request and further recite certain methods of organizing activity and/or mental processes for the same reasons as stated above. As a result, claims 2–7 and 9–14 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include one or more processor (processor(s)), a memory comprising instructions, a computing device, and elements to train and use a machine learning model. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 8 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 8 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claim 1.
Claims 2–7 and 9–14 do not include any additional elements beyond those included with respect to the claims from which claims 2–7 and 9–14 depend. As a result, claims 2–7 and 9–14 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include one or more processor (processor(s)), a memory comprising instructions, a computing device, and elements to train and use a machine learning model. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the remaining additional elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claim 8 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 8 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above with respect to claim 1.
Claims 2–7 and 9–14 do not include any additional elements beyond those included with respect to the claims from which claims 2–7 and 9–14 depend. As a result, claims 2–7 and 9–14 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–14 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–20 are rejected under 35 U.S.C. 103 as being unpatentable over TANG et al. (U.S. 2021/0201393) in view of Li et al. (U.S. 2021/0118079).
Claims 1 and 8: Tang discloses a system for prediction of allocation time in a ridesharing service, the system comprising:
one or more processor (processor(s)) (See FIG. 10 and paragraph 100);
a memory comprising instructions that when executed by the processor(s) cause the processor(s) (See FIG. 10 and paragraph 100) to:
retrieve historical ridesharing allocation records, the records comprising allocation time values and historical request attributes (See paragraphs 82 and 96, wherein historical trip information includes historical request attributes and historical temporal and trip attribute information; see also paragraphs 70–71);
generate training data based on the retrieved historical rideshare allocation records, wherein each record in the training data comprises an allocation status label and a candidate allocation time, the allocation status label indicating a status of allocation with respect to the candidate allocation time (See paragraphs 81–82 and 95–96, wherein historical trip records are utilized as training data for the classifier, wherein the training data records include bundles indicating estimated waiting times for a given price, and wherein each record is labeled as positive or negative samples);
train a machine learning model to predict an allocation probability for each candidate allocation time based on the generated training data (See FIG. 4 and paragraphs 95–96, in view of paragraphs 51 and 81–82, wherein the classifier is trained to generate acceptance probabilities and determine estimated acceptance waiting times; see also paragraphs 76–77, wherein allocation times are variable with respect to matching probabilities);
receive a request for a ride from a computing device, the request comprising request attributes (See FIG. 4 and paragraph 79, wherein a request is received, and wherein the request include request attributes); and
process the request attributes using the trained machine learning model to estimate an allocation time for the request (See FIG. 4 and paragraphs 79 and 82–83, wherein estimated waiting times are generated according to request attributes; see also paragraphs 76–77). Although Tang implicitly discloses retrieving actual time values (See paragraphs 81–82 and 96, in view of paragraphs 76–77, wherein wait times are estimated based on historical data, wherein the wait times are estimated using a learning process, and wherein a learning process implicitly utilizes actual values to improve estimates), Tang does not expressly disclose the remaining claim elements.
Li discloses functionality to retrieve historical ridesharing allocation records, the records comprising actual allocation time values and historical request attributes (See paragraphs 22 and 36, wherein historical features of a pickup request are utilized, and wherein the learning model utilizes historical attributes in the context of ground truth data).
Tang discloses a system directed to managing rideshare request matching. Li discloses a system directed to optimizing wait time periods for transportation requests. Each reference discloses a system directed to managing transportation requests. The technique of utilizing actual time values is applicable to the system of Tang as they each share characteristics and capabilities; namely, they are directed to managing transportation requests.
One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Li to the teachings of Tang would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate transportation request management into similar systems. Further, applying actual time values to Tang would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claims 2 and 9: Tang discloses the system of claim 1, wherein generation of training data comprises generating multiple training records for each historical rideshare allocation record (See paragraph 95, wherein each historical trip includes both a positive example record and negative example records).
Claims 3 and 10: Tang discloses the system of claim 1, wherein the candidate allocation time is based on potential allocation times (See paragraph 77, wherein candidate times are selected, and wherein the candidate time may be selected from the set including waiting times approaching infinity). Tang does not expressly disclose the remaining claim elements.
Li discloses a predetermined list of potential times (See paragraph 22, wherein the system utilizes standard and incremental times as predetermined potential times; see also paragraphs 17, 48, and 66).
One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 4 and 11: Tang discloses the system of claim 3, wherein at least one of the potential allocation times is greater than a largest actual allocation time value in the historical rideshare allocation records (See paragraph 77, wherein candidate times are selected, and wherein the candidate time may be selected from the set including waiting times approaching infinity). Tang does not expressly disclose the remaining claim elements.
Li discloses the predetermined list of potential times (See paragraph 22, wherein the system utilizes standard and incremental times as predetermined potential times; see also paragraphs 17, 48, and 66).
One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 5 and 12: Tang discloses the system of claim 1, wherein the trained machine learning model generates a probability of allocation for one or more of the candidate allocation times (See FIG. 4 and paragraphs 76–77 and 81–83, wherein matching probabilities are generated with respect to estimated waiting times); and
allocation time for the request is estimated based on a predetermined probability threshold (See FIG. 4 and paragraphs 76–77 and 81–83, wherein waiting times are determined based on a fixed matching probability).
Claims 6 and 13: Tang discloses the system of claim 1, wherein the processor(s) is further configured to transmit the estimated allocation time to the computing device (See FIG. 4 and paragraph 86, wherein the estimated waiting time is transmitted to the terminal device).
Claims 7 and 14: Tang discloses the system of claim 1, wherein the historical request attributes comprise one or more than one of: ride request location, ride destination information, ride request time, ride request date, requested vehicle type, driver supply indicator and trip demand indicator (See paragraph 96, wherein origin, destination, time, date, and vehicle type are disclosed as trip attributes).
Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
Vasudevan et al. (U.S. 2022/0188958) discloses a system directed to controlling transport request communications based on match probabilities;
TAN et al. (U.S. 2022/0108339) discloses a system directed to managing rideshare pricing based on temporal conversion probabilities; and
LYE et al. (WO 2018/208232) discloses a system directed to managing transportation requests and provider matching based on temporal likelihood considerations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM S BROCKINGTON III whose telephone number is (571)270-3400. The examiner can normally be reached M-F, 8am-5pm, EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WILLIAM S BROCKINGTON III/ Primary Examiner, Art Unit 3623
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